This section of the Kubernetes documentation contains pages that
show how to do individual tasks. A task page shows how to do a
single thing, typically by giving a short sequence of steps.
The Kubernetes command-line tool, kubectl, allows
you to run commands against Kubernetes clusters.
You can use kubectl to deploy applications, inspect and manage cluster resources,
and view logs. For more information including a complete list of kubectl operations, see the
kubectl reference documentation.
kubectl is installable on a variety of Linux platforms, macOS and Windows.
Find your preferred operating system below.
Like kind, minikube is a tool that lets you run Kubernetes
locally. minikube runs an all-in-one or a multi-node local Kubernetes cluster on your personal
computer (including Windows, macOS and Linux PCs) so that you can try out
Kubernetes, or for daily development work.
You can follow the official
Get Started! guide if your focus is
on getting the tool installed.
You can use the kubeadm tool to create and manage Kubernetes clusters.
It performs the actions necessary to get a minimum viable, secure cluster up and running in a user friendly way.
You must use a kubectl version that is within one minor version difference of
your cluster. For example, a v1.28 client can communicate
with v1.27, v1.28,
and v1.29 control planes.
Using the latest compatible version of kubectl helps avoid unforeseen issues.
Install kubectl on Linux
The following methods exist for installing kubectl on Linux:
Update the apt package index and install packages needed to use the Kubernetes apt repository:
sudo apt-get update
# apt-transport-https may be a dummy package; if so, you can skip that packagesudo apt-get install -y apt-transport-https ca-certificates curl
Download the public signing key for the Kubernetes package repositories. The same signing key is used for all repositories so you can disregard the version in the URL:
Add the appropriate Kubernetes apt repository. If you want to use Kubernetes version different than v1.28,
replace v1.28 with the desired minor version in the command below:
# This overwrites any existing configuration in /etc/apt/sources.list.d/kubernetes.listecho'deb [signed-by=/etc/apt/keyrings/kubernetes-apt-keyring.gpg] https://pkgs.k8s.io/core:/stable:/v1.28/deb/ /' | sudo tee /etc/apt/sources.list.d/kubernetes.list
Note: To upgrade kubectl to another minor release, you'll need to bump the version in /etc/apt/sources.list.d/kubernetes.list before running apt-get update and apt-get upgrade. This procedure is described in more detail in Changing The Kubernetes Package Repository.
Note: In releases older than Debian 12 and Ubuntu 22.04, /etc/apt/keyrings does not exist by default, and can be created using sudo mkdir -m 755 /etc/apt/keyrings
Add the Kubernetes yum repository. If you want to use Kubernetes version
different than v1.28, replace v1.28 with
the desired minor version in the command below.
# This overwrites any existing configuration in /etc/yum.repos.d/kubernetes.repocat <<EOF | sudo tee /etc/yum.repos.d/kubernetes.repo
[kubernetes]
name=Kubernetes
baseurl=https://pkgs.k8s.io/core:/stable:/v1.28/rpm/
enabled=1
gpgcheck=1
gpgkey=https://pkgs.k8s.io/core:/stable:/v1.28/rpm/repodata/repomd.xml.key
EOF
Note: To upgrade kubectl to another minor release, you'll need to bump the version in /etc/yum.repos.d/kubernetes.repo before running yum update. This procedure is described in more detail in Changing The Kubernetes Package Repository.
Install kubectl using yum:
sudo yum install -y kubectl
Add the Kubernetes zypper repository. If you want to use Kubernetes version
different than v1.28, replace v1.28 with
the desired minor version in the command below.
# This overwrites any existing configuration in /etc/zypp/repos.d/kubernetes.repocat <<EOF | sudo tee /etc/zypp/repos.d/kubernetes.repo
[kubernetes]
name=Kubernetes
baseurl=https://pkgs.k8s.io/core:/stable:/v1.28/rpm/
enabled=1
gpgcheck=1
gpgkey=https://pkgs.k8s.io/core:/stable:/v1.28/rpm/repodata/repomd.xml.key
EOF
Note: To upgrade kubectl to another minor release, you'll need to bump the version in /etc/zypp/repos.d/kubernetes.repo
before running zypper update. This procedure is described in more detail in
Changing The Kubernetes Package Repository.
If you are on Ubuntu or another Linux distribution that supports the
snap package manager, kubectl
is available as a snap application.
snap install kubectl --classic
kubectl version --client
If you are on Linux and using Homebrew
package manager, kubectl is available for installation.
brew install kubectl
kubectl version --client
Verify kubectl configuration
In order for kubectl to find and access a Kubernetes cluster, it needs a
kubeconfig file,
which is created automatically when you create a cluster using
kube-up.sh
or successfully deploy a Minikube cluster.
By default, kubectl configuration is located at ~/.kube/config.
Check that kubectl is properly configured by getting the cluster state:
kubectl cluster-info
If you see a URL response, kubectl is correctly configured to access your cluster.
If you see a message similar to the following, kubectl is not configured correctly or is not able to connect to a Kubernetes cluster.
The connection to the server <server-name:port> was refused - did you specify the right host or port?
For example, if you are intending to run a Kubernetes cluster on your laptop (locally), you will need a tool like Minikube to be installed first and then re-run the commands stated above.
If kubectl cluster-info returns the url response but you can't access your cluster, to check whether it is configured properly, use:
kubectl cluster-info dump
Optional kubectl configurations and plugins
Enable shell autocompletion
kubectl provides autocompletion support for Bash, Zsh, Fish, and PowerShell,
which can save you a lot of typing.
Below are the procedures to set up autocompletion for Bash, Fish, and Zsh.
The kubectl completion script for Bash can be generated with the command kubectl completion bash.
Sourcing the completion script in your shell enables kubectl autocompletion.
However, the completion script depends on
bash-completion,
which means that you have to install this software first
(you can test if you have bash-completion already installed by running type _init_completion).
Install bash-completion
bash-completion is provided by many package managers
(see here).
You can install it with apt-get install bash-completion or yum install bash-completion, etc.
The above commands create /usr/share/bash-completion/bash_completion,
which is the main script of bash-completion. Depending on your package manager,
you have to manually source this file in your ~/.bashrc file.
To find out, reload your shell and run type _init_completion.
If the command succeeds, you're already set, otherwise add the following to your ~/.bashrc file:
source /usr/share/bash-completion/bash_completion
Reload your shell and verify that bash-completion is correctly installed by typing type _init_completion.
Enable kubectl autocompletion
Bash
You now need to ensure that the kubectl completion script gets sourced in all
your shell sessions. There are two ways in which you can do this:
If you have an alias for kubectl, you can extend shell completion to work with that alias:
echo'alias k=kubectl' >>~/.bashrc
echo'complete -o default -F __start_kubectl k' >>~/.bashrc
Note: bash-completion sources all completion scripts in /etc/bash_completion.d.
Both approaches are equivalent. After reloading your shell, kubectl autocompletion should be working.
To enable bash autocompletion in current session of shell, source the ~/.bashrc file:
source ~/.bashrc
Note: Autocomplete for Fish requires kubectl 1.23 or later.
The kubectl completion script for Fish can be generated with the command kubectl completion fish. Sourcing the completion script in your shell enables kubectl autocompletion.
To do so in all your shell sessions, add the following line to your ~/.config/fish/config.fish file:
kubectl completion fish | source
After reloading your shell, kubectl autocompletion should be working.
The kubectl completion script for Zsh can be generated with the command kubectl completion zsh. Sourcing the completion script in your shell enables kubectl autocompletion.
To do so in all your shell sessions, add the following to your ~/.zshrc file:
source <(kubectl completion zsh)
If you have an alias for kubectl, kubectl autocompletion will automatically work with it.
After reloading your shell, kubectl autocompletion should be working.
If you get an error like 2: command not found: compdef, then add the following to the beginning of your ~/.zshrc file:
autoload -Uz compinit
compinit
Install kubectl convert plugin
A plugin for Kubernetes command-line tool kubectl, which allows you to convert manifests between different API
versions. This can be particularly helpful to migrate manifests to a non-deprecated api version with newer Kubernetes release.
For more info, visit migrate to non deprecated apis
You must use a kubectl version that is within one minor version difference of
your cluster. For example, a v1.28 client can communicate
with v1.27, v1.28,
and v1.29 control planes.
Using the latest compatible version of kubectl helps avoid unforeseen issues.
Install kubectl on macOS
The following methods exist for installing kubectl on macOS:
Note: Make sure /usr/local/bin is in your PATH environment variable.
Test to ensure the version you installed is up-to-date:
kubectl version --client
Or use this for detailed view of version:
kubectl version --client --output=yaml
After installing the plugin, clean up the installation files:
rm kubectl kubectl.sha256
Install with Homebrew on macOS
If you are on macOS and using Homebrew package manager,
you can install kubectl with Homebrew.
Run the installation command:
brew install kubectl
or
brew install kubernetes-cli
Test to ensure the version you installed is up-to-date:
kubectl version --client
Install with Macports on macOS
If you are on macOS and using Macports package manager,
you can install kubectl with Macports.
Run the installation command:
sudo port selfupdate
sudo port install kubectl
Test to ensure the version you installed is up-to-date:
kubectl version --client
Verify kubectl configuration
In order for kubectl to find and access a Kubernetes cluster, it needs a
kubeconfig file,
which is created automatically when you create a cluster using
kube-up.sh
or successfully deploy a Minikube cluster.
By default, kubectl configuration is located at ~/.kube/config.
Check that kubectl is properly configured by getting the cluster state:
kubectl cluster-info
If you see a URL response, kubectl is correctly configured to access your cluster.
If you see a message similar to the following, kubectl is not configured correctly or is not able to connect to a Kubernetes cluster.
The connection to the server <server-name:port> was refused - did you specify the right host or port?
For example, if you are intending to run a Kubernetes cluster on your laptop (locally), you will need a tool like Minikube to be installed first and then re-run the commands stated above.
If kubectl cluster-info returns the url response but you can't access your cluster, to check whether it is configured properly, use:
kubectl cluster-info dump
Optional kubectl configurations and plugins
Enable shell autocompletion
kubectl provides autocompletion support for Bash, Zsh, Fish, and PowerShell
which can save you a lot of typing.
Below are the procedures to set up autocompletion for Bash, Fish, and Zsh.
The kubectl completion script for Bash can be generated with kubectl completion bash.
Sourcing this script in your shell enables kubectl completion.
However, the kubectl completion script depends on
bash-completion which you thus have to previously install.
Warning: There are two versions of bash-completion, v1 and v2. V1 is for Bash 3.2
(which is the default on macOS), and v2 is for Bash 4.1+. The kubectl completion
script doesn't work correctly with bash-completion v1 and Bash 3.2.
It requires bash-completion v2 and Bash 4.1+. Thus, to be able to
correctly use kubectl completion on macOS, you have to install and use
Bash 4.1+ (instructions).
The following instructions assume that you use Bash 4.1+
(that is, any Bash version of 4.1 or newer).
Upgrade Bash
The instructions here assume you use Bash 4.1+. You can check your Bash's version by running:
echo$BASH_VERSION
If it is too old, you can install/upgrade it using Homebrew:
brew install bash
Reload your shell and verify that the desired version is being used:
echo$BASH_VERSION$SHELL
Homebrew usually installs it at /usr/local/bin/bash.
Install bash-completion
Note: As mentioned, these instructions assume you use Bash 4.1+, which means you will
install bash-completion v2 (in contrast to Bash 3.2 and bash-completion v1,
in which case kubectl completion won't work).
You can test if you have bash-completion v2 already installed with type _init_completion.
If not, you can install it with Homebrew:
brew install bash-completion@2
As stated in the output of this command, add the following to your ~/.bash_profile file:
If you have an alias for kubectl, you can extend shell completion to work with that alias:
echo'alias k=kubectl' >>~/.bash_profile
echo'complete -o default -F __start_kubectl k' >>~/.bash_profile
If you installed kubectl with Homebrew (as explained
here),
then the kubectl completion script should already be in /usr/local/etc/bash_completion.d/kubectl.
In that case, you don't need to do anything.
Note: The Homebrew installation of bash-completion v2 sources all the files in the
BASH_COMPLETION_COMPAT_DIR directory, that's why the latter two methods work.
In any case, after reloading your shell, kubectl completion should be working.
Note: Autocomplete for Fish requires kubectl 1.23 or later.
The kubectl completion script for Fish can be generated with the command kubectl completion fish. Sourcing the completion script in your shell enables kubectl autocompletion.
To do so in all your shell sessions, add the following line to your ~/.config/fish/config.fish file:
kubectl completion fish | source
After reloading your shell, kubectl autocompletion should be working.
The kubectl completion script for Zsh can be generated with the command kubectl completion zsh. Sourcing the completion script in your shell enables kubectl autocompletion.
To do so in all your shell sessions, add the following to your ~/.zshrc file:
source <(kubectl completion zsh)
If you have an alias for kubectl, kubectl autocompletion will automatically work with it.
After reloading your shell, kubectl autocompletion should be working.
If you get an error like 2: command not found: compdef, then add the following to the beginning of your ~/.zshrc file:
autoload -Uz compinit
compinit
Install kubectl convert plugin
A plugin for Kubernetes command-line tool kubectl, which allows you to convert manifests between different API
versions. This can be particularly helpful to migrate manifests to a non-deprecated api version with newer Kubernetes release.
For more info, visit migrate to non deprecated apis
You must use a kubectl version that is within one minor version difference of
your cluster. For example, a v1.28 client can communicate
with v1.27, v1.28,
and v1.29 control planes.
Using the latest compatible version of kubectl helps avoid unforeseen issues.
Install kubectl on Windows
The following methods exist for installing kubectl on Windows:
Append or prepend the kubectl binary folder to your PATH environment variable.
Test to ensure the version of kubectl is the same as downloaded:
kubectl version --client
Or use this for detailed view of version:
kubectl version --client --output=yaml
Note:Docker Desktop for Windows
adds its own version of kubectl to PATH. If you have installed Docker Desktop before,
you may need to place your PATH entry before the one added by the Docker Desktop
installer or remove the Docker Desktop's kubectl.
Install on Windows using Chocolatey, Scoop, or winget
To install kubectl on Windows you can use either Chocolatey
package manager, Scoop command-line installer, or
winget package manager.
Test to ensure the version you installed is up-to-date:
kubectl version --client
Navigate to your home directory:
# If you're using cmd.exe, run: cd %USERPROFILE%cd ~
Create the .kube directory:
mkdir .kube
Change to the .kube directory you just created:
cd .kube
Configure kubectl to use a remote Kubernetes cluster:
New-Item config -type file
Note: Edit the config file with a text editor of your choice, such as Notepad.
Verify kubectl configuration
In order for kubectl to find and access a Kubernetes cluster, it needs a
kubeconfig file,
which is created automatically when you create a cluster using
kube-up.sh
or successfully deploy a Minikube cluster.
By default, kubectl configuration is located at ~/.kube/config.
Check that kubectl is properly configured by getting the cluster state:
kubectl cluster-info
If you see a URL response, kubectl is correctly configured to access your cluster.
If you see a message similar to the following, kubectl is not configured correctly or is not able to connect to a Kubernetes cluster.
The connection to the server <server-name:port> was refused - did you specify the right host or port?
For example, if you are intending to run a Kubernetes cluster on your laptop (locally), you will need a tool like Minikube to be installed first and then re-run the commands stated above.
If kubectl cluster-info returns the url response but you can't access your cluster, to check whether it is configured properly, use:
kubectl cluster-info dump
Optional kubectl configurations and plugins
Enable shell autocompletion
kubectl provides autocompletion support for Bash, Zsh, Fish, and PowerShell,
which can save you a lot of typing.
Below are the procedures to set up autocompletion for PowerShell.
The kubectl completion script for PowerShell can be generated with the command kubectl completion powershell.
To do so in all your shell sessions, add the following line to your $PROFILE file:
This command will regenerate the auto-completion script on every PowerShell start up. You can also add the generated script directly to your $PROFILE file.
To add the generated script to your $PROFILE file, run the following line in your powershell prompt:
kubectl completion powershell >> $PROFILE
After reloading your shell, kubectl autocompletion should be working.
Install kubectl convert plugin
A plugin for Kubernetes command-line tool kubectl, which allows you to convert manifests between different API
versions. This can be particularly helpful to migrate manifests to a non-deprecated api version with newer Kubernetes release.
For more info, visit migrate to non deprecated apis
Client certificates generated by kubeadm expire after 1 year.
This page explains how to manage certificate renewals with kubeadm. It also covers other tasks related
to kubeadm certificate management.
By default, kubeadm generates all the certificates needed for a cluster to run.
You can override this behavior by providing your own certificates.
To do so, you must place them in whatever directory is specified by the
--cert-dir flag or the certificatesDir field of kubeadm's ClusterConfiguration.
By default this is /etc/kubernetes/pki.
If a given certificate and private key pair exists before running kubeadm init,
kubeadm does not overwrite them. This means you can, for example, copy an existing
CA into /etc/kubernetes/pki/ca.crt and /etc/kubernetes/pki/ca.key,
and kubeadm will use this CA for signing the rest of the certificates.
External CA mode
It is also possible to provide only the ca.crt file and not the
ca.key file (this is only available for the root CA file, not other cert pairs).
If all other certificates and kubeconfig files are in place, kubeadm recognizes
this condition and activates the "External CA" mode. kubeadm will proceed without the
CA key on disk.
Instead, run the controller-manager standalone with --controllers=csrsigner and
point to the CA certificate and key.
You can use the check-expiration subcommand to check when certificates expire:
kubeadm certs check-expiration
The output is similar to this:
CERTIFICATE EXPIRES RESIDUAL TIME CERTIFICATE AUTHORITY EXTERNALLY MANAGED
admin.conf Dec 30, 2020 23:36 UTC 364d no
apiserver Dec 30, 2020 23:36 UTC 364d ca no
apiserver-etcd-client Dec 30, 2020 23:36 UTC 364d etcd-ca no
apiserver-kubelet-client Dec 30, 2020 23:36 UTC 364d ca no
controller-manager.conf Dec 30, 2020 23:36 UTC 364d no
etcd-healthcheck-client Dec 30, 2020 23:36 UTC 364d etcd-ca no
etcd-peer Dec 30, 2020 23:36 UTC 364d etcd-ca no
etcd-server Dec 30, 2020 23:36 UTC 364d etcd-ca no
front-proxy-client Dec 30, 2020 23:36 UTC 364d front-proxy-ca no
scheduler.conf Dec 30, 2020 23:36 UTC 364d no
CERTIFICATE AUTHORITY EXPIRES RESIDUAL TIME EXTERNALLY MANAGED
ca Dec 28, 2029 23:36 UTC 9y no
etcd-ca Dec 28, 2029 23:36 UTC 9y no
front-proxy-ca Dec 28, 2029 23:36 UTC 9y no
The command shows expiration/residual time for the client certificates in the
/etc/kubernetes/pki folder and for the client certificate embedded in the kubeconfig files used
by kubeadm (admin.conf, controller-manager.conf and scheduler.conf).
Additionally, kubeadm informs the user if the certificate is externally managed; in this case, the
user should take care of managing certificate renewal manually/using other tools.
Warning:kubeadm cannot manage certificates signed by an external CA.
On nodes created with kubeadm init, prior to kubeadm version 1.17, there is a
bug where you manually have to modify the
contents of kubelet.conf. After kubeadm init finishes, you should update kubelet.conf to
point to the rotated kubelet client certificates, by replacing client-certificate-data and
client-key-data with:
kubeadm renews all the certificates during control plane
upgrade.
This feature is designed for addressing the simplest use cases;
if you don't have specific requirements on certificate renewal and perform Kubernetes version
upgrades regularly (less than 1 year in between each upgrade), kubeadm will take care of keeping
your cluster up to date and reasonably secure.
Note: It is a best practice to upgrade your cluster frequently in order to stay secure.
If you have more complex requirements for certificate renewal, you can opt out from the default
behavior by passing --certificate-renewal=false to kubeadm upgrade apply or to kubeadm upgrade node.
Warning: Prior to kubeadm version 1.17 there is a bug
where the default value for --certificate-renewal is false for the kubeadm upgrade node
command. In that case, you should explicitly set --certificate-renewal=true.
Manual certificate renewal
You can renew your certificates manually at any time with the kubeadm certs renew command, with the appropriate command line options.
This command performs the renewal using CA (or front-proxy-CA) certificate and key stored in /etc/kubernetes/pki.
After running the command you should restart the control plane Pods. This is required since
dynamic certificate reload is currently not supported for all components and certificates.
Static Pods are managed by the local kubelet
and not by the API Server, thus kubectl cannot be used to delete and restart them.
To restart a static Pod you can temporarily remove its manifest file from /etc/kubernetes/manifests/
and wait for 20 seconds (see the fileCheckFrequency value in KubeletConfiguration struct.
The kubelet will terminate the Pod if it's no longer in the manifest directory.
You can then move the file back and after another fileCheckFrequency period, the kubelet will recreate
the Pod and the certificate renewal for the component can complete.
Warning: If you are running an HA cluster, this command needs to be executed on all the control-plane nodes.
Note:certs renew uses the existing certificates as the authoritative source for attributes (Common
Name, Organization, SAN, etc.) instead of the kubeadm-config ConfigMap. It is strongly recommended
to keep them both in sync.
kubeadm certs renew can renew any specific certificate or, with the subcommand all, it can renew all of them, as shown below:
kubeadm certs renew all
Note:
Clusters built with kubeadm often copy the admin.conf certificate into
$HOME/.kube/config, as instructed in Creating a cluster with kubeadm.
On such a system, to update the contents of $HOME/.kube/config
after renewing the admin.conf, you must run the following commands:
Renew certificates with the Kubernetes certificates API
This section provides more details about how to execute manual certificate renewal using the Kubernetes certificates API.
Caution: These are advanced topics for users who need to integrate their organization's certificate
infrastructure into a kubeadm-built cluster. If the default kubeadm configuration satisfies your
needs, you should let kubeadm manage certificates instead.
Set up a signer
The Kubernetes Certificate Authority does not work out of the box.
You can configure an external signer such as cert-manager,
or you can use the built-in signer.
This section provide more details about how to execute manual certificate renewal using an external CA.
To better integrate with external CAs, kubeadm can also produce certificate signing requests (CSRs).
A CSR represents a request to a CA for a signed certificate for a client.
In kubeadm terms, any certificate that would normally be signed by an on-disk CA can be produced
as a CSR instead. A CA, however, cannot be produced as a CSR.
Create certificate signing requests (CSR)
You can create certificate signing requests with kubeadm certs renew --csr-only.
Both the CSR and the accompanying private key are given in the output.
You can pass in a directory with --csr-dir to output the CSRs to the specified location.
If --csr-dir is not specified, the default certificate directory (/etc/kubernetes/pki) is used.
Certificates can be renewed with kubeadm certs renew --csr-only.
As with kubeadm init, an output directory can be specified with the --csr-dir flag.
A CSR contains a certificate's name, domains, and IPs, but it does not specify usages.
It is the responsibility of the CA to specify the correct cert usages
when issuing a certificate.
After a certificate is signed using your preferred method, the certificate and the private key
must be copied to the PKI directory (by default /etc/kubernetes/pki).
Certificate authority (CA) rotation
Kubeadm does not support rotation or replacement of CA certificates out of the box.
By default the kubelet serving certificate deployed by kubeadm is self-signed.
This means a connection from external services like the
metrics-server to a
kubelet cannot be secured with TLS.
To configure the kubelets in a new kubeadm cluster to obtain properly signed serving
certificates you must pass the following minimal configuration to kubeadm init:
If you have already created the cluster you must adapt it by doing the following:
Find and edit the kubelet-config-1.28 ConfigMap in the kube-system namespace.
In that ConfigMap, the kubelet key has a
KubeletConfiguration
document as its value. Edit the KubeletConfiguration document to set serverTLSBootstrap: true.
On each node, add the serverTLSBootstrap: true field in /var/lib/kubelet/config.yaml
and restart the kubelet with systemctl restart kubelet
The field serverTLSBootstrap: true will enable the bootstrap of kubelet serving
certificates by requesting them from the certificates.k8s.io API. One known limitation
is that the CSRs (Certificate Signing Requests) for these certificates cannot be automatically
approved by the default signer in the kube-controller-manager -
kubernetes.io/kubelet-serving.
This will require action from the user or a third party controller.
These CSRs can be viewed using:
kubectl get csr
NAME AGE SIGNERNAME REQUESTOR CONDITION
csr-9wvgt 112s kubernetes.io/kubelet-serving system:node:worker-1 Pending
csr-lz97v 1m58s kubernetes.io/kubelet-serving system:node:control-plane-1 Pending
To approve them you can do the following:
kubectl certificate approve <CSR-name>
By default, these serving certificate will expire after one year. Kubeadm sets the
KubeletConfiguration field rotateCertificates to true, which means that close
to expiration a new set of CSRs for the serving certificates will be created and must
be approved to complete the rotation. To understand more see
Certificate Rotation.
If you are looking for a solution for automatic approval of these CSRs it is recommended
that you contact your cloud provider and ask if they have a CSR signer that verifies
the node identity with an out of band mechanism.
Note:
This section links to third party projects that provide functionality required by Kubernetes. The Kubernetes project authors aren't responsible for these projects, which are listed alphabetically. To add a project to this list, read the content guide before submitting a change. More information.
Such a controller is not a secure mechanism unless it not only verifies the CommonName
in the CSR but also verifies the requested IPs and domain names. This would prevent
a malicious actor that has access to a kubelet client certificate to create
CSRs requesting serving certificates for any IP or domain name.
Generating kubeconfig files for additional users
During cluster creation, kubeadm signs the certificate in the admin.conf to have
Subject: O = system:masters, CN = kubernetes-admin.
system:masters
is a break-glass, super user group that bypasses the authorization layer (for example,
RBAC).
Sharing the admin.conf with additional users is not recommended!
Instead, you can use the kubeadm kubeconfig user
command to generate kubeconfig files for additional users.
The command accepts a mixture of command line flags and
kubeadm configuration options.
The generated kubeconfig will be written to stdout and can be piped to a file using
kubeadm kubeconfig user ... > somefile.conf.
Example configuration file that can be used with --config:
# example.yamlapiVersion:kubeadm.k8s.io/v1beta3kind:ClusterConfiguration# Will be used as the target "cluster" in the kubeconfigclusterName:"kubernetes"# Will be used as the "server" (IP or DNS name) of this cluster in the kubeconfigcontrolPlaneEndpoint:"some-dns-address:6443"# The cluster CA key and certificate will be loaded from this local directorycertificatesDir:"/etc/kubernetes/pki"
Make sure that these settings match the desired target cluster settings.
To see the settings of an existing cluster use:
kubectl get cm kubeadm-config -n kube-system -o=jsonpath="{.data.ClusterConfiguration}"
The following example will generate a kubeconfig file with credentials valid for 24 hours
for a new user johndoe that is part of the appdevs group:
The Container runtimes page
explains that the systemd driver is recommended for kubeadm based setups instead
of the kubelet's defaultcgroupfs driver,
because kubeadm manages the kubelet as a
systemd service.
The page also provides details on how to set up a number of different container runtimes with the
systemd driver by default.
Configuring the kubelet cgroup driver
kubeadm allows you to pass a KubeletConfiguration structure during kubeadm init.
This KubeletConfiguration can include the cgroupDriver field which controls the cgroup
driver of the kubelet.
Note:
In v1.22 and later, if the user does not set the cgroupDriver field under KubeletConfiguration,
kubeadm defaults it to systemd.
In Kubernetes v1.28, you can enable automatic detection of the
cgroup driver as an alpha feature.
See systemd cgroup driver
for more details.
A minimal example of configuring the field explicitly:
Such a configuration file can then be passed to the kubeadm command:
kubeadm init --config kubeadm-config.yaml
Note:
Kubeadm uses the same KubeletConfiguration for all nodes in the cluster.
The KubeletConfiguration is stored in a ConfigMap
object under the kube-system namespace.
Executing the sub commands init, join and upgrade would result in kubeadm
writing the KubeletConfiguration as a file under /var/lib/kubelet/config.yaml
and passing it to the local node kubelet.
Using the cgroupfs driver
To use cgroupfs and to prevent kubeadm upgrade from modifying the
KubeletConfiguration cgroup driver on existing setups, you must be explicit
about its value. This applies to a case where you do not wish future versions
of kubeadm to apply the systemd driver by default.
If you wish to configure a container runtime to use the cgroupfs driver,
you must refer to the documentation of the container runtime of your choice.
Migrating to the systemd driver
To change the cgroup driver of an existing kubeadm cluster from cgroupfs to systemd in-place,
a similar procedure to a kubelet upgrade is required. This must include both
steps outlined below.
Note: Alternatively, it is possible to replace the old nodes in the cluster with new ones
that use the systemd driver. This requires executing only the first step below
before joining the new nodes and ensuring the workloads can safely move to the new
nodes before deleting the old nodes.
Modify the kubelet ConfigMap
Call kubectl edit cm kubelet-config -n kube-system.
Either modify the existing cgroupDriver value or add a new field that looks like this:
cgroupDriver:systemd
This field must be present under the kubelet: section of the ConfigMap.
Update the cgroup driver on all nodes
For each node in the cluster:
Drain the node using kubectl drain <node-name> --ignore-daemonsets
Stop the kubelet using systemctl stop kubelet
Stop the container runtime
Modify the container runtime cgroup driver to systemd
Set cgroupDriver: systemd in /var/lib/kubelet/config.yaml
Execute these steps on nodes one at a time to ensure workloads
have sufficient time to schedule on different nodes.
Once the process is complete ensure that all nodes and workloads are healthy.
2.1.3 - Reconfiguring a kubeadm cluster
kubeadm does not support automated ways of reconfiguring components that
were deployed on managed nodes. One way of automating this would be
by using a custom operator.
To modify the components configuration you must manually edit associated cluster
objects and files on disk.
This guide shows the correct sequence of steps that need to be performed
to achieve kubeadm cluster reconfiguration.
Before you begin
You need a cluster that was deployed using kubeadm
Have administrator credentials (/etc/kubernetes/admin.conf) and network connectivity
to a running kube-apiserver in the cluster from a host that has kubectl installed
Have a text editor installed on all hosts
Reconfiguring the cluster
kubeadm writes a set of cluster wide component configuration options in
ConfigMaps and other objects. These objects must be manually edited. The command kubectl edit
can be used for that.
The kubectl edit command will open a text editor where you can edit and save the object directly.
You can use the environment variables KUBECONFIG and KUBE_EDITOR to specify the location of
the kubectl consumed kubeconfig file and preferred text editor.
Note: Upon saving any changes to these cluster objects, components running on nodes may not be
automatically updated. The steps below instruct you on how to perform that manually.
Warning: Component configuration in ConfigMaps is stored as unstructured data (YAML string).
This means that validation will not be performed upon updating the contents of a ConfigMap.
You have to be careful to follow the documented API format for a particular
component configuration and avoid introducing typos and YAML indentation mistakes.
Applying cluster configuration changes
Updating the ClusterConfiguration
During cluster creation and upgrade, kubeadm writes its
ClusterConfiguration
in a ConfigMap called kubeadm-config in the kube-system namespace.
To change a particular option in the ClusterConfiguration you can edit the ConfigMap with this command:
kubectl edit cm -n kube-system kubeadm-config
The configuration is located under the data.ClusterConfiguration key.
Note: The ClusterConfiguration includes a variety of options that affect the configuration of individual
components such as kube-apiserver, kube-scheduler, kube-controller-manager, CoreDNS, etcd and kube-proxy.
Changes to the configuration must be reflected on node components manually.
Reflecting ClusterConfiguration changes on control plane nodes
kubeadm manages the control plane components as static Pod manifests located in
the directory /etc/kubernetes/manifests.
Any changes to the ClusterConfiguration under the apiServer, controllerManager, scheduler or etcd
keys must be reflected in the associated files in the manifests directory on a control plane node.
Such changes may include:
extraArgs - requires updating the list of flags passed to a component container
extraMounts - requires updated the volume mounts for a component container
*SANs - requires writing new certificates with updated Subject Alternative Names.
Before proceeding with these changes, make sure you have backed up the directory /etc/kubernetes/.
The <config-file> contents must match the updated ClusterConfiguration.
The <component-name> value must be the name of the component.
Note: Updating a file in /etc/kubernetes/manifests will tell the kubelet to restart the static Pod for the corresponding component.
Try doing these changes one node at a time to leave the cluster without downtime.
Applying kubelet configuration changes
Updating the KubeletConfiguration
During cluster creation and upgrade, kubeadm writes its
KubeletConfiguration
in a ConfigMap called kubelet-config in the kube-system namespace.
You can edit the ConfigMap with this command:
kubectl edit cm -n kube-system kubelet-config
The configuration is located under the data.kubelet key.
Reflecting the kubelet changes
To reflect the change on kubeadm nodes you must do the following:
Log in to a kubeadm node
Run kubeadm upgrade node phase kubelet-config to download the latest kubelet-config
ConfigMap contents into the local file /var/lib/kubelet/config.yaml
Edit the file /var/lib/kubelet/kubeadm-flags.env to apply additional configuration with
flags
Restart the kubelet service with systemctl restart kubelet
Note: Do these changes one node at a time to allow workloads to be rescheduled properly.
Note: During kubeadm upgrade, kubeadm downloads the KubeletConfiguration from the
kubelet-config ConfigMap and overwrite the contents of /var/lib/kubelet/config.yaml.
This means that node local configuration must be applied either by flags in
/var/lib/kubelet/kubeadm-flags.env or by manually updating the contents of
/var/lib/kubelet/config.yaml after kubeadm upgrade, and then restarting the kubelet.
Applying kube-proxy configuration changes
Updating the KubeProxyConfiguration
During cluster creation and upgrade, kubeadm writes its
KubeProxyConfiguration
in a ConfigMap in the kube-system namespace called kube-proxy.
This ConfigMap is used by the kube-proxy DaemonSet in the kube-system namespace.
To change a particular option in the KubeProxyConfiguration, you can edit the ConfigMap with this command:
kubectl edit cm -n kube-system kube-proxy
The configuration is located under the data.config.conf key.
Reflecting the kube-proxy changes
Once the kube-proxy ConfigMap is updated, you can restart all kube-proxy Pods:
Obtain the Pod names:
kubectl get po -n kube-system | grep kube-proxy
Delete a Pod with:
kubectl delete po -n kube-system <pod-name>
New Pods that use the updated ConfigMap will be created.
Note: Because kubeadm deploys kube-proxy as a DaemonSet, node specific configuration is unsupported.
Applying CoreDNS configuration changes
Updating the CoreDNS Deployment and Service
kubeadm deploys CoreDNS as a Deployment called coredns and with a Service kube-dns,
both in the kube-system namespace.
To update any of the CoreDNS settings, you can edit the Deployment and
Service objects:
Once the CoreDNS changes are applied you can delete the CoreDNS Pods:
Obtain the Pod names:
kubectl get po -n kube-system | grep coredns
Delete a Pod with:
kubectl delete po -n kube-system <pod-name>
New Pods with the updated CoreDNS configuration will be created.
Note: kubeadm does not allow CoreDNS configuration during cluster creation and upgrade.
This means that if you execute kubeadm upgrade apply, your changes to the CoreDNS
objects will be lost and must be reapplied.
Persisting the reconfiguration
During the execution of kubeadm upgrade on a managed node, kubeadm might overwrite configuration
that was applied after the cluster was created (reconfiguration).
Persisting Node object reconfiguration
kubeadm writes Labels, Taints, CRI socket and other information on the Node object for a particular
Kubernetes node. To change any of the contents of this Node object you can use:
kubectl edit no <node-name>
During kubeadm upgrade the contents of such a Node might get overwritten.
If you would like to persist your modifications to the Node object after upgrade,
you can prepare a kubectl patch
and apply it to the Node object:
kubectl patch no <node-name> --patch-file <patch-file>
Persisting control plane component reconfiguration
The main source of control plane configuration is the ClusterConfiguration
object stored in the cluster. To extend the static Pod manifests configuration,
patches can be used.
These patch files must remain as files on the control plane nodes to ensure that
they can be used by the kubeadm upgrade ... --patches <directory>.
If reconfiguration is done to the ClusterConfiguration and static Pod manifests on disk,
the set of node specific patches must be updated accordingly.
Persisting kubelet reconfiguration
Any changes to the KubeletConfiguration stored in /var/lib/kubelet/config.yaml will be overwritten on
kubeadm upgrade by downloading the contents of the cluster wide kubelet-config ConfigMap.
To persist kubelet node specific configuration either the file /var/lib/kubelet/config.yaml
has to be updated manually post-upgrade or the file /var/lib/kubelet/kubeadm-flags.env can include flags.
The kubelet flags override the associated KubeletConfiguration options, but note that
some of the flags are deprecated.
A kubelet restart will be required after changing /var/lib/kubelet/config.yaml or
/var/lib/kubelet/kubeadm-flags.env.
This page explains how to upgrade a Kubernetes cluster created with kubeadm from version
1.27.x to version 1.28.x, and from version
1.28.x to 1.28.y (where y > x). Skipping MINOR versions
when upgrading is unsupported. For more details, please visit Version Skew Policy.
To see information about upgrading clusters created using older versions of kubeadm,
please refer to following pages instead:
The cluster should use a static control plane and etcd pods or external etcd.
Make sure to back up any important components, such as app-level state stored in a database.
kubeadm upgrade does not touch your workloads, only components internal to Kubernetes, but backups are always a best practice.
The instructions below outline when to drain each node during the upgrade process.
If you are performing a minor version upgrade for any kubelet, you must
first drain the node (or nodes) that you are upgrading. In the case of control plane nodes,
they could be running CoreDNS Pods or other critical workloads. For more information see
Draining nodes.
All containers are restarted after upgrade, because the container spec hash value is changed.
To verify that the kubelet service has successfully restarted after the kubelet has been upgraded,
you can execute systemctl status kubelet or view the service logs with journalctl -xeu kubelet.
If you're using the community-owned package repositories (pkgs.k8s.io), you need to
enable the package repository for the desired Kubernetes minor release. This is explained in
Changing the Kubernetes package repository
document.
Note: The legacy package repositories (apt.kubernetes.io and yum.kubernetes.io) have been
deprecated and frozen starting from September 13, 2023.
Using the new package repositories hosted at pkgs.k8s.io
is strongly recommended and required in order to install Kubernetes versions released after September 13, 2023.
The deprecated legacy repositories, and their contents, might be removed at any time in the future and without
a further notice period. The new package repositories provide downloads for Kubernetes versions starting with v1.24.0.
Determine which version to upgrade to
Find the latest patch release for Kubernetes 1.28 using the OS package manager:
# Find the latest 1.28 version in the list.# It should look like 1.28.x-*, where x is the latest patch.apt update
apt-cache madison kubeadm
# Find the latest 1.28 version in the list.# It should look like 1.28.x-*, where x is the latest patch.yum list --showduplicates kubeadm --disableexcludes=kubernetes
Upgrading control plane nodes
The upgrade procedure on control plane nodes should be executed one node at a time.
Pick a control plane node that you wish to upgrade first. It must have the /etc/kubernetes/admin.conf file.
# replace x in 1.28.x-* with the latest patch versionapt-mark unhold kubeadm &&\
apt-get update && apt-get install -y kubeadm='1.28.x-*'&&\
apt-mark hold kubeadm
# replace x in 1.28.x-* with the latest patch versionyum install -y kubeadm-'1.28.x-*' --disableexcludes=kubernetes
Verify that the download works and has the expected version:
kubeadm version
Verify the upgrade plan:
kubeadm upgrade plan
This command checks that your cluster can be upgraded, and fetches the versions you can upgrade to.
It also shows a table with the component config version states.
Note:kubeadm upgrade also automatically renews the certificates that it manages on this node.
To opt-out of certificate renewal the flag --certificate-renewal=false can be used.
For more information see the certificate management guide.
Note: If kubeadm upgrade plan shows any component configs that require manual upgrade, users must provide
a config file with replacement configs to kubeadm upgrade apply via the --config command line flag.
Failing to do so will cause kubeadm upgrade apply to exit with an error and not perform an upgrade.
Choose a version to upgrade to, and run the appropriate command. For example:
# replace x with the patch version you picked for this upgradesudo kubeadm upgrade apply v1.28.x
Once the command finishes you should see:
[upgrade/successful] SUCCESS! Your cluster was upgraded to "v1.28.x". Enjoy!
[upgrade/kubelet] Now that your control plane is upgraded, please proceed with upgrading your kubelets if you haven't already done so.
Note: For versions earlier than v1.28, kubeadm defaulted to a mode that upgrades the addons (including CoreDNS and kube-proxy)
immediately during kubeadm upgrade apply, regardless of whether there are other control plane instances that have not
been upgraded. This may cause compatibility problems. Since v1.28, kubeadm defaults to a mode that checks whether all
the control plane instances have been upgraded before starting to upgrade the addons. You must perform control plane
instances upgrade sequentially or at least ensure that the last control plane instance upgrade is not started until all
the other control plane instances have been upgraded completely, and the addons upgrade will be performed after the last
control plane instance is upgraded. If you want to keep the old upgrade behavior, please enable the UpgradeAddonsBeforeControlPlane
feature gate by kubeadm upgrade apply --feature-gates=UpgradeAddonsBeforeControlPlane=true. The Kubernetes project does
not in general recommend enabling this feature gate, you should instead change your upgrade process or cluster addons so
that you do not need to enable the legacy behavior. The UpgradeAddonsBeforeControlPlane feature gate will be removed in
a future release.
Manually upgrade your CNI provider plugin.
Your Container Network Interface (CNI) provider may have its own upgrade instructions to follow.
Check the addons page to
find your CNI provider and see whether additional upgrade steps are required.
This step is not required on additional control plane nodes if the CNI provider runs as a DaemonSet.
For the other control plane nodes
Same as the first control plane node but use:
sudo kubeadm upgrade node
instead of:
sudo kubeadm upgrade apply
Also calling kubeadm upgrade plan and upgrading the CNI provider plugin is no longer needed.
Drain the node
Prepare the node for maintenance by marking it unschedulable and evicting the workloads:
# replace <node-to-drain> with the name of your node you are drainingkubectl drain <node-to-drain> --ignore-daemonsets
# replace x in 1.28.x-* with the latest patch versionapt-mark unhold kubelet kubectl &&\
apt-get update && apt-get install -y kubelet='1.28.x-*'kubectl='1.28.x-*'&&\
apt-mark hold kubelet kubectl
# replace x in 1.28.x-* with the latest patch versionyum install -y kubelet-'1.28.x-*' kubectl-'1.28.x-*' --disableexcludes=kubernetes
Bring the node back online by marking it schedulable:
# replace <node-to-uncordon> with the name of your nodekubectl uncordon <node-to-uncordon>
Upgrade worker nodes
The upgrade procedure on worker nodes should be executed one node at a time or few nodes at a time,
without compromising the minimum required capacity for running your workloads.
The following pages show how to upgrade Linux and Windows worker nodes:
After the kubelet is upgraded on all nodes verify that all nodes are available again by running
the following command from anywhere kubectl can access the cluster:
kubectl get nodes
The STATUS column should show Ready for all your nodes, and the version number should be updated.
Recovering from a failure state
If kubeadm upgrade fails and does not roll back, for example because of an unexpected shutdown during execution, you can run kubeadm upgrade again.
This command is idempotent and eventually makes sure that the actual state is the desired state you declare.
To recover from a bad state, you can also run kubeadm upgrade apply --force without changing the version that your cluster is running.
During upgrade kubeadm writes the following backup folders under /etc/kubernetes/tmp:
kubeadm-backup-etcd-<date>-<time>
kubeadm-backup-manifests-<date>-<time>
kubeadm-backup-etcd contains a backup of the local etcd member data for this control plane Node.
In case of an etcd upgrade failure and if the automatic rollback does not work, the contents of this folder
can be manually restored in /var/lib/etcd. In case external etcd is used this backup folder will be empty.
kubeadm-backup-manifests contains a backup of the static Pod manifest files for this control plane Node.
In case of a upgrade failure and if the automatic rollback does not work, the contents of this folder can be
manually restored in /etc/kubernetes/manifests. If for some reason there is no difference between a pre-upgrade
and post-upgrade manifest file for a certain component, a backup file for it will not be written.
How it works
kubeadm upgrade apply does the following:
Checks that your cluster is in an upgradeable state:
The API server is reachable
All nodes are in the Ready state
The control plane is healthy
Enforces the version skew policies.
Makes sure the control plane images are available or available to pull to the machine.
Generates replacements and/or uses user supplied overwrites if component configs require version upgrades.
Upgrades the control plane components or rollbacks if any of them fails to come up.
Applies the new CoreDNS and kube-proxy manifests and makes sure that all necessary RBAC rules are created.
Creates new certificate and key files of the API server and backs up old files if they're about to expire in 180 days.
kubeadm upgrade node does the following on additional control plane nodes:
Fetches the kubeadm ClusterConfiguration from the cluster.
Optionally backups the kube-apiserver certificate.
Upgrades the static Pod manifests for the control plane components.
Upgrades the kubelet configuration for this node.
kubeadm upgrade node does the following on worker nodes:
Fetches the kubeadm ClusterConfiguration from the cluster.
Upgrades the kubelet configuration for this node.
2.1.5 - Upgrading Linux nodes
This page explains how to upgrade a Linux Worker Nodes created with kubeadm.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
If you're using the community-owned package repositories (pkgs.k8s.io), you need to
enable the package repository for the desired Kubernetes minor release. This is explained in
Changing the Kubernetes package repository
document.
Note: The legacy package repositories (apt.kubernetes.io and yum.kubernetes.io) have been
deprecated and frozen starting from September 13, 2023.
Using the new package repositories hosted at pkgs.k8s.io
is strongly recommended and required in order to install Kubernetes versions released after September 13, 2023.
The deprecated legacy repositories, and their contents, might be removed at any time in the future and without
a further notice period. The new package repositories provide downloads for Kubernetes versions starting with v1.24.0.
# replace x in 1.28.x-* with the latest patch versionapt-mark unhold kubeadm &&\
apt-get update && apt-get install -y kubeadm='1.28.x-*'&&\
apt-mark hold kubeadm
# replace x in 1.28.x-* with the latest patch versionyum install -y kubeadm-'1.28.x-*' --disableexcludes=kubernetes
Call "kubeadm upgrade"
For worker nodes this upgrades the local kubelet configuration:
sudo kubeadm upgrade node
Drain the node
Prepare the node for maintenance by marking it unschedulable and evicting the workloads:
# replace <node-to-drain> with the name of your node you are drainingkubectl drain <node-to-drain> --ignore-daemonsets
# replace x in 1.28.x-* with the latest patch versionapt-mark unhold kubelet kubectl &&\
apt-get update && apt-get install -y kubelet='1.28.x-*'kubectl='1.28.x-*'&&\
apt-mark hold kubelet kubectl
# replace x in 1.28.x-* with the latest patch versionyum install -y kubelet-'1.28.x-*' kubectl-'1.28.x-*' --disableexcludes=kubernetes
This page explains how to upgrade a Windows node created with kubeadm.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Note: If you are running kube-proxy in a HostProcess container within a Pod, and not as a Windows Service,
you can upgrade kube-proxy by applying a newer version of your kube-proxy manifests.
Uncordon the node
From a machine with access to the Kubernetes API,
bring the node back online by marking it schedulable:
# replace <node-to-drain> with the name of your nodekubectl uncordon <node-to-drain>
2.1.7 - Changing The Kubernetes Package Repository
This page explains how to enable a package repository for the desired
Kubernetes minor release upon upgrading a cluster. This is only needed
for users of the community-owned package repositories hosted at pkgs.k8s.io.
Unlike the legacy package repositories, the community-owned package
repositories are structured in a way that there's a dedicated package
repository for each Kubernetes minor version.
Note: This guide only covers a part of the Kubernetes upgrade process. Please see the
upgrade guide for
more information about upgrading Kubernetes clusters.
Note: This step is only needed upon upgrading a cluster to another minor release.
If you're upgrading to another patch release within the same minor release (e.g.
v1.28.5 to v1.28.7), you don't
need to follow this guide. However, if you're still using the legacy package
repositories, you'll need to migrate to the new community-owned package
repositories before upgrading (see the next section for more details on how to
do this).
Before you begin
This document assumes that you're already using the community-owned
package repositories (pkgs.k8s.io). If that's not the case, it's strongly
recommended to migrate to the community-owned package repositories as described
in the official announcement.
Note: The legacy package repositories (apt.kubernetes.io and yum.kubernetes.io) have been
deprecated and frozen starting from September 13, 2023.
Using the new package repositories hosted at pkgs.k8s.io
is strongly recommended and required in order to install Kubernetes versions released after September 13, 2023.
The deprecated legacy repositories, and their contents, might be removed at any time in the future and without
a further notice period. The new package repositories provide downloads for Kubernetes versions starting with v1.24.0.
Verifying if the Kubernetes package repositories are used
If you're unsure whether you're using the community-owned package repositories or the
legacy package repositories, take the following steps to verify:
Print the contents of the file that defines the Kubernetes apt repository:
# On your system, this configuration file could have a different namepager /etc/apt/sources.list.d/kubernetes.list
If you see a line similar to:
deb [signed-by=/etc/apt/keyrings/kubernetes-apt-keyring.gpg] https://pkgs.k8s.io/core:/stable:/v1.27/deb/ /
You're using the Kubernetes package repositories and this guide applies to you.
Otherwise, it's strongly recommended to migrate to the Kubernetes package repositories
as described in the official announcement.
Print the contents of the file that defines the Kubernetes yum repository:
# On your system, this configuration file could have a different namecat /etc/yum.repos.d/kubernetes.repo
If you see a baseurl similar to the baseurl in the output below:
You're using the Kubernetes package repositories and this guide applies to you.
Otherwise, it's strongly recommended to migrate to the Kubernetes package repositories
as described in the official announcement.
Print the contents of the file that defines the Kubernetes zypper repository:
# On your system, this configuration file could have a different namecat /etc/zypp/repos.d/kubernetes.repo
If you see a baseurl similar to the baseurl in the output below:
You're using the Kubernetes package repositories and this guide applies to you.
Otherwise, it's strongly recommended to migrate to the Kubernetes package repositories
as described in the official announcement.
Note:
The URL used for the Kubernetes package repositories is not limited to pkgs.k8s.io,
it can also be one of:
pkgs.k8s.io
pkgs.kubernetes.io
packages.kubernetes.io
Switching to another Kubernetes package repository
This step should be done upon upgrading from one to another Kubernetes minor
release in order to get access to the packages of the desired Kubernetes minor
version.
Open the file that defines the Kubernetes apt repository using a text editor of your choice:
nano /etc/apt/sources.list.d/kubernetes.list
You should see a single line with the URL that contains your current Kubernetes
minor version. For example, if you're using v1.27,
you should see this:
deb [signed-by=/etc/apt/keyrings/kubernetes-apt-keyring.gpg] https://pkgs.k8s.io/core:/stable:/v1.27/deb/ /
Change the version in the URL to the next available minor release, for example:
deb [signed-by=/etc/apt/keyrings/kubernetes-apt-keyring.gpg] https://pkgs.k8s.io/core:/stable:/v1.28/deb/ /
Save the file and exit your text editor. Continue following the relevant upgrade instructions.
Open the file that defines the Kubernetes yum repository using a text editor of your choice:
nano /etc/yum.repos.d/kubernetes.repo
You should see a file with two URLs that contain your current Kubernetes
minor version. For example, if you're using v1.27,
you should see this:
This section presents information you need to know when migrating from
dockershim to other container runtimes.
Since the announcement of dockershim deprecation
in Kubernetes 1.20, there were questions on how this will affect various workloads and Kubernetes
installations. Our Dockershim Removal FAQ is there to help you
to understand the problem better.
Dockershim was removed from Kubernetes with the release of v1.24.
If you use Docker Engine via dockershim as your container runtime and wish to upgrade to v1.24,
it is recommended that you either migrate to another runtime or find an alternative means to obtain Docker Engine support.
Check out the container runtimes
section to know your options.
The version of Kubernetes with dockershim (1.23) is out of support and the v1.24
will run out of support soon. Make sure to
report issues you encountered
with the migration so the issues can be fixed in a timely manner and your cluster would be
ready for dockershim removal. After v1.24 running out of support, you will need
to contact your Kubernetes provider for support or upgrade multiple versions at a time
if there are critical issues affecting your cluster.
Your cluster might have more than one kind of node, although this is not a common
configuration.
Check out container runtimes
to understand your options for an alternative.
If you find a defect or other technical concern relating to migrating away from dockershim,
you can report an issue
to the Kubernetes project.
2.2.1 - Changing the Container Runtime on a Node from Docker Engine to containerd
This task outlines the steps needed to update your container runtime to containerd from Docker. It
is applicable for cluster operators running Kubernetes 1.23 or earlier. This also covers an
example scenario for migrating from dockershim to containerd. Alternative container runtimes
can be picked from this page.
Before you begin
Note:
This section links to third party projects that provide functionality required by Kubernetes. The Kubernetes project authors aren't responsible for these projects, which are listed alphabetically. To add a project to this list, read the content guide before submitting a change. More information.
Install the containerd.io package from the official Docker repositories.
Instructions for setting up the Docker repository for your respective Linux distribution and
installing the containerd.io package can be found at
Getting started with containerd.
Configure the kubelet to use containerd as its container runtime
Edit the file /var/lib/kubelet/kubeadm-flags.env and add the containerd runtime to the flags;
--container-runtime-endpoint=unix:///run/containerd/containerd.sock.
Users using kubeadm should be aware that the kubeadm tool stores the CRI socket for each host as
an annotation in the Node object for that host. To change it you can execute the following command
on a machine that has the kubeadm /etc/kubernetes/admin.conf file.
kubectl edit no <node-name>
This will start a text editor where you can edit the Node object.
To choose a text editor you can set the KUBE_EDITOR environment variable.
Change the value of kubeadm.alpha.kubernetes.io/cri-socket from /var/run/dockershim.sock
to the CRI socket path of your choice (for example unix:///run/containerd/containerd.sock).
Note that new CRI socket paths must be prefixed with unix:// ideally.
Save the changes in the text editor, which will update the Node object.
Restart the kubelet
systemctl start kubelet
Verify that the node is healthy
Run kubectl get nodes -o wide and containerd appears as the runtime for the node we just changed.
Remove Docker Engine
Note:
This section links to third party projects that provide functionality required by Kubernetes. The Kubernetes project authors aren't responsible for these projects, which are listed alphabetically. To add a project to this list, read the content guide before submitting a change. More information.
The preceding commands don't remove images, containers, volumes, or customized configuration files on your host.
To delete them, follow Docker's instructions to Uninstall Docker Engine.
Caution: Docker's instructions for uninstalling Docker Engine create a risk of deleting containerd. Be careful when executing commands.
Uncordon the node
kubectl uncordon <node-to-uncordon>
Replace <node-to-uncordon> with the name of your node you previously drained.
2.2.2 - Migrate Docker Engine nodes from dockershim to cri-dockerd
Note:
This section links to third party projects that provide functionality required by Kubernetes. The Kubernetes project authors aren't responsible for these projects, which are listed alphabetically. To add a project to this list, read the content guide before submitting a change. More information.
This page shows you how to migrate your Docker Engine nodes to use cri-dockerd
instead of dockershim. You should follow these steps in these scenarios:
You want to switch away from dockershim and still use Docker Engine to run
containers in Kubernetes.
You want to upgrade to Kubernetes v1.28 and your
existing cluster relies on dockershim, in which case you must migrate
from dockershim and cri-dockerd is one of your options.
To learn more about the removal of dockershim, read the FAQ page.
What is cri-dockerd?
In Kubernetes 1.23 and earlier, you could use Docker Engine with Kubernetes,
relying on a built-in component of Kubernetes named dockershim.
The dockershim component was removed in the Kubernetes 1.24 release; however,
a third-party replacement, cri-dockerd, is available. The cri-dockerd adapter
lets you use Docker Engine through the Container Runtime Interface.
If you want to migrate to cri-dockerd so that you can continue using Docker
Engine as your container runtime, you should do the following for each affected
node:
Install cri-dockerd.
Cordon and drain the node.
Configure the kubelet to use cri-dockerd.
Restart the kubelet.
Verify that the node is healthy.
Test the migration on non-critical nodes first.
You should perform the following steps for each node that you want to migrate
to cri-dockerd.
Cordon the node to stop new Pods scheduling on it:
kubectl cordon <NODE_NAME>
Replace <NODE_NAME> with the name of the node.
Drain the node to safely evict running Pods:
kubectl drain <NODE_NAME> \
--ignore-daemonsets
Configure the kubelet to use cri-dockerd
The following steps apply to clusters set up using the kubeadm tool. If you use
a different tool, you should modify the kubelet using the configuration
instructions for that tool.
Open /var/lib/kubelet/kubeadm-flags.env on each affected node.
Modify the --container-runtime-endpoint flag to
unix:///var/run/cri-dockerd.sock.
Modify the --container-runtime flag to remote
(unavailable in Kubernetes v1.27 and later).
The kubeadm tool stores the node's socket as an annotation on the Node object
in the control plane. To modify this socket for each affected node:
Edit the YAML representation of the Node object:
KUBECONFIG=/path/to/admin.conf kubectl edit no <NODE_NAME>
Replace the following:
/path/to/admin.conf: the path to the kubectl configuration file,
admin.conf.
<NODE_NAME>: the name of the node you want to modify.
Change kubeadm.alpha.kubernetes.io/cri-socket from
/var/run/dockershim.sock to unix:///var/run/cri-dockerd.sock.
Save the changes. The Node object is updated on save.
Restart the kubelet
systemctl restart kubelet
Verify that the node is healthy
To check whether the node uses the cri-dockerd endpoint, follow the
instructions in Find out which runtime you use.
The --container-runtime-endpoint flag for the kubelet should be unix:///var/run/cri-dockerd.sock.
2.2.3 - Find Out What Container Runtime is Used on a Node
This page outlines steps to find out what container runtime
the nodes in your cluster use.
Depending on the way you run your cluster, the container runtime for the nodes may
have been pre-configured or you need to configure it. If you're using a managed
Kubernetes service, there might be vendor-specific ways to check what container runtime is
configured for the nodes. The method described on this page should work whenever
the execution of kubectl is allowed.
Before you begin
Install and configure kubectl. See Install Tools section for details.
Find out the container runtime used on a Node
Use kubectl to fetch and show node information:
kubectl get nodes -o wide
The output is similar to the following. The column CONTAINER-RUNTIME outputs
the runtime and its version.
For Docker Engine, the output is similar to this:
NAME STATUS VERSION CONTAINER-RUNTIME
node-1 Ready v1.16.15 docker://19.3.1
node-2 Ready v1.16.15 docker://19.3.1
node-3 Ready v1.16.15 docker://19.3.1
If your runtime shows as Docker Engine, you still might not be affected by the
removal of dockershim in Kubernetes v1.24.
Check the runtime endpoint to see if you use dockershim.
If you don't use dockershim, you aren't affected.
For containerd, the output is similar to this:
NAME STATUS VERSION CONTAINER-RUNTIME
node-1 Ready v1.19.6 containerd://1.4.1
node-2 Ready v1.19.6 containerd://1.4.1
node-3 Ready v1.19.6 containerd://1.4.1
Find out more information about container runtimes
on Container Runtimes
page.
Find out what container runtime endpoint you use
The container runtime talks to the kubelet over a Unix socket using the CRI
protocol, which is based on the gRPC
framework. The kubelet acts as a client, and the runtime acts as the server.
In some cases, you might find it useful to know which socket your nodes use. For
example, with the removal of dockershim in Kubernetes v1.24 and later, you might
want to know whether you use Docker Engine with dockershim.
Note: If you currently use Docker Engine in your nodes with cri-dockerd, you aren't
affected by the dockershim removal.
You can check which socket you use by checking the kubelet configuration on your
nodes.
Read the starting commands for the kubelet process:
tr \\0 ' ' < /proc/"$(pgrep kubelet)"/cmdline
If you don't have tr or pgrep, check the command line for the kubelet
process manually.
In the output, look for the --container-runtime flag and the
--container-runtime-endpoint flag.
If your nodes use Kubernetes v1.23 and earlier and these flags aren't
present or if the --container-runtime flag is not remote,
you use the dockershim socket with Docker Engine. The --container-runtime command line
argument is not available in Kubernetes v1.27 and later.
If the --container-runtime-endpoint flag is present, check the socket
name to find out which runtime you use. For example,
unix:///run/containerd/containerd.sock is the containerd endpoint.
If you want to change the Container Runtime on a Node from Docker Engine to containerd,
you can find out more information on migrating from Docker Engine to containerd,
or, if you want to continue using Docker Engine in Kubernetes v1.24 and later, migrate to a
CRI-compatible adapter like cri-dockerd.
2.2.4 - Troubleshooting CNI plugin-related errors
To avoid CNI plugin-related errors, verify that you are using or upgrading to a
container runtime that has been tested to work correctly with your version of
Kubernetes.
About the "Incompatible CNI versions" and "Failed to destroy network for sandbox" errors
Service issues exist for pod CNI network setup and tear down in containerd
v1.6.0-v1.6.3 when the CNI plugins have not been upgraded and/or the CNI config
version is not declared in the CNI config files. The containerd team reports, "these issues are resolved in containerd v1.6.4."
With containerd v1.6.0-v1.6.3, if you do not upgrade the CNI plugins and/or
declare the CNI config version, you might encounter the following "Incompatible
CNI versions" or "Failed to destroy network for sandbox" error conditions.
Incompatible CNI versions error
If the version of your CNI plugin does not correctly match the plugin version in
the config because the config version is later than the plugin version, the
containerd log will likely show an error message on startup of a pod similar
to:
If the version of the plugin is missing in the CNI plugin config, the pod may
run. However, stopping the pod generates an error similar to:
ERRO[2022-04-26T00:43:24.518165483Z] StopPodSandbox for "b" failed
error="failed to destroy network for sandbox \"bbc85f891eaf060c5a879e27bba9b6b06450210161dfdecfbb2732959fb6500a\": invalid version \"\": the version is empty"
This error leaves the pod in the not-ready state with a network namespace still
attached. To recover from this problem, edit the CNI config file to add
the missing version information. The next attempt to stop the pod should
be successful.
Updating your CNI plugins and CNI config files
If you're using containerd v1.6.0-v1.6.3 and encountered "Incompatible CNI
versions" or "Failed to destroy network for sandbox" errors, consider updating
your CNI plugins and editing the CNI config files.
Here's an overview of the typical steps for each node:
After stopping your container runtime and kubelet services, perform the
following upgrade operations:
If you're running CNI plugins, upgrade them to the latest version.
If you're using non-CNI plugins, replace them with CNI plugins. Use the
latest version of the plugins.
Update the plugin configuration file to specify or match a version of the
CNI specification that the plugin supports, as shown in the following "An
example containerd configuration
file" section.
For containerd, ensure that you have installed the latest version (v1.0.0
or later) of the CNI loopback plugin.
Upgrade node components (for example, the kubelet) to Kubernetes v1.24
Upgrade to or install the most current version of the container runtime.
Bring the node back into your cluster by restarting your container runtime
and kubelet. Uncordon the node (kubectl uncordon <nodename>).
An example containerd configuration file
The following example shows a configuration for containerd runtime v1.6.x,
which supports a recent version of the CNI specification (v1.0.0).
Please see the documentation from your plugin and networking provider for
further instructions on configuring your system.
On Kubernetes, containerd runtime adds a loopback interface, lo, to pods as a
default behavior. The containerd runtime configures the loopback interface via a
CNI plugin, loopback. The loopback plugin is distributed as part of the
containerd release packages that have the cni designation. containerd
v1.6.0 and later includes a CNI v1.0.0-compatible loopback plugin as well as
other default CNI plugins. The configuration for the loopback plugin is done
internally by containerd, and is set to use CNI v1.0.0. This also means that the
version of the loopback plugin must be v1.0.0 or later when this newer version
containerd is started.
The following bash command generates an example CNI config. Here, the 1.0.0
value for the config version is assigned to the cniVersion field for use when
containerd invokes the CNI bridge plugin.
Update the IP address ranges in the preceding example with ones that are based
on your use case and network addressing plan.
2.2.5 - Check whether dockershim removal affects you
The dockershim component of Kubernetes allows the use of Docker as a Kubernetes's
container runtime.
Kubernetes' built-in dockershim component was removed in release v1.24.
This page explains how your cluster could be using Docker as a container runtime,
provides details on the role that dockershim plays when in use, and shows steps
you can take to check whether any workloads could be affected by dockershim removal.
Finding if your app has a dependencies on Docker
If you are using Docker for building your application containers, you can still
run these containers on any container runtime. This use of Docker does not count
as a dependency on Docker as a container runtime.
When alternative container runtime is used, executing Docker commands may either
not work or yield unexpected output. This is how you can find whether you have a
dependency on Docker:
Make sure no privileged Pods execute Docker commands (like docker ps),
restart the Docker service (commands such as systemctl restart docker.service),
or modify Docker-specific files such as /etc/docker/daemon.json.
Check for any private registries or image mirror settings in the Docker
configuration file (like /etc/docker/daemon.json). Those typically need to
be reconfigured for another container runtime.
Check that scripts and apps running on nodes outside of your Kubernetes
infrastructure do not execute Docker commands. It might be:
SSH to nodes to troubleshoot;
Node startup scripts;
Monitoring and security agents installed on nodes directly.
Make sure there are no indirect dependencies on dockershim behavior.
This is an edge case and unlikely to affect your application. Some tooling may be configured
to react to Docker-specific behaviors, for example, raise alert on specific metrics or search for
a specific log message as part of troubleshooting instructions.
If you have such tooling configured, test the behavior on a test
cluster before migration.
Dependency on Docker explained
A container runtime is software that can
execute the containers that make up a Kubernetes pod. Kubernetes is responsible for orchestration
and scheduling of Pods; on each node, the kubelet
uses the container runtime interface as an abstraction so that you can use any compatible
container runtime.
In its earliest releases, Kubernetes offered compatibility with one container runtime: Docker.
Later in the Kubernetes project's history, cluster operators wanted to adopt additional container runtimes.
The CRI was designed to allow this kind of flexibility - and the kubelet began supporting CRI. However,
because Docker existed before the CRI specification was invented, the Kubernetes project created an
adapter component, dockershim. The dockershim adapter allows the kubelet to interact with Docker as
if Docker were a CRI compatible runtime.
Switching to Containerd as a container runtime eliminates the middleman. All the
same containers can be run by container runtimes like Containerd as before. But
now, since containers schedule directly with the container runtime, they are not visible to Docker.
So any Docker tooling or fancy UI you might have used
before to check on these containers is no longer available.
You cannot get container information using docker ps or docker inspect
commands. As you cannot list containers, you cannot get logs, stop containers,
or execute something inside a container using docker exec.
Note: If you're running workloads via Kubernetes, the best way to stop a container is through
the Kubernetes API rather than directly through the container runtime (this advice applies
for all container runtimes, not only Docker).
You can still pull images or build them using docker build command. But images
built or pulled by Docker would not be visible to container runtime and
Kubernetes. They needed to be pushed to some registry to allow them to be used
by Kubernetes.
Known issues
Some filesystem metrics are missing and the metrics format is different
The Kubelet /metrics/cadvisor endpoint provides Prometheus metrics,
as documented in Metrics for Kubernetes system components.
If you install a metrics collector that depends on that endpoint, you might see the following issues:
The metrics format on the Docker node is k8s_<container-name>_<pod-name>_<namespace>_<pod-uid>_<restart-count>
but the format on other runtime is different. For example, on containerd node it is <container-id>.
2.2.6 - Migrating telemetry and security agents from dockershim
Note:
This section links to third party projects that provide functionality required by Kubernetes. The Kubernetes project authors aren't responsible for these projects, which are listed alphabetically. To add a project to this list, read the content guide before submitting a change. More information.
Kubernetes' support for direct integration with Docker Engine is deprecated and
has been removed. Most apps do not have a direct dependency on runtime hosting
containers. However, there are still a lot of telemetry and monitoring agents
that have a dependency on Docker to collect containers metadata, logs, and
metrics. This document aggregates information on how to detect these
dependencies as well as links on how to migrate these agents to use generic tools or
alternative runtimes.
Telemetry and security agents
Within a Kubernetes cluster there are a few different ways to run telemetry or
security agents. Some agents have a direct dependency on Docker Engine when
they run as DaemonSets or directly on nodes.
Why do some telemetry agents communicate with Docker Engine?
Historically, Kubernetes was written to work specifically with Docker Engine.
Kubernetes took care of networking and scheduling, relying on Docker Engine for
launching and running containers (within Pods) on a node. Some information that
is relevant to telemetry, such as a pod name, is only available from Kubernetes
components. Other data, such as container metrics, is not the responsibility of
the container runtime. Early telemetry agents needed to query the container
runtime and Kubernetes to report an accurate picture. Over time, Kubernetes
gained the ability to support multiple runtimes, and now supports any runtime
that is compatible with the container runtime interface.
Some telemetry agents rely specifically on Docker Engine tooling. For example, an agent
might run a command such as
docker ps
or docker top to list
containers and processes or docker logs
to receive streamed logs. If nodes in your existing cluster use
Docker Engine, and you switch to a different container runtime,
these commands will not work any longer.
Identify DaemonSets that depend on Docker Engine
If a pod wants to make calls to the dockerd running on the node, the pod must either:
mount the filesystem containing the Docker daemon's privileged socket, as a
volume; or
mount the specific path of the Docker daemon's privileged socket directly, also as a volume.
For example: on COS images, Docker exposes its Unix domain socket at
/var/run/docker.sock This means that the pod spec will include a
hostPath volume mount of /var/run/docker.sock.
Here's a sample shell script to find Pods that have a mount directly mapping the
Docker socket. This script outputs the namespace and name of the pod. You can
remove the grep '/var/run/docker.sock' to review other mounts.
Note: There are alternative ways for a pod to access Docker on the host. For instance, the parent
directory /var/run may be mounted instead of the full path (like in this
example).
The script above only detects the most common uses.
Detecting Docker dependency from node agents
If your cluster nodes are customized and install additional security and
telemetry agents on the node, check with the agent vendor
to verify whether it has any dependency on Docker.
Telemetry and security agent vendors
This section is intended to aggregate information about various telemetry and
security agents that may have a dependency on container runtimes.
We keep the work in progress version of migration instructions for various telemetry and security agent vendors
in Google doc.
Please contact the vendor to get up to date instructions for migrating from dockershim.
Migrate Falco from dockershim
Falco supports any CRI-compatible runtime (containerd is used in the default configuration); the documentation explains all details.
The pod accessing Docker may have name containing:
Check documentation for Prisma Cloud,
under the "Install Prisma Cloud on a CRI (non-Docker) cluster" section.
The pod accessing Docker may be named like:
The SignalFx Smart Agent (deprecated) uses several different monitors for Kubernetes including
kubernetes-cluster, kubelet-stats/kubelet-metrics, and docker-container-stats.
The kubelet-stats monitor was previously deprecated by the vendor, in favor of kubelet-metrics.
The docker-container-stats monitor is the one affected by dockershim removal.
Do not use the docker-container-stats with container runtimes other than Docker Engine.
How to migrate from dockershim-dependent agent:
Remove docker-container-stats from the list of configured monitors.
Note, keeping this monitor enabled with non-dockershim runtime will result in incorrect metrics
being reported when docker is installed on node and no metrics when docker is not installed.
The argument --subject-alt-name sets the possible IPs and DNS names the API server will
be accessed with. The MASTER_CLUSTER_IP is usually the first IP from the service CIDR
that is specified as the --service-cluster-ip-range argument for both the API server and
the controller manager component. The argument --days is used to set the number of days
after which the certificate expires.
The sample below also assumes that you are using cluster.local as the default
DNS domain name.
Create a config file for generating a Certificate Signing Request (CSR).
Be sure to substitute the values marked with angle brackets (e.g. <MASTER_IP>)
with real values before saving this to a file (e.g. csr.conf).
Note that the value for MASTER_CLUSTER_IP is the service cluster IP for the
API server as described in previous subsection.
The sample below also assumes that you are using cluster.local as the default
DNS domain name.
Create a JSON config file for CA certificate signing request (CSR), for example,
ca-csr.json. Be sure to replace the values marked with angle brackets with
real values you want to use.
Generate CA key (ca-key.pem) and certificate (ca.pem):
../cfssl gencert -initca ca-csr.json | ../cfssljson -bare ca
Create a JSON config file for generating keys and certificates for the API
server, for example, server-csr.json. Be sure to replace the values in angle brackets with
real values you want to use. The <MASTER_CLUSTER_IP> is the service cluster
IP for the API server as described in previous subsection.
The sample below also assumes that you are using cluster.local as the default
DNS domain name.
A client node may refuse to recognize a self-signed CA certificate as valid.
For a non-production deployment, or for a deployment that runs behind a company
firewall, you can distribute a self-signed CA certificate to all clients and
refresh the local list for valid certificates.
Updating certificates in /etc/ssl/certs...
1 added, 0 removed; done.
Running hooks in /etc/ca-certificates/update.d....
done.
Certificates API
You can use the certificates.k8s.io API to provision
x509 certificates to use for authentication as documented
in the Managing TLS in a cluster
task page.
2.4 - Manage Memory, CPU, and API Resources
2.4.1 - Configure Default Memory Requests and Limits for a Namespace
Define a default memory resource limit for a namespace, so that every new Pod in that namespace has a memory resource limit configured.
This page shows how to configure default memory requests and limits for a
namespace.
A Kubernetes cluster can be divided into namespaces. Once you have a namespace that
has a default memory
limit,
and you then try to create a Pod with a container that does not specify its own memory
limit, then the
control plane assigns the default
memory limit to that container.
Kubernetes assigns a default memory request under certain conditions that are explained later in this topic.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Now if you create a Pod in the default-mem-example namespace, and any container
within that Pod does not specify its own values for memory request and memory limit,
then the control plane
applies default values: a memory request of 256MiB and a memory limit of 512MiB.
Here's an example manifest for a Pod that has one container. The container
does not specify a memory request and limit.
kubectl get pod default-mem-demo --output=yaml --namespace=default-mem-example
The output shows that the Pod's container has a memory request of 256 MiB and
a memory limit of 512 MiB. These are the default values specified by the LimitRange.
kubectl get pod default-mem-demo-2 --output=yaml --namespace=default-mem-example
The output shows that the container's memory request is set to match its memory limit.
Notice that the container was not assigned the default memory request value of 256Mi.
kubectl get pod default-mem-demo-3 --output=yaml --namespace=default-mem-example
The output shows that the container's memory request is set to the value specified in the
container's manifest. The container is limited to use no more than 512MiB of
memory, which matches the default memory limit for the namespace.
Note: A LimitRange does not check the consistency of the default values it applies. This means that a default value for the limit that is set by LimitRange may be less than the request value specified for the container in the spec that a client submits to the API server. If that happens, the final Pod will not be scheduleable.
See Constraints on resource limits and requests for more details.
Motivation for default memory limits and requests
If your namespace has a memory resource quota
configured,
it is helpful to have a default value in place for memory limit.
Here are three of the restrictions that a resource quota imposes on a namespace:
For every Pod that runs in the namespace, the Pod and each of its containers must have a memory limit.
(If you specify a memory limit for every container in a Pod, Kubernetes can infer the Pod-level memory
limit by adding up the limits for its containers).
Memory limits apply a resource reservation on the node where the Pod in question is scheduled.
The total amount of memory reserved for all Pods in the namespace must not exceed a specified limit.
The total amount of memory actually used by all Pods in the namespace must also not exceed a specified limit.
When you add a LimitRange:
If any Pod in that namespace that includes a container does not specify its own memory limit,
the control plane applies the default memory limit to that container, and the Pod can be
allowed to run in a namespace that is restricted by a memory ResourceQuota.
2.4.2 - Configure Default CPU Requests and Limits for a Namespace
Define a default CPU resource limits for a namespace, so that every new Pod in that namespace has a CPU resource limit configured.
This page shows how to configure default CPU requests and limits for a
namespace.
A Kubernetes cluster can be divided into namespaces. If you create a Pod within a
namespace that has a default CPU
limit, and any container in that Pod does not specify
its own CPU limit, then the
control plane assigns the default
CPU limit to that container.
Kubernetes assigns a default CPU
request,
but only under certain conditions that are explained later in this page.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Now if you create a Pod in the default-cpu-example namespace, and any container
in that Pod does not specify its own values for CPU request and CPU limit,
then the control plane applies default values: a CPU request of 0.5 and a default
CPU limit of 1.
Here's a manifest for a Pod that has one container. The container
does not specify a CPU request and limit.
kubectl get pod default-cpu-demo --output=yaml --namespace=default-cpu-example
The output shows that the Pod's only container has a CPU request of 500m cpu
(which you can read as “500 millicpu”), and a CPU limit of 1 cpu.
These are the default values specified by the LimitRange.
kubectl get pod default-cpu-demo-2 --output=yaml --namespace=default-cpu-example
The output shows that the container's CPU request is set to match its CPU limit.
Notice that the container was not assigned the default CPU request value of 0.5 cpu:
resources:
limits:
cpu: "1"
requests:
cpu: "1"
What if you specify a container's request, but not its limit?
Here's an example manifest for a Pod that has one container. The container
specifies a CPU request, but not a limit:
View the specification of the Pod that you created:
kubectl get pod default-cpu-demo-3 --output=yaml --namespace=default-cpu-example
The output shows that the container's CPU request is set to the value you specified at
the time you created the Pod (in other words: it matches the manifest).
However, the same container's CPU limit is set to 1 cpu, which is the default CPU limit
for that namespace.
resources:
limits:
cpu: "1"
requests:
cpu: 750m
Motivation for default CPU limits and requests
If your namespace has a CPU resource quota
configured,
it is helpful to have a default value in place for CPU limit.
Here are two of the restrictions that a CPU resource quota imposes on a namespace:
For every Pod that runs in the namespace, each of its containers must have a CPU limit.
CPU limits apply a resource reservation on the node where the Pod in question is scheduled.
The total amount of CPU that is reserved for use by all Pods in the namespace must not
exceed a specified limit.
When you add a LimitRange:
If any Pod in that namespace that includes a container does not specify its own CPU limit,
the control plane applies the default CPU limit to that container, and the Pod can be
allowed to run in a namespace that is restricted by a CPU ResourceQuota.
2.4.3 - Configure Minimum and Maximum Memory Constraints for a Namespace
Define a range of valid memory resource limits for a namespace, so that every new Pod in that namespace falls within the range you configure.
This page shows how to set minimum and maximum values for memory used by containers
running in a namespace.
You specify minimum and maximum memory values in a
LimitRange
object. If a Pod does not meet the constraints imposed by the LimitRange,
it cannot be created in the namespace.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
kubectl get limitrange mem-min-max-demo-lr --namespace=constraints-mem-example --output=yaml
The output shows the minimum and maximum memory constraints as expected. But
notice that even though you didn't specify default values in the configuration
file for the LimitRange, they were created automatically.
Now whenever you define a Pod within the constraints-mem-example namespace, Kubernetes
performs these steps:
If any container in that Pod does not specify its own memory request and limit,
the control plane assigns the default memory request and limit to that container.
Verify that every container in that Pod requests at least 500 MiB of memory.
Verify that every container in that Pod requests no more than 1024 MiB (1 GiB)
of memory.
Here's a manifest for a Pod that has one container. Within the Pod spec, the sole
container specifies a memory request of 600 MiB and a memory limit of 800 MiB. These satisfy the
minimum and maximum memory constraints imposed by the LimitRange.
Verify that the Pod is running and that its container is healthy:
kubectl get pod constraints-mem-demo --namespace=constraints-mem-example
View detailed information about the Pod:
kubectl get pod constraints-mem-demo --output=yaml --namespace=constraints-mem-example
The output shows that the container within that Pod has a memory request of 600 MiB and
a memory limit of 800 MiB. These satisfy the constraints imposed by the LimitRange for
this namespace:
The output shows that the Pod does not get created, because it defines a container that
requests more memory than is allowed:
Error from server (Forbidden): error when creating "examples/admin/resource/memory-constraints-pod-2.yaml":
pods "constraints-mem-demo-2" is forbidden: maximum memory usage per Container is 1Gi, but limit is 1536Mi.
Attempt to create a Pod that does not meet the minimum memory request
Here's a manifest for a Pod that has one container. That container specifies a
memory request of 100 MiB and a memory limit of 800 MiB.
The output shows that the Pod does not get created, because it defines a container
that requests less memory than the enforced minimum:
Error from server (Forbidden): error when creating "examples/admin/resource/memory-constraints-pod-3.yaml":
pods "constraints-mem-demo-3" is forbidden: minimum memory usage per Container is 500Mi, but request is 100Mi.
Create a Pod that does not specify any memory request or limit
Here's a manifest for a Pod that has one container. The container does not
specify a memory request, and it does not specify a memory limit.
Because your Pod did not define any memory request and limit for that container, the cluster
applied a
default memory request and limit
from the LimitRange.
This means that the definition of that Pod shows those values. You can check it using
kubectl describe:
# Look for the "Requests:" section of the outputkubectl describe pod constraints-mem-demo-4 --namespace=constraints-mem-example
At this point, your Pod might be running or it might not be running. Recall that a prerequisite
for this task is that your Nodes have at least 1 GiB of memory. If each of your Nodes has only
1 GiB of memory, then there is not enough allocatable memory on any Node to accommodate a memory
request of 1 GiB. If you happen to be using Nodes with 2 GiB of memory, then you probably have
enough space to accommodate the 1 GiB request.
Delete your Pod:
kubectl delete pod constraints-mem-demo-4 --namespace=constraints-mem-example
Enforcement of minimum and maximum memory constraints
The maximum and minimum memory constraints imposed on a namespace by a LimitRange are enforced only
when a Pod is created or updated. If you change the LimitRange, it does not affect
Pods that were created previously.
Motivation for minimum and maximum memory constraints
As a cluster administrator, you might want to impose restrictions on the amount of memory that Pods can use.
For example:
Each Node in a cluster has 2 GiB of memory. You do not want to accept any Pod that requests
more than 2 GiB of memory, because no Node in the cluster can support the request.
A cluster is shared by your production and development departments.
You want to allow production workloads to consume up to 8 GiB of memory, but
you want development workloads to be limited to 512 MiB. You create separate namespaces
for production and development, and you apply memory constraints to each namespace.
2.4.4 - Configure Minimum and Maximum CPU Constraints for a Namespace
Define a range of valid CPU resource limits for a namespace, so that every new Pod in that namespace falls within the range you configure.
This page shows how to set minimum and maximum values for the CPU resources used by containers
and Pods in a namespace. You specify minimum
and maximum CPU values in a
LimitRange
object. If a Pod does not meet the constraints imposed by the LimitRange, it cannot be created
in the namespace.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
kubectl get limitrange cpu-min-max-demo-lr --output=yaml --namespace=constraints-cpu-example
The output shows the minimum and maximum CPU constraints as expected. But
notice that even though you didn't specify default values in the configuration
file for the LimitRange, they were created automatically.
Now whenever you create a Pod in the constraints-cpu-example namespace (or some other client
of the Kubernetes API creates an equivalent Pod), Kubernetes performs these steps:
If any container in that Pod does not specify its own CPU request and limit, the control plane
assigns the default CPU request and limit to that container.
Verify that every container in that Pod specifies a CPU request that is greater than or equal to 200 millicpu.
Verify that every container in that Pod specifies a CPU limit that is less than or equal to 800 millicpu.
Note: When creating a LimitRange object, you can specify limits on huge-pages
or GPUs as well. However, when both default and defaultRequest are specified
on these resources, the two values must be the same.
Here's a manifest for a Pod that has one container. The container manifest
specifies a CPU request of 500 millicpu and a CPU limit of 800 millicpu. These satisfy the
minimum and maximum CPU constraints imposed by the LimitRange for this namespace.
Verify that the Pod is running and that its container is healthy:
kubectl get pod constraints-cpu-demo --namespace=constraints-cpu-example
View detailed information about the Pod:
kubectl get pod constraints-cpu-demo --output=yaml --namespace=constraints-cpu-example
The output shows that the Pod's only container has a CPU request of 500 millicpu and CPU limit
of 800 millicpu. These satisfy the constraints imposed by the LimitRange.
resources:limits:cpu:800mrequests:cpu:500m
Delete the Pod
kubectl delete pod constraints-cpu-demo --namespace=constraints-cpu-example
Attempt to create a Pod that exceeds the maximum CPU constraint
Here's a manifest for a Pod that has one container. The container specifies a
CPU request of 500 millicpu and a cpu limit of 1.5 cpu.
The output shows that the Pod does not get created, because it defines an unacceptable container.
That container is not acceptable because it specifies a CPU limit that is too large:
Error from server (Forbidden): error when creating "examples/admin/resource/cpu-constraints-pod-2.yaml":
pods "constraints-cpu-demo-2" is forbidden: maximum cpu usage per Container is 800m, but limit is 1500m.
Attempt to create a Pod that does not meet the minimum CPU request
Here's a manifest for a Pod that has one container. The container specifies a
CPU request of 100 millicpu and a CPU limit of 800 millicpu.
The output shows that the Pod does not get created, because it defines an unacceptable container.
That container is not acceptable because it specifies a CPU request that is lower than the
enforced minimum:
Error from server (Forbidden): error when creating "examples/admin/resource/cpu-constraints-pod-3.yaml":
pods "constraints-cpu-demo-3" is forbidden: minimum cpu usage per Container is 200m, but request is 100m.
Create a Pod that does not specify any CPU request or limit
Here's a manifest for a Pod that has one container. The container does not
specify a CPU request, nor does it specify a CPU limit.
kubectl get pod constraints-cpu-demo-4 --namespace=constraints-cpu-example --output=yaml
The output shows that the Pod's single container has a CPU request of 800 millicpu and a
CPU limit of 800 millicpu.
How did that container get those values?
resources:limits:cpu:800mrequests:cpu:800m
Because that container did not specify its own CPU request and limit, the control plane
applied the
default CPU request and limit
from the LimitRange for this namespace.
At this point, your Pod may or may not be running. Recall that a prerequisite for
this task is that your Nodes must have at least 1 CPU available for use. If each of your Nodes has only 1 CPU,
then there might not be enough allocatable CPU on any Node to accommodate a request of 800 millicpu.
If you happen to be using Nodes with 2 CPU, then you probably have enough CPU to accommodate the 800 millicpu request.
Delete your Pod:
kubectl delete pod constraints-cpu-demo-4 --namespace=constraints-cpu-example
Enforcement of minimum and maximum CPU constraints
The maximum and minimum CPU constraints imposed on a namespace by a LimitRange are enforced only
when a Pod is created or updated. If you change the LimitRange, it does not affect
Pods that were created previously.
Motivation for minimum and maximum CPU constraints
As a cluster administrator, you might want to impose restrictions on the CPU resources that Pods can use.
For example:
Each Node in a cluster has 2 CPU. You do not want to accept any Pod that requests
more than 2 CPU, because no Node in the cluster can support the request.
A cluster is shared by your production and development departments.
You want to allow production workloads to consume up to 3 CPU, but you want development workloads to be limited
to 1 CPU. You create separate namespaces for production and development, and you apply CPU constraints to
each namespace.
2.4.5 - Configure Memory and CPU Quotas for a Namespace
Define overall memory and CPU resource limits for a namespace.
This page shows how to set quotas for the total amount memory and CPU that
can be used by all Pods running in a namespace.
You specify quotas in a
ResourceQuota
object.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Verify that the Pod is running and that its (only) container is healthy:
kubectl get pod quota-mem-cpu-demo --namespace=quota-mem-cpu-example
Once again, view detailed information about the ResourceQuota:
kubectl get resourcequota mem-cpu-demo --namespace=quota-mem-cpu-example --output=yaml
The output shows the quota along with how much of the quota has been used.
You can see that the memory and CPU requests and limits for your Pod do not
exceed the quota.
In the manifest, you can see that the Pod has a memory request of 700 MiB.
Notice that the sum of the used memory request and this new memory
request exceeds the memory request quota: 600 MiB + 700 MiB > 1 GiB.
The second Pod does not get created. The output shows that creating the second Pod
would cause the memory request total to exceed the memory request quota.
Error from server (Forbidden): error when creating "examples/admin/resource/quota-mem-cpu-pod-2.yaml":
pods "quota-mem-cpu-demo-2" is forbidden: exceeded quota: mem-cpu-demo,
requested: requests.memory=700Mi,used: requests.memory=600Mi, limited: requests.memory=1Gi
Discussion
As you have seen in this exercise, you can use a ResourceQuota to restrict
the memory request total for all Pods running in a namespace.
You can also restrict the totals for memory limit, cpu request, and cpu limit.
Instead of managing total resource use within a namespace, you might want to restrict
individual Pods, or the containers in those Pods. To achieve that kind of limiting, use a
LimitRange.
Restrict how many Pods you can create within a namespace.
This page shows how to set a quota for the total number of Pods that can run
in a Namespace. You specify quotas in a
ResourceQuota
object.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
kubectl get deployment pod-quota-demo --namespace=quota-pod-example --output=yaml
The output shows that even though the Deployment specifies three replicas, only two
Pods were created because of the quota you defined earlier:
spec:...replicas:3...status:availableReplicas:2...lastUpdateTime:2021-04-02T20:57:05Zmessage: 'unable to create pods:pods "pod-quota-demo-1650323038-" is forbidden:exceeded quota: pod-demo, requested: pods=1, used: pods=2, limited:pods=2'
Choice of resource
In this task you have defined a ResourceQuota that limited the total number of Pods, but
you could also limit the total number of other kinds of object. For example, you
might decide to limit how many CronJobs
that can live in a single namespace.
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
To get familiar with Cilium easily you can follow the
Cilium Kubernetes Getting Started Guide
to perform a basic DaemonSet installation of Cilium in minikube.
To start minikube, minimal version required is >= v1.5.2, run the with the
following arguments:
minikube version
minikube version: v1.5.2
minikube start --network-plugin=cni
For minikube you can install Cilium using its CLI tool. To do so, first download the latest
version of the CLI with the following command:
Then extract the downloaded file to your /usr/local/bin directory with the following command:
sudo tar xzvfC cilium-linux-amd64.tar.gz /usr/local/bin
rm cilium-linux-amd64.tar.gz
After running the above commands, you can now install Cilium with the following command:
cilium install
Cilium will then automatically detect the cluster configuration and create and
install the appropriate components for a successful installation.
The components are:
Certificate Authority (CA) in Secret cilium-ca and certificates for Hubble (Cilium's observability layer).
Service accounts.
Cluster roles.
ConfigMap.
Agent DaemonSet and an Operator Deployment.
After the installation, you can view the overall status of the Cilium deployment with the cilium status command.
See the expected output of the status command
here.
The remainder of the Getting Started Guide explains how to enforce both L3/L4
(i.e., IP address + port) security policies, as well as L7 (e.g., HTTP) security
policies using an example application.
Deploying Cilium for Production Use
For detailed instructions around deploying Cilium for production, see:
Cilium Kubernetes Installation Guide
This documentation includes detailed requirements, instructions and example
production DaemonSet files.
Understanding Cilium components
Deploying a cluster with Cilium adds Pods to the kube-system namespace. To see
this list of Pods run:
kubectl get pods --namespace=kube-system -l k8s-app=cilium
You'll see a list of Pods similar to this:
NAME READY STATUS RESTARTS AGE
cilium-kkdhz 1/1 Running 0 3m23s
...
A cilium Pod runs on each node in your cluster and enforces network policy
on the traffic to/from Pods on that node using Linux BPF.
What's next
Once your cluster is running, you can follow the
Declare Network Policy
to try out Kubernetes NetworkPolicy with Cilium.
Have fun, and if you have questions, contact us using the
Cilium Slack Channel.
2.5.4 - Use Kube-router for NetworkPolicy
This page shows how to use Kube-router for NetworkPolicy.
Before you begin
You need to have a Kubernetes cluster running. If you do not already have a cluster, you can create one by using any of the cluster installers like Kops, Bootkube, Kubeadm etc.
Installing Kube-router addon
The Kube-router Addon comes with a Network Policy Controller that watches Kubernetes API server for any NetworkPolicy and pods updated and configures iptables rules and ipsets to allow or block traffic as directed by the policies. Please follow the trying Kube-router with cluster installers guide to install Kube-router addon.
What's next
Once you have installed the Kube-router addon, you can follow the Declare Network Policy to try out Kubernetes NetworkPolicy.
2.5.5 - Romana for NetworkPolicy
This page shows how to use Romana for NetworkPolicy.
The Weave Net addon for Kubernetes comes with a
Network Policy Controller
that automatically monitors Kubernetes for any NetworkPolicy annotations on all
namespaces and configures iptables rules to allow or block traffic as directed by the policies.
Test the installation
Verify that the weave works.
Enter the following command:
kubectl get pods -n kube-system -o wide
The output is similar to this:
NAME READY STATUS RESTARTS AGE IP NODE
weave-net-1t1qg 2/2 Running 0 9d 192.168.2.10 worknode3
weave-net-231d7 2/2 Running 1 7d 10.2.0.17 worknodegpu
weave-net-7nmwt 2/2 Running 3 9d 192.168.2.131 masternode
weave-net-pmw8w 2/2 Running 0 9d 192.168.2.216 worknode2
Each Node has a weave Pod, and all Pods are Running and 2/2 READY. (2/2 means that each Pod has weave and weave-npc.)
This page shows how to access clusters using the Kubernetes API.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
When accessing the Kubernetes API for the first time, use the
Kubernetes command-line tool, kubectl.
To access a cluster, you need to know the location of the cluster and have credentials
to access it. Typically, this is automatically set-up when you work through
a Getting started guide,
or someone else set up the cluster and provided you with credentials and a location.
Check the location and credentials that kubectl knows about with this command:
kubectl config view
Many of the examples provide an introduction to using
kubectl. Complete documentation is found in the kubectl manual.
Directly accessing the REST API
kubectl handles locating and authenticating to the API server. If you want to directly access the REST API with an http client like
curl or wget, or a browser, there are multiple ways you can locate and authenticate against the API server:
Run kubectl in proxy mode (recommended). This method is recommended, since it uses the stored apiserver location and verifies the identity of the API server using a self-signed cert. No man-in-the-middle (MITM) attack is possible using this method.
Alternatively, you can provide the location and credentials directly to the http client. This works with client code that is confused by proxies. To protect against man in the middle attacks, you'll need to import a root cert into your browser.
Using the Go or Python client libraries provides accessing kubectl in proxy mode.
Using kubectl proxy
The following command runs kubectl in a mode where it acts as a reverse proxy. It handles
locating the API server and authenticating.
It is possible to avoid using kubectl proxy by passing an authentication token
directly to the API server, like this:
Using grep/cut approach:
# Check all possible clusters, as your .KUBECONFIG may have multiple contexts:kubectl config view -o jsonpath='{"Cluster name\tServer\n"}{range .clusters[*]}{.name}{"\t"}{.cluster.server}{"\n"}{end}'# Select name of cluster you want to interact with from above output:exportCLUSTER_NAME="some_server_name"# Point to the API server referring the cluster nameAPISERVER=$(kubectl config view -o jsonpath="{.clusters[?(@.name==\"$CLUSTER_NAME\")].cluster.server}")# Create a secret to hold a token for the default service accountkubectl apply -f - <<EOF
apiVersion: v1
kind: Secret
metadata:
name: default-token
annotations:
kubernetes.io/service-account.name: default
type: kubernetes.io/service-account-token
EOF# Wait for the token controller to populate the secret with a token:while ! kubectl describe secret default-token | grep -E '^token' >/dev/null; doecho"waiting for token..." >&2 sleep 1done# Get the token valueTOKEN=$(kubectl get secret default-token -o jsonpath='{.data.token}' | base64 --decode)# Explore the API with TOKENcurl -X GET $APISERVER/api --header "Authorization: Bearer $TOKEN" --insecure
The above example uses the --insecure flag. This leaves it subject to MITM
attacks. When kubectl accesses the cluster it uses a stored root certificate
and client certificates to access the server. (These are installed in the
~/.kube directory). Since cluster certificates are typically self-signed, it
may take special configuration to get your http client to use root
certificate.
On some clusters, the API server does not require authentication; it may serve
on localhost, or be protected by a firewall. There is not a standard
for this. Controlling Access to the Kubernetes API
describes how you can configure this as a cluster administrator.
Programmatic access to the API
Kubernetes officially supports client libraries for Go, Python, Java, dotnet, JavaScript, and Haskell. There are other client libraries that are provided and maintained by their authors, not the Kubernetes team. See client libraries for accessing the API from other languages and how they authenticate.
Go client
To get the library, run the following command: go get k8s.io/client-go@kubernetes-<kubernetes-version-number> See https://github.com/kubernetes/client-go/releases to see which versions are supported.
Write an application atop of the client-go clients.
Note: client-go defines its own API objects, so if needed, import API definitions from client-go rather than from the main repository. For example, import "k8s.io/client-go/kubernetes" is correct.
The Go client can use the same kubeconfig file
as the kubectl CLI does to locate and authenticate to the API server. See this example:
package main
import (
"context""fmt""k8s.io/apimachinery/pkg/apis/meta/v1""k8s.io/client-go/kubernetes""k8s.io/client-go/tools/clientcmd")
funcmain() {
// uses the current context in kubeconfig
// path-to-kubeconfig -- for example, /root/.kube/config
config, _ := clientcmd.BuildConfigFromFlags("", "<path-to-kubeconfig>")
// creates the clientset
clientset, _ := kubernetes.NewForConfig(config)
// access the API to list pods
pods, _ := clientset.CoreV1().Pods("").List(context.TODO(), v1.ListOptions{})
fmt.Printf("There are %d pods in the cluster\n", len(pods.Items))
}
The Python client can use the same kubeconfig file
as the kubectl CLI does to locate and authenticate to the API server. See this example:
fromkubernetesimport client, config
config.load_kube_config()
v1=client.CoreV1Api()
print("Listing pods with their IPs:")
ret = v1.list_pod_for_all_namespaces(watch=False)
for i in ret.items:
print("%s\t%s\t%s"% (i.status.pod_ip, i.metadata.namespace, i.metadata.name))
The Java client can use the same kubeconfig file
as the kubectl CLI does to locate and authenticate to the API server. See this example:
packageio.kubernetes.client.examples;importio.kubernetes.client.ApiClient;importio.kubernetes.client.ApiException;importio.kubernetes.client.Configuration;importio.kubernetes.client.apis.CoreV1Api;importio.kubernetes.client.models.V1Pod;importio.kubernetes.client.models.V1PodList;importio.kubernetes.client.util.ClientBuilder;importio.kubernetes.client.util.KubeConfig;importjava.io.FileReader;importjava.io.IOException;/**
* A simple example of how to use the Java API from an application outside a kubernetes cluster
*
* <p>Easiest way to run this: mvn exec:java
* -Dexec.mainClass="io.kubernetes.client.examples.KubeConfigFileClientExample"
*
*/publicclassKubeConfigFileClientExample{publicstaticvoidmain(String[] args)throws IOException, ApiException {// file path to your KubeConfig
String kubeConfigPath ="~/.kube/config";// loading the out-of-cluster config, a kubeconfig from file-system
ApiClient client = ClientBuilder.kubeconfig(KubeConfig.loadKubeConfig(new FileReader(kubeConfigPath))).build();// set the global default api-client to the in-cluster one from above
Configuration.setDefaultApiClient(client);// the CoreV1Api loads default api-client from global configuration.
CoreV1Api api =new CoreV1Api();// invokes the CoreV1Api client
V1PodList list = api.listPodForAllNamespaces(null,null,null,null,null,null,null,null,null); System.out.println("Listing all pods: ");for(V1Pod item : list.getItems()){ System.out.println(item.getMetadata().getName());}}}
This page shows how to specify extended resources for a Node.
Extended resources allow cluster administrators to advertise node-level
resources that would otherwise be unknown to Kubernetes.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Choose one of your Nodes to use for this exercise.
Advertise a new extended resource on one of your Nodes
To advertise a new extended resource on a Node, send an HTTP PATCH request to
the Kubernetes API server. For example, suppose one of your Nodes has four dongles
attached. Here's an example of a PATCH request that advertises four dongle resources
for your Node.
Note that Kubernetes does not need to know what a dongle is or what a dongle is for.
The preceding PATCH request tells Kubernetes that your Node has four things that
you call dongles.
Start a proxy, so that you can easily send requests to the Kubernetes API server:
kubectl proxy
In another command window, send the HTTP PATCH request.
Replace <your-node-name> with the name of your Node:
Note: In the preceding request, ~1 is the encoding for the character / in
the patch path. The operation path value in JSON-Patch is interpreted as a
JSON-Pointer. For more details, see
IETF RFC 6901, section 3.
The output shows that the Node has a capacity of 4 dongles:
Extended resources are similar to memory and CPU resources. For example,
just as a Node has a certain amount of memory and CPU to be shared by all components
running on the Node, it can have a certain number of dongles to be shared
by all components running on the Node. And just as application developers
can create Pods that request a certain amount of memory and CPU, they can
create Pods that request a certain number of dongles.
Extended resources are opaque to Kubernetes; Kubernetes does not
know anything about what they are. Kubernetes knows only that a Node
has a certain number of them. Extended resources must be advertised in integer
amounts. For example, a Node can advertise four dongles, but not 4.5 dongles.
Storage example
Suppose a Node has 800 GiB of a special kind of disk storage. You could
create a name for the special storage, say example.com/special-storage.
Then you could advertise it in chunks of a certain size, say 100 GiB. In that case,
your Node would advertise that it has eight resources of type
example.com/special-storage.
Capacity:...example.com/special-storage:8
If you want to allow arbitrary requests for special storage, you
could advertise special storage in chunks of size 1 byte. In that case, you would advertise
800Gi resources of type example.com/special-storage.
Capacity:...example.com/special-storage:800Gi
Then a Container could request any number of bytes of special storage, up to 800Gi.
Clean up
Here is a PATCH request that removes the dongle advertisement from a Node.
This page shows how to enable and configure autoscaling of the DNS service in
your Kubernetes cluster.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
NAME READY UP-TO-DATE AVAILABLE AGE
...
dns-autoscaler 1/1 1 1 ...
...
If you see "dns-autoscaler" in the output, DNS horizontal autoscaling is
already enabled, and you can skip to
Tuning autoscaling parameters.
Get the name of your DNS Deployment
List the DNS deployments in your cluster in the kube-system namespace:
kubectl get deployment -l k8s-app=kube-dns --namespace=kube-system
The output is similar to this:
NAME READY UP-TO-DATE AVAILABLE AGE
...
coredns 2/2 2 2 ...
...
If you don't see a Deployment for DNS services, you can also look for it by name:
kubectl get deployment --namespace=kube-system
and look for a deployment named coredns or kube-dns.
Your scale target is
Deployment/<your-deployment-name>
where <your-deployment-name> is the name of your DNS Deployment. For example, if
the name of your Deployment for DNS is coredns, your scale target is Deployment/coredns.
Note: CoreDNS is the default DNS service for Kubernetes. CoreDNS sets the label
k8s-app=kube-dns so that it can work in clusters that originally used
kube-dns.
Enable DNS horizontal autoscaling
In this section, you create a new Deployment. The Pods in the Deployment run a
container based on the cluster-proportional-autoscaler-amd64 image.
Create a file named dns-horizontal-autoscaler.yaml with this content:
kind:ServiceAccountapiVersion:v1metadata:name:kube-dns-autoscalernamespace:kube-system---kind:ClusterRoleapiVersion:rbac.authorization.k8s.io/v1metadata:name:system:kube-dns-autoscalerrules:- apiGroups:[""]resources:["nodes"]verbs:["list","watch"]- apiGroups:[""]resources:["replicationcontrollers/scale"]verbs:["get","update"]- apiGroups:["apps"]resources:["deployments/scale","replicasets/scale"]verbs:["get","update"]# Remove the configmaps rule once below issue is fixed:# kubernetes-incubator/cluster-proportional-autoscaler#16- apiGroups:[""]resources:["configmaps"]verbs:["get","create"]---kind:ClusterRoleBindingapiVersion:rbac.authorization.k8s.io/v1metadata:name:system:kube-dns-autoscalersubjects:- kind:ServiceAccountname:kube-dns-autoscalernamespace:kube-systemroleRef:kind:ClusterRolename:system:kube-dns-autoscalerapiGroup:rbac.authorization.k8s.io---apiVersion:apps/v1kind:Deploymentmetadata:name:kube-dns-autoscalernamespace:kube-systemlabels:k8s-app:kube-dns-autoscalerkubernetes.io/cluster-service:"true"spec:selector:matchLabels:k8s-app:kube-dns-autoscalertemplate:metadata:labels:k8s-app:kube-dns-autoscalerspec:priorityClassName:system-cluster-criticalsecurityContext:seccompProfile:type:RuntimeDefaultsupplementalGroups:[65534]fsGroup:65534nodeSelector:kubernetes.io/os:linuxcontainers:- name:autoscalerimage:registry.k8s.io/cpa/cluster-proportional-autoscaler:1.8.4resources:requests:cpu:"20m"memory:"10Mi"command:- /cluster-proportional-autoscaler- --namespace=kube-system- --configmap=kube-dns-autoscaler# Should keep target in sync with cluster/addons/dns/kube-dns.yaml.base- --target=<SCALE_TARGET># When cluster is using large nodes(with more cores), "coresPerReplica" should dominate.# If using small nodes, "nodesPerReplica" should dominate.- --default-params={"linear":{"coresPerReplica":256,"nodesPerReplica":16,"preventSinglePointFailure":true,"includeUnschedulableNodes":true}}- --logtostderr=true- --v=2tolerations:- key:"CriticalAddonsOnly"operator:"Exists"serviceAccountName:kube-dns-autoscaler
In the file, replace <SCALE_TARGET> with your scale target.
Go to the directory that contains your configuration file, and enter this
command to create the Deployment:
Modify the fields according to your needs. The "min" field indicates the
minimal number of DNS backends. The actual number of backends is
calculated using this equation:
Note that the values of both coresPerReplica and nodesPerReplica are
floats.
The idea is that when a cluster is using nodes that have many cores,
coresPerReplica dominates. When a cluster is using nodes that have fewer
cores, nodesPerReplica dominates.
After the manifest file is deleted, the Addon Manager will delete the
dns-autoscaler Deployment.
Understanding how DNS horizontal autoscaling works
The cluster-proportional-autoscaler application is deployed separately from
the DNS service.
An autoscaler Pod runs a client that polls the Kubernetes API server for the
number of nodes and cores in the cluster.
A desired replica count is calculated and applied to the DNS backends based on
the current schedulable nodes and cores and the given scaling parameters.
The scaling parameters and data points are provided via a ConfigMap to the
autoscaler, and it refreshes its parameters table every poll interval to be up
to date with the latest desired scaling parameters.
Changes to the scaling parameters are allowed without rebuilding or restarting
the autoscaler Pod.
The autoscaler provides a controller interface to support two control
patterns: linear and ladder.
This page shows how to change the default Storage Class that is used to
provision volumes for PersistentVolumeClaims that have no special requirements.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Depending on the installation method, your Kubernetes cluster may be deployed with
an existing StorageClass that is marked as default. This default StorageClass
is then used to dynamically provision storage for PersistentVolumeClaims
that do not require any specific storage class. See
PersistentVolumeClaim documentation
for details.
The pre-installed default StorageClass may not fit well with your expected workload;
for example, it might provision storage that is too expensive. If this is the case,
you can either change the default StorageClass or disable it completely to avoid
dynamic provisioning of storage.
Deleting the default StorageClass may not work, as it may be re-created
automatically by the addon manager running in your cluster. Please consult the docs for your installation
for details about addon manager and how to disable individual addons.
Changing the default StorageClass
List the StorageClasses in your cluster:
kubectl get storageclass
The output is similar to this:
NAME PROVISIONER AGE
standard (default) kubernetes.io/gce-pd 1d
gold kubernetes.io/gce-pd 1d
The default StorageClass is marked by (default).
Mark the default StorageClass as non-default:
The default StorageClass has an annotation
storageclass.kubernetes.io/is-default-class set to true. Any other value
or absence of the annotation is interpreted as false.
To mark a StorageClass as non-default, you need to change its value to false:
kubectl patch storageclass standard -p '{"metadata": {"annotations":{"storageclass.kubernetes.io/is-default-class":"false"}}}'
where standard is the name of your chosen StorageClass.
Mark a StorageClass as default:
Similar to the previous step, you need to add/set the annotation
storageclass.kubernetes.io/is-default-class=true.
Please note that at most one StorageClass can be marked as default. If two
or more of them are marked as default, a PersistentVolumeClaim without storageClassName explicitly specified cannot be created.
Verify that your chosen StorageClass is default:
kubectl get storageclass
The output is similar to this:
NAME PROVISIONER AGE
standard kubernetes.io/gce-pd 1d
gold (default) kubernetes.io/gce-pd 1d
2.10 - Switching from Polling to CRI Event-based Updates to Container Status
FEATURE STATE:Kubernetes v1.27 [beta]
This page shows how to migrate nodes to use event based updates for container status. The event-based
implementation reduces node resource consumption by the kubelet, compared to the legacy approach
that relies on polling.
You may know this feature as evented Pod lifecycle event generator (PLEG). That's the name used
internally within the Kubernetes project for a key implementation detail.
The polling based approach is referred to as generic PLEG.
Before you begin
You need to run a version of Kubernetes that provides this feature.
Kubernetes v1.27 includes beta support for event-based container
status updates. The feature is beta but is disabled by default
because it requires support from the container runtime.
Your Kubernetes server must be at or later than version 1.26.
To check the version, enter kubectl version.
If you are running a different version of Kubernetes, check the documentation for that release.
The container runtime in use must support container lifecycle events.
The kubelet automatically switches back to the legacy generic PLEG
mechanism if the container runtime does not announce support for
container lifecycle events, even if you have this feature gate enabled.
Why switch to Evented PLEG?
The Generic PLEG incurs non-negligible overhead due to frequent polling of container statuses.
This overhead is exacerbated by Kubelet's parallelized polling of container states, thus limiting
its scalability and causing poor performance and reliability problems.
The goal of Evented PLEG is to reduce unnecessary work during inactivity
by replacing periodic polling.
Switching to Evented PLEG
Start the Kubelet with the feature gateEventedPLEG enabled. You can manage the kubelet feature gates editing the kubelet
config file and restarting the kubelet service.
You need to do this on each node where you are using this feature.
If you have set --v to 4 and above, you might see more entries that indicate
that the kubelet is using event-based container state monitoring.
I0314 11:12:42.009542 1110177 evented.go:238] "Evented PLEG: Generated pod status from the received event" podUID=3b2c6172-b112-447a-ba96-94e7022912dc
I0314 11:12:44.623326 1110177 evented.go:238] "Evented PLEG: Generated pod status from the received event" podUID=b3fba5ea-a8c5-4b76-8f43-481e17e8ec40
I0314 11:12:44.714564 1110177 evented.go:238] "Evented PLEG: Generated pod status from the received event" podUID=b3fba5ea-a8c5-4b76-8f43-481e17e8ec40
2.11 - Change the Reclaim Policy of a PersistentVolume
This page shows how to change the reclaim policy of a Kubernetes
PersistentVolume.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
PersistentVolumes can have various reclaim policies, including "Retain",
"Recycle", and "Delete". For dynamically provisioned PersistentVolumes,
the default reclaim policy is "Delete". This means that a dynamically provisioned
volume is automatically deleted when a user deletes the corresponding
PersistentVolumeClaim. This automatic behavior might be inappropriate if the volume
contains precious data. In that case, it is more appropriate to use the "Retain"
policy. With the "Retain" policy, if a user deletes a PersistentVolumeClaim,
the corresponding PersistentVolume will not be deleted. Instead, it is moved to the
Released phase, where all of its data can be manually recovered.
where <your-pv-name> is the name of your chosen PersistentVolume.
Note:
On Windows, you must double quote any JSONPath template that contains spaces (not single
quote as shown above for bash). This in turn means that you must use a single quote or escaped
double quote around any literals in the template. For example:
In the preceding output, you can see that the volume bound to claim
default/claim3 has reclaim policy Retain. It will not be automatically
deleted when a user deletes claim default/claim3.
Since cloud providers develop and release at a different pace compared to the
Kubernetes project, abstracting the provider-specific code to the
cloud-controller-manager
binary allows cloud vendors to evolve independently from the core Kubernetes code.
The cloud-controller-manager can be linked to any cloud provider that satisfies
cloudprovider.Interface.
For backwards compatibility, the
cloud-controller-manager
provided in the core Kubernetes project uses the same cloud libraries as kube-controller-manager.
Cloud providers already supported in Kubernetes core are expected to use the in-tree
cloud-controller-manager to transition out of Kubernetes core.
Administration
Requirements
Every cloud has their own set of requirements for running their own cloud provider
integration, it should not be too different from the requirements when running
kube-controller-manager. As a general rule of thumb you'll need:
cloud authentication/authorization: your cloud may require a token or IAM rules
to allow access to their APIs
kubernetes authentication/authorization: cloud-controller-manager may need RBAC
rules set to speak to the kubernetes apiserver
high availability: like kube-controller-manager, you may want a high available
setup for cloud controller manager using leader election (on by default).
Running cloud-controller-manager
Successfully running cloud-controller-manager requires some changes to your cluster configuration.
kubelet, kube-apiserver, and kube-controller-manager must be set according to the
user's usage of external CCM. If the user has an external CCM (not the internal cloud
controller loops in the Kubernetes Controller Manager), then --cloud-provider=external
must be specified. Otherwise, it should not be specified.
Keep in mind that setting up your cluster to use cloud controller manager will
change your cluster behaviour in a few ways:
Components that specify --cloud-provider=external will add a taint
node.cloudprovider.kubernetes.io/uninitialized with an effect NoSchedule
during initialization. This marks the node as needing a second initialization
from an external controller before it can be scheduled work. Note that in the
event that cloud controller manager is not available, new nodes in the cluster
will be left unschedulable. The taint is important since the scheduler may
require cloud specific information about nodes such as their region or type
(high cpu, gpu, high memory, spot instance, etc).
cloud information about nodes in the cluster will no longer be retrieved using
local metadata, but instead all API calls to retrieve node information will go
through cloud controller manager. This may mean you can restrict access to your
cloud API on the kubelets for better security. For larger clusters you may want
to consider if cloud controller manager will hit rate limits since it is now
responsible for almost all API calls to your cloud from within the cluster.
The cloud controller manager can implement:
Node controller - responsible for updating kubernetes nodes using cloud APIs
and deleting kubernetes nodes that were deleted on your cloud.
Service controller - responsible for loadbalancers on your cloud against
services of type LoadBalancer.
Route controller - responsible for setting up network routes on your cloud
any other features you would like to implement if you are running an out-of-tree provider.
Examples
If you are using a cloud that is currently supported in Kubernetes core and would
like to adopt cloud controller manager, see the
cloud controller manager in kubernetes core.
For cloud controller managers not in Kubernetes core, you can find the respective
projects in repositories maintained by cloud vendors or by SIGs.
For providers already in Kubernetes core, you can run the in-tree cloud controller
manager as a DaemonSet in your cluster, use the following as a guideline:
# This is an example of how to set up cloud-controller-manager as a Daemonset in your cluster.# It assumes that your masters can run pods and has the role node-role.kubernetes.io/master# Note that this Daemonset will not work straight out of the box for your cloud, this is# meant to be a guideline.---apiVersion:v1kind:ServiceAccountmetadata:name:cloud-controller-managernamespace:kube-system---apiVersion:rbac.authorization.k8s.io/v1kind:ClusterRoleBindingmetadata:name:system:cloud-controller-managerroleRef:apiGroup:rbac.authorization.k8s.iokind:ClusterRolename:cluster-adminsubjects:- kind:ServiceAccountname:cloud-controller-managernamespace:kube-system---apiVersion:apps/v1kind:DaemonSetmetadata:labels:k8s-app:cloud-controller-managername:cloud-controller-managernamespace:kube-systemspec:selector:matchLabels:k8s-app:cloud-controller-managertemplate:metadata:labels:k8s-app:cloud-controller-managerspec:serviceAccountName:cloud-controller-managercontainers:- name:cloud-controller-manager# for in-tree providers we use registry.k8s.io/cloud-controller-manager# this can be replaced with any other image for out-of-tree providersimage:registry.k8s.io/cloud-controller-manager:v1.8.0command:- /usr/local/bin/cloud-controller-manager- --cloud-provider=[YOUR_CLOUD_PROVIDER] # Add your own cloud provider here!- --leader-elect=true- --use-service-account-credentials# these flags will vary for every cloud provider- --allocate-node-cidrs=true- --configure-cloud-routes=true- --cluster-cidr=172.17.0.0/16tolerations:# this is required so CCM can bootstrap itself- key:node.cloudprovider.kubernetes.io/uninitializedvalue:"true"effect:NoSchedule# these tolerations are to have the daemonset runnable on control plane nodes# remove them if your control plane nodes should not run pods- key:node-role.kubernetes.io/control-planeoperator:Existseffect:NoSchedule- key:node-role.kubernetes.io/masteroperator:Existseffect:NoSchedule# this is to restrict CCM to only run on master nodes# the node selector may vary depending on your cluster setupnodeSelector:node-role.kubernetes.io/master:""
Limitations
Running cloud controller manager comes with a few possible limitations. Although
these limitations are being addressed in upcoming releases, it's important that
you are aware of these limitations for production workloads.
Support for Volumes
Cloud controller manager does not implement any of the volume controllers found
in kube-controller-manager as the volume integrations also require coordination
with kubelets. As we evolve CSI (container storage interface) and add stronger
support for flex volume plugins, necessary support will be added to cloud
controller manager so that clouds can fully integrate with volumes. Learn more
about out-of-tree CSI volume plugins here.
Scalability
The cloud-controller-manager queries your cloud provider's APIs to retrieve
information for all nodes. For very large clusters, consider possible
bottlenecks such as resource requirements and API rate limiting.
Chicken and Egg
The goal of the cloud controller manager project is to decouple development
of cloud features from the core Kubernetes project. Unfortunately, many aspects
of the Kubernetes project has assumptions that cloud provider features are tightly
integrated into the project. As a result, adopting this new architecture can create
several situations where a request is being made for information from a cloud provider,
but the cloud controller manager may not be able to return that information without
the original request being complete.
A good example of this is the TLS bootstrapping feature in the Kubelet.
TLS bootstrapping assumes that the Kubelet has the ability to ask the cloud provider
(or a local metadata service) for all its address types (private, public, etc)
but cloud controller manager cannot set a node's address types without being
initialized in the first place which requires that the kubelet has TLS certificates
to communicate with the apiserver.
As this initiative evolves, changes will be made to address these issues in upcoming releases.
2.13 - Configure a kubelet image credential provider
FEATURE STATE:Kubernetes v1.26 [stable]
Starting from Kubernetes v1.20, the kubelet can dynamically retrieve credentials for a container image registry
using exec plugins. The kubelet and the exec plugin communicate through stdio (stdin, stdout, and stderr) using
Kubernetes versioned APIs. These plugins allow the kubelet to request credentials for a container registry dynamically
as opposed to storing static credentials on disk. For example, the plugin may talk to a local metadata server to retrieve
short-lived credentials for an image that is being pulled by the kubelet.
You may be interested in using this capability if any of the below are true:
API calls to a cloud provider service are required to retrieve authentication information for a registry.
Credentials have short expiration times and requesting new credentials frequently is required.
Storing registry credentials on disk or in imagePullSecrets is not acceptable.
This guide demonstrates how to configure the kubelet's image credential provider plugin mechanism.
Before you begin
You need a Kubernetes cluster with nodes that support kubelet credential
provider plugins. This support is available in Kubernetes 1.28;
Kubernetes v1.24 and v1.25 included this as a beta feature, enabled by default.
A working implementation of a credential provider exec plugin. You can build your own plugin or use one provided by cloud providers.
Your Kubernetes server must be at or later than version v1.26.
To check the version, enter kubectl version.
Installing Plugins on Nodes
A credential provider plugin is an executable binary that will be run by the kubelet. Ensure that the plugin binary exists on
every node in your cluster and stored in a known directory. The directory will be required later when configuring kubelet flags.
Configuring the Kubelet
In order to use this feature, the kubelet expects two flags to be set:
--image-credential-provider-config - the path to the credential provider plugin config file.
--image-credential-provider-bin-dir - the path to the directory where credential provider plugin binaries are located.
Configure a kubelet credential provider
The configuration file passed into --image-credential-provider-config is read by the kubelet to determine which exec plugins
should be invoked for which container images. Here's an example configuration file you may end up using if you are using the
ECR-based plugin:
apiVersion:kubelet.config.k8s.io/v1kind:CredentialProviderConfig# providers is a list of credential provider helper plugins that will be enabled by the kubelet.# Multiple providers may match against a single image, in which case credentials# from all providers will be returned to the kubelet. If multiple providers are called# for a single image, the results are combined. If providers return overlapping# auth keys, the value from the provider earlier in this list is used.providers:# name is the required name of the credential provider. It must match the name of the# provider executable as seen by the kubelet. The executable must be in the kubelet's# bin directory (set by the --image-credential-provider-bin-dir flag).- name:ecr-credential-provider# matchImages is a required list of strings used to match against images in order to# determine if this provider should be invoked. If one of the strings matches the# requested image from the kubelet, the plugin will be invoked and given a chance# to provide credentials. Images are expected to contain the registry domain# and URL path.## Each entry in matchImages is a pattern which can optionally contain a port and a path.# Globs can be used in the domain, but not in the port or the path. Globs are supported# as subdomains like '*.k8s.io' or 'k8s.*.io', and top-level-domains such as 'k8s.*'.# Matching partial subdomains like 'app*.k8s.io' is also supported. Each glob can only match# a single subdomain segment, so `*.io` does **not** match `*.k8s.io`.## A match exists between an image and a matchImage when all of the below are true:# - Both contain the same number of domain parts and each part matches.# - The URL path of an matchImages must be a prefix of the target image URL path.# - If the matchImages contains a port, then the port must match in the image as well.## Example values of matchImages:# - 123456789.dkr.ecr.us-east-1.amazonaws.com# - *.azurecr.io# - gcr.io# - *.*.registry.io# - registry.io:8080/pathmatchImages:- "*.dkr.ecr.*.amazonaws.com"- "*.dkr.ecr.*.amazonaws.com.cn"- "*.dkr.ecr-fips.*.amazonaws.com"- "*.dkr.ecr.us-iso-east-1.c2s.ic.gov"- "*.dkr.ecr.us-isob-east-1.sc2s.sgov.gov"# defaultCacheDuration is the default duration the plugin will cache credentials in-memory# if a cache duration is not provided in the plugin response. This field is required.defaultCacheDuration:"12h"# Required input version of the exec CredentialProviderRequest. The returned CredentialProviderResponse# MUST use the same encoding version as the input. Current supported values are:# - credentialprovider.kubelet.k8s.io/v1apiVersion:credentialprovider.kubelet.k8s.io/v1# Arguments to pass to the command when executing it.# +optional# args:# - --example-argument# Env defines additional environment variables to expose to the process. These# are unioned with the host's environment, as well as variables client-go uses# to pass argument to the plugin.# +optionalenv:- name:AWS_PROFILEvalue:example_profile
The providers field is a list of enabled plugins used by the kubelet. Each entry has a few required fields:
name: the name of the plugin which MUST match the name of the executable binary that exists
in the directory passed into --image-credential-provider-bin-dir.
matchImages: a list of strings used to match against images in order to determine
if this provider should be invoked. More on this below.
defaultCacheDuration: the default duration the kubelet will cache credentials in-memory
if a cache duration was not specified by the plugin.
apiVersion: the API version that the kubelet and the exec plugin will use when communicating.
Each credential provider can also be given optional args and environment variables as well.
Consult the plugin implementors to determine what set of arguments and environment variables are required for a given plugin.
Configure image matching
The matchImages field for each credential provider is used by the kubelet to determine whether a plugin should be invoked
for a given image that a Pod is using. Each entry in matchImages is an image pattern which can optionally contain a port and a path.
Globs can be used in the domain, but not in the port or the path. Globs are supported as subdomains like *.k8s.io or k8s.*.io,
and top-level domains such as k8s.*. Matching partial subdomains like app*.k8s.io is also supported. Each glob can only match
a single subdomain segment, so *.io does NOT match *.k8s.io.
A match exists between an image name and a matchImage entry when all of the below are true:
Both contain the same number of domain parts and each part matches.
The URL path of match image must be a prefix of the target image URL path.
If the matchImages contains a port, then the port must match in the image as well.
This page shows how to configure quotas for API objects, including
PersistentVolumeClaims and Services. A quota restricts the number of
objects, of a particular type, that can be created in a namespace.
You specify quotas in a
ResourceQuota
object.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
View detailed information about the ResourceQuota:
kubectl get resourcequota object-quota-demo --namespace=quota-object-example --output=yaml
The output shows that in the quota-object-example namespace, there can be at most
one PersistentVolumeClaim, at most two Services of type LoadBalancer, and no Services
of type NodePort.
2.15 - Control CPU Management Policies on the Node
FEATURE STATE:Kubernetes v1.26 [stable]
Kubernetes keeps many aspects of how pods execute on nodes abstracted
from the user. This is by design. However, some workloads require
stronger guarantees in terms of latency and/or performance in order to operate
acceptably. The kubelet provides methods to enable more complex workload
placement policies while keeping the abstraction free from explicit placement
directives.
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Your Kubernetes server must be at or later than version v1.26.
To check the version, enter kubectl version.
If you are running an older version of Kubernetes, please look at the documentation for the version you are actually running.
CPU Management Policies
By default, the kubelet uses CFS quota
to enforce pod CPU limits. When the node runs many CPU-bound pods,
the workload can move to different CPU cores depending on
whether the pod is throttled and which CPU cores are available at
scheduling time. Many workloads are not sensitive to this migration and thus
work fine without any intervention.
However, in workloads where CPU cache affinity and scheduling latency
significantly affect workload performance, the kubelet allows alternative CPU
management policies to determine some placement preferences on the node.
Configuration
The CPU Manager policy is set with the --cpu-manager-policy kubelet
flag or the cpuManagerPolicy field in KubeletConfiguration.
There are two supported policies:
static: allows pods with certain resource characteristics to be
granted increased CPU affinity and exclusivity on the node.
The CPU manager periodically writes resource updates through the CRI in
order to reconcile in-memory CPU assignments with cgroupfs. The reconcile
frequency is set through a new Kubelet configuration value
--cpu-manager-reconcile-period. If not specified, it defaults to the same
duration as --node-status-update-frequency.
The behavior of the static policy can be fine-tuned using the --cpu-manager-policy-options flag.
The flag takes a comma-separated list of key=value policy options.
If you disable the CPUManagerPolicyOptionsfeature gate
then you cannot fine-tune CPU manager policies. In that case, the CPU manager
operates only using its default settings.
In addition to the top-level CPUManagerPolicyOptions feature gate, the policy options are split
into two groups: alpha quality (hidden by default) and beta quality (visible by default).
The groups are guarded respectively by the CPUManagerPolicyAlphaOptions
and CPUManagerPolicyBetaOptions feature gates. Diverging from the Kubernetes standard, these
feature gates guard groups of options, because it would have been too cumbersome to add a feature
gate for each individual option.
Changing the CPU Manager Policy
Since the CPU manager policy can only be applied when kubelet spawns new pods, simply changing from
"none" to "static" won't apply to existing pods. So in order to properly change the CPU manager
policy on a node, perform the following steps:
Remove the old CPU manager state file. The path to this file is
/var/lib/kubelet/cpu_manager_state by default. This clears the state maintained by the
CPUManager so that the cpu-sets set up by the new policy won’t conflict with it.
Edit the kubelet configuration to change the CPU manager policy to the desired value.
Start kubelet.
Repeat this process for every node that needs its CPU manager policy changed. Skipping this
process will result in kubelet crashlooping with the following error:
could not restore state from checkpoint: configured policy "static" differs from state checkpoint policy "none", please drain this node and delete the CPU manager checkpoint file "/var/lib/kubelet/cpu_manager_state" before restarting Kubelet
None policy
The none policy explicitly enables the existing default CPU
affinity scheme, providing no affinity beyond what the OS scheduler does
automatically. Limits on CPU usage for
Guaranteed pods and
Burstable pods
are enforced using CFS quota.
Static policy
The static policy allows containers in Guaranteed pods with integer CPU
requests access to exclusive CPUs on the node. This exclusivity is enforced
using the cpuset cgroup controller.
Note: System services such as the container runtime and the kubelet itself can continue to run on these exclusive CPUs. The exclusivity only extends to other pods.
Note: CPU Manager doesn't support offlining and onlining of
CPUs at runtime. Also, if the set of online CPUs changes on the node,
the node must be drained and CPU manager manually reset by deleting the
state file cpu_manager_state in the kubelet root directory.
This policy manages a shared pool of CPUs that initially contains all CPUs in the
node. The amount of exclusively allocatable CPUs is equal to the total
number of CPUs in the node minus any CPU reservations by the kubelet --kube-reserved or
--system-reserved options. From 1.17, the CPU reservation list can be specified
explicitly by kubelet --reserved-cpus option. The explicit CPU list specified by
--reserved-cpus takes precedence over the CPU reservation specified by
--kube-reserved and --system-reserved. CPUs reserved by these options are taken, in
integer quantity, from the initial shared pool in ascending order by physical
core ID. This shared pool is the set of CPUs on which any containers in
BestEffort and Burstable pods run. Containers in Guaranteed pods with fractional
CPU requests also run on CPUs in the shared pool. Only containers that are
both part of a Guaranteed pod and have integer CPU requests are assigned
exclusive CPUs.
Note: The kubelet requires a CPU reservation greater than zero be made
using either --kube-reserved and/or --system-reserved or --reserved-cpus when
the static policy is enabled. This is because zero CPU reservation would allow the shared
pool to become empty.
As Guaranteed pods whose containers fit the requirements for being statically
assigned are scheduled to the node, CPUs are removed from the shared pool and
placed in the cpuset for the container. CFS quota is not used to bound
the CPU usage of these containers as their usage is bound by the scheduling domain
itself. In others words, the number of CPUs in the container cpuset is equal to the integer
CPU limit specified in the pod spec. This static assignment increases CPU
affinity and decreases context switches due to throttling for the CPU-bound
workload.
Consider the containers in the following pod specs:
spec:containers:- name:nginximage:nginx
This pod runs in the BestEffort QoS class because no resource requests or
limits are specified. It runs in the shared pool.
This pod runs in the Burstable QoS class because resource requests do not
equal limits and the cpu quantity is not specified. It runs in the shared
pool.
This pod runs in the Guaranteed QoS class because requests are equal to limits.
And the container's resource limit for the CPU resource is an integer greater than
or equal to one. The nginx container is granted 2 exclusive CPUs.
This pod runs in the Guaranteed QoS class because requests are equal to limits.
But the container's resource limit for the CPU resource is a fraction. It runs in
the shared pool.
This pod runs in the Guaranteed QoS class because only limits are specified
and requests are set equal to limits when not explicitly specified. And the
container's resource limit for the CPU resource is an integer greater than or
equal to one. The nginx container is granted 2 exclusive CPUs.
Static policy options
You can toggle groups of options on and off based upon their maturity level
using the following feature gates:
CPUManagerPolicyBetaOptions default enabled. Disable to hide beta-level options.
CPUManagerPolicyAlphaOptions default disabled. Enable to show alpha-level options.
You will still have to enable each option using the CPUManagerPolicyOptions kubelet option.
The following policy options exist for the static CPUManager policy:
full-pcpus-only (beta, visible by default) (1.22 or higher)
distribute-cpus-across-numa (alpha, hidden by default) (1.23 or higher)
align-by-socket (alpha, hidden by default) (1.25 or higher)
If the full-pcpus-only policy option is specified, the static policy will always allocate full physical cores.
By default, without this option, the static policy allocates CPUs using a topology-aware best-fit allocation.
On SMT enabled systems, the policy can allocate individual virtual cores, which correspond to hardware threads.
This can lead to different containers sharing the same physical cores; this behaviour in turn contributes
to the noisy neighbours problem.
With the option enabled, the pod will be admitted by the kubelet only if the CPU request of all its containers
can be fulfilled by allocating full physical cores.
If the pod does not pass the admission, it will be put in Failed state with the message SMTAlignmentError.
If the distribute-cpus-across-numapolicy option is specified, the static
policy will evenly distribute CPUs across NUMA nodes in cases where more than
one NUMA node is required to satisfy the allocation.
By default, the CPUManager will pack CPUs onto one NUMA node until it is
filled, with any remaining CPUs simply spilling over to the next NUMA node.
This can cause undesired bottlenecks in parallel code relying on barriers (and
similar synchronization primitives), as this type of code tends to run only as
fast as its slowest worker (which is slowed down by the fact that fewer CPUs
are available on at least one NUMA node).
By distributing CPUs evenly across NUMA nodes, application developers can more
easily ensure that no single worker suffers from NUMA effects more than any
other, improving the overall performance of these types of applications.
If the align-by-socket policy option is specified, CPUs will be considered
aligned at the socket boundary when deciding how to allocate CPUs to a
container. By default, the CPUManager aligns CPU allocations at the NUMA
boundary, which could result in performance degradation if CPUs need to be
pulled from more than one NUMA node to satisfy the allocation. Although it
tries to ensure that all CPUs are allocated from the minimum number of NUMA
nodes, there is no guarantee that those NUMA nodes will be on the same socket.
By directing the CPUManager to explicitly align CPUs at the socket boundary
rather than the NUMA boundary, we are able to avoid such issues. Note, this
policy option is not compatible with TopologyManagersingle-numa-node
policy and does not apply to hardware where the number of sockets is greater
than number of NUMA nodes.
The full-pcpus-only option can be enabled by adding full-pcpus-only=true to
the CPUManager policy options.
Likewise, the distribute-cpus-across-numa option can be enabled by adding
distribute-cpus-across-numa=true to the CPUManager policy options.
When both are set, they are "additive" in the sense that CPUs will be
distributed across NUMA nodes in chunks of full-pcpus rather than individual
cores.
The align-by-socket policy option can be enabled by adding align-by-socket=true
to the CPUManager policy options. It is also additive to the full-pcpus-only
and distribute-cpus-across-numa policy options.
2.16 - Control Topology Management Policies on a node
FEATURE STATE:Kubernetes v1.27 [stable]
An increasing number of systems leverage a combination of CPUs and hardware accelerators to
support latency-critical execution and high-throughput parallel computation. These include
workloads in fields such as telecommunications, scientific computing, machine learning, financial
services and data analytics. Such hybrid systems comprise a high performance environment.
In order to extract the best performance, optimizations related to CPU isolation, memory and
device locality are required. However, in Kubernetes, these optimizations are handled by a
disjoint set of components.
Topology Manager is a Kubelet component that aims to coordinate the set of components that are
responsible for these optimizations.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Your Kubernetes server must be at or later than version v1.18.
To check the version, enter kubectl version.
How Topology Manager Works
Prior to the introduction of Topology Manager, the CPU and Device Manager in Kubernetes make
resource allocation decisions independently of each other. This can result in undesirable
allocations on multiple-socketed systems, performance/latency sensitive applications will suffer
due to these undesirable allocations. Undesirable in this case meaning for example, CPUs and
devices being allocated from different NUMA Nodes thus, incurring additional latency.
The Topology Manager is a Kubelet component, which acts as a source of truth so that other Kubelet
components can make topology aligned resource allocation choices.
The Topology Manager provides an interface for components, called Hint Providers, to send and
receive topology information. Topology Manager has a set of node level policies which are
explained below.
The Topology manager receives Topology information from the Hint Providers as a bitmask denoting
NUMA Nodes available and a preferred allocation indication. The Topology Manager policies perform
a set of operations on the hints provided and converge on the hint determined by the policy to
give the optimal result, if an undesirable hint is stored the preferred field for the hint will be
set to false. In the current policies preferred is the narrowest preferred mask.
The selected hint is stored as part of the Topology Manager. Depending on the policy configured
the pod can be accepted or rejected from the node based on the selected hint.
The hint is then stored in the Topology Manager for use by the Hint Providers when making the
resource allocation decisions.
Topology Manager Scopes and Policies
The Topology Manager currently:
Aligns Pods of all QoS classes.
Aligns the requested resources that Hint Provider provides topology hints for.
If these conditions are met, the Topology Manager will align the requested resources.
In order to customise how this alignment is carried out, the Topology Manager provides two
distinct knobs: scope and policy.
The scope defines the granularity at which you would like resource alignment to be performed
(e.g. at the pod or container level). And the policy defines the actual strategy used to
carry out the alignment (e.g. best-effort, restricted, single-numa-node, etc.).
Details on the various scopes and policies available today can be found below.
Note: To align CPU resources with other requested resources in a Pod Spec, the CPU Manager should be
enabled and proper CPU Manager policy should be configured on a Node.
See control CPU Management Policies.
Note: To align memory (and hugepages) resources with other requested resources in a Pod Spec, the Memory
Manager should be enabled and proper Memory Manager policy should be configured on a Node. Examine
Memory Manager documentation.
Topology Manager Scopes
The Topology Manager can deal with the alignment of resources in a couple of distinct scopes:
container (default)
pod
Either option can be selected at a time of the kubelet startup, with --topology-manager-scope
flag.
container scope
The container scope is used by default.
Within this scope, the Topology Manager performs a number of sequential resource alignments, i.e.,
for each container (in a pod) a separate alignment is computed. In other words, there is no notion
of grouping the containers to a specific set of NUMA nodes, for this particular scope. In effect,
the Topology Manager performs an arbitrary alignment of individual containers to NUMA nodes.
The notion of grouping the containers was endorsed and implemented on purpose in the following
scope, for example the pod scope.
pod scope
To select the pod scope, start the kubelet with the command line option --topology-manager-scope=pod.
This scope allows for grouping all containers in a pod to a common set of NUMA nodes. That is, the
Topology Manager treats a pod as a whole and attempts to allocate the entire pod (all containers)
to either a single NUMA node or a common set of NUMA nodes. The following examples illustrate the
alignments produced by the Topology Manager on different occasions:
all containers can be and are allocated to a single NUMA node;
all containers can be and are allocated to a shared set of NUMA nodes.
The total amount of particular resource demanded for the entire pod is calculated according to
effective requests/limits formula, and
thus, this total value is equal to the maximum of:
the sum of all app container requests,
the maximum of init container requests,
for a resource.
Using the pod scope in tandem with single-numa-node Topology Manager policy is specifically
valuable for workloads that are latency sensitive or for high-throughput applications that perform
IPC. By combining both options, you are able to place all containers in a pod onto a single NUMA
node; hence, the inter-NUMA communication overhead can be eliminated for that pod.
In the case of single-numa-node policy, a pod is accepted only if a suitable set of NUMA nodes
is present among possible allocations. Reconsider the example above:
a set containing only a single NUMA node - it leads to pod being admitted,
whereas a set containing more NUMA nodes - it results in pod rejection (because instead of one
NUMA node, two or more NUMA nodes are required to satisfy the allocation).
To recap, Topology Manager first computes a set of NUMA nodes and then tests it against Topology
Manager policy, which either leads to the rejection or admission of the pod.
Topology Manager Policies
Topology Manager supports four allocation policies. You can set a policy via a Kubelet flag,
--topology-manager-policy. There are four supported policies:
none (default)
best-effort
restricted
single-numa-node
Note: If Topology Manager is configured with the pod scope, the container, which is considered by
the policy, is reflecting requirements of the entire pod, and thus each container from the pod
will result with the same topology alignment decision.
none policy
This is the default policy and does not perform any topology alignment.
best-effort policy
For each container in a Pod, the kubelet, with best-effort topology management policy, calls
each Hint Provider to discover their resource availability. Using this information, the Topology
Manager stores the preferred NUMA Node affinity for that container. If the affinity is not
preferred, Topology Manager will store this and admit the pod to the node anyway.
The Hint Providers can then use this information when making the
resource allocation decision.
restricted policy
For each container in a Pod, the kubelet, with restricted topology management policy, calls each
Hint Provider to discover their resource availability. Using this information, the Topology
Manager stores the preferred NUMA Node affinity for that container. If the affinity is not
preferred, Topology Manager will reject this pod from the node. This will result in a pod in a
Terminated state with a pod admission failure.
Once the pod is in a Terminated state, the Kubernetes scheduler will not attempt to
reschedule the pod. It is recommended to use a ReplicaSet or Deployment to trigger a redeploy of
the pod. An external control loop could be also implemented to trigger a redeployment of pods that
have the Topology Affinity error.
If the pod is admitted, the Hint Providers can then use this information when making the
resource allocation decision.
single-numa-node policy
For each container in a Pod, the kubelet, with single-numa-node topology management policy,
calls each Hint Provider to discover their resource availability. Using this information, the
Topology Manager determines if a single NUMA Node affinity is possible. If it is, Topology
Manager will store this and the Hint Providers can then use this information when making the
resource allocation decision. If, however, this is not possible then the Topology Manager will
reject the pod from the node. This will result in a pod in a Terminated state with a pod
admission failure.
Once the pod is in a Terminated state, the Kubernetes scheduler will not attempt to
reschedule the pod. It is recommended to use a Deployment with replicas to trigger a redeploy of
the Pod.An external control loop could be also implemented to trigger a redeployment of pods
that have the Topology Affinity error.
Topology manager policy options
Support for the Topology Manager policy options requires TopologyManagerPolicyOptionsfeature gate to be enabled
(it is enabled by default).
You can toggle groups of options on and off based upon their maturity level using the following feature gates:
TopologyManagerPolicyBetaOptions default enabled. Enable to show beta-level options.
TopologyManagerPolicyAlphaOptions default disabled. Enable to show alpha-level options.
You will still have to enable each option using the TopologyManagerPolicyOptions kubelet option.
The following policy options exists:
prefer-closest-numa-nodes (beta, visible by default; TopologyManagerPolicyOptions and TopologyManagerPolicyBetaOptions feature gates have to be enabled).
The prefer-closest-numa-nodes policy option is beta in Kubernetes 1.28.
If the prefer-closest-numa-nodes policy option is specified, the best-effort and restricted
policies will favor sets of NUMA nodes with shorter distance between them when making admission decisions.
You can enable this option by adding prefer-closest-numa-nodes=true to the Topology Manager policy options.
By default, without this option, Topology Manager aligns resources on either a single NUMA node or
the minimum number of NUMA nodes (in cases where more than one NUMA node is required). However,
the TopologyManager is not aware of NUMA distances and does not take them into account when making admission decisions.
This limitation surfaces in multi-socket, as well as single-socket multi NUMA systems,
and can cause significant performance degradation in latency-critical execution and high-throughput applications if the
Topology Manager decides to align resources on non-adjacent NUMA nodes.
Pod Interactions with Topology Manager Policies
Consider the containers in the following pod specs:
spec:containers:- name:nginximage:nginx
This pod runs in the BestEffort QoS class because no resource requests or limits are specified.
This pod runs in the Burstable QoS class because requests are less than limits.
If the selected policy is anything other than none, Topology Manager would consider these Pod
specifications. The Topology Manager would consult the Hint Providers to get topology hints.
In the case of the static, the CPU Manager policy would return default topology hint, because
these Pods do not have explicitly request CPU resources.
This pod runs in the BestEffort QoS class because there are no CPU and memory requests.
The Topology Manager would consider the above pods. The Topology Manager would consult the Hint
Providers, which are CPU and Device Manager to get topology hints for the pods.
In the case of the Guaranteed pod with integer CPU request, the static CPU Manager policy
would return topology hints relating to the exclusive CPU and the Device Manager would send back
hints for the requested device.
In the case of the Guaranteed pod with sharing CPU request, the static CPU Manager policy
would return default topology hint as there is no exclusive CPU request and the Device Manager
would send back hints for the requested device.
In the above two cases of the Guaranteed pod, the none CPU Manager policy would return default
topology hint.
In the case of the BestEffort pod, the static CPU Manager policy would send back the default
topology hint as there is no CPU request and the Device Manager would send back the hints for each
of the requested devices.
Using this information the Topology Manager calculates the optimal hint for the pod and stores
this information, which will be used by the Hint Providers when they are making their resource
assignments.
Known Limitations
The maximum number of NUMA nodes that Topology Manager allows is 8. With more than 8 NUMA nodes
there will be a state explosion when trying to enumerate the possible NUMA affinities and
generating their hints.
The scheduler is not topology-aware, so it is possible to be scheduled on a node and then fail
on the node due to the Topology Manager.
2.17 - Customizing DNS Service
This page explains how to configure your DNS
Pod(s) and customize the
DNS resolution process in your cluster.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Your Kubernetes server must be at or later than version v1.12.
To check the version, enter kubectl version.
Introduction
DNS is a built-in Kubernetes service launched automatically
using the addon managercluster add-on.
Note: The CoreDNS Service is named kube-dns in the metadata.name field.
The intent is to ensure greater interoperability with workloads that relied on
the legacy kube-dns Service name to resolve addresses internal to the cluster.
Using a Service named kube-dns abstracts away the implementation detail of
which DNS provider is running behind that common name.
If you are running CoreDNS as a Deployment, it will typically be exposed as
a Kubernetes Service with a static IP address.
The kubelet passes DNS resolver information to each container with the
--cluster-dns=<dns-service-ip> flag.
DNS names also need domains. You configure the local domain in the kubelet
with the flag --cluster-domain=<default-local-domain>.
The DNS server supports forward lookups (A and AAAA records), port lookups (SRV records),
reverse IP address lookups (PTR records), and more. For more information, see
DNS for Services and Pods.
If a Pod's dnsPolicy is set to default, it inherits the name resolution
configuration from the node that the Pod runs on. The Pod's DNS resolution
should behave the same as the node.
But see Known issues.
If you don't want this, or if you want a different DNS config for pods, you can
use the kubelet's --resolv-conf flag. Set this flag to "" to prevent Pods from
inheriting DNS. Set it to a valid file path to specify a file other than
/etc/resolv.conf for DNS inheritance.
CoreDNS
CoreDNS is a general-purpose authoritative DNS server that can serve as cluster DNS,
complying with the DNS specifications.
CoreDNS ConfigMap options
CoreDNS is a DNS server that is modular and pluggable, with plugins adding new functionalities.
The CoreDNS server can be configured by maintaining a Corefile,
which is the CoreDNS configuration file. As a cluster administrator, you can modify the
ConfigMap for the CoreDNS Corefile to
change how DNS service discovery behaves for that cluster.
In Kubernetes, CoreDNS is installed with the following default Corefile configuration:
health: Health of CoreDNS is reported to
http://localhost:8080/health. In this extended syntax lameduck will make the process
unhealthy then wait for 5 seconds before the process is shut down.
ready: An HTTP endpoint on port 8181 will return 200 OK,
when all plugins that are able to signal readiness have done so.
kubernetes: CoreDNS will reply to DNS queries
based on IP of the Services and Pods. You can find more details
about this plugin on the CoreDNS website.
ttl allows you to set a custom TTL for responses. The default is 5 seconds.
The minimum TTL allowed is 0 seconds, and the maximum is capped at 3600 seconds.
Setting TTL to 0 will prevent records from being cached.
The pods insecure option is provided for backward compatibility with kube-dns.
You can use the pods verified option, which returns an A record only if there exists a pod
in the same namespace with a matching IP.
The pods disabled option can be used if you don't use pod records.
prometheus: Metrics of CoreDNS are available at
http://localhost:9153/metrics in the Prometheus format
(also known as OpenMetrics).
forward: Any queries that are not within the Kubernetes
cluster domain are forwarded to predefined resolvers (/etc/resolv.conf).
loop: Detects simple forwarding loops and
halts the CoreDNS process if a loop is found.
reload: Allows automatic reload of a changed Corefile.
After you edit the ConfigMap configuration, allow two minutes for your changes to take effect.
loadbalance: This is a round-robin DNS loadbalancer
that randomizes the order of A, AAAA, and MX records in the answer.
You can modify the default CoreDNS behavior by modifying the ConfigMap.
Configuration of Stub-domain and upstream nameserver using CoreDNS
CoreDNS has the ability to configure stub-domains and upstream nameservers
using the forward plugin.
Example
If a cluster operator has a Consul domain server located at "10.150.0.1",
and all Consul names have the suffix ".consul.local". To configure it in CoreDNS,
the cluster administrator creates the following stanza in the CoreDNS ConfigMap.
To explicitly force all non-cluster DNS lookups to go through a specific nameserver at 172.16.0.1,
point the forward to the nameserver instead of /etc/resolv.conf
forward . 172.16.0.1
The final ConfigMap along with the default Corefile configuration looks like:
Note: CoreDNS does not support FQDNs for stub-domains and nameservers (eg: "ns.foo.com").
During translation, all FQDN nameservers will be omitted from the CoreDNS config.
This page provides hints on diagnosing DNS problems.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Note: This example creates a pod in the default namespace. DNS name resolution for
services depends on the namespace of the pod. For more information, review
DNS for Services and Pods.
Use the kubectl get pods command to verify that the DNS pod is running.
kubectl get pods --namespace=kube-system -l k8s-app=kube-dns
NAME READY STATUS RESTARTS AGE
...
coredns-7b96bf9f76-5hsxb 1/1 Running 0 1h
coredns-7b96bf9f76-mvmmt 1/1 Running 0 1h
...
Note: The value for label k8s-app is kube-dns for both CoreDNS and kube-dns deployments.
If you see that no CoreDNS Pod is running or that the Pod has failed/completed,
the DNS add-on may not be deployed by default in your current environment and you
will have to deploy it manually.
Check for errors in the DNS pod
Use the kubectl logs command to see logs for the DNS containers.
See if there are any suspicious or unexpected messages in the logs.
Is DNS service up?
Verify that the DNS service is up by using the kubectl get service command.
kubectl get svc --namespace=kube-system
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
...
kube-dns ClusterIP 10.0.0.10 <none> 53/UDP,53/TCP 1h
...
Note: The service name is kube-dns for both CoreDNS and kube-dns deployments.
If you have created the Service or in the case it should be created by default
but it does not appear, see
debugging Services for
more information.
Are DNS endpoints exposed?
You can verify that DNS endpoints are exposed by using the kubectl get endpoints
command.
kubectl get endpoints kube-dns --namespace=kube-system
NAME ENDPOINTS AGE
kube-dns 10.180.3.17:53,10.180.3.17:53 1h
If you do not see the endpoints, see the endpoints section in the
debugging Services documentation.
For additional Kubernetes DNS examples, see the
cluster-dns examples
in the Kubernetes GitHub repository.
Are DNS queries being received/processed?
You can verify if queries are being received by CoreDNS by adding the log plugin to the CoreDNS configuration (aka Corefile).
The CoreDNS Corefile is held in a ConfigMap named coredns. To edit it, use the command:
kubectl -n kube-system edit configmap coredns
Then add log in the Corefile section per the example below:
After saving the changes, it may take up to minute or two for Kubernetes to propagate these changes to the CoreDNS pods.
Next, make some queries and view the logs per the sections above in this document. If CoreDNS pods are receiving the queries, you should see them in the logs.
Some Linux distributions (e.g. Ubuntu) use a local DNS resolver by default (systemd-resolved).
Systemd-resolved moves and replaces /etc/resolv.conf with a stub file that can cause a fatal forwarding
loop when resolving names in upstream servers. This can be fixed manually by using kubelet's --resolv-conf flag
to point to the correct resolv.conf (With systemd-resolved, this is /run/systemd/resolve/resolv.conf).
kubeadm automatically detects systemd-resolved, and adjusts the kubelet flags accordingly.
Kubernetes installs do not configure the nodes' resolv.conf files to use the
cluster DNS by default, because that process is inherently distribution-specific.
This should probably be implemented eventually.
Linux's libc (a.k.a. glibc) has a limit for the DNS nameserver records to 3 by
default and Kubernetes needs to consume 1 nameserver record. This means that
if a local installation already uses 3 nameservers, some of those entries will
be lost. To work around this limit, the node can run dnsmasq, which will
provide more nameserver entries. You can also use kubelet's --resolv-conf
flag.
If you are using Alpine version 3.17 or earlier as your base image, DNS may not
work properly due to a design issue with Alpine.
Until musl version 1.24 didn't include TCP fallback to the DNS stub resolver meaning any DNS call above 512 bytes would fail.
Please upgrade your images to Alpine version 3.18 or above.
This document helps you get started using the Kubernetes NetworkPolicy API to declare network policies that govern how pods communicate with each other.
Note:
This section links to third party projects that provide functionality required by Kubernetes. The Kubernetes project authors aren't responsible for these projects, which are listed alphabetically. To add a project to this list, read the content guide before submitting a change. More information.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Your Kubernetes server must be at or later than version v1.8.
To check the version, enter kubectl version.
Make sure you've configured a network provider with network policy support. There are a number of network providers that support NetworkPolicy, including:
Create an nginx deployment and expose it via a service
To see how Kubernetes network policy works, start off by creating an nginx Deployment.
kubectl create deployment nginx --image=nginx
deployment.apps/nginx created
Expose the Deployment through a Service called nginx.
kubectl expose deployment nginx --port=80
service/nginx exposed
The above commands create a Deployment with an nginx Pod and expose the Deployment through a Service named nginx. The nginx Pod and Deployment are found in the default namespace.
kubectl get svc,pod
NAME CLUSTER-IP EXTERNAL-IP PORT(S) AGE
service/kubernetes 10.100.0.1 <none> 443/TCP 46m
service/nginx 10.100.0.16 <none> 80/TCP 33s
NAME READY STATUS RESTARTS AGE
pod/nginx-701339712-e0qfq 1/1 Running 0 35s
Test the service by accessing it from another Pod
You should be able to access the new nginx service from other Pods. To access the nginx Service from another Pod in the default namespace, start a busybox container:
kubectl run busybox --rm -ti --image=busybox:1.28 -- /bin/sh
In your shell, run the following command:
wget --spider --timeout=1 nginx
Connecting to nginx (10.100.0.16:80)
remote file exists
Limit access to the nginx service
To limit the access to the nginx service so that only Pods with the label access: true can query it, create a NetworkPolicy object as follows:
Note: NetworkPolicy includes a podSelector which selects the grouping of Pods to which the policy applies. You can see this policy selects Pods with the label app=nginx. The label was automatically added to the Pod in the nginx Deployment. An empty podSelector selects all pods in the namespace.
Assign the policy to the service
Use kubectl to create a NetworkPolicy from the above nginx-policy.yaml file:
networkpolicy.networking.k8s.io/access-nginx created
Test access to the service when access label is not defined
When you attempt to access the nginx Service from a Pod without the correct labels, the request times out:
kubectl run busybox --rm -ti --image=busybox:1.28 -- /bin/sh
In your shell, run the command:
wget --spider --timeout=1 nginx
Connecting to nginx (10.100.0.16:80)
wget: download timed out
Define access label and test again
You can create a Pod with the correct labels to see that the request is allowed:
kubectl run busybox --rm -ti --labels="access=true" --image=busybox:1.28 -- /bin/sh
In your shell, run the command:
wget --spider --timeout=1 nginx
Connecting to nginx (10.100.0.16:80)
remote file exists
2.20 - Developing Cloud Controller Manager
FEATURE STATE:Kubernetes v1.11 [beta]
The cloud-controller-manager is a Kubernetes control plane component
that embeds cloud-specific control logic. The cloud controller manager lets you link your
cluster into your cloud provider's API, and separates out the components that interact
with that cloud platform from components that only interact with your cluster.
By decoupling the interoperability logic between Kubernetes and the underlying cloud
infrastructure, the cloud-controller-manager component enables cloud providers to release
features at a different pace compared to the main Kubernetes project.
Background
Since cloud providers develop and release at a different pace compared to the Kubernetes project, abstracting the provider-specific code to the cloud-controller-manager binary allows cloud vendors to evolve independently from the core Kubernetes code.
The Kubernetes project provides skeleton cloud-controller-manager code with Go interfaces to allow you (or your cloud provider) to plug in your own implementations. This means that a cloud provider can implement a cloud-controller-manager by importing packages from Kubernetes core; each cloudprovider will register their own code by calling cloudprovider.RegisterCloudProvider to update a global variable of available cloud providers.
Developing
Out of tree
To build an out-of-tree cloud-controller-manager for your cloud:
Use main.go in cloud-controller-manager from Kubernetes core as a template for your main.go. As mentioned above, the only difference should be the cloud package that will be imported.
Many cloud providers publish their controller manager code as open source. If you are creating
a new cloud-controller-manager from scratch, you could take an existing out-of-tree cloud
controller manager as your starting point.
This page shows how to enable or disable an API version from your cluster's
control plane.
Specific API versions can be turned on or off by passing --runtime-config=api/<version> as a
command line argument to the API server. The values for this argument are a comma-separated
list of API versions. Later values override earlier values.
The runtime-config command line argument also supports 2 special keys:
api/all, representing all known APIs
api/legacy, representing only legacy APIs. Legacy APIs are any APIs that have been
explicitly deprecated.
For example, to turn off all API versions except v1, pass --runtime-config=api/all=false,api/v1=true
to the kube-apiserver.
All of the APIs in Kubernetes that let you write persistent API resource data support
at-rest encryption. For example, you can enable at-rest encryption for
Secrets.
This at-rest encryption is additional to any system-level encryption for the
etcd cluster or for the filesystem(s) on hosts where you are running the
kube-apiserver.
This page shows how to enable and configure encryption of API data at rest.
Note:
This task covers encryption for resource data stored using the
Kubernetes API. For example, you can
encrypt Secret objects, including the key-value data they contain.
If you want to encrypt data in filesystems that are mounted into containers, you instead need
to either:
use a storage integration that provides encrypted
volumes
encrypt the data within your own application
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
This task assumes that you are running the Kubernetes API server as a
static pod on each control
plane node.
Your cluster's control plane must use etcd v3.x (major version 3, any minor version).
To encrypt a custom resource, your cluster must be running Kubernetes v1.26 or newer.
To use a wildcard to match resources, your cluster must be running Kubernetes v1.27 or newer.
To check the version, enter kubectl version.
Configuration and determining whether encryption at rest is already enabled
The kube-apiserver process accepts an argument --encryption-provider-config
that controls how API data is encrypted in etcd.
The configuration is provided as an API named
EncryptionConfiguration. An example configuration is provided below.
Caution:IMPORTANT: For high-availability configurations (with two or more control plane nodes), the
encryption configuration file must be the same! Otherwise, the kube-apiserver component cannot
decrypt data stored in the etcd.
Understanding the encryption at rest configuration
---## CAUTION: this is an example configuration.# Do not use this for your own cluster!#apiVersion:apiserver.config.k8s.io/v1kind:EncryptionConfigurationresources:- resources:- secrets- configmaps- pandas.awesome.bears.example# a custom resource APIproviders:# This configuration does not provide data confidentiality. The first# configured provider is specifying the "identity" mechanism, which# stores resources as plain text.#- identity:{}# plain text, in other words NO encryption- aesgcm:keys:- name:key1secret:c2VjcmV0IGlzIHNlY3VyZQ==- name:key2secret:dGhpcyBpcyBwYXNzd29yZA==- aescbc:keys:- name:key1secret:c2VjcmV0IGlzIHNlY3VyZQ==- name:key2secret:dGhpcyBpcyBwYXNzd29yZA==- secretbox:keys:- name:key1secret:YWJjZGVmZ2hpamtsbW5vcHFyc3R1dnd4eXoxMjM0NTY=- resources:- eventsproviders:- identity:{}# do not encrypt Events even though *.* is specified below- resources:- '*.apps'# wildcard match requires Kubernetes 1.27 or laterproviders:- aescbc:keys:- name:key2secret:c2VjcmV0IGlzIHNlY3VyZSwgb3IgaXMgaXQ/Cg==- resources:- '*.*'# wildcard match requires Kubernetes 1.27 or laterproviders:- aescbc:keys:- name:key3secret:c2VjcmV0IGlzIHNlY3VyZSwgSSB0aGluaw==
Each resources array item is a separate config and contains a complete configuration. The
resources.resources field is an array of Kubernetes resource names (resource or resource.group)
that should be encrypted like Secrets, ConfigMaps, or other resources.
If custom resources are added to EncryptionConfiguration and the cluster version is 1.26 or newer,
any newly created custom resources mentioned in the EncryptionConfiguration will be encrypted.
Any custom resources that existed in etcd prior to that version and configuration will be unencrypted
until they are next written to storage. This is the same behavior as built-in resources.
See the Ensure all secrets are encrypted section.
The providers array is an ordered list of the possible encryption providers to use for the APIs that you listed.
Each provider supports multiple keys - the keys are tried in order for decryption, and if the provider
is the first provider, the first key is used for encryption.
Only one provider type may be specified per entry (identity or aescbc may be provided,
but not both in the same item).
The first provider in the list is used to encrypt resources written into the storage. When reading
resources from storage, each provider that matches the stored data attempts in order to decrypt the
data. If no provider can read the stored data due to a mismatch in format or secret key, an error
is returned which prevents clients from accessing that resource.
EncryptionConfiguration supports the use of wildcards to specify the resources that should be encrypted.
Use '*.<group>' to encrypt all resources within a group (for eg '*.apps' in above example) or '*.*'
to encrypt all resources. '*.' can be used to encrypt all resource in the core group. '*.*' will
encrypt all resources, even custom resources that are added after API server start.
Note: Use of wildcards that overlap within the same resource list or across multiple entries are not allowed
since part of the configuration would be ineffective. The resources list's processing order and precedence
are determined by the order it's listed in the configuration.
Opting out of encryption for specific resources while wildcard is enabled can be achieved by adding a new
resources array item with the resource name, followed by the providers array item with the identity provider.
For example, if '*.*' is enabled and you want to opt-out encryption for the events resource, add a new item
to the resources array with events as the resource name, followed by the providers array item with identity.
The new item should look like this:
- resources:- eventsproviders:- identity:{}
Ensure that the new item is listed before the wildcard '*.*' item in the resources array to give it precedence.
For more detailed information about the EncryptionConfiguration struct, please refer to the
encryption configuration API.
Caution: If any resource is not readable via the encryption config (because keys were changed),
the only recourse is to delete that key from the underlying etcd directly. Calls that attempt to
read that resource will fail until it is deleted or a valid decryption key is provided.
Available providers
Before you configure encryption-at-rest for data in your cluster's Kubernetes API, you
need to select which provider(s) you will use.
The following table describes each available provider.
Providers for Kubernetes encryption at rest
Name
Encryption
Strength
Speed
Key length
identity
None
N/A
N/A
N/A
Resources written as-is without encryption. When set as the first provider, the resource will be decrypted as new values are written. Existing encrypted resources are not automatically overwritten with the plaintext data.
The identity provider is the default if you do not specify otherwise.
Not recommended due to CBC's vulnerability to padding oracle attacks. Key material accessible from control plane host.
aesgcm
AES-GCM with random nonce
Must be rotated every 200,000 writes
Fastest
16, 24, or 32-byte
Not recommended for use except when an automated key rotation scheme is implemented. Key material accessible from control plane host.
kms v1 (deprecated since Kubernetes v1.28)
Uses envelope encryption scheme with DEK per resource.
Strongest
Slow (compared to kms version 2)
32-bytes
Data is encrypted by data encryption keys (DEKs) using AES-GCM;
DEKs are encrypted by key encryption keys (KEKs) according to
configuration in Key Management Service (KMS).
Simple key rotation, with a new DEK generated for each encryption, and
KEK rotation controlled by the user.
Read how to configure the KMS V1 provider.
kms v2 (beta)
Uses envelope encryption scheme with DEK per API server.
Strongest
Fast
32-bytes
Data is encrypted by data encryption keys (DEKs) using AES-GCM; DEKs
are encrypted by key encryption keys (KEKs) according to configuration
in Key Management Service (KMS).
Kubernetes defaults to generating a new DEK at API server startup, which is then
reused for object encryption.
If you enable the KMSv2KDFfeature gate,
Kubernetes instead generates a new DEK per encryption from a secret seed.
Whichever approach you configure, the DEK or seed is also rotated whenever the KEK is rotated.
A good choice if using a third party tool for key management.
Available in beta from Kubernetes v1.27.
Read how to configure the KMS V2 provider.
secretbox
XSalsa20 and Poly1305
Strong
Faster
32-byte
Uses relatively new encryption technologies that may not be considered acceptable in environments that require high levels of review. Key material accessible from control plane host.
The identity provider is the default if you do not specify otherwise. The identity provider does not
encrypt stored data and provides no additional confidentiality protection.
Key storage
Local key storage
Encrypting secret data with a locally managed key protects against an etcd compromise, but it fails to
protect against a host compromise. Since the encryption keys are stored on the host in the
EncryptionConfiguration YAML file, a skilled attacker can access that file and extract the encryption
keys.
Managed (KMS) key storage
The KMS provider uses envelope encryption: Kubernetes encrypts resources using a data key, and then
encrypts that data key using the managed encryption service. Kubernetes generates a unique data key for
each resource. The API server stores an encrypted version of the data key in etcd alongside the ciphertext;
when reading the resource, the API server calls the managed encryption service and provides both the
ciphertext and the (encrypted) data key.
Within the managed encryption service, the provider use a key encryption key to decipher the data key,
deciphers the data key, and finally recovers the plain text. Communication between the control plane
and the KMS requires in-transit protection, such as TLS.
Using envelope encryption creates dependence on the key encryption key, which is not stored in Kubernetes.
In the KMS case, an attacker who intends to get unauthorised access to the plaintext
values would need to compromise etcd and the third-party KMS provider.
Write an encryption configuration file
Caution: The encryption configuration file may contain keys that can decrypt content in etcd.
If the configuration file contains any key material, you must properly
restrict permissions on all your control plane hosts so only the user
who runs the kube-apiserver can read this configuration.
Create a new encryption configuration file. The contents should be similar to:
---apiVersion:apiserver.config.k8s.io/v1kind:EncryptionConfigurationresources:- resources:- secrets- configmaps- pandas.awesome.bears.exampleproviders:- aescbc:keys:- name:key1# See the following text for more details about the secret valuesecret:<BASE 64 ENCODED SECRET>- identity:{}# this fallback allows reading unencrypted secrets;# for example, during initial migration
To create a new Secret, perform the following steps:
Generate a 32-byte random key and base64 encode it. If you're on Linux or macOS, run the following command:
head -c 32 /dev/urandom | base64
Place that value in the secret field of the EncryptionConfiguration struct.
Set the --encryption-provider-config flag on the kube-apiserver to point to
the location of the config file.
You will need to mount the new encryption config file to the kube-apiserver static pod. Here is an example on how to do that:
Save the new encryption config file to /etc/kubernetes/enc/enc.yaml on the control-plane node.
Edit the manifest for the kube-apiserver static pod: /etc/kubernetes/manifests/kube-apiserver.yaml similarly to this:
---## This is a fragment of a manifest for a static Pod.# Check whether this is correct for your cluster and for your API server.#apiVersion:v1kind:Podmetadata:annotations:kubeadm.kubernetes.io/kube-apiserver.advertise-address.endpoint:10.20.30.40:443creationTimestamp:nulllabels:app.kubernetes.io/component:kube-apiservertier:control-planename:kube-apiservernamespace:kube-systemspec:containers:- command:- kube-apiserver...- --encryption-provider-config=/etc/kubernetes/enc/enc.yaml # add this linevolumeMounts:...- name:enc # add this linemountPath:/etc/kubernetes/enc # add this linereadOnly:true# add this line...volumes:...- name:enc # add this linehostPath:# add this linepath:/etc/kubernetes/enc # add this linetype:DirectoryOrCreate # add this line...
Restart your API server.
Caution: Your config file contains keys that can decrypt the contents in etcd, so you must properly restrict
permissions on your control-plane nodes so only the user who runs the kube-apiserver can read it.
Reconfigure other control plane hosts
If you have multiple API servers in your cluster, you should deploy the
changes in turn to each API server.
Make sure that you use the same encryption configuration on each
control plane host.
Verify that newly written data is encrypted
Data is encrypted when written to etcd. After restarting your kube-apiserver, any newly
created or updated Secret (or other resource kinds configured in EncryptionConfiguration)
should be encrypted when stored.
To check this, you can use the etcdctl command line
program to retrieve the contents of your secret data.
This example shows how to check this for encrypting the Secret API.
Create a new Secret called secret1 in the default namespace:
Verify the stored Secret is prefixed with k8s:enc:aescbc:v1: which indicates
the aescbc provider has encrypted the resulting data. Confirm that the key name shown in etcd
matches the key name specified in the EncryptionConfiguration mentioned above. In this example,
you can see that the encryption key named key1 is used in etcd and in EncryptionConfiguration.
Verify the Secret is correctly decrypted when retrieved via the API:
kubectl get secret secret1 -n default -o yaml
The output should contain mykey: bXlkYXRh, with contents of mydata encoded using base64;
read
decoding a Secret
to learn how to completely decode the Secret.
Ensure all relevant data are encrypted
It's often not enough to make sure that new objects get encrypted: you also want that
encryption to apply to the objects that are already stored.
For this example, you have configured your cluster so that Secrets are encrypted on write.
Performing a replace operation for each Secret will encrypt that content at rest,
where the objects are unchanged.
You can make this change across all Secrets in your cluster:
# Run this as an administrator that can read and write all Secretskubectl get secrets --all-namespaces -o json | kubectl replace -f -
The command above reads all Secrets and then updates them with the same data, in order to
apply server side encryption.
Note:
If an error occurs due to a conflicting write, retry the command.
It is safe to run that command more than once.
For larger clusters, you may wish to subdivide the Secrets by namespace,
or script an update.
Rotating a decryption key
Changing a Secret without incurring downtime requires a multi-step operation, especially in
the presence of a highly-available deployment where multiple kube-apiserver processes are running.
Generate a new key and add it as the second key entry for the current provider on all servers
Restart all kube-apiserver processes to ensure each server can decrypt using the new key
Make the new key the first entry in the keys array so that it is used for encryption in the config
Restart all kube-apiserver processes to ensure each server now encrypts using the new key
Run kubectl get secrets --all-namespaces -o json | kubectl replace -f - to encrypt all
existing Secrets with the new key
Remove the old decryption key from the config after you have backed up etcd with the new key in use
and updated all Secrets
When running a single kube-apiserver instance, step 2 may be skipped.
Configure automatic reloading
You can configure automatic reloading of encryption provider configuration.
That setting determines whether the
API server should
load the file you specify for --encryption-provider-config only once at
startup, or automatically whenever you change that file. Enabling this option
allows you to change the keys for encryption at rest without restarting the
API server.
To allow automatic reloading, configure the API server to run with:
--encryption-provider-config-automatic-reload=true
2.23 - Decrypt Confidential Data that is Already Encrypted at Rest
All of the APIs in Kubernetes that let you write persistent API resource data support
at-rest encryption. For example, you can enable at-rest encryption for
Secrets.
This at-rest encryption is additional to any system-level encryption for the
etcd cluster or for the filesystem(s) on hosts where you are running the
kube-apiserver.
This page shows how to switch from encryption of API data at rest, so that API data
are stored unencrypted. You might want to do this to improve performance; usually,
though, if it was a good idea to encrypt some data, it's also a good idea to leave them
encrypted.
Note:
This task covers encryption for resource data stored using the
Kubernetes API. For example, you can
encrypt Secret objects, including the key-value data they contain.
If you wanted to manage encryption for data in filesystems that are mounted into containers, you instead
need to either:
use a storage integration that provides encrypted
volumes
encrypt the data within your own application
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
This task assumes that you are running the Kubernetes API server as a
static pod on each control
plane node.
Your cluster's control plane must use etcd v3.x (major version 3, any minor version).
To encrypt a custom resource, your cluster must be running Kubernetes v1.26 or newer.
You should have some API data that are already encrypted.
To check the version, enter kubectl version.
Determine whether encryption at rest is already enabled
By default, the API server uses an identity provider that stores plain-text representations
of resources.
The default identity provider does not provide any confidentiality protection.
The kube-apiserver process accepts an argument --encryption-provider-config
that specifies a path to a configuration file. The contents of that file, if you specify one,
control how Kubernetes API data is encrypted in etcd.
If it is not specified, you do not have encryption at rest enabled.
If --encryption-provider-config is set, check which resources (such as secrets) are
configured for encryption, and what provider is used.
Make sure that the preferred provider for that resource type is notidentity; you
only set identity (no encryption) as default when you want to disable encryption at
rest.
Verify that the first-listed provider for a resource is something other than identity,
which means that any new information written to resources of that type will be encrypted as
configured. If you do see identity as the first-listed provider for any resource, this
means that those resources are being written out to etcd without encryption.
Decrypt all data
This example shows how to stop encrypting the Secret API at rest. If you are encrypting
other API kinds, adjust the steps to match.
Locate the encryption configuration file
First, find the API server configuration files. On each control plane node, static Pod manifest
for the kube-apiserver specifies a command line argument, --encryption-provider-config.
You are likely to find that this file is mounted into the static Pod using a
hostPath volume mount. Once you locate the volume
you can find the file on the node filesystem and inspect it.
Configure the API server to decrypt objects
To disable encryption at rest, place the identity provider as the first
entry in your encryption configuration file.
For example, if your existing EncryptionConfiguration file reads:
---apiVersion:apiserver.config.k8s.io/v1kind:EncryptionConfigurationresources:- resources:- secretsproviders:- aescbc:keys:# Do not use this (invalid) example key for encryption- name:examplesecret:2KfZgdiq2K0g2YrYpyDYs9mF2LPZhQ==
then change it to:
---apiVersion:apiserver.config.k8s.io/v1kind:EncryptionConfigurationresources:- resources:- secretsproviders:- identity:{}# add this line- aescbc:keys:- name:examplesecret:2KfZgdiq2K0g2YrYpyDYs9mF2LPZhQ==
and restart the kube-apiserver Pod on this node.
Reconfigure other control plane hosts
If you have multiple API servers in your cluster, you should deploy the changes in turn to each API server.
Make sure that you use the same encryption configuration on each control plane host.
Force decryption
Then run the following command to force decryption of all Secrets:
# If you are decrypting a different kind of object, change "secrets" to match.kubectl get secrets --all-namespaces -o json | kubectl replace -f -
Once you have replaced all existing encrypted resources with backing data that
don't use encryption, you can remove the encryption settings from the
kube-apiserver.
The command line options to remove are:
--encryption-provider-config
--encryption-provider-config-automatic-reload
Restart the kube-apiserver Pod again to apply the new configuration.
Reconfigure other control plane hosts
If you have multiple API servers in your cluster, you should again deploy the changes in turn to each API server.
Make sure that you use the same encryption configuration on each control plane host.
2.24 - Guaranteed Scheduling For Critical Add-On Pods
Kubernetes core components such as the API server, scheduler, and controller-manager run on a control plane node. However, add-ons must run on a regular cluster node.
Some of these add-ons are critical to a fully functional cluster, such as metrics-server, DNS, and UI.
A cluster may stop working properly if a critical add-on is evicted (either manually or as a side effect of another operation like upgrade)
and becomes pending (for example when the cluster is highly utilized and either there are other pending pods that schedule into the space
vacated by the evicted critical add-on pod or the amount of resources available on the node changed for some other reason).
Note that marking a pod as critical is not meant to prevent evictions entirely; it only prevents the pod from becoming permanently unavailable.
A static pod marked as critical can't be evicted. However, non-static pods marked as critical are always rescheduled.
Marking pod as critical
To mark a Pod as critical, set priorityClassName for that Pod to system-cluster-critical or system-node-critical. system-node-critical is the highest available priority, even higher than system-cluster-critical.
2.25 - IP Masquerade Agent User Guide
This page shows how to configure and enable the ip-masq-agent.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
The ip-masq-agent configures iptables rules to hide a pod's IP address behind the cluster
node's IP address. This is typically done when sending traffic to destinations outside the
cluster's pod CIDR range.
Key Terms
NAT (Network Address Translation):
Is a method of remapping one IP address to another by modifying either the source and/or
destination address information in the IP header. Typically performed by a device doing IP routing.
Masquerading:
A form of NAT that is typically used to perform a many to one address translation, where
multiple source IP addresses are masked behind a single address, which is typically the
device doing the IP routing. In Kubernetes this is the Node's IP address.
CIDR (Classless Inter-Domain Routing):
Based on the variable-length subnet masking, allows specifying arbitrary-length prefixes.
CIDR introduced a new method of representation for IP addresses, now commonly known as
CIDR notation, in which an address or routing prefix is written with a suffix indicating
the number of bits of the prefix, such as 192.168.2.0/24.
Link Local:
A link-local address is a network address that is valid only for communications within the
network segment or the broadcast domain that the host is connected to. Link-local addresses
for IPv4 are defined in the address block 169.254.0.0/16 in CIDR notation.
The ip-masq-agent configures iptables rules to handle masquerading node/pod IP addresses when
sending traffic to destinations outside the cluster node's IP and the Cluster IP range. This
essentially hides pod IP addresses behind the cluster node's IP address. In some environments,
traffic to "external" addresses must come from a known machine address. For example, in Google
Cloud, any traffic to the internet must come from a VM's IP. When containers are used, as in
Google Kubernetes Engine, the Pod IP will be rejected for egress. To avoid this, we must hide
the Pod IP behind the VM's own IP address - generally known as "masquerade". By default, the
agent is configured to treat the three private IP ranges specified by
RFC 1918 as non-masquerade
CIDR.
These ranges are 10.0.0.0/8, 172.16.0.0/12, and 192.168.0.0/16.
The agent will also treat link-local (169.254.0.0/16) as a non-masquerade CIDR by default.
The agent is configured to reload its configuration from the location
/etc/config/ip-masq-agent every 60 seconds, which is also configurable.
The agent configuration file must be written in YAML or JSON syntax, and may contain three
optional keys:
nonMasqueradeCIDRs: A list of strings in
CIDR notation that specify
the non-masquerade ranges.
masqLinkLocal: A Boolean (true/false) which indicates whether to masquerade traffic to the
link local prefix 169.254.0.0/16. False by default.
resyncInterval: A time interval at which the agent attempts to reload config from disk.
For example: '30s', where 's' means seconds, 'ms' means milliseconds.
Traffic to 10.0.0.0/8, 172.16.0.0/12 and 192.168.0.0/16 ranges will NOT be masqueraded. Any
other traffic (assumed to be internet) will be masqueraded. An example of a local destination
from a pod could be its Node's IP address as well as another node's address or one of the IP
addresses in Cluster's IP range. Any other traffic will be masqueraded by default. The
below entries show the default set of rules that are applied by the ip-masq-agent:
iptables -t nat -L IP-MASQ-AGENT
target prot opt source destination
RETURN all -- anywhere 169.254.0.0/16 /* ip-masq-agent: cluster-local traffic should not be subject to MASQUERADE */ ADDRTYPE match dst-type !LOCAL
RETURN all -- anywhere 10.0.0.0/8 /* ip-masq-agent: cluster-local traffic should not be subject to MASQUERADE */ ADDRTYPE match dst-type !LOCAL
RETURN all -- anywhere 172.16.0.0/12 /* ip-masq-agent: cluster-local traffic should not be subject to MASQUERADE */ ADDRTYPE match dst-type !LOCAL
RETURN all -- anywhere 192.168.0.0/16 /* ip-masq-agent: cluster-local traffic should not be subject to MASQUERADE */ ADDRTYPE match dst-type !LOCAL
MASQUERADE all -- anywhere anywhere /* ip-masq-agent: outbound traffic should be subject to MASQUERADE (this match must come after cluster-local CIDR matches) */ ADDRTYPE match dst-type !LOCAL
By default, in GCE/Google Kubernetes Engine, if network policy is enabled or
you are using a cluster CIDR not in the 10.0.0.0/8 range, the ip-masq-agent
will run in your cluster. If you are running in another environment,
you can add the ip-masq-agentDaemonSet
to your cluster.
Create an ip-masq-agent
To create an ip-masq-agent, run the following kubectl command:
More information can be found in the ip-masq-agent documentation here.
In most cases, the default set of rules should be sufficient; however, if this is not the case
for your cluster, you can create and apply a
ConfigMap to customize the IP
ranges that are affected. For example, to allow
only 10.0.0.0/8 to be considered by the ip-masq-agent, you can create the following
ConfigMap in a file called
"config".
Note:
It is important that the file is called config since, by default, that will be used as the key
for lookup by the ip-masq-agent:
nonMasqueradeCIDRs:- 10.0.0.0/8resyncInterval:60s
Run the following command to add the configmap to your cluster:
This will update a file located at /etc/config/ip-masq-agent which is periodically checked
every resyncInterval and applied to the cluster node.
After the resync interval has expired, you should see the iptables rules reflect your changes:
iptables -t nat -L IP-MASQ-AGENT
Chain IP-MASQ-AGENT (1 references)
target prot opt source destination
RETURN all -- anywhere 169.254.0.0/16 /* ip-masq-agent: cluster-local traffic should not be subject to MASQUERADE */ ADDRTYPE match dst-type !LOCAL
RETURN all -- anywhere 10.0.0.0/8 /* ip-masq-agent: cluster-local
MASQUERADE all -- anywhere anywhere /* ip-masq-agent: outbound traffic should be subject to MASQUERADE (this match must come after cluster-local CIDR matches) */ ADDRTYPE match dst-type !LOCAL
By default, the link local range (169.254.0.0/16) is also handled by the ip-masq agent, which
sets up the appropriate iptables rules. To have the ip-masq-agent ignore link local, you can
set masqLinkLocal to true in the ConfigMap.
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
The cluster-admin is operating a cluster on behalf of a user population and the admin wants to control
how much storage a single namespace can consume in order to control cost.
The admin would like to limit:
The number of persistent volume claims in a namespace
The amount of storage each claim can request
The amount of cumulative storage the namespace can have
LimitRange to limit requests for storage
Adding a LimitRange to a namespace enforces storage request sizes to a minimum and maximum. Storage is requested
via PersistentVolumeClaim. The admission controller that enforces limit ranges will reject any PVC that is above or below
the values set by the admin.
In this example, a PVC requesting 10Gi of storage would be rejected because it exceeds the 2Gi max.
Minimum storage requests are used when the underlying storage provider requires certain minimums. For example,
AWS EBS volumes have a 1Gi minimum requirement.
StorageQuota to limit PVC count and cumulative storage capacity
Admins can limit the number of PVCs in a namespace as well as the cumulative capacity of those PVCs. New PVCs that exceed
either maximum value will be rejected.
In this example, a 6th PVC in the namespace would be rejected because it exceeds the maximum count of 5. Alternatively,
a 5Gi maximum quota when combined with the 2Gi max limit above, cannot have 3 PVCs where each has 2Gi. That would be 6Gi requested
for a namespace capped at 5Gi.
A limit range can put a ceiling on how much storage is requested while a resource quota can effectively cap the storage
consumed by a namespace through claim counts and cumulative storage capacity. The allows a cluster-admin to plan their
cluster's storage budget without risk of any one project going over their allotment.
2.27 - Migrate Replicated Control Plane To Use Cloud Controller Manager
The cloud-controller-manager is a Kubernetes control plane component
that embeds cloud-specific control logic. The cloud controller manager lets you link your
cluster into your cloud provider's API, and separates out the components that interact
with that cloud platform from components that only interact with your cluster.
By decoupling the interoperability logic between Kubernetes and the underlying cloud
infrastructure, the cloud-controller-manager component enables cloud providers to release
features at a different pace compared to the main Kubernetes project.
Background
As part of the cloud provider extraction effort,
all cloud specific controllers must be moved out of the kube-controller-manager.
All existing clusters that run cloud controllers in the kube-controller-manager
must migrate to instead run the controllers in a cloud provider specific
cloud-controller-manager.
Leader Migration provides a mechanism in which HA clusters can safely migrate "cloud
specific" controllers between the kube-controller-manager and the
cloud-controller-manager via a shared resource lock between the two components
while upgrading the replicated control plane. For a single-node control plane, or if
unavailability of controller managers can be tolerated during the upgrade, Leader
Migration is not needed and this guide can be ignored.
Leader Migration can be enabled by setting --enable-leader-migration on
kube-controller-manager or cloud-controller-manager. Leader Migration only
applies during the upgrade and can be safely disabled or left enabled after the
upgrade is complete.
This guide walks you through the manual process of upgrading the control plane from
kube-controller-manager with built-in cloud provider to running both
kube-controller-manager and cloud-controller-manager. If you use a tool to deploy
and manage the cluster, please refer to the documentation of the tool and the cloud
provider for specific instructions of the migration.
Before you begin
It is assumed that the control plane is running Kubernetes version N and to be
upgraded to version N + 1. Although it is possible to migrate within the same
version, ideally the migration should be performed as part of an upgrade so that
changes of configuration can be aligned to each release. The exact versions of N and
N + 1 depend on each cloud provider. For example, if a cloud provider builds a
cloud-controller-manager to work with Kubernetes 1.24, then N can be 1.23 and N + 1
can be 1.24.
The control plane nodes should run kube-controller-manager with Leader Election
enabled, which is the default. As of version N, an in-tree cloud provider must be set
with --cloud-provider flag and cloud-controller-manager should not yet be
deployed.
The out-of-tree cloud provider must have built a cloud-controller-manager with
Leader Migration implementation. If the cloud provider imports
k8s.io/cloud-provider and k8s.io/controller-manager of version v0.21.0 or later,
Leader Migration will be available. However, for version before v0.22.0, Leader
Migration is alpha and requires feature gate ControllerManagerLeaderMigration to be
enabled in cloud-controller-manager.
This guide assumes that kubelet of each control plane node starts
kube-controller-manager and cloud-controller-manager as static pods defined by
their manifests. If the components run in a different setting, please adjust the
steps accordingly.
For authorization, this guide assumes that the cluster uses RBAC. If another
authorization mode grants permissions to kube-controller-manager and
cloud-controller-manager components, please grant the needed access in a way that
matches the mode.
Grant access to Migration Lease
The default permissions of the controller manager allow only accesses to their main
Lease. In order for the migration to work, accesses to another Lease are required.
You can grant kube-controller-manager full access to the leases API by modifying
the system::leader-locking-kube-controller-manager role. This task guide assumes
that the name of the migration lease is cloud-provider-extraction-migration.
Leader Migration optionally takes a configuration file representing the state of
controller-to-manager assignment. At this moment, with in-tree cloud provider,
kube-controller-manager runs route, service, and cloud-node-lifecycle. The
following example configuration shows the assignment.
Leader Migration can be enabled without a configuration. Please see
Default Configuration for details.
Alternatively, because the controllers can run under either controller managers,
setting component to * for both sides makes the configuration file consistent
between both parties of the migration.
On each control plane node, save the content to /etc/leadermigration.conf, and
update the manifest of kube-controller-manager so that the file is mounted inside
the container at the same location. Also, update the same manifest to add the
following arguments:
--enable-leader-migration to enable Leader Migration on the controller manager
--leader-migration-config=/etc/leadermigration.conf to set configuration file
Restart kube-controller-manager on each node. At this moment,
kube-controller-manager has leader migration enabled and is ready for the
migration.
Deploy Cloud Controller Manager
In version N + 1, the desired state of controller-to-manager assignment can be
represented by a new configuration file, shown as follows. Please note component
field of each controllerLeaders changing from kube-controller-manager to
cloud-controller-manager. Alternatively, use the wildcard version mentioned above,
which has the same effect.
When creating control plane nodes of version N + 1, the content should be deployed to
/etc/leadermigration.conf. The manifest of cloud-controller-manager should be
updated to mount the configuration file in the same manner as
kube-controller-manager of version N. Similarly, add --enable-leader-migration
and --leader-migration-config=/etc/leadermigration.conf to the arguments of
cloud-controller-manager.
Create a new control plane node of version N + 1 with the updated
cloud-controller-manager manifest, and with the --cloud-provider flag set to
external for kube-controller-manager. kube-controller-manager of version N + 1
MUST NOT have Leader Migration enabled because, with an external cloud provider, it
does not run the migrated controllers anymore, and thus it is not involved in the
migration.
The control plane now contains nodes of both version N and N + 1. The nodes of
version N run kube-controller-manager only, and these of version N + 1 run both
kube-controller-manager and cloud-controller-manager. The migrated controllers,
as specified in the configuration, are running under either kube-controller-manager
of version N or cloud-controller-manager of version N + 1 depending on which
controller manager holds the migration lease. No controller will ever be running
under both controller managers at any time.
In a rolling manner, create a new control plane node of version N + 1 and bring down
one of version N until the control plane contains only nodes of version N + 1.
If a rollback from version N + 1 to N is required, add nodes of version N with Leader
Migration enabled for kube-controller-manager back to the control plane, replacing
one of version N + 1 each time until there are only nodes of version N.
(Optional) Disable Leader Migration
Now that the control plane has been upgraded to run both kube-controller-manager
and cloud-controller-manager of version N + 1, Leader Migration has finished its
job and can be safely disabled to save one Lease resource. It is safe to re-enable
Leader Migration for the rollback in the future.
In a rolling manager, update manifest of cloud-controller-manager to unset both
--enable-leader-migration and --leader-migration-config= flag, also remove the
mount of /etc/leadermigration.conf, and finally remove /etc/leadermigration.conf.
To re-enable Leader Migration, recreate the configuration file and add its mount and
the flags that enable Leader Migration back to cloud-controller-manager.
Default Configuration
Starting Kubernetes 1.22, Leader Migration provides a default configuration suitable
for the default controller-to-manager assignment.
The default configuration can be enabled by setting --enable-leader-migration but
without --leader-migration-config=.
For kube-controller-manager and cloud-controller-manager, if there are no flags
that enable any in-tree cloud provider or change ownership of controllers, the
default configuration can be used to avoid manual creation of the configuration file.
Special case: migrating the Node IPAM controller
If your cloud provider provides an implementation of Node IPAM controller, you should
switch to the implementation in cloud-controller-manager. Disable Node IPAM
controller in kube-controller-manager of version N + 1 by adding
--controllers=*,-nodeipam to its flags. Then add nodeipam to the list of migrated
controllers.
# wildcard version, with nodeipamkind:LeaderMigrationConfigurationapiVersion:controllermanager.config.k8s.io/v1leaderName:cloud-provider-extraction-migrationcontrollerLeaders:- name:routecomponent:*- name:servicecomponent:*- name:cloud-node-lifecyclecomponent:*- name:nodeipam- component:*
A mechanism to attach authorization and policy to a subsection of the cluster.
Use of multiple namespaces is optional.
This example demonstrates how to use Kubernetes namespaces to subdivide your cluster.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
By default, a Kubernetes cluster will instantiate a default namespace when provisioning the cluster to hold the default set of Pods,
Services, and Deployments used by the cluster.
Assuming you have a fresh cluster, you can inspect the available namespaces by doing the following:
kubectl get namespaces
NAME STATUS AGE
default Active 13m
Create new namespaces
For this exercise, we will create two additional Kubernetes namespaces to hold our content.
Let's imagine a scenario where an organization is using a shared Kubernetes cluster for development and production use cases.
The development team would like to maintain a space in the cluster where they can get a view on the list of Pods, Services, and Deployments
they use to build and run their application. In this space, Kubernetes resources come and go, and the restrictions on who can or cannot modify resources
are relaxed to enable agile development.
The operations team would like to maintain a space in the cluster where they can enforce strict procedures on who can or cannot manipulate the set of
Pods, Services, and Deployments that run the production site.
One pattern this organization could follow is to partition the Kubernetes cluster into two namespaces: development and production.
Let's create two new namespaces to hold our work.
Use the file namespace-dev.yaml which describes a development namespace:
The next step is to define a context for the kubectl client to work in each namespace. The value of "cluster" and "user" fields are copied from the current context.
By default, the above commands add two contexts that are saved into file
.kube/config. You can now view the contexts and alternate against the two
new request contexts depending on which namespace you wish to work against.
We have created a deployment whose replica size is 2 that is running the pod called snowflake with a basic container that serves the hostname.
kubectl get deployment
NAME READY UP-TO-DATE AVAILABLE AGE
snowflake 2/2 2 2 2m
kubectl get pods -l app=snowflake
NAME READY STATUS RESTARTS AGE
snowflake-3968820950-9dgr8 1/1 Running 0 2m
snowflake-3968820950-vgc4n 1/1 Running 0 2m
And this is great, developers are able to do what they want, and they do not have to worry about affecting content in the production namespace.
Let's switch to the production namespace and show how resources in one namespace are hidden from the other.
kubectl config use-context prod
The production namespace should be empty, and the following commands should return nothing.
kubectl get deployment
kubectl get pods
Production likes to run cattle, so let's create some cattle pods.
kubectl create deployment cattle --image=registry.k8s.io/serve_hostname --replicas=5kubectl get deployment
NAME READY UP-TO-DATE AVAILABLE AGE
cattle 5/5 5 5 10s
kubectl get pods -l app=cattle
NAME READY STATUS RESTARTS AGE
cattle-2263376956-41xy6 1/1 Running 0 34s
cattle-2263376956-kw466 1/1 Running 0 34s
cattle-2263376956-n4v97 1/1 Running 0 34s
cattle-2263376956-p5p3i 1/1 Running 0 34s
cattle-2263376956-sxpth 1/1 Running 0 34s
At this point, it should be clear that the resources users create in one namespace are hidden from the other namespace.
As the policy support in Kubernetes evolves, we will extend this scenario to show how you can provide different
authorization rules for each namespace.
2.29 - Operating etcd clusters for Kubernetes
etcd is a consistent and highly-available key value store used as Kubernetes' backing store for all cluster data.
If your Kubernetes cluster uses etcd as its backing store, make sure you have a
back up plan
for the data.
You can find in-depth information about etcd in the official documentation.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this
task on a cluster with at least two nodes that are not acting as control plane
nodes . If you do not already have a cluster, you can create one by using
minikube.
Prerequisites
Run etcd as a cluster of odd members.
etcd is a leader-based distributed system. Ensure that the leader
periodically send heartbeats on time to all followers to keep the cluster
stable.
Ensure that no resource starvation occurs.
Performance and stability of the cluster is sensitive to network and disk
I/O. Any resource starvation can lead to heartbeat timeout, causing instability
of the cluster. An unstable etcd indicates that no leader is elected. Under
such circumstances, a cluster cannot make any changes to its current state,
which implies no new pods can be scheduled.
Keeping etcd clusters stable is critical to the stability of Kubernetes
clusters. Therefore, run etcd clusters on dedicated machines or isolated
environments for guaranteed resource requirements.
The minimum recommended etcd versions to run in production are 3.4.22+ and 3.5.6+.
Resource requirements
Operating etcd with limited resources is suitable only for testing purposes.
For deploying in production, advanced hardware configuration is required.
Before deploying etcd in production, see
resource requirement reference.
Starting etcd clusters
This section covers starting a single-node and multi-node etcd cluster.
Single-node etcd cluster
Use a single-node etcd cluster only for testing purpose.
Start the Kubernetes API server with the flag
--etcd-servers=$PRIVATE_IP:2379.
Make sure PRIVATE_IP is set to your etcd client IP.
Multi-node etcd cluster
For durability and high availability, run etcd as a multi-node cluster in
production and back it up periodically. A five-member cluster is recommended
in production. For more information, see
FAQ documentation.
Configure an etcd cluster either by static member information or by dynamic
discovery. For more information on clustering, see
etcd clustering documentation.
For an example, consider a five-member etcd cluster running with the following
client URLs: http://$IP1:2379, http://$IP2:2379, http://$IP3:2379,
http://$IP4:2379, and http://$IP5:2379. To start a Kubernetes API server:
Start the Kubernetes API servers with the flag
--etcd-servers=$IP1:2379,$IP2:2379,$IP3:2379,$IP4:2379,$IP5:2379.
Make sure the IP<n> variables are set to your client IP addresses.
Multi-node etcd cluster with load balancer
To run a load balancing etcd cluster:
Set up an etcd cluster.
Configure a load balancer in front of the etcd cluster.
For example, let the address of the load balancer be $LB.
Start Kubernetes API Servers with the flag --etcd-servers=$LB:2379.
Securing etcd clusters
Access to etcd is equivalent to root permission in the cluster so ideally only
the API server should have access to it. Considering the sensitivity of the
data, it is recommended to grant permission to only those nodes that require
access to etcd clusters.
To secure etcd, either set up firewall rules or use the security features
provided by etcd. etcd security features depend on x509 Public Key
Infrastructure (PKI). To begin, establish secure communication channels by
generating a key and certificate pair. For example, use key pairs peer.key
and peer.cert for securing communication between etcd members, and
client.key and client.cert for securing communication between etcd and its
clients. See the example scripts
provided by the etcd project to generate key pairs and CA files for client
authentication.
Securing communication
To configure etcd with secure peer communication, specify flags
--peer-key-file=peer.key and --peer-cert-file=peer.cert, and use HTTPS as
the URL schema.
Similarly, to configure etcd with secure client communication, specify flags
--key-file=k8sclient.key and --cert-file=k8sclient.cert, and use HTTPS as
the URL schema. Here is an example on a client command that uses secure
communication:
ETCDCTL_API=3 etcdctl --endpoints 10.2.0.9:2379 \
--cert=/etc/kubernetes/pki/etcd/server.crt \
--key=/etc/kubernetes/pki/etcd/server.key \
--cacert=/etc/kubernetes/pki/etcd/ca.crt \
member list
Limiting access of etcd clusters
After configuring secure communication, restrict the access of etcd cluster to
only the Kubernetes API servers. Use TLS authentication to do so.
For example, consider key pairs k8sclient.key and k8sclient.cert that are
trusted by the CA etcd.ca. When etcd is configured with --client-cert-auth
along with TLS, it verifies the certificates from clients by using system CAs
or the CA passed in by --trusted-ca-file flag. Specifying flags
--client-cert-auth=true and --trusted-ca-file=etcd.ca will restrict the
access to clients with the certificate k8sclient.cert.
Once etcd is configured correctly, only clients with valid certificates can
access it. To give Kubernetes API servers the access, configure them with the
flags --etcd-certfile=k8sclient.cert, --etcd-keyfile=k8sclient.key and
--etcd-cafile=ca.cert.
Note: etcd authentication is not currently supported by Kubernetes. For more
information, see the related issue
Support Basic Auth for Etcd v2.
Replacing a failed etcd member
etcd cluster achieves high availability by tolerating minor member failures.
However, to improve the overall health of the cluster, replace failed members
immediately. When multiple members fail, replace them one by one. Replacing a
failed member involves two steps: removing the failed member and adding a new
member.
Though etcd keeps unique member IDs internally, it is recommended to use a
unique name for each member to avoid human errors. For example, consider a
three-member etcd cluster. Let the URLs be, member1=http://10.0.0.1,
member2=http://10.0.0.2, and member3=http://10.0.0.3. When member1 fails,
replace it with member4=http://10.0.0.4.
Get the member ID of the failed member1:
etcdctl --endpoints=http://10.0.0.2,http://10.0.0.3 member list
If each Kubernetes API server is configured to communicate with all etcd
members, remove the failed member from the --etcd-servers flag, then
restart each Kubernetes API server.
If each Kubernetes API server communicates with a single etcd member,
then stop the Kubernetes API server that communicates with the failed
etcd.
Stop the etcd server on the broken node. It is possible that other
clients besides the Kubernetes API server is causing traffic to etcd
and it is desirable to stop all traffic to prevent writes to the data
dir.
Remove the failed member:
etcdctl member remove 8211f1d0f64f3269
The following message is displayed:
Removed member 8211f1d0f64f3269 from cluster
Add the new member:
etcdctl member add member4 --peer-urls=http://10.0.0.4:2380
The following message is displayed:
Member 2be1eb8f84b7f63e added to cluster ef37ad9dc622a7c4
Start the newly added member on a machine with the IP 10.0.0.4:
If each Kubernetes API server is configured to communicate with all etcd
members, add the newly added member to the --etcd-servers flag, then
restart each Kubernetes API server.
If each Kubernetes API server communicates with a single etcd member,
start the Kubernetes API server that was stopped in step 2. Then
configure Kubernetes API server clients to again route requests to the
Kubernetes API server that was stopped. This can often be done by
configuring a load balancer.
All Kubernetes objects are stored on etcd. Periodically backing up the etcd
cluster data is important to recover Kubernetes clusters under disaster
scenarios, such as losing all control plane nodes. The snapshot file contains
all the Kubernetes states and critical information. In order to keep the
sensitive Kubernetes data safe, encrypt the snapshot files.
Backing up an etcd cluster can be accomplished in two ways: etcd built-in
snapshot and volume snapshot.
Built-in snapshot
etcd supports built-in snapshot. A snapshot may either be taken from a live
member with the etcdctl snapshot save command or by copying the
member/snap/db file from an etcd
data directory
that is not currently used by an etcd process. Taking the snapshot will
not affect the performance of the member.
Below is an example for taking a snapshot of the keyspace served by
$ENDPOINT to the file snapshot.db:
ETCDCTL_API=3 etcdctl --endpoints $ENDPOINT snapshot save snapshot.db
Verify the snapshot:
ETCDCTL_API=3 etcdctl --write-out=table snapshot status snapshot.db
If etcd is running on a storage volume that supports backup, such as Amazon
Elastic Block Store, back up etcd data by taking a snapshot of the storage
volume.
Snapshot using etcdctl options
We can also take the snapshot using various options given by etcdctl. For example
ETCDCTL_API=3 etcdctl -h
will list various options available from etcdctl. For example, you can take a snapshot by specifying
the endpoint, certificates etc as shown below:
where trusted-ca-file, cert-file and key-file can be obtained from the description of the etcd Pod.
Scaling out etcd clusters
Scaling out etcd clusters increases availability by trading off performance.
Scaling does not increase cluster performance nor capability. A general rule
is not to scale out or in etcd clusters. Do not configure any auto scaling
groups for etcd clusters. It is highly recommended to always run a static
five-member etcd cluster for production Kubernetes clusters at any officially
supported scale.
A reasonable scaling is to upgrade a three-member cluster to a five-member
one, when more reliability is desired. See
etcd reconfiguration documentation
for information on how to add members into an existing cluster.
Restoring an etcd cluster
etcd supports restoring from snapshots that are taken from an etcd process of
the major.minor version. Restoring a version from a
different patch version of etcd also is supported. A restore operation is
employed to recover the data of a failed cluster.
Before starting the restore operation, a snapshot file must be present. It can
either be a snapshot file from a previous backup operation, or from a remaining
data directory.
If the access URLs of the restored cluster is changed from the previous
cluster, the Kubernetes API server must be reconfigured accordingly. In this
case, restart Kubernetes API servers with the flag
--etcd-servers=$NEW_ETCD_CLUSTER instead of the flag
--etcd-servers=$OLD_ETCD_CLUSTER. Replace $NEW_ETCD_CLUSTER and
$OLD_ETCD_CLUSTER with the respective IP addresses. If a load balancer is
used in front of an etcd cluster, you might need to update the load balancer
instead.
If the majority of etcd members have permanently failed, the etcd cluster is
considered failed. In this scenario, Kubernetes cannot make any changes to its
current state. Although the scheduled pods might continue to run, no new pods
can be scheduled. In such cases, recover the etcd cluster and potentially
reconfigure Kubernetes API servers to fix the issue.
Note:
If any API servers are running in your cluster, you should not attempt to
restore instances of etcd. Instead, follow these steps to restore etcd:
stop all API server instances
restore state in all etcd instances
restart all API server instances
We also recommend restarting any components (e.g. kube-scheduler,
kube-controller-manager, kubelet) to ensure that they don't rely on some
stale data. Note that in practice, the restore takes a bit of time. During the
restoration, critical components will lose leader lock and restart themselves.
Upgrading etcd clusters
For more details on etcd upgrade, please refer to the etcd upgrades documentation.
Note: Before you start an upgrade, please back up your etcd cluster first.
Maintaining etcd clusters
For more details on etcd maintenance, please refer to the etcd maintenance documentation.
🛇 This item links to a third party project or product that is not part of Kubernetes itself. More information
Note:
Defragmentation is an expensive operation, so it should be executed as infrequent
as possible. On the other hand, it's also necessary to make sure any etcd member
will not run out of the storage quota. The Kubernetes project recommends that when
you perform defragmentation, you use a tool such as etcd-defrag.
You can also run the defragmentation tool as a Kubernetes CronJob, to make sure that
defragmentation happens regularly. See etcd-defrag-cronjob.yaml
for details.
2.30 - Reserve Compute Resources for System Daemons
Kubernetes nodes can be scheduled to Capacity. Pods can consume all the
available capacity on a node by default. This is an issue because nodes
typically run quite a few system daemons that power the OS and Kubernetes
itself. Unless resources are set aside for these system daemons, pods and system
daemons compete for resources and lead to resource starvation issues on the
node.
The kubelet exposes a feature named 'Node Allocatable' that helps to reserve
compute resources for system daemons. Kubernetes recommends cluster
administrators to configure 'Node Allocatable' based on their workload density
on each node.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Your Kubernetes server must be at or later than version 1.8.
To check the version, enter kubectl version.
Your Kubernetes server must be at or later than version 1.17 to use
the kubelet command line option --reserved-cpus to set an
explicitly reserved CPU list.
Node Allocatable
'Allocatable' on a Kubernetes node is defined as the amount of compute resources
that are available for pods. The scheduler does not over-subscribe
'Allocatable'. 'CPU', 'memory' and 'ephemeral-storage' are supported as of now.
Node Allocatable is exposed as part of v1.Node object in the API and as part
of kubectl describe node in the CLI.
Resources can be reserved for two categories of system daemons in the kubelet.
Enabling QoS and Pod level cgroups
To properly enforce node allocatable constraints on the node, you must
enable the new cgroup hierarchy via the --cgroups-per-qos flag. This flag is
enabled by default. When enabled, the kubelet will parent all end-user pods
under a cgroup hierarchy managed by the kubelet.
Configuring a cgroup driver
The kubelet supports manipulation of the cgroup hierarchy on
the host using a cgroup driver. The driver is configured via the
--cgroup-driver flag.
The supported values are the following:
cgroupfs is the default driver that performs direct manipulation of the
cgroup filesystem on the host in order to manage cgroup sandboxes.
systemd is an alternative driver that manages cgroup sandboxes using
transient slices for resources that are supported by that init system.
Depending on the configuration of the associated container runtime,
operators may have to choose a particular cgroup driver to ensure
proper system behavior. For example, if operators use the systemd
cgroup driver provided by the containerd runtime, the kubelet must
be configured to use the systemd cgroup driver.
kube-reserved is meant to capture resource reservation for kubernetes system
daemons like the kubelet, container runtime, node problem detector, etc.
It is not meant to reserve resources for system daemons that are run as pods.
kube-reserved is typically a function of pod density on the nodes.
In addition to cpu, memory, and ephemeral-storage, pid may be
specified to reserve the specified number of process IDs for
kubernetes system daemons.
To optionally enforce kube-reserved on kubernetes system daemons, specify the parent
control group for kube daemons as the value for --kube-reserved-cgroup kubelet
flag.
It is recommended that the kubernetes system daemons are placed under a top
level control group (runtime.slice on systemd machines for example). Each
system daemon should ideally run within its own child control group. Refer to
the design proposal
for more details on recommended control group hierarchy.
Note that Kubelet does not create --kube-reserved-cgroup if it doesn't
exist. The kubelet will fail to start if an invalid cgroup is specified. With systemd
cgroup driver, you should follow a specific pattern for the name of the cgroup you
define: the name should be the value you set for --kube-reserved-cgroup,
with .slice appended.
system-reserved is meant to capture resource reservation for OS system daemons
like sshd, udev, etc. system-reserved should reserve memory for the
kernel too since kernel memory is not accounted to pods in Kubernetes at this time.
Reserving resources for user login sessions is also recommended (user.slice in
systemd world).
In addition to cpu, memory, and ephemeral-storage, pid may be
specified to reserve the specified number of process IDs for OS system
daemons.
To optionally enforce system-reserved on system daemons, specify the parent
control group for OS system daemons as the value for --system-reserved-cgroup
kubelet flag.
It is recommended that the OS system daemons are placed under a top level
control group (system.slice on systemd machines for example).
Note that kubeletdoes not create --system-reserved-cgroup if it doesn't
exist. kubelet will fail if an invalid cgroup is specified. With systemd
cgroup driver, you should follow a specific pattern for the name of the cgroup you
define: the name should be the value you set for --system-reserved-cgroup,
with .slice appended.
reserved-cpus is meant to define an explicit CPU set for OS system daemons and
kubernetes system daemons. reserved-cpus is for systems that do not intend to
define separate top level cgroups for OS system daemons and kubernetes system daemons
with regard to cpuset resource.
If the Kubelet does not have --system-reserved-cgroup and --kube-reserved-cgroup,
the explicit cpuset provided by reserved-cpus will take precedence over the CPUs
defined by --kube-reserved and --system-reserved options.
This option is specifically designed for Telco/NFV use cases where uncontrolled
interrupts/timers may impact the workload performance. you can use this option
to define the explicit cpuset for the system/kubernetes daemons as well as the
interrupts/timers, so the rest CPUs on the system can be used exclusively for
workloads, with less impact from uncontrolled interrupts/timers. To move the
system daemon, kubernetes daemons and interrupts/timers to the explicit cpuset
defined by this option, other mechanism outside Kubernetes should be used.
For example: in Centos, you can do this using the tuned toolset.
Memory pressure at the node level leads to System OOMs which affects the entire
node and all pods running on it. Nodes can go offline temporarily until memory
has been reclaimed. To avoid (or reduce the probability of) system OOMs kubelet
provides out of resource
management. Evictions are
supported for memory and ephemeral-storage only. By reserving some memory via
--eviction-hard flag, the kubelet attempts to evict pods whenever memory
availability on the node drops below the reserved value. Hypothetically, if
system daemons did not exist on a node, pods cannot use more than capacity - eviction-hard. For this reason, resources reserved for evictions are not
available for pods.
The scheduler treats 'Allocatable' as the available capacity for pods.
kubelet enforce 'Allocatable' across pods by default. Enforcement is performed
by evicting pods whenever the overall usage across all pods exceeds
'Allocatable'. More details on eviction policy can be found
on the node pressure eviction
page. This enforcement is controlled by
specifying pods value to the kubelet flag --enforce-node-allocatable.
Optionally, kubelet can be made to enforce kube-reserved and
system-reserved by specifying kube-reserved & system-reserved values in
the same flag. Note that to enforce kube-reserved or system-reserved,
--kube-reserved-cgroup or --system-reserved-cgroup needs to be specified
respectively.
General Guidelines
System daemons are expected to be treated similar to
Guaranteed pods.
System daemons can burst within their bounding control groups and this behavior needs
to be managed as part of kubernetes deployments. For example, kubelet should
have its own control group and share kube-reserved resources with the
container runtime. However, Kubelet cannot burst and use up all available Node
resources if kube-reserved is enforced.
Be extra careful while enforcing system-reserved reservation since it can lead
to critical system services being CPU starved, OOM killed, or unable
to fork on the node. The
recommendation is to enforce system-reserved only if a user has profiled their
nodes exhaustively to come up with precise estimates and is confident in their
ability to recover if any process in that group is oom-killed.
To begin with enforce 'Allocatable' on pods.
Once adequate monitoring and alerting is in place to track kube system
daemons, attempt to enforce kube-reserved based on usage heuristics.
If absolutely necessary, enforce system-reserved over time.
The resource requirements of kube system daemons may grow over time as more and
more features are added. Over time, kubernetes project will attempt to bring
down utilization of node system daemons, but that is not a priority as of now.
So expect a drop in Allocatable capacity in future releases.
Example Scenario
Here is an example to illustrate Node Allocatable computation:
Node has 32Gi of memory, 16 CPUs and 100Gi of Storage
--kube-reserved is set to cpu=1,memory=2Gi,ephemeral-storage=1Gi
--system-reserved is set to cpu=500m,memory=1Gi,ephemeral-storage=1Gi
--eviction-hard is set to memory.available<500Mi,nodefs.available<10%
Under this scenario, 'Allocatable' will be 14.5 CPUs, 28.5Gi of memory and
88Gi of local storage.
Scheduler ensures that the total memory requests across all pods on this node does
not exceed 28.5Gi and storage doesn't exceed 88Gi.
Kubelet evicts pods whenever the overall memory usage across pods exceeds 28.5Gi,
or if overall disk usage exceeds 88Gi. If all processes on the node consume as
much CPU as they can, pods together cannot consume more than 14.5 CPUs.
If kube-reserved and/or system-reserved is not enforced and system daemons
exceed their reservation, kubelet evicts pods whenever the overall node memory
usage is higher than 31.5Gi or storage is greater than 90Gi.
2.31 - Running Kubernetes Node Components as a Non-root User
FEATURE STATE:Kubernetes v1.22 [alpha]
This document describes how to run Kubernetes Node components such as kubelet, CRI, OCI, and CNI
without root privileges, by using a user namespace.
This technique is also known as rootless mode.
Note:
This document describes how to run Kubernetes Node components (and hence pods) as a non-root user.
If you are just looking for how to run a pod as a non-root user, see SecurityContext.
Before you begin
Your Kubernetes server must be at or later than version 1.22.
To check the version, enter kubectl version.
Note:
This section links to third party projects that provide functionality required by Kubernetes. The Kubernetes project authors aren't responsible for these projects, which are listed alphabetically. To add a project to this list, read the content guide before submitting a change. More information.
sysbox
Sysbox is an open-source container runtime
(similar to "runc") that supports running system-level workloads such as Docker
and Kubernetes inside unprivileged containers isolated with the Linux user
namespace.
Sysbox supports running Kubernetes inside unprivileged containers without
requiring Cgroup v2 and without the KubeletInUserNamespace feature gate. It
does this by exposing specially crafted /proc and /sys filesystems inside
the container plus several other advanced OS virtualization techniques.
Running Rootless Kubernetes directly on a host
Note:
This section links to third party projects that provide functionality required by Kubernetes. The Kubernetes project authors aren't responsible for these projects, which are listed alphabetically. To add a project to this list, read the content guide before submitting a change. More information.
If you are trying to run Kubernetes in a user-namespaced container such as
Rootless Docker/Podman or LXC/LXD, you are all set, and you can go to the next subsection.
Otherwise you have to create a user namespace by yourself, by calling unshare(2) with CLONE_NEWUSER.
A user namespace can be also unshared by using command line tools such as:
After unsharing the user namespace, you will also have to unshare other namespaces such as mount namespace.
You do not need to call chroot() nor pivot_root() after unsharing the mount namespace,
however, you have to mount writable filesystems on several directories in the namespace.
At least, the following directories need to be writable in the namespace (not outside the namespace):
/etc
/run
/var/logs
/var/lib/kubelet
/var/lib/cni
/var/lib/containerd (for containerd)
/var/lib/containers (for CRI-O)
Creating a delegated cgroup tree
In addition to the user namespace, you also need to have a writable cgroup tree with cgroup v2.
Note: Kubernetes support for running Node components in user namespaces requires cgroup v2.
Cgroup v1 is not supported.
If you are trying to run Kubernetes in Rootless Docker/Podman or LXC/LXD on a systemd-based host, you are all set.
Otherwise you have to create a systemd unit with Delegate=yes property to delegate a cgroup tree with writable permission.
On your node, systemd must already be configured to allow delegation; for more details, see
cgroup v2 in the Rootless
Containers documentation.
Configuring network
Note:
This section links to third party projects that provide functionality required by Kubernetes. The Kubernetes project authors aren't responsible for these projects, which are listed alphabetically. To add a project to this list, read the content guide before submitting a change. More information.
The network namespace of the Node components has to have a non-loopback interface, which can be for example configured with
slirp4netns,
VPNKit, or
lxc-user-nic(1).
The network namespaces of the Pods can be configured with regular CNI plugins.
For multi-node networking, Flannel (VXLAN, 8472/UDP) is known to work.
Ports such as the kubelet port (10250/TCP) and NodePort service ports have to be exposed from the Node network namespace to
the host with an external port forwarder, such as RootlessKit, slirp4netns, or
socat(1).
The kubelet relies on a container runtime. You should deploy a container runtime such as
containerd or CRI-O and ensure that it is running within the user namespace before the kubelet starts.
Running CRI plugin of containerd in a user namespace is supported since containerd 1.4.
Running containerd within a user namespace requires the following configurations.
version = 2[plugins."io.containerd.grpc.v1.cri"]
# Disable AppArmor disable_apparmor = true# Ignore an error during setting oom_score_adj restrict_oom_score_adj = true# Disable hugetlb cgroup v2 controller (because systemd does not support delegating hugetlb controller) disable_hugetlb_controller = true[plugins."io.containerd.grpc.v1.cri".containerd]
# Using non-fuse overlayfs is also possible for kernel >= 5.11, but requires SELinux to be disabled snapshotter = "fuse-overlayfs"[plugins."io.containerd.grpc.v1.cri".containerd.runtimes.runc.options]
# We use cgroupfs that is delegated by systemd, so we do not use SystemdCgroup driver# (unless you run another systemd in the namespace) SystemdCgroup = false
The default path of the configuration file is /etc/containerd/config.toml.
The path can be specified with containerd -c /path/to/containerd/config.toml.
Running CRI-O in a user namespace is supported since CRI-O 1.22.
CRI-O requires an environment variable _CRIO_ROOTLESS=1 to be set.
The following configurations are also recommended:
[crio]
storage_driver = "overlay"# Using non-fuse overlayfs is also possible for kernel >= 5.11, but requires SELinux to be disabled storage_option = ["overlay.mount_program=/usr/local/bin/fuse-overlayfs"]
[crio.runtime]
# We use cgroupfs that is delegated by systemd, so we do not use "systemd" driver# (unless you run another systemd in the namespace) cgroup_manager = "cgroupfs"
The default path of the configuration file is /etc/crio/crio.conf.
The path can be specified with crio --config /path/to/crio/crio.conf.
Configuring kubelet
Running kubelet in a user namespace requires the following configuration:
apiVersion:kubelet.config.k8s.io/v1beta1kind:KubeletConfigurationfeatureGates:KubeletInUserNamespace:true# We use cgroupfs that is delegated by systemd, so we do not use "systemd" driver# (unless you run another systemd in the namespace)cgroupDriver:"cgroupfs"
When the KubeletInUserNamespace feature gate is enabled, the kubelet ignores errors
that may happen during setting the following sysctl values on the node.
vm.overcommit_memory
vm.panic_on_oom
kernel.panic
kernel.panic_on_oops
kernel.keys.root_maxkeys
kernel.keys.root_maxbytes.
Within a user namespace, the kubelet also ignores any error raised from trying to open /dev/kmsg.
This feature gate also allows kube-proxy to ignore an error during setting RLIMIT_NOFILE.
The KubeletInUserNamespace feature gate was introduced in Kubernetes v1.22 with "alpha" status.
Running kubelet in a user namespace without using this feature gate is also possible
by mounting a specially crafted proc filesystem (as done by Sysbox), but not officially supported.
Configuring kube-proxy
Running kube-proxy in a user namespace requires the following configuration:
apiVersion:kubeproxy.config.k8s.io/v1alpha1kind:KubeProxyConfigurationmode:"iptables"# or "userspace"conntrack:# Skip setting sysctl value "net.netfilter.nf_conntrack_max"maxPerCore:0# Skip setting "net.netfilter.nf_conntrack_tcp_timeout_established"tcpEstablishedTimeout:0s# Skip setting "net.netfilter.nf_conntrack_tcp_timeout_close"tcpCloseWaitTimeout:0s
Caveats
Most of "non-local" volume drivers such as nfs and iscsi do not work.
Local volumes like local, hostPath, emptyDir, configMap, secret, and downwardAPI are known to work.
Some CNI plugins may not work. Flannel (VXLAN) is known to work.
To ensure that your workloads remain available during maintenance, you can
configure a PodDisruptionBudget.
If availability is important for any applications that run or could run on the node(s)
that you are draining, configure a PodDisruptionBudgets
first and then continue following this guide.
It is recommended to set AlwaysAllowUnhealthy Pod Eviction Policy
to your PodDisruptionBudgets to support eviction of misbehaving applications during a node drain.
The default behavior is to wait for the application pods to become healthy
before the drain can proceed.
Use kubectl drain to remove a node from service
You can use kubectl drain to safely evict all of your pods from a
node before you perform maintenance on the node (e.g. kernel upgrade,
hardware maintenance, etc.). Safe evictions allow the pod's containers
to gracefully terminate
and will respect the PodDisruptionBudgets you have specified.
Note: By default kubectl drain ignores certain system pods on the node
that cannot be killed; see
the kubectl drain
documentation for more details.
When kubectl drain returns successfully, that indicates that all of
the pods (except the ones excluded as described in the previous paragraph)
have been safely evicted (respecting the desired graceful termination period,
and respecting the PodDisruptionBudget you have defined). It is then safe to
bring down the node by powering down its physical machine or, if running on a
cloud platform, deleting its virtual machine.
Note:
If any new Pods tolerate the node.kubernetes.io/unschedulable taint, then those Pods
might be scheduled to the node you have drained. Avoid tolerating that taint other than
for DaemonSets.
If you or another API user directly set the nodeName
field for a Pod (bypassing the scheduler), then the Pod is bound to the specified node
and will run there, even though you have drained that node and marked it unschedulable.
First, identify the name of the node you wish to drain. You can list all of the nodes in your cluster with
kubectl get nodes
Next, tell Kubernetes to drain the node:
kubectl drain --ignore-daemonsets <node name>
If there are pods managed by a DaemonSet, you will need to specify
--ignore-daemonsets with kubectl to successfully drain the node. The kubectl drain subcommand on its own does not actually drain
a node of its DaemonSet pods:
the DaemonSet controller (part of the control plane) immediately replaces missing Pods with
new equivalent Pods. The DaemonSet controller also creates Pods that ignore unschedulable
taints, which allows the new Pods to launch onto a node that you are draining.
Once it returns (without giving an error), you can power down the node
(or equivalently, if on a cloud platform, delete the virtual machine backing the node).
If you leave the node in the cluster during the maintenance operation, you need to run
kubectl uncordon <node name>
afterwards to tell Kubernetes that it can resume scheduling new pods onto the node.
Draining multiple nodes in parallel
The kubectl drain command should only be issued to a single node at a
time. However, you can run multiple kubectl drain commands for
different nodes in parallel, in different terminals or in the
background. Multiple drain commands running concurrently will still
respect the PodDisruptionBudget you specify.
For example, if you have a StatefulSet with three replicas and have
set a PodDisruptionBudget for that set specifying minAvailable: 2,
kubectl drain only evicts a pod from the StatefulSet if all three
replicas pods are healthy;
if then you issue multiple drain commands in parallel,
Kubernetes respects the PodDisruptionBudget and ensures that
only 1 (calculated as replicas - minAvailable) Pod is unavailable
at any given time. Any drains that would cause the number of healthy
replicas to fall below the specified budget are blocked.
The Eviction API
If you prefer not to use kubectl drain (such as
to avoid calling to an external command, or to get finer control over the pod
eviction process), you can also programmatically cause evictions using the
eviction API.
This document covers topics related to protecting a cluster from accidental or malicious access
and provides recommendations on overall security.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
As Kubernetes is entirely API-driven, controlling and limiting who can access the cluster and what actions
they are allowed to perform is the first line of defense.
Use Transport Layer Security (TLS) for all API traffic
Kubernetes expects that all API communication in the cluster is encrypted by default with TLS, and the
majority of installation methods will allow the necessary certificates to be created and distributed to
the cluster components. Note that some components and installation methods may enable local ports over
HTTP and administrators should familiarize themselves with the settings of each component to identify
potentially unsecured traffic.
API Authentication
Choose an authentication mechanism for the API servers to use that matches the common access patterns
when you install a cluster. For instance, small, single-user clusters may wish to use a simple certificate
or static Bearer token approach. Larger clusters may wish to integrate an existing OIDC or LDAP server that
allow users to be subdivided into groups.
All API clients must be authenticated, even those that are part of the infrastructure like nodes,
proxies, the scheduler, and volume plugins. These clients are typically service accounts or use x509 client certificates, and they are created automatically at cluster startup or are setup as part of the cluster installation.
Once authenticated, every API call is also expected to pass an authorization check. Kubernetes ships
an integrated Role-Based Access Control (RBAC) component that matches an incoming user or group to a
set of permissions bundled into roles. These permissions combine verbs (get, create, delete) with
resources (pods, services, nodes) and can be namespace-scoped or cluster-scoped. A set of out-of-the-box
roles are provided that offer reasonable default separation of responsibility depending on what
actions a client might want to perform. It is recommended that you use the
Node and
RBAC authorizers together, in combination with the
NodeRestriction admission plugin.
As with authentication, simple and broad roles may be appropriate for smaller clusters, but as
more users interact with the cluster, it may become necessary to separate teams into separate
namespaces with more limited roles.
With authorization, it is important to understand how updates on one object may cause actions in
other places. For instance, a user may not be able to create pods directly, but allowing them to
create a deployment, which creates pods on their behalf, will let them create those pods
indirectly. Likewise, deleting a node from the API will result in the pods scheduled to that node
being terminated and recreated on other nodes. The out-of-the box roles represent a balance
between flexibility and common use cases, but more limited roles should be carefully reviewed
to prevent accidental escalation. You can make roles specific to your use case if the out-of-box ones don't meet your needs.
Kubelets expose HTTPS endpoints which grant powerful control over the node and containers.
By default Kubelets allow unauthenticated access to this API.
Production clusters should enable Kubelet authentication and authorization.
Controlling the capabilities of a workload or user at runtime
Authorization in Kubernetes is intentionally high level, focused on coarse actions on resources.
More powerful controls exist as policies to limit by use case how those objects act on the
cluster, themselves, and other resources.
Limiting resource usage on a cluster
Resource quota limits the number or capacity of
resources granted to a namespace. This is most often used to limit the amount of CPU, memory,
or persistent disk a namespace can allocate, but can also control how many pods, services, or
volumes exist in each namespace.
Limit ranges restrict the maximum or minimum size of some of the
resources above, to prevent users from requesting unreasonably high or low values for commonly
reserved resources like memory, or to provide default limits when none are specified.
Controlling what privileges containers run with
A pod definition contains a security context
that allows it to request access to run as a specific Linux user on a node (like root),
access to run privileged or access the host network, and other controls that would otherwise
allow it to run unfettered on a hosting node.
Generally, most application workloads need limited access to host resources so they can
successfully run as a root process (uid 0) without access to host information. However,
considering the privileges associated with the root user, you should write application
containers to run as a non-root user. Similarly, administrators who wish to prevent
client applications from escaping their containers should apply the Baseline
or Restricted Pod Security Standard.
Preventing containers from loading unwanted kernel modules
The Linux kernel automatically loads kernel modules from disk if needed in certain
circumstances, such as when a piece of hardware is attached or a filesystem is mounted. Of
particular relevance to Kubernetes, even unprivileged processes can cause certain
network-protocol-related kernel modules to be loaded, just by creating a socket of the
appropriate type. This may allow an attacker to exploit a security hole in a kernel module
that the administrator assumed was not in use.
To prevent specific modules from being automatically loaded, you can uninstall them from
the node, or add rules to block them. On most Linux distributions, you can do that by
creating a file such as /etc/modprobe.d/kubernetes-blacklist.conf with contents like:
# DCCP is unlikely to be needed, has had multiple serious
# vulnerabilities, and is not well-maintained.
blacklist dccp
# SCTP is not used in most Kubernetes clusters, and has also had
# vulnerabilities in the past.
blacklist sctp
To block module loading more generically, you can use a Linux Security Module (such as
SELinux) to completely deny the module_request permission to containers, preventing the
kernel from loading modules for containers under any circumstances. (Pods would still be
able to use modules that had been loaded manually, or modules that were loaded by the
kernel on behalf of some more-privileged process.)
Restricting network access
The network policies for a namespace
allows application authors to restrict which pods in other namespaces may access pods and ports
within their namespaces. Many of the supported Kubernetes networking providers
now respect network policy.
Quota and limit ranges can also be used to control whether users may request node ports or
load-balanced services, which on many clusters can control whether those users applications
are visible outside of the cluster.
Additional protections may be available that control network rules on a per-plugin or per-
environment basis, such as per-node firewalls, physically separating cluster nodes to
prevent cross talk, or advanced networking policy.
Restricting cloud metadata API access
Cloud platforms (AWS, Azure, GCE, etc.) often expose metadata services locally to instances.
By default these APIs are accessible by pods running on an instance and can contain cloud
credentials for that node, or provisioning data such as kubelet credentials. These credentials
can be used to escalate within the cluster or to other cloud services under the same account.
When running Kubernetes on a cloud platform, limit permissions given to instance credentials, use
network policies to restrict pod access
to the metadata API, and avoid using provisioning data to deliver secrets.
As an administrator, a beta admission plugin PodNodeSelector can be used to force pods
within a namespace to default or require a specific node selector, and if end users cannot
alter namespaces, this can strongly limit the placement of all of the pods in a specific workload.
Protecting cluster components from compromise
This section describes some common patterns for protecting clusters from compromise.
Restrict access to etcd
Write access to the etcd backend for the API is equivalent to gaining root on the entire cluster,
and read access can be used to escalate fairly quickly. Administrators should always use strong
credentials from the API servers to their etcd server, such as mutual auth via TLS client certificates,
and it is often recommended to isolate the etcd servers behind a firewall that only the API servers
may access.
Caution: Allowing other components within the cluster to access the master etcd instance with
read or write access to the full keyspace is equivalent to granting cluster-admin access. Using
separate etcd instances for non-master components or using etcd ACLs to restrict read and write
access to a subset of the keyspace is strongly recommended.
Enable audit logging
The audit logger is a beta feature that records actions taken by the
API for later analysis in the event of a compromise. It is recommended to enable audit logging
and archive the audit file on a secure server.
Restrict access to alpha or beta features
Alpha and beta Kubernetes features are in active development and may have limitations or bugs
that result in security vulnerabilities. Always assess the value an alpha or beta feature may
provide against the possible risk to your security posture. When in doubt, disable features you
do not use.
Rotate infrastructure credentials frequently
The shorter the lifetime of a secret or credential the harder it is for an attacker to make
use of that credential. Set short lifetimes on certificates and automate their rotation. Use
an authentication provider that can control how long issued tokens are available and use short
lifetimes where possible. If you use service-account tokens in external integrations, plan to
rotate those tokens frequently. For example, once the bootstrap phase is complete, a bootstrap
token used for setting up nodes should be revoked or its authorization removed.
Review third party integrations before enabling them
Many third party integrations to Kubernetes may alter the security profile of your cluster. When
enabling an integration, always review the permissions that an extension requests before granting
it access. For example, many security integrations may request access to view all secrets on
your cluster which is effectively making that component a cluster admin. When in doubt,
restrict the integration to functioning in a single namespace if possible.
Components that create pods may also be unexpectedly powerful if they can do so inside namespaces
like the kube-system namespace, because those pods can gain access to service account secrets
or run with elevated permissions if those service accounts are granted access to permissive
PodSecurityPolicies.
If you use Pod Security admission and allow
any component to create Pods within a namespace that permits privileged Pods, those Pods may
be able to escape their containers and use this widened access to elevate their privileges.
You should not allow untrusted components to create Pods in any system namespace (those with
names that start with kube-) nor in any namespace where that access grant allows the possibility
of privilege escalation.
Encrypt secrets at rest
In general, the etcd database will contain any information accessible via the Kubernetes API
and may grant an attacker significant visibility into the state of your cluster. Always encrypt
your backups using a well reviewed backup and encryption solution, and consider using full disk
encryption where possible.
Kubernetes supports optional encryption at rest for information in the Kubernetes API.
This lets you ensure that when Kubernetes stores data for objects (for example, Secret or
ConfigMap objects), the API server writes an encrypted representation of the object.
That encryption means that even someone who has access to etcd backup data is unable
to view the content of those objects.
In Kubernetes 1.28 you can also encrypt custom resources;
encryption-at-rest for extension APIs defined in CustomResourceDefinitions was added to
Kubernetes as part of the v1.26 release.
Receiving alerts for security updates and reporting vulnerabilities
Join the kubernetes-announce
group for emails about security announcements. See the
security reporting
page for more on how to report vulnerabilities.
What's next
Security Checklist for additional information on Kubernetes security guidance.
2.34 - Set Kubelet Parameters Via A Configuration File
A subset of the kubelet's configuration parameters may be
set via an on-disk config file, as a substitute for command-line flags.
Providing parameters via a config file is the recommended approach because
it simplifies node deployment and configuration management.
Create the config file
The subset of the kubelet's configuration that can be configured via a file
is defined by the
KubeletConfiguration
struct.
The configuration file must be a JSON or YAML representation of the parameters
in this struct. Make sure the kubelet has read permissions on the file.
Here is an example of what this file might look like:
In this example, the kubelet is configured with the following settings:
address: The kubelet will serve on IP address 192.168.0.8.
port: The kubelet will serve on port 20250.
serializeImagePulls: Image pulls will be done in parallel.
evictionHard: The kubelet will evict Pods under one of the following conditions:
When the node's available memory drops below 100MiB.
When the node's main filesystem's available space is less than 10%.
When the image filesystem's available space is less than 15%.
When more than 95% of the node's main filesystem's inodes are in use.
Note: In the example, by changing the default value of only one parameter for
evictionHard, the default values of other parameters will not be inherited and
will be set to zero. In order to provide custom values, you should provide all
the threshold values respectively.
The imagefs is an optional filesystem that container runtimes use to store container
images and container writable layers.
Start a kubelet process configured via the config file
Note: If you use kubeadm to initialize your cluster, use the kubelet-config while creating your cluster with kubeadm init.
See configuring kubelet using kubeadm for details.
Start the kubelet with the --config flag set to the path of the kubelet's config file.
The kubelet will then load its config from this file.
Note that command line flags which target the same value as a config file will override that value.
This helps ensure backwards compatibility with the command-line API.
Note that relative file paths in the kubelet config file are resolved relative to the
location of the kubelet config file, whereas relative paths in command line flags are resolved
relative to the kubelet's current working directory.
Note that some default values differ between command-line flags and the kubelet config file.
If --config is provided and the values are not specified via the command line, the
defaults for the KubeletConfiguration version apply.
In the above example, this version is kubelet.config.k8s.io/v1beta1.
Drop-in directory for kubelet configuration files
As of Kubernetes v1.28.0, the kubelet has been extended to support a drop-in configuration directory. The location of it can be specified with
--config-dir flag, and it defaults to "", or disabled, by default.
You can only set --config-dir if you set the environment variable KUBELET_CONFIG_DROPIN_DIR_ALPHA for the kubelet process (the value of that variable does not matter).
For Kubernetes v1.28, the kubelet returns an error if you specify --config-dir without that variable set, and startup fails.
You cannot specify the drop-in configuration directory using the kubelet configuration file; only the CLI argument --config-dir can set it.
One can use the kubelet configuration directory in a similar way to the kubelet config file.
Note: The suffix of a valid kubelet drop-in configuration file must be .conf. For instance: 99-kubelet-address.conf
For instance, you may want a baseline kubelet configuration for all nodes, but you may want to customize the address field. This can be done as follows:
NAME STATUS AGE
default Active 11d
kube-node-lease Active 11d
kube-public Active 11d
kube-system Active 11d
Kubernetes starts with four initial namespaces:
default The default namespace for objects with no other namespace
kube-node-lease This namespace holds Lease objects associated with each node. Node leases allow the kubelet to send heartbeats so that the control plane can detect node failure.
kube-public This namespace is created automatically and is readable by all users
(including those not authenticated). This namespace is mostly reserved for cluster usage,
in case that some resources should be visible and readable publicly throughout the whole cluster.
The public aspect of this namespace is only a convention, not a requirement.
kube-system The namespace for objects created by the Kubernetes system
You can also get the summary of a specific namespace using:
kubectl get namespaces <name>
Or you can get detailed information with:
kubectl describe namespaces <name>
Name: default
Labels: <none>
Annotations: <none>
Status: Active
No resource quota.
Resource Limits
Type Resource Min Max Default
---- -------- --- --- ---
Container cpu - - 100m
Note that these details show both resource quota (if present) as well as resource limit ranges.
Resource quota tracks aggregate usage of resources in the Namespace and allows cluster operators
to define Hard resource usage limits that a Namespace may consume.
A limit range defines min/max constraints on the amount of resources a single entity can consume in
a Namespace.
The name of your namespace must be a valid
DNS label.
There's an optional field finalizers, which allows observables to purge resources whenever the
namespace is deleted. Keep in mind that if you specify a nonexistent finalizer, the namespace will
be created but will get stuck in the Terminating state if the user tries to delete it.
More information on finalizers can be found in the namespace
design doc.
Warning: This deletes everything under the namespace!
This delete is asynchronous, so for a time you will see the namespace in the Terminating state.
Subdividing your cluster using Kubernetes namespaces
By default, a Kubernetes cluster will instantiate a default namespace when provisioning the
cluster to hold the default set of Pods, Services, and Deployments used by the cluster.
Assuming you have a fresh cluster, you can introspect the available namespaces by doing the following:
kubectl get namespaces
NAME STATUS AGE
default Active 13m
Create new namespaces
For this exercise, we will create two additional Kubernetes namespaces to hold our content.
In a scenario where an organization is using a shared Kubernetes cluster for development and
production use cases:
The development team would like to maintain a space in the cluster where they can get a view on
the list of Pods, Services, and Deployments they use to build and run their application.
In this space, Kubernetes resources come and go, and the restrictions on who can or cannot modify
resources are relaxed to enable agile development.
The operations team would like to maintain a space in the cluster where they can enforce strict
procedures on who can or cannot manipulate the set of Pods, Services, and Deployments that run
the production site.
One pattern this organization could follow is to partition the Kubernetes cluster into two
namespaces: development and production. Let's create two new namespaces to hold our work.
To be sure things are right, list all of the namespaces in our cluster.
kubectl get namespaces --show-labels
NAME STATUS AGE LABELS
default Active 32m <none>
development Active 29s name=development
production Active 23s name=production
Create pods in each namespace
A Kubernetes namespace provides the scope for Pods, Services, and Deployments in the cluster.
Users interacting with one namespace do not see the content in another namespace.
To demonstrate this, let's spin up a simple Deployment and Pods in the development namespace.
We have created a deployment whose replica size is 2 that is running the pod called snowflake
with a basic container that serves the hostname.
kubectl get deployment -n=development
NAME READY UP-TO-DATE AVAILABLE AGE
snowflake 2/2 2 2 2m
kubectl get pods -l app=snowflake -n=development
NAME READY STATUS RESTARTS AGE
snowflake-3968820950-9dgr8 1/1 Running 0 2m
snowflake-3968820950-vgc4n 1/1 Running 0 2m
And this is great, developers are able to do what they want, and they do not have to worry about
affecting content in the production namespace.
Let's switch to the production namespace and show how resources in one namespace are hidden from
the other. The production namespace should be empty, and the following commands should return nothing.
kubectl get deployment -n=production
kubectl get pods -n=production
Production likes to run cattle, so let's create some cattle pods.
NAME READY UP-TO-DATE AVAILABLE AGE
cattle 5/5 5 5 10s
kubectl get pods -l app=cattle -n=production
NAME READY STATUS RESTARTS AGE
cattle-2263376956-41xy6 1/1 Running 0 34s
cattle-2263376956-kw466 1/1 Running 0 34s
cattle-2263376956-n4v97 1/1 Running 0 34s
cattle-2263376956-p5p3i 1/1 Running 0 34s
cattle-2263376956-sxpth 1/1 Running 0 34s
At this point, it should be clear that the resources users create in one namespace are hidden from
the other namespace.
As the policy support in Kubernetes evolves, we will extend this scenario to show how you can provide different
authorization rules for each namespace.
Understanding the motivation for using namespaces
A single cluster should be able to satisfy the needs of multiple users or groups of users
(henceforth in this document a user community).
Kubernetes namespaces help different projects, teams, or customers to share a Kubernetes cluster.
policies (who can or cannot perform actions in their community)
constraints (this community is allowed this much quota, etc.)
A cluster operator may create a Namespace for each unique user community.
The Namespace provides a unique scope for:
named resources (to avoid basic naming collisions)
delegated management authority to trusted users
ability to limit community resource consumption
Use cases include:
As a cluster operator, I want to support multiple user communities on a single cluster.
As a cluster operator, I want to delegate authority to partitions of the cluster to trusted
users in those communities.
As a cluster operator, I want to limit the amount of resources each community can consume in
order to limit the impact to other communities using the cluster.
As a cluster user, I want to interact with resources that are pertinent to my user community in
isolation of what other user communities are doing on the cluster.
Understanding namespaces and DNS
When you create a Service, it creates a corresponding
DNS entry.
This entry is of the form <service-name>.<namespace-name>.svc.cluster.local, which means
that if a container uses <service-name> it will resolve to the service which
is local to a namespace. This is useful for using the same configuration across
multiple namespaces such as Development, Staging and Production. If you want to reach
across namespaces, you need to use the fully qualified domain name (FQDN).
Adjust manifests and other resources based on the API changes that accompany the
new Kubernetes version
Before you begin
You must have an existing cluster. This page is about upgrading from Kubernetes
1.27 to Kubernetes 1.28. If your cluster
is not currently running Kubernetes 1.27 then please check
the documentation for the version of Kubernetes that you plan to upgrade to.
Upgrade approaches
kubeadm
If your cluster was deployed using the kubeadm tool, refer to
Upgrading kubeadm clusters
for detailed information on how to upgrade the cluster.
For each node in your cluster, drain
that node and then either replace it with a new node that uses the 1.28
kubelet, or upgrade the kubelet on that node and bring the node back into service.
Caution: Draining nodes before upgrading kubelet ensures that pods are re-admitted and containers are
re-created, which may be necessary to resolve some security issues or other important bugs.
Other deployments
Refer to the documentation for your cluster deployment tool to learn the recommended set
up steps for maintenance.
Post-upgrade tasks
Switch your cluster's storage API version
The objects that are serialized into etcd for a cluster's internal
representation of the Kubernetes resources active in the cluster are
written using a particular version of the API.
When the supported API changes, these objects may need to be rewritten
in the newer API. Failure to do this will eventually result in resources
that are no longer decodable or usable by the Kubernetes API server.
For each affected object, fetch it using the latest supported API and then
write it back also using the latest supported API.
Update manifests
Upgrading to a new Kubernetes version can provide new APIs.
You can use kubectl convert command to convert manifests between different API versions.
For example:
kubectl convert -f pod.yaml --output-version v1
The kubectl tool replaces the contents of pod.yaml with a manifest that sets kind to
Pod (unchanged), but with a revised apiVersion.
Device Plugins
If your cluster is running device plugins and the node needs to be upgraded to a Kubernetes
release with a newer device plugin API version, device plugins must be upgraded to support
both version before the node is upgraded in order to guarantee that device allocations
continue to complete successfully during the upgrade.
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
You also need to create a sample Deployment
to experiment with the different types of cascading deletion. You will need to
recreate the Deployment for each type.
Check owner references on your pods
Check that the ownerReferences field is present on your pods:
kubectl get pods -l app=nginx --output=yaml
The output has an ownerReferences field similar to this:
By default, Kubernetes uses background cascading deletion
to delete dependents of an object. You can switch to foreground cascading deletion
using either kubectl or the Kubernetes API, depending on the Kubernetes
version your cluster runs.
To check the version, enter kubectl version.
You can delete objects using foreground cascading deletion using kubectl or the
Kubernetes API.
Use either kubectl or the Kubernetes API to delete the Deployment,
depending on the Kubernetes version your cluster runs.
To check the version, enter kubectl version.
You can delete objects using background cascading deletion using kubectl
or the Kubernetes API.
Kubernetes uses background cascading deletion by default, and does so
even if you run the following commands without the --cascade flag or the
propagationPolicy argument.
By default, when you tell Kubernetes to delete an object, the
controller also deletes
dependent objects. You can make Kubernetes orphan these dependents using
kubectl or the Kubernetes API, depending on the Kubernetes version your
cluster runs.
To check the version, enter kubectl version.
This page shows how to configure a Key Management Service (KMS) provider and plugin to enable secret data encryption.
In Kubernetes 1.28 there are two versions of KMS at-rest encryption.
You should use KMS v2 if feasible because KMS v1 is deprecated (since Kubernetes v1.28).
However, you should also read and observe the Caution notices in this page that highlight specific
cases when you must not use KMS v2. KMS v2 offers significantly better performance characteristics than KMS v1.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
The version of Kubernetes that you need depends on which KMS API version
you have selected. Kubernetes recommends using KMS v2.
If you selected KMS API v2, you should use Kubernetes v1.28
(if you are running a different version of Kubernetes that also supports the v2 KMS
API, switch to the documentation for that version of Kubernetes).
If you selected KMS API v1 to support clusters prior to version v1.27
or if you have a legacy KMS plugin that only supports KMS v1,
any supported Kubernetes version will work. This API is deprecated as of Kubernetes v1.28.
Kubernetes does not recommend the use of this API.
To check the version, enter kubectl version.
KMS v1
FEATURE STATE:Kubernetes v1.28 [deprecated]
Kubernetes version 1.10.0 or later is required
Your cluster must use etcd v3 or later
KMS v2
FEATURE STATE:Kubernetes v1.27 [beta]
For version 1.25 and 1.26, enabling the feature via kube-apiserver feature gate is required.
Set --feature-gates=KMSv2=true to configure a KMS v2 provider.
For environments where all API servers are running version 1.28 or later, and you do not require the ability
to downgrade to Kubernetes v1.27, you can enable the KMSv2KDF feature gate (a beta feature) for more
robust data encryption key generation. The Kubernetes project recommends enabling KMS v2 KDF if those
preconditions are met.
Your cluster must use etcd v3 or later
Caution:
The KMS v2 API and implementation changed in incompatible ways in-between the alpha release in v1.25
and the beta release in v1.27. Attempting to upgrade from old versions with the alpha feature
enabled will result in data loss.
Running mixed API server versions with some servers at v1.27, and others at v1.28 with the
KMSv2KDF feature gate enabled is not supported - and is likely to result in data loss.
The KMS encryption provider uses an envelope encryption scheme to encrypt data in etcd.
The data is encrypted using a data encryption key (DEK).
The DEKs are encrypted with a key encryption key (KEK) that is stored and managed in a remote KMS.
With KMS v1, a new DEK is generated for each encryption.
With KMS v2, there are two ways for the API server to generate a DEK.
Kubernetes defaults to generating a new DEK at API server startup, which is then reused
for resource encryption. However, if you use KMS v2 and enable the KMSv2KDFfeature gate, then
Kubernetes instead generates a new DEK per encryption: the API server uses a
key derivation function to generate single use data encryption keys from a secret seed
combined with some random data.
Whichever approach you configure, the DEK or seed is also rotated whenever the KEK is rotated
(see Understanding key_id and Key Rotation section below for more details).
The KMS provider uses gRPC to communicate with a specific KMS plugin over a UNIX domain socket.
The KMS plugin, which is implemented as a gRPC server and deployed on the same host(s)
as the Kubernetes control plane, is responsible for all communication with the remote KMS.
Caution:
If you are running virtual machine (VM) based nodes that leverage VM state store with this feature,
using KMS v2 is insecure and an information security risk unless you also explicitly enable
the KMSv2KDFfeature gate.
With KMS v2, the API server uses AES-GCM with a 12 byte nonce (8 byte atomic counter and 4 bytes random data) for encryption.
The following issues could occur if the VM is saved and restored:
The counter value may be lost or corrupted if the VM is saved in an inconsistent state or restored improperly.
This can lead to a situation where the same counter value is used twice, resulting in the same nonce being used
for two different messages.
If the VM is restored to a previous state, the counter value may be set back to its previous value,
resulting in the same nonce being used again.
Although both of these cases are partially mitigated by the 4 byte random nonce, this can compromise
the security of the encryption.
If you have enabled the KMSv2KDFfeature gateand are using KMS v2
(not KMS v1), the API server generates single use data encryption keys from a secret seed.
This eliminates the need for a counter based nonce while avoiding nonce collision concerns.
It also removes any specific concerns with using KMS v2 and VM state store.
Configuring the KMS provider
To configure a KMS provider on the API server, include a provider of type kms in the
providers array in the encryption configuration file and set the following properties:
KMS v1
apiVersion: API Version for KMS provider. Leave this value empty or set it to v1.
name: Display name of the KMS plugin. Cannot be changed once set.
endpoint: Listen address of the gRPC server (KMS plugin). The endpoint is a UNIX domain socket.
cachesize: Number of data encryption keys (DEKs) to be cached in the clear.
When cached, DEKs can be used without another call to the KMS;
whereas DEKs that are not cached require a call to the KMS to unwrap.
timeout: How long should kube-apiserver wait for kms-plugin to respond before
returning an error (default is 3 seconds).
KMS v2
apiVersion: API Version for KMS provider. Set this to v2.
name: Display name of the KMS plugin. Cannot be changed once set.
endpoint: Listen address of the gRPC server (KMS plugin). The endpoint is a UNIX domain socket.
timeout: How long should kube-apiserver wait for kms-plugin to respond before
returning an error (default is 3 seconds).
KMS v2 does not support the cachesize property. All data encryption keys (DEKs) will be cached in
the clear once the server has unwrapped them via a call to the KMS. Once cached, DEKs can be used
to perform decryption indefinitely without making a call to the KMS.
To implement a KMS plugin, you can develop a new plugin gRPC server or enable a KMS plugin
already provided by your cloud provider.
You then integrate the plugin with the remote KMS and deploy it on the Kubernetes control plane.
Enabling the KMS supported by your cloud provider
Refer to your cloud provider for instructions on enabling the cloud provider-specific KMS plugin.
Developing a KMS plugin gRPC server
You can develop a KMS plugin gRPC server using a stub file available for Go. For other languages,
you use a proto file to create a stub file that you can use to develop the gRPC server code.
KMS v1
Using Go: Use the functions and data structures in the stub file:
api.pb.go
to develop the gRPC server code
Using languages other than Go: Use the protoc compiler with the proto file:
api.proto
to generate a stub file for the specific language
KMS v2
Using Go: A high level
library
is provided to make the process easier. Low level implementations
can use the functions and data structures in the stub file:
api.pb.go
to develop the gRPC server code
Using languages other than Go: Use the protoc compiler with the proto file:
api.proto
to generate a stub file for the specific language
Then use the functions and data structures in the stub file to develop the server code.
Notes
KMS v1
kms plugin version: v1beta1
In response to procedure call Version, a compatible KMS plugin should return v1beta1 as VersionResponse.version.
message version: v1beta1
All messages from KMS provider have the version field set to v1beta1.
protocol: UNIX domain socket (unix)
The plugin is implemented as a gRPC server that listens at UNIX domain socket. The plugin deployment should create a file on the file system to run the gRPC unix domain socket connection. The API server (gRPC client) is configured with the KMS provider (gRPC server) unix domain socket endpoint in order to communicate with it. An abstract Linux socket may be used by starting the endpoint with /@, i.e. unix:///@foo. Care must be taken when using this type of socket as they do not have concept of ACL (unlike traditional file based sockets). However, they are subject to Linux networking namespace, so will only be accessible to containers within the same pod unless host networking is used.
KMS v2
KMS plugin version: v2beta1
In response to procedure call Status, a compatible KMS plugin should return v2beta1 as StatusResponse.version,
"ok" as StatusResponse.healthz and a key_id (remote KMS KEK ID) as StatusResponse.key_id.
The API server polls the Status procedure call approximately every minute when everything is healthy,
and every 10 seconds when the plugin is not healthy. Plugins must take care to optimize this call as it will be
under constant load.
Encryption
The EncryptRequest procedure call provides the plaintext and a UID for logging purposes. The response must include
the ciphertext, the key_id for the KEK used, and, optionally, any metadata that the KMS plugin needs to aid in
future DecryptRequest calls (via the annotations field). The plugin must guarantee that any distinct plaintext
results in a distinct response (ciphertext, key_id, annotations).
If the plugin returns a non-empty annotations map, all map keys must be fully qualified domain names such as
example.com. An example use case of annotation is {"kms.example.io/remote-kms-auditid":"<audit ID used by the remote KMS>"}
The API server does not perform the EncryptRequest procedure call at a high rate. Plugin implementations should
still aim to keep each request's latency at under 100 milliseconds.
Decryption
The DecryptRequest procedure call provides the (ciphertext, key_id, annotations) from EncryptRequest and a UID
for logging purposes. As expected, it is the inverse of the EncryptRequest call. Plugins must verify that the
key_id is one that they understand - they must not attempt to decrypt data unless they are sure that it was
encrypted by them at an earlier time.
The API server may perform thousands of DecryptRequest procedure calls on startup to fill its watch cache. Thus
plugin implementations must perform these calls as quickly as possible, and should aim to keep each request's latency
at under 10 milliseconds.
Understanding key_id and Key Rotation
The key_id is the public, non-secret name of the remote KMS KEK that is currently in use. It may be logged
during regular operation of the API server, and thus must not contain any private data. Plugin implementations
are encouraged to use a hash to avoid leaking any data. The KMS v2 metrics take care to hash this value before
exposing it via the /metrics endpoint.
The API server considers the key_id returned from the Status procedure call to be authoritative. Thus, a change
to this value signals to the API server that the remote KEK has changed, and data encrypted with the old KEK should
be marked stale when a no-op write is performed (as described below). If an EncryptRequest procedure call returns a
key_id that is different from Status, the response is thrown away and the plugin is considered unhealthy. Thus
implementations must guarantee that the key_id returned from Status will be the same as the one returned by
EncryptRequest. Furthermore, plugins must ensure that the key_id is stable and does not flip-flop between values
(i.e. during a remote KEK rotation).
Plugins must not re-use key_ids, even in situations where a previously used remote KEK has been reinstated. For
example, if a plugin was using key_id=A, switched to key_id=B, and then went back to key_id=A - instead of
reporting key_id=A the plugin should report some derivative value such as key_id=A_001 or use a new value such
as key_id=C.
Since the API server polls Status about every minute, key_id rotation is not immediate. Furthermore, the API
server will coast on the last valid state for about three minutes. Thus if a user wants to take a passive approach
to storage migration (i.e. by waiting), they must schedule a migration to occur at 3 + N + M minutes after the
remote KEK has been rotated (N is how long it takes the plugin to observe the key_id change and M is the
desired buffer to allow config changes to be processed - a minimum M of five minutes is recommend). Note that no
API server restart is required to perform KEK rotation.
Caution: Because you don't control the number of writes performed with the DEK,
the Kubernetes project recommends rotating the KEK at least every 90 days.
protocol: UNIX domain socket (unix)
The plugin is implemented as a gRPC server that listens at UNIX domain socket.
The plugin deployment should create a file on the file system to run the gRPC unix domain socket connection.
The API server (gRPC client) is configured with the KMS provider (gRPC server) unix
domain socket endpoint in order to communicate with it.
An abstract Linux socket may be used by starting the endpoint with /@, i.e. unix:///@foo.
Care must be taken when using this type of socket as they do not have concept of ACL
(unlike traditional file based sockets).
However, they are subject to Linux networking namespace, so will only be accessible to
containers within the same pod unless host networking is used.
Integrating a KMS plugin with the remote KMS
The KMS plugin can communicate with the remote KMS using any protocol supported by the KMS.
All configuration data, including authentication credentials the KMS plugin uses to communicate with the remote KMS,
are stored and managed by the KMS plugin independently.
The KMS plugin can encode the ciphertext with additional metadata that may be required before sending it to the KMS
for decryption (KMS v2 makes this process easier by providing a dedicated annotations field).
Deploying the KMS plugin
Ensure that the KMS plugin runs on the same host(s) as the Kubernetes API server(s).
Encrypting your data with the KMS provider
To encrypt the data:
Create a new EncryptionConfiguration file using the appropriate properties for the kms provider
to encrypt resources like Secrets and ConfigMaps. If you want to encrypt an extension API that is
defined in a CustomResourceDefinition, your cluster must be running Kubernetes v1.26 or newer.
Set the --encryption-provider-config flag on the kube-apiserver to point to the location of the configuration file.
--encryption-provider-config-automatic-reload boolean argument determines if the file set by --encryption-provider-config should be automatically reloaded if the disk contents change. This enables key rotation without API server restarts.
Setting --encryption-provider-config-automatic-reload to true collapses all health checks to a single health check endpoint. Individual health checks are only available when KMS v1 providers are in use and the encryption config is not auto-reloaded.
The following table summarizes the health check endpoints for each KMS version:
KMS configurations
Without Automatic Reload
With Automatic Reload
KMS v1 only
Individual Healthchecks
Single Healthcheck
KMS v2 only
Single Healthcheck
Single Healthcheck
Both KMS v1 and v2
Individual Healthchecks
Single Healthcheck
No KMS
None
Single Healthcheck
Single Healthcheck means that the only health check endpoint is /healthz/kms-providers.
Individual Healthchecks means that each KMS plugin has an associated health check endpoint based on its location in the encryption config: /healthz/kms-provider-0, /healthz/kms-provider-1 etc.
These healthcheck endpoint paths are hard coded and generated/controlled by the server. The indices for individual healthchecks corresponds to the order in which the KMS encryption config is processed.
At a high level, restarting an API server when a KMS plugin is unhealthy is unlikely to make the situation better.
It can make the situation significantly worse by throwing away the API server's DEK cache. Thus the general
recommendation is to ignore the API server KMS healthz checks for liveness purposes, i.e. /livez?exclude=kms-providers.
Until the steps defined in Ensuring all secrets are encrypted are performed, the providers list should end with the identity: {} provider to allow unencrypted data to be read. Once all resources are encrypted, the identity provider should be removed to prevent the API server from honoring unencrypted data.
When encryption at rest is correctly configured, resources are encrypted on write.
After restarting your kube-apiserver, any newly created or updated Secret or other resource types
configured in EncryptionConfiguration should be encrypted when stored. To verify,
you can use the etcdctl command line program to retrieve the contents of your secret data.
Create a new secret called secret1 in the default namespace:
Using the etcdctl command line, read that secret out of etcd:
ETCDCTL_API=3 etcdctl get /kubernetes.io/secrets/default/secret1 [...] | hexdump -C
where [...] contains the additional arguments for connecting to the etcd server.
Verify the stored secret is prefixed with k8s:enc:kms:v1: for KMS v1 or prefixed with k8s:enc:kms:v2: for KMS v2, which indicates that the kms provider has encrypted the resulting data.
Verify that the secret is correctly decrypted when retrieved via the API:
kubectl describe secret secret1 -n default
The Secret should contain mykey: mydata
Ensuring all secrets are encrypted
When encryption at rest is correctly configured, resources are encrypted on write.
Thus we can perform an in-place no-op update to ensure that data is encrypted.
The following command reads all secrets and then updates them to apply server side encryption.
If an error occurs due to a conflicting write, retry the command.
For larger clusters, you may wish to subdivide the secrets by namespace or script an update.
This page describes the CoreDNS upgrade process and how to install CoreDNS instead of kube-dns.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Your Kubernetes server must be at or later than version v1.9.
To check the version, enter kubectl version.
About CoreDNS
CoreDNS is a flexible, extensible DNS server
that can serve as the Kubernetes cluster DNS.
Like Kubernetes, the CoreDNS project is hosted by the
CNCF.
You can use CoreDNS instead of kube-dns in your cluster by replacing
kube-dns in an existing deployment, or by using tools like kubeadm
that will deploy and upgrade the cluster for you.
Installing CoreDNS
For manual deployment or replacement of kube-dns, see the documentation at the
CoreDNS GitHub project.
Migrating to CoreDNS
Upgrading an existing cluster with kubeadm
In Kubernetes version 1.21, kubeadm removed its support for kube-dns as a DNS application.
For kubeadm v1.28, the only supported cluster DNS application
is CoreDNS.
You can move to CoreDNS when you use kubeadm to upgrade a cluster that is
using kube-dns. In this case, kubeadm generates the CoreDNS configuration
("Corefile") based upon the kube-dns ConfigMap, preserving configurations for
stub domains, and upstream name server.
Upgrading CoreDNS
You can check the version of CoreDNS that kubeadm installs for each version of
Kubernetes in the page
CoreDNS version in Kubernetes.
CoreDNS can be upgraded manually in case you want to only upgrade CoreDNS
or use your own custom image.
There is a helpful guideline and walkthrough
available to ensure a smooth upgrade.
Make sure the existing CoreDNS configuration ("Corefile") is retained when
upgrading your cluster.
If you are upgrading your cluster using the kubeadm tool, kubeadm
can take care of retaining the existing CoreDNS configuration automatically.
Tuning CoreDNS
When resource utilisation is a concern, it may be useful to tune the
configuration of CoreDNS. For more details, check out the
documentation on scaling CoreDNS.
What's next
You can configure CoreDNS to support many more use cases than
kube-dns does by modifying the CoreDNS configuration ("Corefile").
For more information, see the documentation
for the kubernetes CoreDNS plugin, or read the
Custom DNS Entries for Kubernetes.
in the CoreDNS blog.
2.40 - Using NodeLocal DNSCache in Kubernetes Clusters
FEATURE STATE:Kubernetes v1.18 [stable]
This page provides an overview of NodeLocal DNSCache feature in Kubernetes.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
NodeLocal DNSCache improves Cluster DNS performance by running a DNS caching agent
on cluster nodes as a DaemonSet. In today's architecture, Pods in 'ClusterFirst' DNS mode
reach out to a kube-dns serviceIP for DNS queries. This is translated to a
kube-dns/CoreDNS endpoint via iptables rules added by kube-proxy.
With this new architecture, Pods will reach out to the DNS caching agent
running on the same node, thereby avoiding iptables DNAT rules and connection tracking.
The local caching agent will query kube-dns service for cache misses of cluster
hostnames ("cluster.local" suffix by default).
Motivation
With the current DNS architecture, it is possible that Pods with the highest DNS QPS
have to reach out to a different node, if there is no local kube-dns/CoreDNS instance.
Having a local cache will help improve the latency in such scenarios.
Skipping iptables DNAT and connection tracking will help reduce
conntrack races
and avoid UDP DNS entries filling up conntrack table.
Connections from the local caching agent to kube-dns service can be upgraded to TCP.
TCP conntrack entries will be removed on connection close in contrast with
UDP entries that have to timeout
(defaultnf_conntrack_udp_timeout is 30 seconds)
Upgrading DNS queries from UDP to TCP would reduce tail latency attributed to
dropped UDP packets and DNS timeouts usually up to 30s (3 retries + 10s timeout).
Since the nodelocal cache listens for UDP DNS queries, applications don't need to be changed.
Metrics & visibility into DNS requests at a node level.
Negative caching can be re-enabled, thereby reducing the number of queries for the kube-dns service.
Architecture Diagram
This is the path followed by DNS Queries after NodeLocal DNSCache is enabled:
Configuration
Note: The local listen IP address for NodeLocal DNSCache can be any address that
can be guaranteed to not collide with any existing IP in your cluster.
It's recommended to use an address with a local scope, for example,
from the 'link-local' range '169.254.0.0/16' for IPv4 or from the
'Unique Local Address' range in IPv6 'fd00::/8'.
This feature can be enabled using the following steps:
Prepare a manifest similar to the sample
nodelocaldns.yaml
and save it as nodelocaldns.yaml.
If using IPv6, the CoreDNS configuration file needs to enclose all the IPv6 addresses
into square brackets if used in 'IP:Port' format.
If you are using the sample manifest from the previous point, this will require you to modify
the configuration line L70
like this: "health [__PILLAR__LOCAL__DNS__]:8080"
Substitute the variables in the manifest with the right values:
kubedns=`kubectl get svc kube-dns -n kube-system -o jsonpath={.spec.clusterIP}`domain=<cluster-domain>
localdns=<node-local-address>
<cluster-domain> is "cluster.local" by default. <node-local-address> is the
local listen IP address chosen for NodeLocal DNSCache.
If kube-proxy is running in IPTABLES mode:
sed -i "s/__PILLAR__LOCAL__DNS__/$localdns/g; s/__PILLAR__DNS__DOMAIN__/$domain/g; s/__PILLAR__DNS__SERVER__/$kubedns/g" nodelocaldns.yaml
__PILLAR__CLUSTER__DNS__ and __PILLAR__UPSTREAM__SERVERS__ will be populated by
the node-local-dns pods.
In this mode, the node-local-dns pods listen on both the kube-dns service IP
as well as <node-local-address>, so pods can look up DNS records using either IP address.
If kube-proxy is running in IPVS mode:
sed -i "s/__PILLAR__LOCAL__DNS__/$localdns/g; s/__PILLAR__DNS__DOMAIN__/$domain/g; s/,__PILLAR__DNS__SERVER__//g; s/__PILLAR__CLUSTER__DNS__/$kubedns/g" nodelocaldns.yaml
In this mode, the node-local-dns pods listen only on <node-local-address>.
The node-local-dns interface cannot bind the kube-dns cluster IP since the
interface used for IPVS loadbalancing already uses this address.
__PILLAR__UPSTREAM__SERVERS__ will be populated by the node-local-dns pods.
Run kubectl create -f nodelocaldns.yaml
If using kube-proxy in IPVS mode, --cluster-dns flag to kubelet needs to be modified
to use <node-local-address> that NodeLocal DNSCache is listening on.
Otherwise, there is no need to modify the value of the --cluster-dns flag,
since NodeLocal DNSCache listens on both the kube-dns service IP as well as
<node-local-address>.
Once enabled, the node-local-dns Pods will run in the kube-system namespace
on each of the cluster nodes. This Pod runs CoreDNS
in cache mode, so all CoreDNS metrics exposed by the different plugins will
be available on a per-node basis.
You can disable this feature by removing the DaemonSet, using kubectl delete -f <manifest>.
You should also revert any changes you made to the kubelet configuration.
StubDomains and Upstream server Configuration
StubDomains and upstream servers specified in the kube-dns ConfigMap in the kube-system namespace
are automatically picked up by node-local-dns pods. The ConfigMap contents need to follow the format
shown in the example.
The node-local-dns ConfigMap can also be modified directly with the stubDomain configuration
in the Corefile format. Some cloud providers might not allow modifying node-local-dns ConfigMap directly.
In those cases, the kube-dns ConfigMap can be updated.
Setting memory limits
The node-local-dns Pods use memory for storing cache entries and processing queries.
Since they do not watch Kubernetes objects, the cluster size or the number of Services / EndpointSlices do not directly affect memory usage. Memory usage is influenced by the DNS query pattern.
From CoreDNS docs,
The default cache size is 10000 entries, which uses about 30 MB when completely filled.
This would be the memory usage for each server block (if the cache gets completely filled).
Memory usage can be reduced by specifying smaller cache sizes.
The number of concurrent queries is linked to the memory demand, because each extra
goroutine used for handling a query requires an amount of memory. You can set an upper limit
using the max_concurrent option in the forward plugin.
If a node-local-dns Pod attempts to use more memory than is available (because of total system
resources, or because of a configured
resource limit), the operating system
may shut down that pod's container.
If this happens, the container that is terminated (“OOMKilled”) does not clean up the custom
packet filtering rules that it previously added during startup.
The node-local-dns container should get restarted (since managed as part of a DaemonSet), but this
will lead to a brief DNS downtime each time that the container fails: the packet filtering rules direct
DNS queries to a local Pod that is unhealthy.
You can determine a suitable memory limit by running node-local-dns pods without a limit and
measuring the peak usage. You can also set up and use a
VerticalPodAutoscaler
in recommender mode, and then check its recommendations.
2.41 - Using sysctls in a Kubernetes Cluster
FEATURE STATE:Kubernetes v1.21 [stable]
This document describes how to configure and use kernel parameters within a
Kubernetes cluster using the sysctl
interface.
Note: Starting from Kubernetes version 1.23, the kubelet supports the use of either / or .
as separators for sysctl names.
Starting from Kubernetes version 1.25, setting Sysctls for a Pod supports setting sysctls with slashes.
For example, you can represent the same sysctl name as kernel.shm_rmid_forced using a
period as the separator, or as kernel/shm_rmid_forced using a slash as a separator.
For more sysctl parameter conversion method details, please refer to
the page sysctl.d(5) from
the Linux man-pages project.
Before you begin
Note:sysctl is a Linux-specific command-line tool used to configure various kernel parameters
and it is not available on non-Linux operating systems.
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
For some steps, you also need to be able to reconfigure the command line
options for the kubelets running on your cluster.
Listing all Sysctl Parameters
In Linux, the sysctl interface allows an administrator to modify kernel
parameters at runtime. Parameters are available via the /proc/sys/ virtual
process file system. The parameters cover various subsystems such as:
Kubernetes classes sysctls as either safe or unsafe. In addition to proper
namespacing, a safe sysctl must be properly isolated between pods on the
same node. This means that setting a safe sysctl for one pod
must not have any influence on any other pod on the node
must not allow to harm the node's health
must not allow to gain CPU or memory resources outside of the resource limits
of a pod.
By far, most of the namespaced sysctls are not necessarily considered safe.
The following sysctls are supported in the safe set:
There are some exceptions to the set of safe sysctls:
The net.* sysctls are not allowed with host networking enabled.
The net.ipv4.tcp_syncookies sysctl is not namespaced on Linux kernel version 4.4 or lower.
This list will be extended in future Kubernetes versions when the kubelet
supports better isolation mechanisms.
Enabling Unsafe Sysctls
All safe sysctls are enabled by default.
All unsafe sysctls are disabled by default and must be allowed manually by the
cluster admin on a per-node basis. Pods with disabled unsafe sysctls will be
scheduled, but will fail to launch.
With the warning above in mind, the cluster admin can allow certain unsafe
sysctls for very special situations such as high-performance or real-time
application tuning. Unsafe sysctls are enabled on a node-by-node basis with a
flag of the kubelet; for example:
A number of sysctls are namespaced in today's Linux kernels. This means that
they can be set independently for each pod on a node. Only namespaced sysctls
are configurable via the pod securityContext within Kubernetes.
The following sysctls are known to be namespaced. This list could change
in future versions of the Linux kernel.
kernel.shm*,
kernel.msg*,
kernel.sem,
fs.mqueue.*,
Those net.* that can be set in container networking namespace. However,
there are exceptions (e.g., net.netfilter.nf_conntrack_max and
net.netfilter.nf_conntrack_expect_max can be set in container networking
namespace but are unnamespaced before Linux 5.12.2).
Sysctls with no namespace are called node-level sysctls. If you need to set
them, you must manually configure them on each node's operating system, or by
using a DaemonSet with privileged containers.
Use the pod securityContext to configure namespaced sysctls. The securityContext
applies to all containers in the same pod.
This example uses the pod securityContext to set a safe sysctl
kernel.shm_rmid_forced and two unsafe sysctls net.core.somaxconn and
kernel.msgmax. There is no distinction between safe and unsafe sysctls in
the specification.
Warning: Only modify sysctl parameters after you understand their effects, to avoid
destabilizing your operating system.
Warning: Due to their nature of being unsafe, the use of unsafe sysctls
is at-your-own-risk and can lead to severe problems like wrong behavior of
containers, resource shortage or complete breakage of a node.
It is good practice to consider nodes with special sysctl settings as
tainted within a cluster, and only schedule pods onto them which need those
sysctl settings. It is suggested to use the Kubernetes taints and toleration
feature to implement this.
A pod with the unsafe sysctls will fail to launch on any node which has not
enabled those two unsafe sysctls explicitly. As with node-level sysctls it
is recommended to use
taints and toleration feature or
taints on nodes
to schedule those pods onto the right nodes.
2.42 - Utilizing the NUMA-aware Memory Manager
FEATURE STATE:Kubernetes v1.22 [beta]
The Kubernetes Memory Manager enables the feature of guaranteed memory (and hugepages)
allocation for pods in the GuaranteedQoS class.
The Memory Manager employs hint generation protocol to yield the most suitable NUMA affinity for a pod.
The Memory Manager feeds the central manager (Topology Manager) with these affinity hints.
Based on both the hints and Topology Manager policy, the pod is rejected or admitted to the node.
Moreover, the Memory Manager ensures that the memory which a pod requests
is allocated from a minimum number of NUMA nodes.
The Memory Manager is only pertinent to Linux based hosts.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Your Kubernetes server must be at or later than version v1.21.
To check the version, enter kubectl version.
To align memory resources with other requested resources in a Pod spec:
the CPU Manager should be enabled and proper CPU Manager policy should be configured on a Node.
See control CPU Management Policies;
the Topology Manager should be enabled and proper Topology Manager policy should be configured on a Node.
See control Topology Management Policies.
Starting from v1.22, the Memory Manager is enabled by default through MemoryManagerfeature gate.
Preceding v1.22, the kubelet must be started with the following flag:
--feature-gates=MemoryManager=true
in order to enable the Memory Manager feature.
How Memory Manager Operates?
The Memory Manager currently offers the guaranteed memory (and hugepages) allocation
for Pods in Guaranteed QoS class.
To immediately put the Memory Manager into operation follow the guidelines in the section
Memory Manager configuration, and subsequently,
prepare and deploy a Guaranteed pod as illustrated in the section
Placing a Pod in the Guaranteed QoS class.
The Memory Manager is a Hint Provider, and it provides topology hints for
the Topology Manager which then aligns the requested resources according to these topology hints.
It also enforces cgroups (i.e. cpuset.mems) for pods.
The complete flow diagram concerning pod admission and deployment process is illustrated in
Memory Manager KEP: Design Overview and below:
During this process, the Memory Manager updates its internal counters stored in
Node Map and Memory Maps to manage guaranteed memory allocation.
The Memory Manager updates the Node Map during the startup and runtime as follows.
The administrator must provide --reserved-memory flag when Static policy is configured.
Runtime
Reference Memory Manager KEP: Memory Maps at runtime (with examples) illustrates
how a successful pod deployment affects the Node Map, and it also relates to
how potential Out-of-Memory (OOM) situations are handled further by Kubernetes or operating system.
Other Managers should be first pre-configured. Next, the Memory Manager feature should be enabled
and be run with Static policy (section Static policy).
Optionally, some amount of memory can be reserved for system or kubelet processes to increase
node stability (section Reserved memory flag).
Policies
Memory Manager supports two policies. You can select a policy via a kubelet flag --memory-manager-policy:
None (default)
Static
None policy
This is the default policy and does not affect the memory allocation in any way.
It acts the same as if the Memory Manager is not present at all.
The None policy returns default topology hint. This special hint denotes that Hint Provider
(Memory Manager in this case) has no preference for NUMA affinity with any resource.
Static policy
In the case of the Guaranteed pod, the Static Memory Manager policy returns topology hints
relating to the set of NUMA nodes where the memory can be guaranteed,
and reserves the memory through updating the internal NodeMap object.
In the case of the BestEffort or Burstable pod, the Static Memory Manager policy sends back
the default topology hint as there is no request for the guaranteed memory,
and does not reserve the memory in the internal NodeMap object.
Reserved memory flag
The Node Allocatable mechanism
is commonly used by node administrators to reserve K8S node system resources for the kubelet
or operating system processes in order to enhance the node stability.
A dedicated set of flags can be used for this purpose to set the total amount of reserved memory
for a node. This pre-configured value is subsequently utilized to calculate
the real amount of node's "allocatable" memory available to pods.
The Kubernetes scheduler incorporates "allocatable" to optimise pod scheduling process.
The foregoing flags include --kube-reserved, --system-reserved and --eviction-threshold.
The sum of their values will account for the total amount of reserved memory.
A new --reserved-memory flag was added to Memory Manager to allow for this total reserved memory
to be split (by a node administrator) and accordingly reserved across many NUMA nodes.
The flag specifies a comma-separated list of memory reservations of different memory types per NUMA node.
Memory reservations across multiple NUMA nodes can be specified using semicolon as separator.
This parameter is only useful in the context of the Memory Manager feature.
The Memory Manager will not use this reserved memory for the allocation of container workloads.
For example, if you have a NUMA node "NUMA0" with 10Gi of memory available, and
the --reserved-memory was specified to reserve 1Gi of memory at "NUMA0",
the Memory Manager assumes that only 9Gi is available for containers.
You can omit this parameter, however, you should be aware that the quantity of reserved memory
from all NUMA nodes should be equal to the quantity of memory specified by the
Node Allocatable feature.
If at least one node allocatable parameter is non-zero, you will need to specify
--reserved-memory for at least one NUMA node.
In fact, eviction-hard threshold value is equal to 100Mi by default, so
if Static policy is used, --reserved-memory is obligatory.
Also, avoid the following configurations:
duplicates, i.e. the same NUMA node or memory type, but with a different value;
setting zero limit for any of memory types;
NUMA node IDs that do not exist in the machine hardware;
memory type names different than memory or hugepages-<size>
(hugepages of particular <size> should also exist).
When you specify values for --reserved-memory flag, you must comply with the setting that
you prior provided via Node Allocatable Feature flags.
That is, the following rule must be obeyed for each memory type:
An example of kubelet command-line arguments relevant to the node Allocatable configuration:
--kube-reserved=cpu=500m,memory=50Mi
--system-reserved=cpu=123m,memory=333Mi
--eviction-hard=memory.available<500Mi
Note: The default hard eviction threshold is 100MiB, and not zero.
Remember to increase the quantity of memory that you reserve by setting --reserved-memory
by that hard eviction threshold. Otherwise, the kubelet will not start Memory Manager and
display an error.
If the selected policy is anything other than None, the Memory Manager identifies pods
that are in the Guaranteed QoS class.
The Memory Manager provides specific topology hints to the Topology Manager for each Guaranteed pod.
For pods in a QoS class other than Guaranteed, the Memory Manager provides default topology hints
to the Topology Manager.
The following excerpts from pod manifests assign a pod to the Guaranteed QoS class.
Pod with integer CPU(s) runs in the Guaranteed QoS class, when requests are equal to limits:
Notice that both CPU and memory requests must be specified for a Pod to lend it to Guaranteed QoS class.
Troubleshooting
The following means can be used to troubleshoot the reason why a pod could not be deployed or
became rejected at a node:
pod status - indicates topology affinity errors
system logs - include valuable information for debugging, e.g., about generated hints
state file - the dump of internal state of the Memory Manager
(includes Node Map and Memory Maps)
starting from v1.22, the device plugin resource API can be used
to retrieve information about the memory reserved for containers
Pod status (TopologyAffinityError)
This error typically occurs in the following situations:
a node has not enough resources available to satisfy the pod's request
the pod's request is rejected due to particular Topology Manager policy constraints
The error appears in the status of a pod:
kubectl get pods
NAME READY STATUS RESTARTS AGE
guaranteed 0/1 TopologyAffinityError 0 113s
Use kubectl describe pod <id> or kubectl get events to obtain detailed error message:
Warning TopologyAffinityError 10m kubelet, dell8 Resources cannot be allocated with Topology locality
System logs
Search system logs with respect to a particular pod.
The set of hints that Memory Manager generated for the pod can be found in the logs.
Also, the set of hints generated by CPU Manager should be present in the logs.
Topology Manager merges these hints to calculate a single best hint.
The best hint should be also present in the logs.
The best hint indicates where to allocate all the resources.
Topology Manager tests this hint against its current policy, and based on the verdict,
it either admits the pod to the node or rejects it.
Also, search the logs for occurrences associated with the Memory Manager,
e.g. to find out information about cgroups and cpuset.mems updates.
Examine the memory manager state on a node
Let us first deploy a sample Guaranteed pod whose specification is as follows:
It can be deduced from the state file that the pod was pinned to both NUMA nodes, i.e.:
"numaAffinity":[
0,
1],
Pinned term means that pod's memory consumption is constrained (through cgroups configuration)
to these NUMA nodes.
This automatically implies that Memory Manager instantiated a new group that
comprises these two NUMA nodes, i.e. 0 and 1 indexed NUMA nodes.
Notice that the management of groups is handled in a relatively complex manner, and
further elaboration is provided in Memory Manager KEP in this and this sections.
In order to analyse memory resources available in a group,the corresponding entries from
NUMA nodes belonging to the group must be added up.
For example, the total amount of free "conventional" memory in the group can be computed
by adding up the free memory available at every NUMA node in the group,
i.e., in the "memory" section of NUMA node 0 ("free":0) and NUMA node 1 ("free":103739236352).
So, the total amount of free "conventional" memory in this group is equal to 0 + 103739236352 bytes.
The line "systemReserved":3221225472 indicates that the administrator of this node reserved
3221225472 bytes (i.e. 3Gi) to serve kubelet and system processes at NUMA node 0,
by using --reserved-memory flag.
Device plugin resource API
The kubelet provides a PodResourceLister gRPC service to enable discovery of resources and associated metadata.
By using its List gRPC endpoint,
information about reserved memory for each container can be retrieved, which is contained
in protobuf ContainerMemory message.
This information can be retrieved solely for pods in Guaranteed QoS class.
The Kubernetes release process signs all binary artifacts (tarballs, SPDX files,
standalone binaries) by using cosign's keyless signing. To verify a particular
binary, retrieve it together with its signature and certificate:
URL=https://dl.k8s.io/release/v1.28.4/bin/linux/amd64
BINARY=kubectl
FILES=("$BINARY""$BINARY.sig""$BINARY.cert")for FILE in "${FILES[@]}"; do curl -sSfL --retry 3 --retry-delay 3"$URL/$FILE" -o "$FILE"done
Perform common configuration tasks for Pods and containers.
3.1 - Assign Memory Resources to Containers and Pods
This page shows how to assign a memory request and a memory limit to a
Container. A Container is guaranteed to have as much memory as it requests,
but is not allowed to use more memory than its limit.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Each node in your cluster must have at least 300 MiB of memory.
A few of the steps on this page require you to run the
metrics-server
service in your cluster. If you have the metrics-server
running, you can skip those steps.
If you are running Minikube, run the following command to enable the
metrics-server:
minikube addons enable metrics-server
To see whether the metrics-server is running, or another provider of the resource metrics
API (metrics.k8s.io), run the following command:
kubectl get apiservices
If the resource metrics API is available, the output includes a
reference to metrics.k8s.io.
NAME
v1beta1.metrics.k8s.io
Create a namespace
Create a namespace so that the resources you create in this exercise are
isolated from the rest of your cluster.
kubectl create namespace mem-example
Specify a memory request and a memory limit
To specify a memory request for a Container, include the resources:requests field
in the Container's resource manifest. To specify a memory limit, include resources:limits.
In this exercise, you create a Pod that has one Container. The Container has a memory
request of 100 MiB and a memory limit of 200 MiB. Here's the configuration file
for the Pod:
The args section in the configuration file provides arguments for the Container when it starts.
The "--vm-bytes", "150M" arguments tell the Container to attempt to allocate 150 MiB of memory.
kubectl top pod memory-demo --namespace=mem-example
The output shows that the Pod is using about 162,900,000 bytes of memory, which
is about 150 MiB. This is greater than the Pod's 100 MiB request, but within the
Pod's 200 MiB limit.
NAME CPU(cores) MEMORY(bytes)
memory-demo <something> 162856960
Delete your Pod:
kubectl delete pod memory-demo --namespace=mem-example
Exceed a Container's memory limit
A Container can exceed its memory request if the Node has memory available. But a Container
is not allowed to use more than its memory limit. If a Container allocates more memory than
its limit, the Container becomes a candidate for termination. If the Container continues to
consume memory beyond its limit, the Container is terminated. If a terminated Container can be
restarted, the kubelet restarts it, as with any other type of runtime failure.
In this exercise, you create a Pod that attempts to allocate more memory than its limit.
Here is the configuration file for a Pod that has one Container with a
memory request of 50 MiB and a memory limit of 100 MiB:
In the args section of the configuration file, you can see that the Container
will attempt to allocate 250 MiB of memory, which is well above the 100 MiB limit.
The Container in this exercise can be restarted, so the kubelet restarts it. Repeat
this command several times to see that the Container is repeatedly killed and restarted:
kubectl get pod memory-demo-2 --namespace=mem-example
The output shows that the Container is killed, restarted, killed again, restarted again, and so on:
kubectl get pod memory-demo-2 --namespace=mem-example
NAME READY STATUS RESTARTS AGE
memory-demo-2 0/1 OOMKilled 1 37s
kubectl get pod memory-demo-2 --namespace=mem-example
NAME READY STATUS RESTARTS AGE
memory-demo-2 1/1 Running 2 40s
View detailed information about the Pod history:
kubectl describe pod memory-demo-2 --namespace=mem-example
The output shows that the Container starts and fails repeatedly:
... Normal Created Created container with id 66a3a20aa7980e61be4922780bf9d24d1a1d8b7395c09861225b0eba1b1f8511
... Warning BackOff Back-off restarting failed container
View detailed information about your cluster's Nodes:
kubectl describe nodes
The output includes a record of the Container being killed because of an out-of-memory condition:
Warning OOMKilling Memory cgroup out of memory: Kill process 4481 (stress) score 1994 or sacrifice child
Delete your Pod:
kubectl delete pod memory-demo-2 --namespace=mem-example
Specify a memory request that is too big for your Nodes
Memory requests and limits are associated with Containers, but it is useful to think
of a Pod as having a memory request and limit. The memory request for the Pod is the
sum of the memory requests for all the Containers in the Pod. Likewise, the memory
limit for the Pod is the sum of the limits of all the Containers in the Pod.
Pod scheduling is based on requests. A Pod is scheduled to run on a Node only if the Node
has enough available memory to satisfy the Pod's memory request.
In this exercise, you create a Pod that has a memory request so big that it exceeds the
capacity of any Node in your cluster. Here is the configuration file for a Pod that has one
Container with a request for 1000 GiB of memory, which likely exceeds the capacity
of any Node in your cluster.
kubectl get pod memory-demo-3 --namespace=mem-example
The output shows that the Pod status is PENDING. That is, the Pod is not scheduled to run on any Node, and it will remain in the PENDING state indefinitely:
kubectl get pod memory-demo-3 --namespace=mem-example
NAME READY STATUS RESTARTS AGE
memory-demo-3 0/1 Pending 0 25s
View detailed information about the Pod, including events:
kubectl describe pod memory-demo-3 --namespace=mem-example
The output shows that the Container cannot be scheduled because of insufficient memory on the Nodes:
Events:
... Reason Message
------ -------
... FailedScheduling No nodes are available that match all of the following predicates:: Insufficient memory (3).
Memory units
The memory resource is measured in bytes. You can express memory as a plain integer or a
fixed-point integer with one of these suffixes: E, P, T, G, M, K, Ei, Pi, Ti, Gi, Mi, Ki.
For example, the following represent approximately the same value:
128974848, 129e6, 129M, 123Mi
Delete your Pod:
kubectl delete pod memory-demo-3 --namespace=mem-example
If you do not specify a memory limit
If you do not specify a memory limit for a Container, one of the following situations applies:
The Container has no upper bound on the amount of memory it uses. The Container
could use all of the memory available on the Node where it is running which in turn could invoke the OOM Killer. Further, in case of an OOM Kill, a container with no resource limits will have a greater chance of being killed.
The Container is running in a namespace that has a default memory limit, and the
Container is automatically assigned the default limit. Cluster administrators can use a
LimitRange
to specify a default value for the memory limit.
Motivation for memory requests and limits
By configuring memory requests and limits for the Containers that run in your
cluster, you can make efficient use of the memory resources available on your cluster's
Nodes. By keeping a Pod's memory request low, you give the Pod a good chance of being
scheduled. By having a memory limit that is greater than the memory request, you accomplish two things:
The Pod can have bursts of activity where it makes use of memory that happens to be available.
The amount of memory a Pod can use during a burst is limited to some reasonable amount.
Clean up
Delete your namespace. This deletes all the Pods that you created for this task:
This page shows how to assign a CPU request and a CPU limit to
a container. Containers cannot use more CPU than the configured limit.
Provided the system has CPU time free, a container is guaranteed to be
allocated as much CPU as it requests.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Your cluster must have at least 1 CPU available for use to run the task examples.
A few of the steps on this page require you to run the
metrics-server
service in your cluster. If you have the metrics-server
running, you can skip those steps.
If you are running Minikube, run the
following command to enable metrics-server:
minikube addons enable metrics-server
To see whether metrics-server (or another provider of the resource metrics
API, metrics.k8s.io) is running, type the following command:
kubectl get apiservices
If the resource metrics API is available, the output will include a
reference to metrics.k8s.io.
NAME
v1beta1.metrics.k8s.io
Create a namespace
Create a Namespace so that the resources you
create in this exercise are isolated from the rest of your cluster.
kubectl create namespace cpu-example
Specify a CPU request and a CPU limit
To specify a CPU request for a container, include the resources:requests field
in the Container resource manifest. To specify a CPU limit, include resources:limits.
In this exercise, you create a Pod that has one container. The container has a request
of 0.5 CPU and a limit of 1 CPU. Here is the configuration file for the Pod:
The args section of the configuration file provides arguments for the container when it starts.
The -cpus "2" argument tells the Container to attempt to use 2 CPUs.
kubectl get pod cpu-demo --output=yaml --namespace=cpu-example
The output shows that the one container in the Pod has a CPU request of 500 milliCPU
and a CPU limit of 1 CPU.
resources:limits:cpu:"1"requests:cpu:500m
Use kubectl top to fetch the metrics for the Pod:
kubectl top pod cpu-demo --namespace=cpu-example
This example output shows that the Pod is using 974 milliCPU, which is
slightly less than the limit of 1 CPU specified in the Pod configuration.
NAME CPU(cores) MEMORY(bytes)
cpu-demo 974m <something>
Recall that by setting -cpu "2", you configured the Container to attempt to use 2 CPUs, but the Container is only being allowed to use about 1 CPU. The container's CPU use is being throttled, because the container is attempting to use more CPU resources than its limit.
Note: Another possible explanation for the CPU use being below 1.0 is that the Node might not have
enough CPU resources available. Recall that the prerequisites for this exercise require your cluster to have at least 1 CPU available for use. If your Container runs on a Node that has only 1 CPU, the Container cannot use more than 1 CPU regardless of the CPU limit specified for the Container.
CPU units
The CPU resource is measured in CPU units. One CPU, in Kubernetes, is equivalent to:
1 AWS vCPU
1 GCP Core
1 Azure vCore
1 Hyperthread on a bare-metal Intel processor with Hyperthreading
Fractional values are allowed. A Container that requests 0.5 CPU is guaranteed half as much
CPU as a Container that requests 1 CPU. You can use the suffix m to mean milli. For example
100m CPU, 100 milliCPU, and 0.1 CPU are all the same. Precision finer than 1m is not allowed.
CPU is always requested as an absolute quantity, never as a relative quantity; 0.1 is the same
amount of CPU on a single-core, dual-core, or 48-core machine.
Delete your Pod:
kubectl delete pod cpu-demo --namespace=cpu-example
Specify a CPU request that is too big for your Nodes
CPU requests and limits are associated with Containers, but it is useful to think
of a Pod as having a CPU request and limit. The CPU request for a Pod is the sum
of the CPU requests for all the Containers in the Pod. Likewise, the CPU limit for
a Pod is the sum of the CPU limits for all the Containers in the Pod.
Pod scheduling is based on requests. A Pod is scheduled to run on a Node only if
the Node has enough CPU resources available to satisfy the Pod CPU request.
In this exercise, you create a Pod that has a CPU request so big that it exceeds
the capacity of any Node in your cluster. Here is the configuration file for a Pod
that has one Container. The Container requests 100 CPU, which is likely to exceed the
capacity of any Node in your cluster.
kubectl get pod cpu-demo-2 --namespace=cpu-example
The output shows that the Pod status is Pending. That is, the Pod has not been
scheduled to run on any Node, and it will remain in the Pending state indefinitely:
NAME READY STATUS RESTARTS AGE
cpu-demo-2 0/1 Pending 0 7m
View detailed information about the Pod, including events:
kubectl describe pod cpu-demo-2 --namespace=cpu-example
The output shows that the Container cannot be scheduled because of insufficient
CPU resources on the Nodes:
Events:
Reason Message
------ -------
FailedScheduling No nodes are available that match all of the following predicates:: Insufficient cpu (3).
Delete your Pod:
kubectl delete pod cpu-demo-2 --namespace=cpu-example
If you do not specify a CPU limit
If you do not specify a CPU limit for a Container, then one of these situations applies:
The Container has no upper bound on the CPU resources it can use. The Container
could use all of the CPU resources available on the Node where it is running.
The Container is running in a namespace that has a default CPU limit, and the
Container is automatically assigned the default limit. Cluster administrators can use a
LimitRange
to specify a default value for the CPU limit.
If you specify a CPU limit but do not specify a CPU request
If you specify a CPU limit for a Container but do not specify a CPU request, Kubernetes automatically
assigns a CPU request that matches the limit. Similarly, if a Container specifies its own memory limit,
but does not specify a memory request, Kubernetes automatically assigns a memory request that matches
the limit.
Motivation for CPU requests and limits
By configuring the CPU requests and limits of the Containers that run in your
cluster, you can make efficient use of the CPU resources available on your cluster
Nodes. By keeping a Pod CPU request low, you give the Pod a good chance of being
scheduled. By having a CPU limit that is greater than the CPU request, you accomplish two things:
The Pod can have bursts of activity where it makes use of CPU resources that happen to be available.
The amount of CPU resources a Pod can use during a burst is limited to some reasonable amount.
3.3 - Configure GMSA for Windows Pods and containers
FEATURE STATE:Kubernetes v1.18 [stable]
This page shows how to configure
Group Managed Service Accounts (GMSA)
for Pods and containers that will run on Windows nodes. Group Managed Service Accounts
are a specific type of Active Directory account that provides automatic password management,
simplified service principal name (SPN) management, and the ability to delegate the management
to other administrators across multiple servers.
In Kubernetes, GMSA credential specs are configured at a Kubernetes cluster-wide scope
as Custom Resources. Windows Pods, as well as individual containers within a Pod,
can be configured to use a GMSA for domain based functions (e.g. Kerberos authentication)
when interacting with other Windows services.
Before you begin
You need to have a Kubernetes cluster and the kubectl command-line tool must be
configured to communicate with your cluster. The cluster is expected to have Windows worker nodes.
This section covers a set of initial steps required once for each cluster:
Install the GMSACredentialSpec CRD
A CustomResourceDefinition(CRD)
for GMSA credential spec resources needs to be configured on the cluster to define
the custom resource type GMSACredentialSpec. Download the GMSA CRD
YAML
and save it as gmsa-crd.yaml. Next, install the CRD with kubectl apply -f gmsa-crd.yaml
Install webhooks to validate GMSA users
Two webhooks need to be configured on the Kubernetes cluster to populate and
validate GMSA credential spec references at the Pod or container level:
A mutating webhook that expands references to GMSAs (by name from a Pod specification)
into the full credential spec in JSON form within the Pod spec.
A validating webhook ensures all references to GMSAs are authorized to be used by the Pod service account.
Installing the above webhooks and associated objects require the steps below:
Create a certificate key pair (that will be used to allow the webhook container to communicate to the cluster)
Install a secret with the certificate from above.
Create a deployment for the core webhook logic.
Create the validating and mutating webhook configurations referring to the deployment.
A script
can be used to deploy and configure the GMSA webhooks and associated objects
mentioned above. The script can be run with a --dry-run=server option to
allow you to review the changes that would be made to your cluster.
The YAML template
used by the script may also be used to deploy the webhooks and associated objects
manually (with appropriate substitutions for the parameters)
Configure GMSAs and Windows nodes in Active Directory
Before Pods in Kubernetes can be configured to use GMSAs, the desired GMSAs need
to be provisioned in Active Directory as described in the
Windows GMSA documentation.
Windows worker nodes (that are part of the Kubernetes cluster) need to be configured
in Active Directory to access the secret credentials associated with the desired GMSA as described in the
Windows GMSA documentation.
Create GMSA credential spec resources
With the GMSACredentialSpec CRD installed (as described earlier), custom resources
containing GMSA credential specs can be configured. The GMSA credential spec does
not contain secret or sensitive data. It is information that a container runtime
can use to describe the desired GMSA of a container to Windows. GMSA credential
specs can be generated in YAML format with a utility
PowerShell script.
Following are the steps for generating a GMSA credential spec YAML manually in JSON format and then converting it:
Import the CredentialSpec
module: ipmo CredentialSpec.psm1
Create a credential spec in JSON format using New-CredentialSpec.
To create a GMSA credential spec named WebApp1, invoke
New-CredentialSpec -Name WebApp1 -AccountName WebApp1 -Domain $(Get-ADDomain -Current LocalComputer)
Use Get-CredentialSpec to show the path of the JSON file.
Convert the credspec file from JSON to YAML format and apply the necessary
header fields apiVersion, kind, metadata and credspec to make it a
GMSACredentialSpec custom resource that can be configured in Kubernetes.
The following YAML configuration describes a GMSA credential spec named gmsa-WebApp1:
apiVersion:windows.k8s.io/v1kind:GMSACredentialSpecmetadata:name:gmsa-WebApp1 # This is an arbitrary name but it will be used as a referencecredspec:ActiveDirectoryConfig:GroupManagedServiceAccounts:- Name:WebApp1 # Username of the GMSA accountScope:CONTOSO # NETBIOS Domain Name- Name:WebApp1 # Username of the GMSA accountScope:contoso.com# DNS Domain NameCmsPlugins:- ActiveDirectoryDomainJoinConfig:DnsName:contoso.com # DNS Domain NameDnsTreeName:contoso.com# DNS Domain Name RootGuid:244818ae-87ac-4fcd-92ec-e79e5252348a # GUIDMachineAccountName:WebApp1# Username of the GMSA accountNetBiosName:CONTOSO # NETBIOS Domain NameSid:S-1-5-21-2126449477-2524075714-3094792973# SID of GMSA
The above credential spec resource may be saved as gmsa-Webapp1-credspec.yaml
and applied to the cluster using: kubectl apply -f gmsa-Webapp1-credspec.yml
Configure cluster role to enable RBAC on specific GMSA credential specs
A cluster role needs to be defined for each GMSA credential spec resource. This
authorizes the use verb on a specific GMSA resource by a subject which is typically
a service account. The following example shows a cluster role that authorizes usage
of the gmsa-WebApp1 credential spec from above. Save the file as gmsa-webapp1-role.yaml
and apply using kubectl apply -f gmsa-webapp1-role.yaml
# Create the Role to read the credspecapiVersion:rbac.authorization.k8s.io/v1kind:ClusterRolemetadata:name:webapp1-rolerules:- apiGroups:["windows.k8s.io"]resources:["gmsacredentialspecs"]verbs:["use"]resourceNames:["gmsa-WebApp1"]
Assign role to service accounts to use specific GMSA credspecs
A service account (that Pods will be configured with) needs to be bound to the
cluster role create above. This authorizes the service account to use the desired
GMSA credential spec resource. The following shows the default service account
being bound to a cluster role webapp1-role to use gmsa-WebApp1 credential spec resource created above.
Configure GMSA credential spec reference in Pod spec
The Pod spec field securityContext.windowsOptions.gmsaCredentialSpecName is used to
specify references to desired GMSA credential spec custom resources in Pod specs.
This configures all containers in the Pod spec to use the specified GMSA. A sample
Pod spec with the annotation populated to refer to gmsa-WebApp1:
Individual containers in a Pod spec can also specify the desired GMSA credspec
using a per-container securityContext.windowsOptions.gmsaCredentialSpecName field. For example:
As Pod specs with GMSA fields populated (as described above) are applied in a cluster,
the following sequence of events take place:
The mutating webhook resolves and expands all references to GMSA credential spec
resources to the contents of the GMSA credential spec.
The validating webhook ensures the service account associated with the Pod is
authorized for the use verb on the specified GMSA credential spec.
The container runtime configures each Windows container with the specified GMSA
credential spec so that the container can assume the identity of the GMSA in
Active Directory and access services in the domain using that identity.
Authenticating to network shares using hostname or FQDN
If you are experiencing issues connecting to SMB shares from Pods using hostname or FQDN,
but are able to access the shares via their IPv4 address then make sure the following registry key is set on the Windows nodes.
Running Pods will then need to be recreated to pick up the behavior changes.
More information on how this registry key is used can be found
here
Troubleshooting
If you are having difficulties getting GMSA to work in your environment,
there are a few troubleshooting steps you can take.
First, make sure the credspec has been passed to the Pod. To do this you will need
to exec into one of your Pods and check the output of the nltest.exe /parentdomain command.
In the example below the Pod did not get the credspec correctly:
nltest.exe /parentdomain results in the following error:
Getting parent domain failed: Status = 1722 0x6ba RPC_S_SERVER_UNAVAILABLE
If your Pod did get the credspec correctly, then next check communication with the domain.
First, from inside of your Pod, quickly do an nslookup to find the root of your domain.
This will tell us 3 things:
The Pod can reach the DC
The DC can reach the Pod
DNS is working correctly.
If the DNS and communication test passes, next you will need to check if the Pod has
established secure channel communication with the domain. To do this, again,
exec into your Pod and run the nltest.exe /query command.
nltest.exe /query
Results in the following output:
I_NetLogonControl failed: Status = 1722 0x6ba RPC_S_SERVER_UNAVAILABLE
This tells us that for some reason, the Pod was unable to logon to the domain using
the account specified in the credspec. You can try to repair the secure channel by running the following:
nltest /sc_reset:domain.example
If the command is successful you will see and output similar to this:
Flags: 30 HAS_IP HAS_TIMESERV
Trusted DC Name \\dc10.domain.example
Trusted DC Connection Status Status = 0 0x0 NERR_Success
The command completed successfully
If the above corrects the error, you can automate the step by adding the following
lifecycle hook to your Pod spec. If it did not correct the error, you will need to
examine your credspec again and confirm that it is correct and complete.
If you add the lifecycle section show above to your Pod spec, the Pod will execute
the commands listed to restart the netlogon service until the nltest.exe /query command exits without error.
3.4 - Resize CPU and Memory Resources assigned to Containers
FEATURE STATE:Kubernetes v1.27 [alpha]
This page assumes that you are familiar with Quality of Service
for Kubernetes Pods.
This page shows how to resize CPU and memory resources assigned to containers
of a running pod without restarting the pod or its containers. A Kubernetes node
allocates resources for a pod based on its requests, and restricts the pod's
resource usage based on the limits specified in the pod's containers.
For in-place resize of pod resources:
Container's resource requests and limits are mutable for CPU
and memory resources.
allocatedResources field in containerStatuses of the Pod's status reflects
the resources allocated to the pod's containers.
resources field in containerStatuses of the Pod's status reflects the
actual resource requests and limits that are configured on the running
containers as reported by the container runtime.
resize field in the Pod's status shows the status of the last requested
pending resize. It can have the following values:
Proposed: This value indicates an acknowledgement of the requested resize
and that the request was validated and recorded.
InProgress: This value indicates that the node has accepted the resize
request and is in the process of applying it to the pod's containers.
Deferred: This value means that the requested resize cannot be granted at
this time, and the node will keep retrying. The resize may be granted when
other pods leave and free up node resources.
Infeasible: is a signal that the node cannot accommodate the requested
resize. This can happen if the requested resize exceeds the maximum
resources the node can ever allocate for a pod.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Your Kubernetes server must be at or later than version 1.27.
To check the version, enter kubectl version.
Container Resize Policies
Resize policies allow for a more fine-grained control over how pod's containers
are resized for CPU and memory resources. For example, the container's
application may be able to handle CPU resources resized without being restarted,
but resizing memory may require that the application hence the containers be restarted.
To enable this, the Container specification allows users to specify a resizePolicy.
The following restart policies can be specified for resizing CPU and memory:
NotRequired: Resize the container's resources while it is running.
RestartContainer: Restart the container and apply new resources upon restart.
If resizePolicy[*].restartPolicy is not specified, it defaults to NotRequired.
Note: If the Pod's restartPolicy is Never, container's resize restart policy must be
set to NotRequired for all Containers in the Pod.
Below example shows a Pod whose Container's CPU can be resized without restart, but
resizing memory requires the container to be restarted.
Note: In the above example, if desired requests or limits for both CPU and memory
have changed, the container will be restarted in order to resize its memory.
Create a pod with resource requests and limits
You can create a Guaranteed or Burstable Quality of Service
class pod by specifying requests and/or limits for a pod's containers.
Consider the following manifest for a Pod that has one Container.
This pod is classified as a Guaranteed QoS class requesting 700m CPU and 200Mi
memory.
View detailed information about the pod:
kubectl get pod qos-demo-5 --output=yaml --namespace=qos-example
Also notice that the values of resizePolicy[*].restartPolicy defaulted to
NotRequired, indicating that CPU and memory can be resized while container
is running.
Let's say the CPU requirements have increased, and 0.8 CPU is now desired. This
is typically determined, and may be programmatically applied, by an entity such as
VerticalPodAutoscaler (VPA).
Note: While you can change a Pod's requests and limits to express new desired
resources, you cannot change the QoS class in which the Pod was created.
Now, patch the Pod's Container with CPU requests & limits both set to 800m:
kubectl -n qos-example patch pod qos-demo-5 --patch '{"spec":{"containers":[{"name":"qos-demo-ctr-5", "resources":{"requests":{"cpu":"800m"}, "limits":{"cpu":"800m"}}}]}}'
Query the Pod's detailed information after the Pod has been patched.
kubectl get pod qos-demo-5 --output=yaml --namespace=qos-example
The Pod's spec below reflects the updated CPU requests and limits.
Observe that the allocatedResources values have been updated to reflect the new
desired CPU requests. This indicates that node was able to accommodate the
increased CPU resource needs.
In the Container's status, updated CPU resource values shows that new CPU
resources have been applied. The Container's restartCount remains unchanged,
indicating that container's CPU resources were resized without restarting the container.
3.5 - Configure RunAsUserName for Windows pods and containers
FEATURE STATE:Kubernetes v1.18 [stable]
This page shows how to use the runAsUserName setting for Pods and containers that will run on Windows nodes. This is roughly equivalent of the Linux-specific runAsUser setting, allowing you to run applications in a container as a different username than the default.
Before you begin
You need to have a Kubernetes cluster and the kubectl command-line tool must be configured to communicate with your cluster. The cluster is expected to have Windows worker nodes where pods with containers running Windows workloads will get scheduled.
Set the Username for a Pod
To specify the username with which to execute the Pod's container processes, include the securityContext field (PodSecurityContext) in the Pod specification, and within it, the windowsOptions (WindowsSecurityContextOptions) field containing the runAsUserName field.
The Windows security context options that you specify for a Pod apply to all Containers and init Containers in the Pod.
Here is a configuration file for a Windows Pod that has the runAsUserName field set:
Check that the shell is running user the correct username:
echo $env:USERNAME
The output should be:
ContainerUser
Set the Username for a Container
To specify the username with which to execute a Container's processes, include the securityContext field (SecurityContext) in the Container manifest, and within it, the windowsOptions (WindowsSecurityContextOptions) field containing the runAsUserName field.
The Windows security context options that you specify for a Container apply only to that individual Container, and they override the settings made at the Pod level.
Here is the configuration file for a Pod that has one Container, and the runAsUserName field is set at the Pod level and the Container level:
Check that the shell is running user the correct username (the one set at the Container level):
echo $env:USERNAME
The output should be:
ContainerAdministrator
Windows Username limitations
In order to use this feature, the value set in the runAsUserName field must be a valid username. It must have the following format: DOMAIN\USER, where DOMAIN\ is optional. Windows user names are case insensitive. Additionally, there are some restrictions regarding the DOMAIN and USER:
The runAsUserName field cannot be empty, and it cannot contain control characters (ASCII values: 0x00-0x1F, 0x7F)
The DOMAIN must be either a NetBios name, or a DNS name, each with their own restrictions:
NetBios names: maximum 15 characters, cannot start with . (dot), and cannot contain the following characters: \ / : * ? " < > |
DNS names: maximum 255 characters, contains only alphanumeric characters, dots, and dashes, and it cannot start or end with a . (dot) or - (dash).
The USER must have at most 20 characters, it cannot contain only dots or spaces, and it cannot contain the following characters: " / \ [ ] : ; | = , + * ? < > @.
Examples of acceptable values for the runAsUserName field: ContainerAdministrator, ContainerUser, NT AUTHORITY\NETWORK SERVICE, NT AUTHORITY\LOCAL SERVICE.
For more information about these limtations, check here and here.
Windows HostProcess containers enable you to run containerized
workloads on a Windows host. These containers operate as
normal processes but have access to the host network namespace,
storage, and devices when given the appropriate user privileges.
HostProcess containers can be used to deploy network plugins,
storage configurations, device plugins, kube-proxy, and other
components to Windows nodes without the need for dedicated proxies or
the direct installation of host services.
Administrative tasks such as installation of security patches, event
log collection, and more can be performed without requiring cluster operators to
log onto each Windows node. HostProcess containers can run as any user that is
available on the host or is in the domain of the host machine, allowing administrators
to restrict resource access through user permissions. While neither filesystem or process
isolation are supported, a new volume is created on the host upon starting the container
to give it a clean and consolidated workspace. HostProcess containers can also be built on
top of existing Windows base images and do not inherit the same
compatibility requirements
as Windows server containers, meaning that the version of the base images does not need
to match that of the host. It is, however, recommended that you use the same base image
version as your Windows Server container workloads to ensure you do not have any unused
images taking up space on the node. HostProcess containers also support
volume mounts within the container volume.
When should I use a Windows HostProcess container?
When you need to perform tasks which require the networking namespace of the host.
HostProcess containers have access to the host's network interfaces and IP addresses.
You need access to resources on the host such as the filesystem, event logs, etc.
Installation of specific device drivers or Windows services.
Consolidation of administrative tasks and security policies. This reduces the degree of
privileges needed by Windows nodes.
Before you begin
This task guide is specific to Kubernetes v1.28.
If you are not running Kubernetes v1.28, check the documentation for
that version of Kubernetes.
In Kubernetes 1.28, the HostProcess container feature is enabled by default. The kubelet will
communicate with containerd directly by passing the hostprocess flag via CRI. You can use the
latest version of containerd (v1.6+) to run HostProcess containers.
How to install containerd.
Limitations
These limitations are relevant for Kubernetes v1.28:
HostProcess containers require containerd 1.6 or higher
container runtime and
containerd 1.7 is recommended.
HostProcess pods can only contain HostProcess containers. This is a current limitation
of the Windows OS; non-privileged Windows containers cannot share a vNIC with the host IP namespace.
HostProcess containers run as a process on the host and do not have any degree of
isolation other than resource constraints imposed on the HostProcess user account. Neither
filesystem or Hyper-V isolation are supported for HostProcess containers.
Volume mounts are supported and are mounted under the container volume. See
Volume Mounts
A limited set of host user accounts are available for HostProcess containers by default.
See Choosing a User Account.
Resource limits (disk, memory, cpu count) are supported in the same fashion as processes
on the host.
Both Named pipe mounts and Unix domain sockets are not supported and should instead
be accessed via their path on the host (e.g. \\.\pipe\*)
HostProcess Pod configuration requirements
Enabling a Windows HostProcess pod requires setting the right configurations in the pod security
configuration. Of the policies defined in the Pod Security Standards
HostProcess pods are disallowed by the baseline and restricted policies. It is therefore recommended
that HostProcess pods run in alignment with the privileged profile.
When running under the privileged policy, here are
the configurations which need to be set to enable the creation of a HostProcess pod:
Because HostProcess containers have privileged access to the host, the runAsNonRoot field cannot be set to true.
Allowed Values
Undefined/Nil
false
Example manifest (excerpt)
spec:securityContext:windowsOptions:hostProcess:truerunAsUserName:"NT AUTHORITY\\Local service"hostNetwork:truecontainers:- name:testimage:image1:latestcommand:- ping- -t- 127.0.0.1nodeSelector:"kubernetes.io/os": windows
Volume mounts
HostProcess containers support the ability to mount volumes within the container volume space.
Volume mount behavior differs depending on the version of containerd runtime used by on the node.
Containerd v1.6
Applications running inside the container can access volume mounts directly via relative or
absolute paths. An environment variable $CONTAINER_SANDBOX_MOUNT_POINT is set upon container
creation and provides the absolute host path to the container volume. Relative paths are based
upon the .spec.containers.volumeMounts.mountPath configuration.
To access service account tokens (for example) the following path structures are supported within the container:
Applications running inside the container can access volume mounts directly via the volumeMount's
specified mountPath (just like Linux and non-HostProcess Windows containers).
For backwards compatibility volumes can also be accessed via using the same relative paths configured
by containerd v1.6.
As an example, to access service account tokens within the container you would use one of the following paths:
Resource limits (disk, memory, cpu count) are applied to the job and are job wide.
For example, with a limit of 10MB set, the memory allocated for any HostProcess job object
will be capped at 10MB. This is the same behavior as other Windows container types.
These limits would be specified the same way they are currently for whatever orchestrator
or runtime is being used. The only difference is in the disk resource usage calculation
used for resource tracking due to the difference in how HostProcess containers are bootstrapped.
Choosing a user account
System accounts
By default, HostProcess containers support the ability to run as one of three supported Windows service accounts:
You should select an appropriate Windows service account for each HostProcess
container, aiming to limit the degree of privileges so as to avoid accidental (or even
malicious) damage to the host. The LocalSystem service account has the highest level
of privilege of the three and should be used only if absolutely necessary. Where possible,
use the LocalService service account as it is the least privileged of the three options.
Local accounts
If configured, HostProcess containers can also run as local user accounts which allows for node operators to give
fine-grained access to workloads.
To run HostProcess containers as a local user; A local usergroup must first be created on the node
and the name of that local usergroup must be specified in the runAsUserName field in the deployment.
Prior to initializing the HostProcess container, a new ephemeral local user account to be created and joined to the specified usergroup, from which the container is run.
This provides a number a benefits including eliminating the need to manage passwords for local user accounts.
An initial HostProcess container running as a service account can be used to
prepare the user groups for later HostProcess containers.
Note: Running HostProcess containers as local user accounts requires containerd v1.7+
Example:
Create a local user group on the node (this can be done in another HostProcess container).
net localgroup hpc-localgroup /add
Grant access to desired resources on the node to the local usergroup.
This can be done with tools like icacls.
Set runAsUserName to the name of the local usergroup for the pod or individual containers.
HostProcess containers fail to start with failed to create user process token: failed to logon user: Access is denied.: unknown
Ensure containerd is running as LocalSystem or LocalService service accounts. User accounts (even Administrator accounts) do not have permissions to create logon tokens for any of the supported user accounts.
3.7 - Configure Quality of Service for Pods
This page shows how to configure Pods so that they will be assigned particular
Quality of Service (QoS) classes.
Kubernetes uses QoS classes to make decisions about evicting Pods when Node resources are exceeded.
When Kubernetes creates a Pod it assigns one of these QoS classes to the Pod:
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
You also need to be able to create and delete namespaces.
Create a namespace
Create a namespace so that the resources you create in this exercise are
isolated from the rest of your cluster.
kubectl create namespace qos-example
Create a Pod that gets assigned a QoS class of Guaranteed
For a Pod to be given a QoS class of Guaranteed:
Every Container in the Pod must have a memory limit and a memory request.
For every Container in the Pod, the memory limit must equal the memory request.
Every Container in the Pod must have a CPU limit and a CPU request.
For every Container in the Pod, the CPU limit must equal the CPU request.
These restrictions apply to init containers and app containers equally.
Ephemeral containers
cannot define resources so these restrictions do not apply.
Here is a manifest for a Pod that has one Container. The Container has a memory limit and a
memory request, both equal to 200 MiB. The Container has a CPU limit and a CPU request, both equal to 700 milliCPU:
kubectl get pod qos-demo --namespace=qos-example --output=yaml
The output shows that Kubernetes gave the Pod a QoS class of Guaranteed. The output also
verifies that the Pod Container has a memory request that matches its memory limit, and it has
a CPU request that matches its CPU limit.
Note: If a Container specifies its own memory limit, but does not specify a memory request, Kubernetes
automatically assigns a memory request that matches the limit. Similarly, if a Container specifies its own
CPU limit, but does not specify a CPU request, Kubernetes automatically assigns a CPU request that matches
the limit.
Clean up
Delete your Pod:
kubectl delete pod qos-demo --namespace=qos-example
Create a Pod that gets assigned a QoS class of Burstable
A Pod is given a QoS class of Burstable if:
The Pod does not meet the criteria for QoS class Guaranteed.
At least one Container in the Pod has a memory or CPU request or limit.
Here is a manifest for a Pod that has one Container. The Container has a memory limit of 200 MiB
and a memory request of 100 MiB.
kubectl delete pod qos-demo-3 --namespace=qos-example
Create a Pod that has two Containers
Here is a manifest for a Pod that has two Containers. One container specifies a memory
request of 200 MiB. The other Container does not specify any requests or limits.
Notice that this Pod meets the criteria for QoS class Burstable. That is, it does not meet the
criteria for QoS class Guaranteed, and one of its Containers has a memory request.
This page shows how to assign extended resources to a Container.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Before you do this exercise, do the exercise in
Advertise Extended Resources for a Node.
That will configure one of your Nodes to advertise a dongle resource.
Assign an extended resource to a Pod
To request an extended resource, include the resources:requests field in your
Container manifest. Extended resources are fully qualified with any domain outside of
*.kubernetes.io/. Valid extended resource names have the form example.com/foo where
example.com is replaced with your organization's domain and foo is a
descriptive resource name.
Here is the configuration file for a Pod that has one Container:
The output shows that the Pod cannot be scheduled, because there is no Node that has
2 dongles available:
Conditions:
Type Status
PodScheduled False
...
Events:
...
... Warning FailedScheduling pod (extended-resource-demo-2) failed to fit in any node
fit failure summary on nodes : Insufficient example.com/dongle (1)
View the Pod status:
kubectl get pod extended-resource-demo-2
The output shows that the Pod was created, but not scheduled to run on a Node.
It has a status of Pending:
NAME READY STATUS RESTARTS AGEextended-resource-demo-2 0/1 Pending 0 6m
Clean up
Delete the Pods that you created for this exercise:
kubectl delete pod extended-resource-demo
kubectl delete pod extended-resource-demo-2
This page shows how to configure a Pod to use a Volume for storage.
A Container's file system lives only as long as the Container does. So when a
Container terminates and restarts, filesystem changes are lost. For more
consistent storage that is independent of the Container, you can use a
Volume. This is especially important for stateful
applications, such as key-value stores (such as Redis) and databases.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
In this exercise, you create a Pod that runs one Container. This Pod has a
Volume of type
emptyDir
that lasts for the life of the Pod, even if the Container terminates and
restarts. Here is the configuration file for the Pod:
In addition to the local disk storage provided by emptyDir, Kubernetes
supports many different network-attached storage solutions, including PD on
GCE and EBS on EC2, which are preferred for critical data and will handle
details such as mounting and unmounting the devices on the nodes. See
Volumes for more details.
3.10 - Configure a Pod to Use a PersistentVolume for Storage
This page shows you how to configure a Pod to use a
PersistentVolumeClaim
for storage.
Here is a summary of the process:
You, as cluster administrator, create a PersistentVolume backed by physical
storage. You do not associate the volume with any Pod.
You, now taking the role of a developer / cluster user, create a
PersistentVolumeClaim that is automatically bound to a suitable
PersistentVolume.
You create a Pod that uses the above PersistentVolumeClaim for storage.
Before you begin
You need to have a Kubernetes cluster that has only one Node, and the
kubectl
command-line tool must be configured to communicate with your cluster. If you
do not already have a single-node cluster, you can create one by using
Minikube.
Open a shell to the single Node in your cluster. How you open a shell depends
on how you set up your cluster. For example, if you are using Minikube, you
can open a shell to your Node by entering minikube ssh.
In your shell on that Node, create a /mnt/data directory:
# This assumes that your Node uses "sudo" to run commands# as the superusersudo mkdir /mnt/data
In the /mnt/data directory, create an index.html file:
# This again assumes that your Node uses "sudo" to run commands# as the superusersudo sh -c "echo 'Hello from Kubernetes storage' > /mnt/data/index.html"
Note: If your Node uses a tool for superuser access other than sudo, you can
usually make this work if you replace sudo with the name of the other tool.
Test that the index.html file exists:
cat /mnt/data/index.html
The output should be:
Hello from Kubernetes storage
You can now close the shell to your Node.
Create a PersistentVolume
In this exercise, you create a hostPath PersistentVolume. Kubernetes supports
hostPath for development and testing on a single-node cluster. A hostPath
PersistentVolume uses a file or directory on the Node to emulate network-attached storage.
In a production cluster, you would not use hostPath. Instead a cluster administrator
would provision a network resource like a Google Compute Engine persistent disk,
an NFS share, or an Amazon Elastic Block Store volume. Cluster administrators can also
use StorageClasses
to set up
dynamic provisioning.
Here is the configuration file for the hostPath PersistentVolume:
The configuration file specifies that the volume is at /mnt/data on the
cluster's Node. The configuration also specifies a size of 10 gibibytes and
an access mode of ReadWriteOnce, which means the volume can be mounted as
read-write by a single Node. It defines the StorageClass namemanual for the PersistentVolume, which will be used to bind
PersistentVolumeClaim requests to this PersistentVolume.
The output shows that the PersistentVolume has a STATUS of Available. This
means it has not yet been bound to a PersistentVolumeClaim.
NAME CAPACITY ACCESSMODES RECLAIMPOLICY STATUS CLAIM STORAGECLASS REASON AGE
task-pv-volume 10Gi RWO Retain Available manual 4s
Create a PersistentVolumeClaim
The next step is to create a PersistentVolumeClaim. Pods use PersistentVolumeClaims
to request physical storage. In this exercise, you create a PersistentVolumeClaim
that requests a volume of at least three gibibytes that can provide read-write
access for at most one Node at a time.
Here is the configuration file for the PersistentVolumeClaim:
After you create the PersistentVolumeClaim, the Kubernetes control plane looks
for a PersistentVolume that satisfies the claim's requirements. If the control
plane finds a suitable PersistentVolume with the same StorageClass, it binds the
claim to the volume.
Look again at the PersistentVolume:
kubectl get pv task-pv-volume
Now the output shows a STATUS of Bound.
NAME CAPACITY ACCESSMODES RECLAIMPOLICY STATUS CLAIM STORAGECLASS REASON AGE
task-pv-volume 10Gi RWO Retain Bound default/task-pv-claim manual 2m
Look at the PersistentVolumeClaim:
kubectl get pvc task-pv-claim
The output shows that the PersistentVolumeClaim is bound to your PersistentVolume,
task-pv-volume.
NAME STATUS VOLUME CAPACITY ACCESSMODES STORAGECLASS AGE
task-pv-claim Bound task-pv-volume 10Gi RWO manual 30s
Create a Pod
The next step is to create a Pod that uses your PersistentVolumeClaim as a volume.
Notice that the Pod's configuration file specifies a PersistentVolumeClaim, but
it does not specify a PersistentVolume. From the Pod's point of view, the claim
is a volume.
In your shell, verify that nginx is serving the index.html file from the
hostPath volume:
# Be sure to run these 3 commands inside the root shell that comes from# running "kubectl exec" in the previous stepapt update
apt install curl
curl http://localhost/
The output shows the text that you wrote to the index.html file on the
hostPath volume:
Hello from Kubernetes storage
If you see that message, you have successfully configured a Pod to
use storage from a PersistentVolumeClaim.
Clean up
Delete the Pod, the PersistentVolumeClaim and the PersistentVolume:
apiVersion:v1kind:Podmetadata:name:testspec:containers:- name:testimage:nginxvolumeMounts:# a mount for site-data- name:configmountPath:/usr/share/nginx/htmlsubPath:html# another mount for nginx config- name:configmountPath:/etc/nginx/nginx.confsubPath:nginx.confvolumes:- name:configpersistentVolumeClaim:claimName:test-nfs-claim
You can perform 2 volume mounts on your nginx container:
/usr/share/nginx/html for the static website
/etc/nginx/nginx.conf for the default config
Access control
Storage configured with a group ID (GID) allows writing only by Pods using the same
GID. Mismatched or missing GIDs cause permission denied errors. To reduce the
need for coordination with users, an administrator can annotate a PersistentVolume
with a GID. Then the GID is automatically added to any Pod that uses the
PersistentVolume.
Use the pv.beta.kubernetes.io/gid annotation as follows:
When a Pod consumes a PersistentVolume that has a GID annotation, the annotated GID
is applied to all containers in the Pod in the same way that GIDs specified in the
Pod's security context are. Every GID, whether it originates from a PersistentVolume
annotation or the Pod's specification, is applied to the first process run in
each container.
Note: When a Pod consumes a PersistentVolume, the GIDs associated with the
PersistentVolume are not present on the Pod resource itself.
3.11 - Configure a Pod to Use a Projected Volume for Storage
This page shows how to use a projected Volume to mount
several existing volume sources into the same directory. Currently, secret, configMap, downwardAPI,
and serviceAccountToken volumes can be projected.
Note:serviceAccountToken is not a volume type.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
In this exercise, you create username and password Secrets from local files. You then create a Pod that runs one container, using a projected Volume to mount the Secrets into the same shared directory.
allowPrivilegeEscalation: Controls whether a process can gain more privileges than
its parent process. This bool directly controls whether the
no_new_privs
flag gets set on the container process.
allowPrivilegeEscalation is always true when the container:
is run as privileged, or
has CAP_SYS_ADMIN
readOnlyRootFilesystem: Mounts the container's root filesystem as read-only.
The above bullets are not a complete set of security context settings -- please see
SecurityContext
for a comprehensive list.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
To specify security settings for a Pod, include the securityContext field
in the Pod specification. The securityContext field is a
PodSecurityContext object.
The security settings that you specify for a Pod apply to all Containers in the Pod.
Here is a configuration file for a Pod that has a securityContext and an emptyDir volume:
In the configuration file, the runAsUser field specifies that for any Containers in
the Pod, all processes run with user ID 1000. The runAsGroup field specifies the primary group ID of 3000 for
all processes within any containers of the Pod. If this field is omitted, the primary group ID of the containers
will be root(0). Any files created will also be owned by user 1000 and group 3000 when runAsGroup is specified.
Since fsGroup field is specified, all processes of the container are also part of the supplementary group ID 2000.
The owner for volume /data/demo and any files created in that volume will be Group ID 2000.
The output shows that the processes are running as user 1000, which is the value of runAsUser:
PID USER TIME COMMAND
1 1000 0:00 sleep 1h
6 1000 0:00 sh
...
In your shell, navigate to /data, and list the one directory:
cd /data
ls -l
The output shows that the /data/demo directory has group ID 2000, which is
the value of fsGroup.
drwxrwsrwx 2 root 2000 4096 Jun 6 20:08 demo
In your shell, navigate to /data/demo, and create a file:
cd demo
echo hello > testfile
List the file in the /data/demo directory:
ls -l
The output shows that testfile has group ID 2000, which is the value of fsGroup.
-rw-r--r-- 1 1000 2000 6 Jun 6 20:08 testfile
Run the following command:
id
The output is similar to this:
uid=1000 gid=3000 groups=2000
From the output, you can see that gid is 3000 which is same as the runAsGroup field.
If the runAsGroup was omitted, the gid would remain as 0 (root) and the process will
be able to interact with files that are owned by the root(0) group and groups that have
the required group permissions for the root (0) group.
Exit your shell:
exit
Configure volume permission and ownership change policy for Pods
FEATURE STATE:Kubernetes v1.23 [stable]
By default, Kubernetes recursively changes ownership and permissions for the contents of each
volume to match the fsGroup specified in a Pod's securityContext when that volume is
mounted.
For large volumes, checking and changing ownership and permissions can take a lot of time,
slowing Pod startup. You can use the fsGroupChangePolicy field inside a securityContext
to control the way that Kubernetes checks and manages ownership and permissions
for a volume.
fsGroupChangePolicy - fsGroupChangePolicy defines behavior for changing ownership
and permission of the volume before being exposed inside a Pod.
This field only applies to volume types that support fsGroup controlled ownership and permissions.
This field has two possible values:
OnRootMismatch: Only change permissions and ownership if the permission and the ownership of
root directory does not match with expected permissions of the volume.
This could help shorten the time it takes to change ownership and permission of a volume.
Always: Always change permission and ownership of the volume when volume is mounted.
Note: This field has no effect on ephemeral volume types such as
secret,
configMap,
and emptydir.
Delegating volume permission and ownership change to CSI driver
FEATURE STATE:Kubernetes v1.26 [stable]
If you deploy a Container Storage Interface (CSI)
driver which supports the VOLUME_MOUNT_GROUPNodeServiceCapability, the
process of setting file ownership and permissions based on the
fsGroup specified in the securityContext will be performed by the CSI driver
instead of Kubernetes. In this case, since Kubernetes doesn't perform any
ownership and permission change, fsGroupChangePolicy does not take effect, and
as specified by CSI, the driver is expected to mount the volume with the
provided fsGroup, resulting in a volume that is readable/writable by the
fsGroup.
Set the security context for a Container
To specify security settings for a Container, include the securityContext field
in the Container manifest. The securityContext field is a
SecurityContext object.
Security settings that you specify for a Container apply only to
the individual Container, and they override settings made at the Pod level when
there is overlap. Container settings do not affect the Pod's Volumes.
Here is the configuration file for a Pod that has one Container. Both the Pod
and the Container have a securityContext field:
The output shows that the processes are running as user 2000. This is the value
of runAsUser specified for the Container. It overrides the value 1000 that is
specified for the Pod.
USER PID %CPU %MEM VSZ RSS TTY STAT START TIME COMMAND
2000 1 0.0 0.0 4336 764 ? Ss 20:36 0:00 /bin/sh -c node server.js
2000 8 0.1 0.5 772124 22604 ? Sl 20:36 0:00 node server.js
...
Exit your shell:
exit
Set capabilities for a Container
With Linux capabilities,
you can grant certain privileges to a process without granting all the privileges
of the root user. To add or remove Linux capabilities for a Container, include the
capabilities field in the securityContext section of the Container manifest.
First, see what happens when you don't include a capabilities field.
Here is configuration file that does not add or remove any Container capabilities:
In the capability bitmap of the first container, bits 12 and 25 are clear. In the second container,
bits 12 and 25 are set. Bit 12 is CAP_NET_ADMIN, and bit 25 is CAP_SYS_TIME.
See capability.h
for definitions of the capability constants.
Note: Linux capability constants have the form CAP_XXX.
But when you list capabilities in your container manifest, you must
omit the CAP_ portion of the constant.
For example, to add CAP_SYS_TIME, include SYS_TIME in your list of capabilities.
Set the Seccomp Profile for a Container
To set the Seccomp profile for a Container, include the seccompProfile field
in the securityContext section of your Pod or Container manifest. The
seccompProfile field is a
SeccompProfile object consisting of type and localhostProfile.
Valid options for type include RuntimeDefault, Unconfined, and
Localhost. localhostProfile must only be set if type: Localhost. It
indicates the path of the pre-configured profile on the node, relative to the
kubelet's configured Seccomp profile location (configured with the --root-dir
flag).
Here is an example that sets the Seccomp profile to the node's container runtime
default profile:
To assign SELinux labels to a Container, include the seLinuxOptions field in
the securityContext section of your Pod or Container manifest. The
seLinuxOptions field is an
SELinuxOptions
object. Here's an example that applies an SELinux level:
Note: To assign SELinux labels, the SELinux security module must be loaded on the host operating system.
Efficient SELinux volume relabeling
FEATURE STATE:Kubernetes v1.27 [beta]
By default, the container runtime recursively assigns SELinux label to all
files on all Pod volumes. To speed up this process, Kubernetes can change the
SELinux label of a volume instantly by using a mount option
-o context=<label>.
To benefit from this speedup, all these conditions must be met:
The feature gatesReadWriteOncePod
and SELinuxMountReadWriteOncePod must be enabled.
Pod must use PersistentVolumeClaim with accessModes: ["ReadWriteOncePod"].
Pod (or all its Containers that use the PersistentVolumeClaim) must
have seLinuxOptions set.
The corresponding PersistentVolume must be either:
A volume that uses the legacy in-tree iscsi, rbd or fc volume type.
Or a volume that uses a CSI driver.
The CSI driver must announce that it supports mounting with -o context by setting
spec.seLinuxMount: true in its CSIDriver instance.
For any other volume types, SELinux relabelling happens another way: the container
runtime recursively changes the SELinux label for all inodes (files and directories)
in the volume.
The more files and directories in the volume, the longer that relabelling takes.
Note:
If you are running Kubernetes v1.25, refer to the v1.25 version of this task page:
Configure a Security Context for a Pod or Container (v1.25).
There is an important note in that documentation about a situation where the kubelet
can lose track of volume labels after restart. This deficiency has been fixed
in Kubernetes 1.26.
Discussion
The security context for a Pod applies to the Pod's Containers and also to
the Pod's Volumes when applicable. Specifically fsGroup and seLinuxOptions are
applied to Volumes as follows:
fsGroup: Volumes that support ownership management are modified to be owned
and writable by the GID specified in fsGroup. See the
Ownership Management design document
for more details.
seLinuxOptions: Volumes that support SELinux labeling are relabeled to be accessible
by the label specified under seLinuxOptions. Usually you only
need to set the level section. This sets the
Multi-Category Security (MCS)
label given to all Containers in the Pod as well as the Volumes.
Warning: After you specify an MCS label for a Pod, all Pods with the same label can access the Volume.
If you need inter-Pod protection, you must assign a unique MCS label to each Pod.
Clean up
Delete the Pod:
kubectl delete pod security-context-demo
kubectl delete pod security-context-demo-2
kubectl delete pod security-context-demo-3
kubectl delete pod security-context-demo-4
Kubernetes offers two distinct ways for clients that run within your
cluster, or that otherwise have a relationship to your cluster's
control plane
to authenticate to the
API server.
A service account provides an identity for processes that run in a Pod,
and maps to a ServiceAccount object. When you authenticate to the API
server, you identify yourself as a particular user. Kubernetes recognises
the concept of a user, however, Kubernetes itself does not have a User
API.
This task guide is about ServiceAccounts, which do exist in the Kubernetes
API. The guide shows you some ways to configure ServiceAccounts for Pods.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Use the default service account to access the API server
When Pods contact the API server, Pods authenticate as a particular
ServiceAccount (for example, default). There is always at least one
ServiceAccount in each namespace.
Every Kubernetes namespace contains at least one ServiceAccount: the default
ServiceAccount for that namespace, named default.
If you do not specify a ServiceAccount when you create a Pod, Kubernetes
automatically assigns the ServiceAccount named default in that namespace.
You can fetch the details for a Pod you have created. For example:
kubectl get pods/<podname> -o yaml
In the output, you see a field spec.serviceAccountName.
Kubernetes automatically
sets that value if you don't specify it when you create a Pod.
An application running inside a Pod can access the Kubernetes API using
automatically mounted service account credentials.
See accessing the Cluster to learn more.
If you don't want the kubelet
to automatically mount a ServiceAccount's API credentials, you can opt out of
the default behavior.
You can opt out of automounting API credentials on /var/run/secrets/kubernetes.io/serviceaccount/token
for a service account by setting automountServiceAccountToken: false on the ServiceAccount:
If both the ServiceAccount and the Pod's .spec specify a value for
automountServiceAccountToken, the Pod spec takes precedence.
Use more than one ServiceAccount
Every namespace has at least one ServiceAccount: the default ServiceAccount
resource, called default. You can list all ServiceAccount resources in your
current namespace
with:
kubectl get serviceaccounts
The output is similar to this:
NAME SECRETS AGE
default 1 1d
You can create additional ServiceAccount objects like this:
To use a non-default service account, set the spec.serviceAccountName
field of a Pod to the name of the ServiceAccount you wish to use.
You can only set the serviceAccountName field when creating a Pod, or in a
template for a new Pod. You cannot update the .spec.serviceAccountName field
of a Pod that already exists.
Note: The .spec.serviceAccount field is a deprecated alias for .spec.serviceAccountName.
If you want to remove the fields from a workload resource, set both fields to empty explicitly
on the pod template.
Cleanup
If you tried creating build-robot ServiceAccount from the example above,
you can clean it up by running:
kubectl delete serviceaccount/build-robot
Manually create an API token for a ServiceAccount
Suppose you have an existing service account named "build-robot" as mentioned earlier.
You can get a time-limited API token for that ServiceAccount using kubectl:
kubectl create token build-robot
The output from that command is a token that you can use to authenticate as that
ServiceAccount. You can request a specific token duration using the --duration
command line argument to kubectl create token (the actual duration of the issued
token might be shorter, or could even be longer).
Note:
Versions of Kubernetes before v1.22 automatically created long term credentials for
accessing the Kubernetes API. This older mechanism was based on creating token Secrets
that could then be mounted into running Pods. In more recent versions, including
Kubernetes v1.28, API credentials are obtained directly by using the
TokenRequest API,
and are mounted into Pods using a
projected volume.
The tokens obtained using this method have bounded lifetimes, and are automatically
invalidated when the Pod they are mounted into is deleted.
You can still manually create a service account token Secret; for example,
if you need a token that never expires. However, using the
TokenRequest
subresource to obtain a token to access the API is recommended instead.
Manually create a long-lived API token for a ServiceAccount
If you want to obtain an API token for a ServiceAccount, you create a new Secret
with a special annotation, kubernetes.io/service-account.name.
you can see that the Secret now contains an API token for the "build-robot" ServiceAccount.
Because of the annotation you set, the control plane automatically generates a token for that
ServiceAccounts, and stores them into the associated Secret. The control plane also cleans up
tokens for deleted ServiceAccounts.
Take care not to display the contents of a kubernetes.io/service-account-token
Secret somewhere that your terminal / computer screen could be seen by an onlooker.
When you delete a ServiceAccount that has an associated Secret, the Kubernetes
control plane automatically cleans up the long-lived token from that Secret.
Note:
If you view the ServiceAccount using:
kubectl get serviceaccount build-robot -o yaml
You can't see the build-robot-secret Secret in the ServiceAccount API objects
.secrets field
because that field is only populated with auto-generated Secrets.
Using your editor, delete the line with key resourceVersion, add lines for
imagePullSecrets: and save it. Leave the uid value set the same as you found it.
After you made those changes, the edited ServiceAccount looks something like this:
Now, when a new Pod is created in the current namespace and using the default
ServiceAccount, the new Pod has its spec.imagePullSecrets field set automatically:
kubectl run nginx --image=nginx --restart=Never
kubectl get pod nginx -o=jsonpath='{.spec.imagePullSecrets[0].name}{"\n"}'
The output is:
myregistrykey
ServiceAccount token volume projection
FEATURE STATE:Kubernetes v1.20 [stable]
Note:
To enable and use token request projection, you must specify each of the following
command line arguments to kube-apiserver:
--service-account-issuer
defines the Identifier of the service account token issuer. You can specify the
--service-account-issuer argument multiple times, this can be useful to enable
a non-disruptive change of the issuer. When this flag is specified multiple times,
the first is used to generate tokens and all are used to determine which issuers
are accepted. You must be running Kubernetes v1.22 or later to be able to specify
--service-account-issuer multiple times.
--service-account-key-file
specifies the path to a file containing PEM-encoded X.509 private or public keys
(RSA or ECDSA), used to verify ServiceAccount tokens. The specified file can contain
multiple keys, and the flag can be specified multiple times with different files.
If specified multiple times, tokens signed by any of the specified keys are considered
valid by the Kubernetes API server.
--service-account-signing-key-file
specifies the path to a file that contains the current private key of the service
account token issuer. The issuer signs issued ID tokens with this private key.
--api-audiences (can be omitted)
defines audiences for ServiceAccount tokens. The service account token authenticator
validates that tokens used against the API are bound to at least one of these audiences.
If api-audiences is specified multiple times, tokens for any of the specified audiences
are considered valid by the Kubernetes API server. If you specify the --service-account-issuer
command line argument but you don't set --api-audiences, the control plane defaults to
a single element audience list that contains only the issuer URL.
The kubelet can also project a ServiceAccount token into a Pod. You can
specify desired properties of the token, such as the audience and the validity
duration. These properties are not configurable on the default ServiceAccount
token. The token will also become invalid against the API when either the Pod
or the ServiceAccount is deleted.
You can configure this behavior for the spec of a Pod using a
projected volume type called
ServiceAccountToken.
Launch a Pod using service account token projection
To provide a Pod with a token with an audience of vault and a validity duration
of two hours, you could define a Pod manifest that is similar to:
The kubelet will: request and store the token on behalf of the Pod; make
the token available to the Pod at a configurable file path; and refresh
the token as it approaches expiration. The kubelet proactively requests rotation
for the token if it is older than 80% of its total time-to-live (TTL),
or if the token is older than 24 hours.
The application is responsible for reloading the token when it rotates. It's
often good enough for the application to load the token on a schedule
(for example: once every 5 minutes), without tracking the actual expiry time.
Service account issuer discovery
FEATURE STATE:Kubernetes v1.21 [stable]
If you have enabled token projection
for ServiceAccounts in your cluster, then you can also make use of the discovery
feature. Kubernetes provides a way for clients to federate as an identity provider,
so that one or more external systems can act as a relying party.
Note:
The issuer URL must comply with the
OIDC Discovery Spec. In
practice, this means it must use the https scheme, and should serve an OpenID
provider configuration at {service-account-issuer}/.well-known/openid-configuration.
If the URL does not comply, ServiceAccount issuer discovery endpoints are not
registered or accessible.
When enabled, the Kubernetes API server publishes an OpenID Provider
Configuration document via HTTP. The configuration document is published at
/.well-known/openid-configuration.
The OpenID Provider Configuration is sometimes referred to as the discovery document.
The Kubernetes API server publishes the related
JSON Web Key Set (JWKS), also via HTTP, at /openid/v1/jwks.
Note: The responses served at /.well-known/openid-configuration and
/openid/v1/jwks are designed to be OIDC compatible, but not strictly OIDC
compliant. Those documents contain only the parameters necessary to perform
validation of Kubernetes service account tokens.
Clusters that use RBAC include a
default ClusterRole called system:service-account-issuer-discovery.
A default ClusterRoleBinding assigns this role to the system:serviceaccounts group,
which all ServiceAccounts implicitly belong to.
This allows pods running on the cluster to access the service account discovery document
via their mounted service account token. Administrators may, additionally, choose to
bind the role to system:authenticated or system:unauthenticated depending on their
security requirements and which external systems they intend to federate with.
The JWKS response contains public keys that a relying party can use to validate
the Kubernetes service account tokens. Relying parties first query for the
OpenID Provider Configuration, and use the jwks_uri field in the response to
find the JWKS.
In many cases, Kubernetes API servers are not available on the public internet,
but public endpoints that serve cached responses from the API server can be made
available by users or by service providers. In these cases, it is possible to
override the jwks_uri in the OpenID Provider Configuration so that it points
to the public endpoint, rather than the API server's address, by passing the
--service-account-jwks-uri flag to the API server. Like the issuer URL, the
JWKS URI is required to use the https scheme.
but also bear in mind that using Secrets for authenticating as a ServiceAccount
is deprecated. The recommended alternative is
ServiceAccount token volume projection.
This page shows how to create a Pod that uses a
Secret to pull an image
from a private container image registry or repository. There are many private
registries in use. This task uses Docker Hub
as an example registry.
🛇 This item links to a third party project or product that is not part of Kubernetes itself. More information
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Note: If you use a Docker credentials store, you won't see that auth entry but a credsStore entry with the name of the store as value.
In that case, you can create a secret directly.
See Create a Secret by providing credentials on the command line.
Create a Secret based on existing credentials
A Kubernetes cluster uses the Secret of kubernetes.io/dockerconfigjson type to authenticate with
a container registry to pull a private image.
If you already ran docker login, you can copy
that credential into Kubernetes:
If you need more control (for example, to set a namespace or a label on the new
secret) then you can customise the Secret before storing it.
Be sure to:
set the name of the data item to .dockerconfigjson
base64 encode the Docker configuration file and then paste that string, unbroken
as the value for field data[".dockerconfigjson"]
If you get the error message error: no objects passed to create, it may mean the base64 encoded string is invalid.
If you get an error message like Secret "myregistrykey" is invalid: data[.dockerconfigjson]: invalid value ..., it means
the base64 encoded string in the data was successfully decoded, but could not be parsed as a .docker/config.json file.
Create a Secret by providing credentials on the command line
<your-registry-server> is your Private Docker Registry FQDN.
Use https://index.docker.io/v1/ for DockerHub.
<your-name> is your Docker username.
<your-pword> is your Docker password.
<your-email> is your Docker email.
You have successfully set your Docker credentials in the cluster as a Secret called regcred.
Note: Typing secrets on the command line may store them in your shell history unprotected, and
those secrets might also be visible to other users on your PC during the time that
kubectl is running.
Inspecting the Secret regcred
To understand the contents of the regcred Secret you created, start by viewing the Secret in YAML format:
To pull the image from the private registry, Kubernetes needs credentials.
The imagePullSecrets field in the configuration file specifies that
Kubernetes should get the credentials from a Secret named regcred.
Create a Pod that uses your Secret, and verify that the Pod is running:
kubectl apply -f my-private-reg-pod.yaml
kubectl get pod private-reg
Note: To use image pull secrets for a Pod (or a Deployment, or other object that
has a pod template that you are using), you need to make sure that the appropriate
Secret does exist in the right namespace. The namespace to use is the same
namespace where you defined the Pod.
Also, in case the Pod fails to start with the status ImagePullBackOff, view the Pod events:
kubectl describe pod private-reg
If you then see an event with the reason set to FailedToRetrieveImagePullSecret,
Kubernetes can't find a Secret with name (regcred, in this example).
If you specify that a Pod needs image pull credentials, the kubelet checks that it can
access that Secret before attempting to pull the image.
Make sure that the Secret you have specified exists, and that its name is spelled properly.
Events:
... Reason ... Message
------ -------
... FailedToRetrieveImagePullSecret ... Unable to retrieve some image pull secrets (<regcred>); attempting to pull the image may not succeed.
3.15 - Configure Liveness, Readiness and Startup Probes
This page shows how to configure liveness, readiness and startup probes for containers.
The kubelet uses
liveness probes to know when to restart a container. For example, liveness
probes could catch a deadlock, where an application is running, but unable to
make progress. Restarting a container in such a state can help to make the
application more available despite bugs.
A common pattern for liveness probes is to use the same low-cost HTTP endpoint
as for readiness probes, but with a higher failureThreshold. This ensures that the pod
is observed as not-ready for some period of time before it is hard killed.
The kubelet uses readiness probes to know when a container is ready to start
accepting traffic. A Pod is considered ready when all of its containers are ready.
One use of this signal is to control which Pods are used as backends for Services.
When a Pod is not ready, it is removed from Service load balancers.
The kubelet uses startup probes to know when a container application has started.
If such a probe is configured, liveness and readiness probes do not start until
it succeeds, making sure those probes don't interfere with the application startup.
This can be used to adopt liveness checks on slow starting containers, avoiding them
getting killed by the kubelet before they are up and running.
Caution: Liveness probes can be a powerful way to recover from application failures, but
they should be used with caution. Liveness probes must be configured carefully
to ensure that they truly indicate unrecoverable application failure, for example a deadlock.
Note: Incorrect implementation of liveness probes can lead to cascading failures. This results in
restarting of container under high load; failed client requests as your application became less
scalable; and increased workload on remaining pods due to some failed pods.
Understand the difference between readiness and liveness probes and when to apply them for your app.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Many applications running for long periods of time eventually transition to
broken states, and cannot recover except by being restarted. Kubernetes provides
liveness probes to detect and remedy such situations.
In this exercise, you create a Pod that runs a container based on the
registry.k8s.io/busybox image. Here is the configuration file for the Pod:
In the configuration file, you can see that the Pod has a single Container.
The periodSeconds field specifies that the kubelet should perform a liveness
probe every 5 seconds. The initialDelaySeconds field tells the kubelet that it
should wait 5 seconds before performing the first probe. To perform a probe, the
kubelet executes the command cat /tmp/healthy in the target container. If the
command succeeds, it returns 0, and the kubelet considers the container to be alive and
healthy. If the command returns a non-zero value, the kubelet kills the container
and restarts it.
When the container starts, it executes this command:
For the first 30 seconds of the container's life, there is a /tmp/healthy file.
So during the first 30 seconds, the command cat /tmp/healthy returns a success
code. After 30 seconds, cat /tmp/healthy returns a failure code.
The output indicates that no liveness probes have failed yet:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal Scheduled 11s default-scheduler Successfully assigned default/liveness-exec to node01
Normal Pulling 9s kubelet, node01 Pulling image "registry.k8s.io/busybox"
Normal Pulled 7s kubelet, node01 Successfully pulled image "registry.k8s.io/busybox"
Normal Created 7s kubelet, node01 Created container liveness
Normal Started 7s kubelet, node01 Started container liveness
After 35 seconds, view the Pod events again:
kubectl describe pod liveness-exec
At the bottom of the output, there are messages indicating that the liveness
probes have failed, and the failed containers have been killed and recreated.
Type Reason Age From Message
---- ------ ---- ---- -------
Normal Scheduled 57s default-scheduler Successfully assigned default/liveness-exec to node01
Normal Pulling 55s kubelet, node01 Pulling image "registry.k8s.io/busybox"
Normal Pulled 53s kubelet, node01 Successfully pulled image "registry.k8s.io/busybox"
Normal Created 53s kubelet, node01 Created container liveness
Normal Started 53s kubelet, node01 Started container liveness
Warning Unhealthy 10s (x3 over 20s) kubelet, node01 Liveness probe failed: cat: can't open '/tmp/healthy': No such file or directory
Normal Killing 10s kubelet, node01 Container liveness failed liveness probe, will be restarted
Wait another 30 seconds, and verify that the container has been restarted:
kubectl get pod liveness-exec
The output shows that RESTARTS has been incremented. Note that the RESTARTS counter
increments as soon as a failed container comes back to the running state:
NAME READY STATUS RESTARTS AGE
liveness-exec 1/1 Running 1 1m
Define a liveness HTTP request
Another kind of liveness probe uses an HTTP GET request. Here is the configuration
file for a Pod that runs a container based on the registry.k8s.io/liveness image.
In the configuration file, you can see that the Pod has a single container.
The periodSeconds field specifies that the kubelet should perform a liveness
probe every 3 seconds. The initialDelaySeconds field tells the kubelet that it
should wait 3 seconds before performing the first probe. To perform a probe, the
kubelet sends an HTTP GET request to the server that is running in the container
and listening on port 8080. If the handler for the server's /healthz path
returns a success code, the kubelet considers the container to be alive and
healthy. If the handler returns a failure code, the kubelet kills the container
and restarts it.
Any code greater than or equal to 200 and less than 400 indicates success. Any
other code indicates failure.
You can see the source code for the server in
server.go.
For the first 10 seconds that the container is alive, the /healthz handler
returns a status of 200. After that, the handler returns a status of 500.
The kubelet starts performing health checks 3 seconds after the container starts.
So the first couple of health checks will succeed. But after 10 seconds, the health
checks will fail, and the kubelet will kill and restart the container.
After 10 seconds, view Pod events to verify that liveness probes have failed and
the container has been restarted:
kubectl describe pod liveness-http
In releases after v1.13, local HTTP proxy environment variable settings do not
affect the HTTP liveness probe.
Define a TCP liveness probe
A third type of liveness probe uses a TCP socket. With this configuration, the
kubelet will attempt to open a socket to your container on the specified port.
If it can establish a connection, the container is considered healthy, if it
can't it is considered a failure.
As you can see, configuration for a TCP check is quite similar to an HTTP check.
This example uses both readiness and liveness probes. The kubelet will send the
first readiness probe 15 seconds after the container starts. This will attempt to
connect to the goproxy container on port 8080. If the probe succeeds, the Pod
will be marked as ready. The kubelet will continue to run this check every 10
seconds.
In addition to the readiness probe, this configuration includes a liveness probe.
The kubelet will run the first liveness probe 15 seconds after the container
starts. Similar to the readiness probe, this will attempt to connect to the
goproxy container on port 8080. If the liveness probe fails, the container
will be restarted.
After 15 seconds, view Pod events to verify that liveness probes:
kubectl describe pod goproxy
Define a gRPC liveness probe
FEATURE STATE:Kubernetes v1.27 [stable]
If your application implements the
gRPC Health Checking Protocol,
this example shows how to configure Kubernetes to use it for application liveness checks.
Similarly you can configure readiness and startup probes.
To use a gRPC probe, port must be configured. If you want to distinguish probes of different types
and probes for different features you can use the service field.
You can set service to the value liveness and make your gRPC Health Checking endpoint
respond to this request differently than when you set service set to readiness.
This lets you use the same endpoint for different kinds of container health check
rather than listening on two different ports.
If you want to specify your own custom service name and also specify a probe type,
the Kubernetes project recommends that you use a name that concatenates
those. For example: myservice-liveness (using - as a separator).
Note: Unlike HTTP or TCP probes, you cannot specify the health check port by name, and you
cannot configure a custom hostname.
Configuration problems (for example: incorrect port or service, unimplemented health checking protocol)
are considered a probe failure, similar to HTTP and TCP probes.
To try the gRPC liveness check, create a Pod using the command below.
In the example below, the etcd pod is configured to use gRPC liveness probe.
After 15 seconds, view Pod events to verify that the liveness check has not failed:
kubectl describe pod etcd-with-grpc
When using a gRPC probe, there are some technical details to be aware of:
The probes run against the pod IP address or its hostname.
Be sure to configure your gRPC endpoint to listen on the Pod's IP address.
The probes do not support any authentication parameters (like -tls).
There are no error codes for built-in probes. All errors are considered as probe failures.
If ExecProbeTimeout feature gate is set to false, grpc-health-probe does not
respect the timeoutSeconds setting (which defaults to 1s), while built-in probe would fail on timeout.
Use a named port
You can use a named port
for HTTP and TCP probes. gRPC probes do not support named ports.
Protect slow starting containers with startup probes
Sometimes, you have to deal with legacy applications that might require
an additional startup time on their first initialization.
In such cases, it can be tricky to set up liveness probe parameters without
compromising the fast response to deadlocks that motivated such a probe.
The trick is to set up a startup probe with the same command, HTTP or TCP
check, with a failureThreshold * periodSeconds long enough to cover the
worst case startup time.
Thanks to the startup probe, the application will have a maximum of 5 minutes
(30 * 10 = 300s) to finish its startup.
Once the startup probe has succeeded once, the liveness probe takes over to
provide a fast response to container deadlocks.
If the startup probe never succeeds, the container is killed after 300s and
subject to the pod's restartPolicy.
Define readiness probes
Sometimes, applications are temporarily unable to serve traffic.
For example, an application might need to load large data or configuration
files during startup, or depend on external services after startup.
In such cases, you don't want to kill the application,
but you don't want to send it requests either. Kubernetes provides
readiness probes to detect and mitigate these situations. A pod with containers
reporting that they are not ready does not receive traffic through Kubernetes
Services.
Note: Readiness probes runs on the container during its whole lifecycle.
Caution: The readiness and liveness probes do not depend on each other to succeed.
If you want to wait before executing a readiness probe, you should use
initialDelaySeconds or a startupProbe.
Readiness probes are configured similarly to liveness probes. The only difference
is that you use the readinessProbe field instead of the livenessProbe field.
Configuration for HTTP and TCP readiness probes also remains identical to
liveness probes.
Readiness and liveness probes can be used in parallel for the same container.
Using both can ensure that traffic does not reach a container that is not ready
for it, and that containers are restarted when they fail.
Configure Probes
Probes
have a number of fields that you can use to more precisely control the behavior of startup,
liveness and readiness checks:
initialDelaySeconds: Number of seconds after the container has started before startup,
liveness or readiness probes are initiated. If a startup probe is defined, liveness and
readiness probe delays do not begin until the startup probe has succeeded. If the value of
periodSeconds is greater than initialDelaySeconds then the initialDelaySeconds would be
ignored. Defaults to 0 seconds. Minimum value is 0.
periodSeconds: How often (in seconds) to perform the probe. Default to 10 seconds.
The minimum value is 1.
timeoutSeconds: Number of seconds after which the probe times out.
Defaults to 1 second. Minimum value is 1.
successThreshold: Minimum consecutive successes for the probe to be considered successful
after having failed. Defaults to 1. Must be 1 for liveness and startup Probes.
Minimum value is 1.
failureThreshold: After a probe fails failureThreshold times in a row, Kubernetes
considers that the overall check has failed: the container is not ready/healthy/live.
For the case of a startup or liveness probe, if at least failureThreshold probes have
failed, Kubernetes treats the container as unhealthy and triggers a restart for that
specific container. The kubelet honors the setting of terminationGracePeriodSeconds
for that container.
For a failed readiness probe, the kubelet continues running the container that failed
checks, and also continues to run more probes; because the check failed, the kubelet
sets the Readycondition
on the Pod to false.
terminationGracePeriodSeconds: configure a grace period for the kubelet to wait between
triggering a shut down of the failed container, and then forcing the container runtime to stop
that container.
The default is to inherit the Pod-level value for terminationGracePeriodSeconds
(30 seconds if not specified), and the minimum value is 1.
See probe-level terminationGracePeriodSeconds
for more detail.
Caution: Incorrect implementation of readiness probes may result in an ever growing number
of processes in the container, and resource starvation if this is left unchecked.
HTTP probes
HTTP probes
have additional fields that can be set on httpGet:
host: Host name to connect to, defaults to the pod IP. You probably want to
set "Host" in httpHeaders instead.
scheme: Scheme to use for connecting to the host (HTTP or HTTPS). Defaults to "HTTP".
path: Path to access on the HTTP server. Defaults to "/".
httpHeaders: Custom headers to set in the request. HTTP allows repeated headers.
port: Name or number of the port to access on the container. Number must be
in the range 1 to 65535.
For an HTTP probe, the kubelet sends an HTTP request to the specified port and
path to perform the check. The kubelet sends the probe to the Pod's IP address,
unless the address is overridden by the optional host field in httpGet. If
scheme field is set to HTTPS, the kubelet sends an HTTPS request skipping the
certificate verification. In most scenarios, you do not want to set the host field.
Here's one scenario where you would set it. Suppose the container listens on 127.0.0.1
and the Pod's hostNetwork field is true. Then host, under httpGet, should be set
to 127.0.0.1. If your pod relies on virtual hosts, which is probably the more common
case, you should not use host, but rather set the Host header in httpHeaders.
For an HTTP probe, the kubelet sends two request headers in addition to the mandatory Host header:
User-Agent: The default value is kube-probe/1.28,
where 1.28 is the version of the kubelet.
Accept: The default value is */*.
You can override the default headers by defining httpHeaders for the probe.
For example:
For a TCP probe, the kubelet makes the probe connection at the node, not in the Pod, which
means that you can not use a service name in the host parameter since the kubelet is unable
to resolve it.
Probe-level terminationGracePeriodSeconds
FEATURE STATE:Kubernetes v1.28 [stable]
In 1.25 and above, users can specify a probe-level terminationGracePeriodSeconds
as part of the probe specification. When both a pod- and probe-level
terminationGracePeriodSeconds are set, the kubelet will use the probe-level value.
When setting the terminationGracePeriodSeconds, please note the following:
The kubelet always honors the probe-level terminationGracePeriodSeconds field if
it is present on a Pod.
If you have existing Pods where the terminationGracePeriodSeconds field is set and
you no longer wish to use per-probe termination grace periods, you must delete
those existing Pods.
This page shows how to assign a Kubernetes Pod to a particular node in a
Kubernetes cluster.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
List the nodes in your cluster, along with their labels:
kubectl get nodes --show-labels
The output is similar to this:
NAME STATUS ROLES AGE VERSION LABELS
worker0 Ready <none> 1d v1.13.0 ...,kubernetes.io/hostname=worker0
worker1 Ready <none> 1d v1.13.0 ...,kubernetes.io/hostname=worker1
worker2 Ready <none> 1d v1.13.0 ...,kubernetes.io/hostname=worker2
Choose one of your nodes, and add a label to it:
kubectl label nodes <your-node-name> disktype=ssd
where <your-node-name> is the name of your chosen node.
Verify that your chosen node has a disktype=ssd label:
kubectl get nodes --show-labels
The output is similar to this:
NAME STATUS ROLES AGE VERSION LABELS
worker0 Ready <none> 1d v1.13.0 ...,disktype=ssd,kubernetes.io/hostname=worker0
worker1 Ready <none> 1d v1.13.0 ...,kubernetes.io/hostname=worker1
worker2 Ready <none> 1d v1.13.0 ...,kubernetes.io/hostname=worker2
In the preceding output, you can see that the worker0 node has a
disktype=ssd label.
Create a pod that gets scheduled to your chosen node
This pod configuration file describes a pod that has a node selector,
disktype: ssd. This means that the pod will get scheduled on a node that has
a disktype=ssd label.
apiVersion:v1kind:Podmetadata:name:nginxspec:nodeName:foo-node# schedule pod to specific nodecontainers:- name:nginximage:nginximagePullPolicy:IfNotPresent
Use the configuration file to create a pod that will get scheduled on foo-node only.
This page shows how to assign a Kubernetes Pod to a particular node using Node Affinity in a
Kubernetes cluster.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Your Kubernetes server must be at or later than version v1.10.
To check the version, enter kubectl version.
Add a label to a node
List the nodes in your cluster, along with their labels:
kubectl get nodes --show-labels
The output is similar to this:
NAME STATUS ROLES AGE VERSION LABELS
worker0 Ready <none> 1d v1.13.0 ...,kubernetes.io/hostname=worker0
worker1 Ready <none> 1d v1.13.0 ...,kubernetes.io/hostname=worker1
worker2 Ready <none> 1d v1.13.0 ...,kubernetes.io/hostname=worker2
Choose one of your nodes, and add a label to it:
kubectl label nodes <your-node-name> disktype=ssd
where <your-node-name> is the name of your chosen node.
Verify that your chosen node has a disktype=ssd label:
kubectl get nodes --show-labels
The output is similar to this:
NAME STATUS ROLES AGE VERSION LABELS
worker0 Ready <none> 1d v1.13.0 ...,disktype=ssd,kubernetes.io/hostname=worker0
worker1 Ready <none> 1d v1.13.0 ...,kubernetes.io/hostname=worker1
worker2 Ready <none> 1d v1.13.0 ...,kubernetes.io/hostname=worker2
In the preceding output, you can see that the worker0 node has a
disktype=ssd label.
Schedule a Pod using required node affinity
This manifest describes a Pod that has a requiredDuringSchedulingIgnoredDuringExecution node affinity,disktype: ssd.
This means that the pod will get scheduled only on a node that has a disktype=ssd label.
Verify that the pod is running on your chosen node:
kubectl get pods --output=wide
The output is similar to this:
NAME READY STATUS RESTARTS AGE IP NODE
nginx 1/1 Running 0 13s 10.200.0.4 worker0
Schedule a Pod using preferred node affinity
This manifest describes a Pod that has a preferredDuringSchedulingIgnoredDuringExecution node affinity,disktype: ssd.
This means that the pod will prefer a node that has a disktype=ssd label.
This page shows how to use an Init Container to initialize a Pod before an
application Container runs.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
In this exercise you create a Pod that has one application Container and one
Init Container. The init container runs to completion before the application
container starts.
apiVersion:v1kind:Podmetadata:name:init-demospec:containers:- name:nginximage:nginxports:- containerPort:80volumeMounts:- name:workdirmountPath:/usr/share/nginx/html# These containers are run during pod initializationinitContainers:- name:installimage:busybox:1.28command:- wget- "-O"- "/work-dir/index.html"- http://info.cern.chvolumeMounts:- name:workdirmountPath:"/work-dir"dnsPolicy:Defaultvolumes:- name:workdiremptyDir:{}
In the configuration file, you can see that the Pod has a Volume that the init
container and the application container share.
The init container mounts the
shared Volume at /work-dir, and the application container mounts the shared
Volume at /usr/share/nginx/html. The init container runs the following command
and then terminates:
wget -O /work-dir/index.html http://info.cern.ch
Notice that the init container writes the index.html file in the root directory
of the nginx server.
The output shows that nginx is serving the web page that was written by the init container:
<html><head></head><body><header>
<title>http://info.cern.ch</title>
</header>
<h1>http://info.cern.ch - home of the first website</h1>
...
<li><ahref="http://info.cern.ch/hypertext/WWW/TheProject.html">Browse the first website</a></li>
...
3.19 - Attach Handlers to Container Lifecycle Events
This page shows how to attach handlers to Container lifecycle events. Kubernetes supports
the postStart and preStop events. Kubernetes sends the postStart event immediately
after a Container is started, and it sends the preStop event immediately before the
Container is terminated. A Container may specify one handler per event.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
apiVersion:v1kind:Podmetadata:name:lifecycle-demospec:containers:- name:lifecycle-demo-containerimage:nginxlifecycle:postStart:exec:command:["/bin/sh","-c","echo Hello from the postStart handler > /usr/share/message"]preStop:exec:command:["/bin/sh","-c","nginx -s quit; while killall -0 nginx; do sleep 1; done"]
In the configuration file, you can see that the postStart command writes a message
file to the Container's /usr/share directory. The preStop command shuts down
nginx gracefully. This is helpful if the Container is being terminated because of a failure.
Get a shell into the Container running in your Pod:
kubectl exec -it lifecycle-demo -- /bin/bash
In your shell, verify that the postStart handler created the message file:
root@lifecycle-demo:/# cat /usr/share/message
The output shows the text written by the postStart handler:
Hello from the postStart handler
Discussion
Kubernetes sends the postStart event immediately after the Container is created.
There is no guarantee, however, that the postStart handler is called before
the Container's entrypoint is called. The postStart handler runs asynchronously
relative to the Container's code, but Kubernetes' management of the container
blocks until the postStart handler completes. The Container's status is not
set to RUNNING until the postStart handler completes.
Kubernetes sends the preStop event immediately before the Container is terminated.
Kubernetes' management of the Container blocks until the preStop handler completes,
unless the Pod's grace period expires. For more details, see
Pod Lifecycle.
Note: Kubernetes only sends the preStop event when a Pod or a container in the Pod is terminated.
This means that the preStop hook is not invoked when the Pod is completed.
About this limitation, please see Container hooks for the detail.
Many applications rely on configuration which is used during either application initialization or runtime.
Most times, there is a requirement to adjust values assigned to configuration parameters.
ConfigMaps are a Kubernetes mechanism that let you inject configuration data into application
pods.
The ConfigMap concept allow you to decouple configuration artifacts from image content to
keep containerized applications portable. For example, you can download and run the same
container image to spin up containers for
the purposes of local development, system test, or running a live end-user workload.
This page provides a series of usage examples demonstrating how to create ConfigMaps and
configure Pods using data stored in ConfigMaps.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
You need to have the wget tool installed. If you have a different tool
such as curl, and you do not have wget, you will need to adapt the
step that downloads example data.
Create a ConfigMap
You can use either kubectl create configmap or a ConfigMap generator in kustomization.yaml
to create a ConfigMap.
where <map-name> is the name you want to assign to the ConfigMap and <data-source> is the
directory, file, or literal value to draw the data from.
The name of a ConfigMap object must be a valid
DNS subdomain name.
When you are creating a ConfigMap based on a file, the key in the <data-source> defaults to
the basename of the file, and the value defaults to the file content.
You can use kubectl create configmap to create a ConfigMap from multiple files in the same
directory. When you are creating a ConfigMap based on a directory, kubectl identifies files
whose filename is a valid key in the directory and packages each of those files into the new
ConfigMap. Any directory entries except regular files are ignored (for example: subdirectories,
symlinks, devices, pipes, and more).
Note:
Each filename being used for ConfigMap creation must consist of only acceptable characters,
which are: letters (A to Z and a to z), digits (0 to 9), '-', '_', or '.'.
If you use kubectl create configmap with a directory where any of the file names contains
an unacceptable character, the kubectl command may fail.
The kubectl command does not print an error when it encounters an invalid filename.
Create the local directory:
mkdir -p configure-pod-container/configmap/
Now, download the sample configuration and create the ConfigMap:
# Download the sample files into `configure-pod-container/configmap/` directorywget https://kubernetes.io/examples/configmap/game.properties -O configure-pod-container/configmap/game.properties
wget https://kubernetes.io/examples/configmap/ui.properties -O configure-pod-container/configmap/ui.properties
# Create the ConfigMapkubectl create configmap game-config --from-file=configure-pod-container/configmap/
The above command packages each file, in this case, game.properties and ui.properties
in the configure-pod-container/configmap/ directory into the game-config ConfigMap. You can
display details of the ConfigMap using the following command:
Use the option --from-env-file to create a ConfigMap from an env-file, for example:
# Env-files contain a list of environment variables.# These syntax rules apply:# Each line in an env file has to be in VAR=VAL format.# Lines beginning with # (i.e. comments) are ignored.# Blank lines are ignored.# There is no special handling of quotation marks (i.e. they will be part of the ConfigMap value)).# Download the sample files into `configure-pod-container/configmap/` directorywget https://kubernetes.io/examples/configmap/game-env-file.properties -O configure-pod-container/configmap/game-env-file.properties
wget https://kubernetes.io/examples/configmap/ui-env-file.properties -O configure-pod-container/configmap/ui-env-file.properties
# The env-file `game-env-file.properties` looks like belowcat configure-pod-container/configmap/game-env-file.properties
enemies=aliens
lives=3allowed="true"# This comment and the empty line above it are ignored
Starting with Kubernetes v1.23, kubectl supports the --from-env-file argument to be
specified multiple times to create a ConfigMap from multiple data sources.
where <my-key-name> is the key you want to use in the ConfigMap and <path-to-file> is the
location of the data source file you want the key to represent.
You can pass in multiple key-value pairs. Each pair provided on the command line is represented
as a separate entry in the data section of the ConfigMap.
You can also create a ConfigMap from generators and then apply it to create the object
in the cluster's API server.
You should specify the generators in a kustomization.yaml file within a directory.
Generate ConfigMaps from files
For example, to generate a ConfigMap from files configure-pod-container/configmap/game.properties
Notice that the generated ConfigMap name has a suffix appended by hashing the contents. This
ensures that a new ConfigMap is generated each time the content is modified.
Define the key to use when generating a ConfigMap from a file
You can define a key other than the file name to use in the ConfigMap generator.
For example, to generate a ConfigMap from files configure-pod-container/configmap/game.properties
with the key game-special-key
Apply the kustomization directory to create the ConfigMap object.
kubectl apply -k .
configmap/game-config-5-m67dt67794 created
Generate ConfigMaps from literals
This example shows you how to create a ConfigMap from two literal key/value pairs:
special.type=charm and special.how=very, using Kustomize and kubectl. To achieve
this, you can specify the ConfigMap generator. Create (or replace)
kustomization.yaml so that it has the following contents:
---# kustomization.yaml contents for creating a ConfigMap from literalsconfigMapGenerator:- name:special-config-2literals:- special.how=very- special.type=charm
Apply the kustomization directory to create the ConfigMap object:
kubectl apply -k .
configmap/special-config-2-c92b5mmcf2 created
Interim cleanup
Before proceeding, clean up some of the ConfigMaps you made:
apiVersion:v1kind:Podmetadata:name:dapi-test-podspec:containers:- name:test-containerimage:registry.k8s.io/busyboxcommand:["/bin/sh","-c","env"]env:# Define the environment variable- name:SPECIAL_LEVEL_KEYvalueFrom:configMapKeyRef:# The ConfigMap containing the value you want to assign to SPECIAL_LEVEL_KEYname:special-config# Specify the key associated with the valuekey:special.howrestartPolicy:Never
Use envFrom to define all of the ConfigMap's data as container environment variables. The
key from the ConfigMap becomes the environment variable name in the Pod.
That pod produces the following output from the test-container container:
kubectl logs dapi-test-pod
very charm
Once you're happy to move on, delete that Pod:
kubectl delete pod dapi-test-pod --now
Add ConfigMap data to a Volume
As explained in Create ConfigMaps from files, when you create
a ConfigMap using --from-file, the filename becomes a key stored in the data section of
the ConfigMap. The file contents become the key's value.
The examples in this section refer to a ConfigMap named special-config:
Add the ConfigMap name under the volumes section of the Pod specification.
This adds the ConfigMap data to the directory specified as volumeMounts.mountPath (in this
case, /etc/config). The command section lists directory files with names that match the
keys in ConfigMap.
apiVersion:v1kind:Podmetadata:name:dapi-test-podspec:containers:- name:test-containerimage:registry.k8s.io/busyboxcommand:["/bin/sh","-c","ls /etc/config/"]volumeMounts:- name:config-volumemountPath:/etc/configvolumes:- name:config-volumeconfigMap:# Provide the name of the ConfigMap containing the files you want# to add to the containername:special-configrestartPolicy:Never
When the pod runs, the command ls /etc/config/ produces the output below:
SPECIAL_LEVEL
SPECIAL_TYPE
Text data is exposed as files using the UTF-8 character encoding. To use some other
character encoding, use binaryData
(see ConfigMap object for more details).
Note: If there are any files in the /etc/config directory of that container image, the volume
mount will make those files from the image inaccessible.
Once you're happy to move on, delete that Pod:
kubectl delete pod dapi-test-pod --now
Add ConfigMap data to a specific path in the Volume
Use the path field to specify the desired file path for specific ConfigMap items.
In this case, the SPECIAL_LEVEL item will be mounted in the config-volume volume at /etc/config/keys.
When the pod runs, the command cat /etc/config/keys produces the output below:
very
Caution: Like before, all previous files in the /etc/config/ directory will be deleted.
Delete that Pod:
kubectl delete pod dapi-test-pod --now
Project keys to specific paths and file permissions
You can project keys to specific paths and specific permissions on a per-file
basis. The
Secrets
guide explains the syntax.
Optional references
A ConfigMap reference may be marked optional. If the ConfigMap is non-existent, the mounted
volume will be empty. If the ConfigMap exists, but the referenced key is non-existent, the path
will be absent beneath the mount point. See Optional ConfigMaps for more
details.
Mounted ConfigMaps are updated automatically
When a mounted ConfigMap is updated, the projected content is eventually updated too.
This applies in the case where an optionally referenced ConfigMap comes into
existence after a pod has started.
Kubelet checks whether the mounted ConfigMap is fresh on every periodic sync. However,
it uses its local TTL-based cache for getting the current value of the ConfigMap. As a
result, the total delay from the moment when the ConfigMap is updated to the moment
when new keys are projected to the pod can be as long as kubelet sync period (1
minute by default) + TTL of ConfigMaps cache (1 minute by default) in kubelet. You
can trigger an immediate refresh by updating one of the pod's annotations.
Note: A container using a ConfigMap as a subPath
volume will not receive ConfigMap updates.
Understanding ConfigMaps and Pods
The ConfigMap API resource stores configuration data as key-value pairs. The data can be consumed
in pods or provide the configurations for system components such as controllers. ConfigMap is
similar to Secrets, but provides a means of working
with strings that don't contain sensitive information. Users and system components alike can
store configuration data in ConfigMap.
Note: ConfigMaps should reference properties files, not replace them. Think of the ConfigMap as
representing something similar to the Linux /etc directory and its contents. For example,
if you create a Kubernetes Volume from a ConfigMap, each
data item in the ConfigMap is represented by an individual file in the volume.
The ConfigMap's data field contains the configuration data. As shown in the example below,
this can be simple (like individual properties defined using --from-literal) or complex
(like configuration files or JSON blobs defined using --from-file).
apiVersion:v1kind:ConfigMapmetadata:creationTimestamp:2016-02-18T19:14:38Zname:example-confignamespace:defaultdata:# example of a simple property defined using --from-literalexample.property.1:helloexample.property.2:world# example of a complex property defined using --from-fileexample.property.file:|- property.1=value-1
property.2=value-2
property.3=value-3
When kubectl creates a ConfigMap from inputs that are not ASCII or UTF-8, the tool puts
these into the binaryData field of the ConfigMap, and not in data. Both text and binary
data sources can be combined in one ConfigMap.
If you want to view the binaryData keys (and their values) in a ConfigMap, you can run
kubectl get configmap -o jsonpath='{.binaryData}' <name>.
Pods can load data from a ConfigMap that uses either data or binaryData.
Optional ConfigMaps
You can mark a reference to a ConfigMap as optional in a Pod specification.
If the ConfigMap doesn't exist, the configuration for which it provides data in the Pod
(for example: environment variable, mounted volume) will be empty.
If the ConfigMap exists, but the referenced key is non-existent the data is also empty.
For example, the following Pod specification marks an environment variable from a ConfigMap
as optional:
apiVersion:v1kind:Podmetadata:name:dapi-test-podspec:containers:- name:test-containerimage:gcr.io/google_containers/busyboxcommand:["/bin/sh","-c","env"]env:- name:SPECIAL_LEVEL_KEYvalueFrom:configMapKeyRef:name:a-configkey:akeyoptional:true# mark the variable as optionalrestartPolicy:Never
If you run this pod, and there is no ConfigMap named a-config, the output is empty.
If you run this pod, and there is a ConfigMap named a-config but that ConfigMap doesn't have
a key named akey, the output is also empty. If you do set a value for akey in the a-config
ConfigMap, this pod prints that value and then terminates.
You can also mark the volumes and files provided by a ConfigMap as optional. Kubernetes always
creates the mount paths for the volume, even if the referenced ConfigMap or key doesn't exist. For
example, the following Pod specification marks a volume that references a ConfigMap as optional:
apiVersion:v1kind:Podmetadata:name:dapi-test-podspec:containers:- name:test-containerimage:gcr.io/google_containers/busyboxcommand:["/bin/sh","-c","ls /etc/config"]volumeMounts:- name:config-volumemountPath:/etc/configvolumes:- name:config-volumeconfigMap:name:no-configoptional:true# mark the source ConfigMap as optionalrestartPolicy:Never
Restrictions
You must create the ConfigMap object before you reference it in a Pod
specification. Alternatively, mark the ConfigMap reference as optional in the Pod spec (see
Optional ConfigMaps). If you reference a ConfigMap that doesn't exist
and you don't mark the reference as optional, the Pod won't start. Similarly, references
to keys that don't exist in the ConfigMap will also prevent the Pod from starting, unless
you mark the key references as optional.
If you use envFrom to define environment variables from ConfigMaps, keys that are considered
invalid will be skipped. The pod will be allowed to start, but the invalid names will be
recorded in the event log (InvalidVariableNames). The log message lists each skipped
key. For example:
kubectl get events
The output is similar to this:
LASTSEEN FIRSTSEEN COUNT NAME KIND SUBOBJECT TYPE REASON SOURCE MESSAGE
0s 0s 1 dapi-test-pod Pod Warning InvalidEnvironmentVariableNames {kubelet, 127.0.0.1} Keys [1badkey, 2alsobad] from the EnvFrom configMap default/myconfig were skipped since they are considered invalid environment variable names.
ConfigMaps reside in a specific Namespace.
Pods can only refer to ConfigMaps that are in the same namespace as the Pod.
You can't use ConfigMaps for
static pods, because the
kubelet does not support this.
Cleaning up
Delete the ConfigMaps and Pods that you made:
kubectl delete configmaps/game-config configmaps/game-config-2 configmaps/game-config-3 \
configmaps/game-config-env-file
kubectl delete pod dapi-test-pod --now
# You might already have removed the next setkubectl delete configmaps/special-config configmaps/env-config
kubectl delete configmap -l 'game-config in (config-4,config-5)’
If you created a directory configure-pod-container and no longer need it, you should remove that too,
or move it into the trash can / deleted files location.
3.21 - Share Process Namespace between Containers in a Pod
This page shows how to configure process namespace sharing for a pod. When
process namespace sharing is enabled, processes in a container are visible
to all other containers in the same pod.
You can use this feature to configure cooperating containers, such as a log
handler sidecar container, or to troubleshoot container images that don't
include debugging utilities like a shell.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
If you don't see a command prompt, try pressing enter. In the container shell:
# run this inside the "shell" containerps ax
The output is similar to this:
PID USER TIME COMMAND
1 root 0:00 /pause
8 root 0:00 nginx: master process nginx -g daemon off;
14 101 0:00 nginx: worker process
15 root 0:00 sh
21 root 0:00 ps ax
You can signal processes in other containers. For example, send SIGHUP to
nginx to restart the worker process. This requires the SYS_PTRACE capability.
# run this inside the "shell" containerkill -HUP 8# change "8" to match the PID of the nginx leader process, if necessaryps ax
The output is similar to this:
PID USER TIME COMMAND
1 root 0:00 /pause
8 root 0:00 nginx: master process nginx -g daemon off;
15 root 0:00 sh
22 101 0:00 nginx: worker process
23 root 0:00 ps ax
It's even possible to access the file system of another container using the
/proc/$pid/root link.
# run this inside the "shell" container# change "8" to the PID of the Nginx process, if necessaryhead /proc/8/root/etc/nginx/nginx.conf
Pods share many resources so it makes sense they would also share a process
namespace. Some containers may expect to be isolated from others, though,
so it's important to understand the differences:
The container process no longer has PID 1. Some containers refuse
to start without PID 1 (for example, containers using systemd) or run
commands like kill -HUP 1 to signal the container process. In pods with a
shared process namespace, kill -HUP 1 will signal the pod sandbox
(/pause in the above example).
Processes are visible to other containers in the pod. This includes all
information visible in /proc, such as passwords that were passed as arguments
or environment variables. These are protected only by regular Unix permissions.
Container filesystems are visible to other containers in the pod through the
/proc/$pid/root link. This makes debugging easier, but it also means
that filesystem secrets are protected only by filesystem permissions.
3.22 - Use a User Namespace With a Pod
FEATURE STATE:Kubernetes v1.25 [alpha]
This page shows how to configure a user namespace for pods. This allows you to
isolate the user running inside the container from the one in the host.
A process running as root in a container can run as a different (non-root) user
in the host; in other words, the process has full privileges for operations
inside the user namespace, but is unprivileged for operations outside the
namespace.
You can use this feature to reduce the damage a compromised container can do to
the host or other pods in the same node. There are several security
vulnerabilities rated either HIGH or CRITICAL that were not
exploitable when user namespaces is active. It is expected user namespace will
mitigate some future vulnerabilities too.
Without using a user namespace a container running as root, in the case of a
container breakout, has root privileges on the node. And if some capability were
granted to the container, the capabilities are valid on the host too. None of
this is true when user namespaces are used.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Your Kubernetes server must be at or later than version v1.25.
To check the version, enter kubectl version.
🛇 This item links to a third party project or product that is not part of Kubernetes itself. More information
The node OS needs to be Linux
You need to exec commands in the host
You need to be able to exec into pods
You need to enable the UserNamespacesSupportfeature gate
Note: The feature gate to enable user namespaces was previously named
UserNamespacesStatelessPodsSupport, when only stateless pods were supported.
Only Kubernetes v1.25 through to v1.27 recognise UserNamespacesStatelessPodsSupport.
The cluster that you're using must include at least one node that meets the
requirements
for using user namespaces with Pods.
If you have a mixture of nodes and only some of the nodes provide user namespace support for
Pods, you also need to ensure that the user namespace Pods are
scheduled to suitable nodes.
Please note that if your container runtime doesn't support user namespaces, the
hostUsers field in the pod spec will be silently ignored and the pod will be
created without user namespaces.
Run a Pod that uses a user namespace
A user namespace for a pod is enabled setting the hostUsers field of .spec
to false. For example:
Then, open a shell in the host and run the same command.
The output must be different. This means the host and the pod are using a
different user namespace. When user namespaces are not enabled, the host and the
pod use the same user namespace.
If you are running the kubelet inside a user namespace, you need to compare the
output from running the command in the pod to the output of running in the host:
readlink /proc/$pid/ns/user
user:[4026534732]
replacing $pid with the kubelet PID.
3.23 - Create static Pods
Static Pods are managed directly by the kubelet daemon on a specific node,
without the API server
observing them.
Unlike Pods that are managed by the control plane (for example, a
Deployment);
instead, the kubelet watches each static Pod (and restarts it if it fails).
Static Pods are always bound to one Kubelet on a specific node.
The kubelet automatically tries to create a mirror Pod
on the Kubernetes API server for each static Pod.
This means that the Pods running on a node are visible on the API server,
but cannot be controlled from there.
The Pod names will be suffixed with the node hostname with a leading hyphen.
Note: If you are running clustered Kubernetes and are using static
Pods to run a Pod on every node, you should probably be using a
DaemonSet instead.
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
This page assumes you're using CRI-O to run Pods,
and that your nodes are running the Fedora operating system.
Instructions for other distributions or Kubernetes installations may vary.
Manifests are standard Pod definitions in JSON or YAML format in a specific directory.
Use the staticPodPath: <the directory> field in the
kubelet configuration file,
which periodically scans the directory and creates/deletes static Pods as YAML/JSON files appear/disappear there.
Note that the kubelet will ignore files starting with dots when scanning the specified directory.
For example, this is how to start a simple web server as a static Pod:
Choose a node where you want to run the static Pod. In this example, it's my-node1.
ssh my-node1
Choose a directory, say /etc/kubernetes/manifests and place a web server
Pod definition there, for example /etc/kubernetes/manifests/static-web.yaml:
# Run this command on the node where kubelet is runningmkdir -p /etc/kubernetes/manifests/
cat <<EOF >/etc/kubernetes/manifests/static-web.yaml
apiVersion: v1
kind: Pod
metadata:
name: static-web
labels:
role: myrole
spec:
containers:
- name: web
image: nginx
ports:
- name: web
containerPort: 80
protocol: TCP
EOF
Configure your kubelet on the node to use this directory by running it with
--pod-manifest-path=/etc/kubernetes/manifests/ argument.
On Fedora, edit /etc/kubernetes/kubelet to include this line:
# Run this command on the node where the kubelet is runningsystemctl restart kubelet
Web-hosted static pod manifest
Kubelet periodically downloads a file specified by --manifest-url=<URL> argument
and interprets it as a JSON/YAML file that contains Pod definitions.
Similar to how filesystem-hosted manifests work, the kubelet
refetches the manifest on a schedule. If there are changes to the list of static
Pods, the kubelet applies them.
To use this approach:
Create a YAML file and store it on a web server so that you can pass the URL of that file to the kubelet.
Configure the kubelet on your selected node to use this web manifest by
running it with --manifest-url=<manifest-url>.
On Fedora, edit /etc/kubernetes/kubelet to include this line:
# Run this command on the node where the kubelet is runningsystemctl restart kubelet
Observe static pod behavior
When the kubelet starts, it automatically starts all defined static Pods. As you have
defined a static Pod and restarted the kubelet, the new static Pod should
already be running.
You can view running containers (including static Pods) by running (on the node):
# Run this command on the node where the kubelet is runningcrictl ps
The output might be something like:
CONTAINER IMAGE CREATED STATE NAME ATTEMPT POD ID
129fd7d382018 docker.io/library/nginx@sha256:... 11 minutes ago Running web 0 34533c6729106
Note:crictl outputs the image URI and SHA-256 checksum. NAME will look more like:
docker.io/library/nginx@sha256:0d17b565c37bcbd895e9d92315a05c1c3c9a29f762b011a10c54a66cd53c9b31.
You can see the mirror Pod on the API server:
kubectl get pods
NAME READY STATUS RESTARTS AGE
static-web-my-node1 1/1 Running 0 2m
Note: Make sure the kubelet has permission to create the mirror Pod in the API server.
If not, the creation request is rejected by the API server.
Labels from the static Pod are
propagated into the mirror Pod. You can use those labels as normal via
selectors, etc.
If you try to use kubectl to delete the mirror Pod from the API server,
the kubelet doesn't remove the static Pod:
kubectl delete pod static-web-my-node1
pod "static-web-my-node1" deleted
You can see that the Pod is still running:
kubectl get pods
NAME READY STATUS RESTARTS AGE
static-web-my-node1 1/1 Running 0 4s
Back on your node where the kubelet is running, you can try to stop the container manually.
You'll see that, after a time, the kubelet will notice and will restart the Pod
automatically:
# Run these commands on the node where the kubelet is runningcrictl stop 129fd7d382018 # replace with the ID of your containersleep 20crictl ps
CONTAINER IMAGE CREATED STATE NAME ATTEMPT POD ID
89db4553e1eeb docker.io/library/nginx@sha256:... 19 seconds ago Running web 1 34533c6729106
Once you identify the right container, you can get the logs for that container with crictl:
# Run these commands on the node where the container is runningcrictl logs <container_id>
The running kubelet periodically scans the configured directory
(/etc/kubernetes/manifests in our example) for changes and
adds/removes Pods as files appear/disappear in this directory.
# This assumes you are using filesystem-hosted static Pod configuration# Run these commands on the node where the container is running#mv /etc/kubernetes/manifests/static-web.yaml /tmp
sleep 20crictl ps
# You see that no nginx container is runningmv /tmp/static-web.yaml /etc/kubernetes/manifests/
sleep 20crictl ps
CONTAINER IMAGE CREATED STATE NAME ATTEMPT POD ID
f427638871c35 docker.io/library/nginx@sha256:... 19 seconds ago Running web 1 34533c6729106
3.24 - Translate a Docker Compose File to Kubernetes Resources
What's Kompose? It's a conversion tool for all things compose (namely Docker Compose) to container orchestrators (Kubernetes or OpenShift).
More information can be found on the Kompose website at http://kompose.io.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Installing using go get pulls from the master branch with the latest development changes.
go get -u github.com/kubernetes/kompose
Kompose is in EPEL CentOS repository.
If you don't have EPEL repository already installed and enabled you can do it by running sudo yum install epel-release.
If you have EPEL enabled in your system, you can install Kompose like any other package.
sudo yum -y install kompose
Kompose is in Fedora 24, 25 and 26 repositories. You can install it like any other package.
sudo dnf -y install kompose
On macOS you can install the latest release via Homebrew:
brew install kompose
Use Kompose
In a few steps, we'll take you from Docker Compose to Kubernetes. All
you need is an existing docker-compose.yml file.
Go to the directory containing your docker-compose.yml file. If you don't have one, test using this one.
To convert the docker-compose.yml file to files that you can use with
kubectl, run kompose convert and then kubectl apply -f <output file>.
kompose convert
The output is similar to:
INFO Kubernetes file "frontend-tcp-service.yaml" created
INFO Kubernetes file "redis-master-service.yaml" created
INFO Kubernetes file "redis-slave-service.yaml" created
INFO Kubernetes file "frontend-deployment.yaml" created
INFO Kubernetes file "redis-master-deployment.yaml" created
INFO Kubernetes file "redis-slave-deployment.yaml" created
service/frontend-tcp created
service/redis-master created
service/redis-slave created
deployment.apps/frontend created
deployment.apps/redis-master created
deployment.apps/redis-slave created
Your deployments are running in Kubernetes.
Access your application.
If you're already using minikube for your development process:
minikube service frontend
Otherwise, let's look up what IP your service is using!
kubectl describe svc frontend
Name: frontend-tcp
Namespace: default
Labels: io.kompose.service=frontend-tcp
Annotations: kompose.cmd: kompose convert
kompose.service.type: LoadBalancer
kompose.version: 1.26.0 (40646f47)
Selector: io.kompose.service=frontend
Type: LoadBalancer
IP Family Policy: SingleStack
IP Families: IPv4
IP: 10.43.67.174
IPs: 10.43.67.174
Port: 80 80/TCP
TargetPort: 80/TCP
NodePort: 80 31254/TCP
Endpoints: 10.42.0.25:80
Session Affinity: None
External Traffic Policy: Cluster
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal EnsuringLoadBalancer 62s service-controller Ensuring load balancer
Normal AppliedDaemonSet 62s service-controller Applied LoadBalancer DaemonSet kube-system/svclb-frontend-tcp-9362d276
If you're using a cloud provider, your IP will be listed next to LoadBalancer Ingress.
curl http://192.0.2.89
Clean-up.
After you are finished testing out the example application deployment, simply run the following command in your shell to delete the
resources used.
Kompose has support for two providers: OpenShift and Kubernetes.
You can choose a targeted provider using global option --provider. If no provider is specified, Kubernetes is set by default.
kompose convert
Kompose supports conversion of V1, V2, and V3 Docker Compose files into Kubernetes and OpenShift objects.
Kubernetes kompose convert example
kompose --file docker-voting.yml convert
WARN Unsupported key networks - ignoring
WARN Unsupported key build - ignoring
INFO Kubernetes file "worker-svc.yaml" created
INFO Kubernetes file "db-svc.yaml" created
INFO Kubernetes file "redis-svc.yaml" created
INFO Kubernetes file "result-svc.yaml" created
INFO Kubernetes file "vote-svc.yaml" created
INFO Kubernetes file "redis-deployment.yaml" created
INFO Kubernetes file "result-deployment.yaml" created
INFO Kubernetes file "vote-deployment.yaml" created
INFO Kubernetes file "worker-deployment.yaml" created
INFO Kubernetes file "db-deployment.yaml" created
INFO Kubernetes file "frontend-service.yaml" created
INFO Kubernetes file "mlbparks-service.yaml" created
INFO Kubernetes file "mongodb-service.yaml" created
INFO Kubernetes file "redis-master-service.yaml" created
INFO Kubernetes file "redis-slave-service.yaml" created
INFO Kubernetes file "frontend-deployment.yaml" created
INFO Kubernetes file "mlbparks-deployment.yaml" created
INFO Kubernetes file "mongodb-deployment.yaml" created
INFO Kubernetes file "mongodb-claim0-persistentvolumeclaim.yaml" created
INFO Kubernetes file "redis-master-deployment.yaml" created
INFO Kubernetes file "redis-slave-deployment.yaml" created
WARN [worker] Service cannot be created because of missing port.
INFO OpenShift file "vote-service.yaml" created
INFO OpenShift file "db-service.yaml" created
INFO OpenShift file "redis-service.yaml" created
INFO OpenShift file "result-service.yaml" created
INFO OpenShift file "vote-deploymentconfig.yaml" created
INFO OpenShift file "vote-imagestream.yaml" created
INFO OpenShift file "worker-deploymentconfig.yaml" created
INFO OpenShift file "worker-imagestream.yaml" created
INFO OpenShift file "db-deploymentconfig.yaml" created
INFO OpenShift file "db-imagestream.yaml" created
INFO OpenShift file "redis-deploymentconfig.yaml" created
INFO OpenShift file "redis-imagestream.yaml" created
INFO OpenShift file "result-deploymentconfig.yaml" created
INFO OpenShift file "result-imagestream.yaml" created
It also supports creating buildconfig for build directive in a service. By default, it uses the remote repo for the current git branch as the source repo, and the current branch as the source branch for the build. You can specify a different source repo and branch using --build-repo and --build-branch options respectively.
WARN [foo] Service cannot be created because of missing port.
INFO OpenShift Buildconfig using git@github.com:rtnpro/kompose.git::master as source.
INFO OpenShift file "foo-deploymentconfig.yaml" created
INFO OpenShift file "foo-imagestream.yaml" created
INFO OpenShift file "foo-buildconfig.yaml" created
Note: If you are manually pushing the OpenShift artifacts using oc create -f, you need to ensure that you push the imagestream artifact before the buildconfig artifact, to workaround this OpenShift issue: https://github.com/openshift/origin/issues/4518 .
Alternative Conversions
The default kompose transformation will generate Kubernetes Deployments and Services, in yaml format. You have alternative option to generate json with -j. Also, you can alternatively generate Replication Controllers objects, Daemon Sets, or Helm charts.
kompose convert -j
INFO Kubernetes file "redis-svc.json" created
INFO Kubernetes file "web-svc.json" created
INFO Kubernetes file "redis-deployment.json" created
INFO Kubernetes file "web-deployment.json" created
The *-deployment.json files contain the Deployment objects.
kompose convert --replication-controller
INFO Kubernetes file "redis-svc.yaml" created
INFO Kubernetes file "web-svc.yaml" created
INFO Kubernetes file "redis-replicationcontroller.yaml" created
INFO Kubernetes file "web-replicationcontroller.yaml" created
The *-replicationcontroller.yaml files contain the Replication Controller objects. If you want to specify replicas (default is 1), use --replicas flag: kompose convert --replication-controller --replicas 3.
kompose convert --daemon-set
INFO Kubernetes file "redis-svc.yaml" created
INFO Kubernetes file "web-svc.yaml" created
INFO Kubernetes file "redis-daemonset.yaml" created
INFO Kubernetes file "web-daemonset.yaml" created
The *-daemonset.yaml files contain the DaemonSet objects.
If you want to generate a Chart to be used with Helm run:
kompose convert -c
INFO Kubernetes file "web-svc.yaml" created
INFO Kubernetes file "redis-svc.yaml" created
INFO Kubernetes file "web-deployment.yaml" created
INFO Kubernetes file "redis-deployment.yaml" created
chart created in "./docker-compose/"
kompose.service.expose defines if the service needs to be made accessible from outside the cluster or not. If the value is set to "true", the provider sets the endpoint automatically, and for any other value, the value is set as the hostname. If multiple ports are defined in a service, the first one is chosen to be the exposed.
For the Kubernetes provider, an ingress resource is created and it is assumed that an ingress controller has already been configured.
Note: The kompose.service.type label should be defined with ports only, otherwise kompose will fail.
Restart
If you want to create normal pods without controllers you can use restart construct of docker-compose to define that. Follow table below to see what happens on the restart value.
docker-composerestart
object created
Pod restartPolicy
""
controller object
Always
always
controller object
Always
on-failure
Pod
OnFailure
no
Pod
Never
Note: The controller object could be deployment or replicationcontroller.
For example, the pival service will become pod down here. This container calculated value of pi.
If the Docker Compose file has a volume specified for a service, the Deployment (Kubernetes) or DeploymentConfig (OpenShift) strategy is changed to "Recreate" instead of "RollingUpdate" (default). This is done to avoid multiple instances of a service from accessing a volume at the same time.
If the Docker Compose file has service name with _ in it (for example, web_service), then it will be replaced by - and the service name will be renamed accordingly (for example, web-service). Kompose does this because "Kubernetes" doesn't allow _ in object name.
Please note that changing service name might break some docker-compose files.
Docker Compose Versions
Kompose supports Docker Compose versions: 1, 2 and 3. We have limited support on versions 2.1 and 3.2 due to their experimental nature.
A full list on compatibility between all three versions is listed in our conversion document including a list of all incompatible Docker Compose keys.
3.25 - Enforce Pod Security Standards by Configuring the Built-in Admission Controller
Following an alpha release in Kubernetes v1.22,
Pod Security Admission became available by default in Kubernetes v1.23, as
a beta. From version 1.25 onwards, Pod Security Admission is generally
available.
To check the version, enter kubectl version.
If you are not running Kubernetes 1.28, you can switch
to viewing this page in the documentation for the Kubernetes version that you
are running.
Configure the Admission Controller
Note:pod-security.admission.config.k8s.io/v1 configuration requires v1.25+.
For v1.23 and v1.24, use v1beta1.
For v1.22, use v1alpha1.
apiVersion:apiserver.config.k8s.io/v1kind:AdmissionConfigurationplugins:- name:PodSecurityconfiguration:apiVersion:pod-security.admission.config.k8s.io/v1# see compatibility notekind:PodSecurityConfiguration# Defaults applied when a mode label is not set.## Level label values must be one of:# - "privileged" (default)# - "baseline"# - "restricted"## Version label values must be one of:# - "latest" (default) # - specific version like "v1.28"defaults:enforce:"privileged"enforce-version:"latest"audit:"privileged"audit-version:"latest"warn:"privileged"warn-version:"latest"exemptions:# Array of authenticated usernames to exempt.usernames:[]# Array of runtime class names to exempt.runtimeClasses:[]# Array of namespaces to exempt.namespaces:[]
Note: The above manifest needs to be specified via the --admission-control-config-file to kube-apiserver.
3.26 - Enforce Pod Security Standards with Namespace Labels
Pod Security Admission was available by default in Kubernetes v1.23, as
a beta. From version 1.25 onwards, Pod Security Admission is generally
available.
To check the version, enter kubectl version.
Requiring the baseline Pod Security Standard with namespace labels
This manifest defines a Namespace my-baseline-namespace that:
Blocks any pods that don't satisfy the baseline policy requirements.
Generates a user-facing warning and adds an audit annotation to any created pod that does not
meet the restricted policy requirements.
Pins the versions of the baseline and restricted policies to v1.28.
apiVersion:v1kind:Namespacemetadata:name:my-baseline-namespacelabels:pod-security.kubernetes.io/enforce:baselinepod-security.kubernetes.io/enforce-version:v1.28# We are setting these to our _desired_ `enforce` level.pod-security.kubernetes.io/audit:restrictedpod-security.kubernetes.io/audit-version:v1.28pod-security.kubernetes.io/warn:restrictedpod-security.kubernetes.io/warn-version:v1.28
Add labels to existing namespaces with kubectl label
Note: When an enforce policy (or version) label is added or changed, the admission plugin will test
each pod in the namespace against the new policy. Violations are returned to the user as warnings.
It is helpful to apply the --dry-run flag when initially evaluating security profile changes for
namespaces. The Pod Security Standard checks will still be run in dry run mode, giving you
information about how the new policy would treat existing pods, without actually updating a policy.
If you're just getting started with the Pod Security Standards, a suitable first step would be to
configure all namespaces with audit annotations for a stricter level such as baseline:
Note that this is not setting an enforce level, so that namespaces that haven't been explicitly
evaluated can be distinguished. You can list namespaces without an explicitly set enforce level
using this command:
kubectl get namespaces --selector='!pod-security.kubernetes.io/enforce'
Applying to a single namespace
You can update a specific namespace as well. This command adds the enforce=restricted
policy to my-existing-namespace, pinning the restricted policy version to v1.28.
3.27 - Migrate from PodSecurityPolicy to the Built-In PodSecurity Admission Controller
This page describes the process of migrating from PodSecurityPolicies to the built-in PodSecurity
admission controller. This can be done effectively using a combination of dry-run and audit and
warn modes, although this becomes harder if mutating PSPs are used.
Before you begin
Your Kubernetes server must be at or later than version v1.22.
To check the version, enter kubectl version.
If you are currently running a version of Kubernetes other than
1.28, you may want to switch to viewing this
page in the documentation for the version of Kubernetes that you
are actually running.
This page assumes you are already familiar with the basic Pod Security Admission
concepts.
Overall approach
There are multiple strategies you can take for migrating from PodSecurityPolicy to Pod Security
Admission. The following steps are one possible migration path, with a goal of minimizing both the
risks of a production outage and of a security gap.
Decide whether Pod Security Admission is the right fit for your use case.
Review namespace permissions
Simplify & standardize PodSecurityPolicies
Update namespaces
Identify an appropriate Pod Security level
Verify the Pod Security level
Enforce the Pod Security level
Bypass PodSecurityPolicy
Review namespace creation processes
Disable PodSecurityPolicy
0. Decide whether Pod Security Admission is right for you
Pod Security Admission was designed to meet the most common security needs out of the box, and to
provide a standard set of security levels across clusters. However, it is less flexible than
PodSecurityPolicy. Notably, the following features are supported by PodSecurityPolicy but not Pod
Security Admission:
Setting default security constraints - Pod Security Admission is a non-mutating admission
controller, meaning it won't modify pods before validating them. If you were relying on this
aspect of PSP, you will need to either modify your workloads to meet the Pod Security constraints,
or use a Mutating Admission Webhook
to make those changes. See Simplify & Standardize PodSecurityPolicies below for more detail.
Fine-grained control over policy definition - Pod Security Admission only supports
3 standard levels.
If you require more control over specific constraints, then you will need to use a
Validating Admission Webhook
to enforce those policies.
Sub-namespace policy granularity - PodSecurityPolicy lets you bind different policies to
different Service Accounts or users, even within a single namespace. This approach has many
pitfalls and is not recommended, but if you require this feature anyway you will
need to use a 3rd party webhook instead. The exception to this is if you only need to completely exempt
specific users or RuntimeClasses, in which case Pod
Security Admission does expose some
static configuration for exemptions.
Even if Pod Security Admission does not meet all of your needs it was designed to be complementary
to other policy enforcement mechanisms, and can provide a useful fallback running alongside other
admission webhooks.
1. Review namespace permissions
Pod Security Admission is controlled by labels on
namespaces.
This means that anyone who can update (or patch or create) a namespace can also modify the Pod
Security level for that namespace, which could be used to bypass a more restrictive policy. Before
proceeding, ensure that only trusted, privileged users have these namespace permissions. It is not
recommended to grant these powerful permissions to users that shouldn't have elevated permissions,
but if you must you will need to use an
admission webhook
to place additional restrictions on setting Pod Security labels on Namespace objects.
2. Simplify & standardize PodSecurityPolicies
In this section, you will reduce mutating PodSecurityPolicies and remove options that are outside
the scope of the Pod Security Standards. You should make the changes recommended here to an offline
copy of the original PodSecurityPolicy being modified. The cloned PSP should have a different
name that is alphabetically before the original (for example, prepend a 0 to it). Do not create the
new policies in Kubernetes yet - that will be covered in the Rollout the updated
policies section below.
2.a. Eliminate purely mutating fields
If a PodSecurityPolicy is mutating pods, then you could end up with pods that don't meet the Pod
Security level requirements when you finally turn PodSecurityPolicy off. In order to avoid this, you
should eliminate all PSP mutation prior to switching over. Unfortunately PSP does not cleanly
separate mutating & validating fields, so this is not a straightforward migration.
You can start by eliminating the fields that are purely mutating, and don't have any bearing on the
validating policy. These fields (also listed in the
Mapping PodSecurityPolicies to Pod Security Standards
reference) are:
.spec.defaultAddCapabilities - Although technically a mutating & validating field, these should
be merged into .spec.allowedCapabilities which performs the same validation without mutation.
Caution: Removing these could result in workloads missing required configuration, and cause problems. See
Rollout the updated policies below for advice on how to roll these changes
out safely.
2.b. Eliminate options not covered by the Pod Security Standards
There are several fields in PodSecurityPolicy that are not covered by the Pod Security Standards. If
you must enforce these options, you will need to supplement Pod Security Admission with an
admission webhook,
which is outside the scope of this guide.
First, you can remove the purely validating fields that the Pod Security Standards do not cover.
These fields (also listed in the
Mapping PodSecurityPolicies to Pod Security Standards
reference with "no opinion") are:
.spec.allowedHostPaths
.spec.allowedFlexVolumes
.spec.allowedCSIDrivers
.spec.forbiddenSysctls
.spec.runtimeClass
You can also remove the following fields, that are related to POSIX / UNIX group controls.
Caution: If any of these use the MustRunAs strategy they may be mutating! Removing these could result in
workloads not setting the required groups, and cause problems. See
Rollout the updated policies below for advice on how to roll these changes
out safely.
.spec.runAsGroup
.spec.supplementalGroups
.spec.fsGroup
The remaining mutating fields are required to properly support the Pod Security Standards, and will
need to be handled on a case-by-case basis later:
.spec.requiredDropCapabilities - Required to drop ALL for the Restricted profile.
.spec.seLinux - (Only mutating with the MustRunAs rule) required to enforce the SELinux
requirements of the Baseline & Restricted profiles.
.spec.runAsUser - (Non-mutating with the RunAsAny rule) required to enforce RunAsNonRoot for
the Restricted profile.
.spec.allowPrivilegeEscalation - (Only mutating if set to false) required for the Restricted
profile.
2.c. Rollout the updated PSPs
Next, you can rollout the updated policies to your cluster. You should proceed with caution, as
removing the mutating options may result in workloads missing required configuration.
For each updated PodSecurityPolicy:
Identify pods running under the original PSP. This can be done using the kubernetes.io/psp
annotation. For example, using kubectl:
PSP_NAME="original"# Set the name of the PSP you're checking forkubectl get pods --all-namespaces -o jsonpath="{range .items[?(@.metadata.annotations.kubernetes\.io\/psp=='$PSP_NAME')]}{.metadata.namespace} {.metadata.name}{'\n'}{end}"
Compare these running pods against the original pod spec to determine whether PodSecurityPolicy
has modified the pod. For pods created by a workload resource
you can compare the pod with the PodTemplate in the controller resource. If any changes are
identified, the original Pod or PodTemplate should be updated with the desired configuration.
The fields to review are:
.metadata.annotations['container.apparmor.security.beta.kubernetes.io/*'] (replace * with each container name)
.spec.runtimeClassName
.spec.securityContext.fsGroup
.spec.securityContext.seccompProfile
.spec.securityContext.seLinuxOptions
.spec.securityContext.supplementalGroups
On containers, under .spec.containers[*] and .spec.initContainers[*]:
.securityContext.allowPrivilegeEscalation
.securityContext.capabilities.add
.securityContext.capabilities.drop
.securityContext.readOnlyRootFilesystem
.securityContext.runAsGroup
.securityContext.runAsNonRoot
.securityContext.runAsUser
.securityContext.seccompProfile
.securityContext.seLinuxOptions
Create the new PodSecurityPolicies. If any Roles or ClusterRoles are granting use on all PSPs
this could cause the new PSPs to be used instead of their mutating counter-parts.
Update your authorization to grant access to the new PSPs. In RBAC this means updating any Roles
or ClusterRoles that grant the use permission on the original PSP to also grant it to the
updated PSP.
Verify: after some soak time, rerun the command from step 1 to see if any pods are still using
the original PSPs. Note that pods need to be recreated after the new policies have been rolled
out before they can be fully verified.
(optional) Once you have verified that the original PSPs are no longer in use, you can delete
them.
3. Update Namespaces
The following steps will need to be performed on every namespace in the cluster. Commands referenced
in these steps use the $NAMESPACE variable to refer to the namespace being updated.
3.a. Identify an appropriate Pod Security level
Start reviewing the Pod Security Standards and
familiarizing yourself with the 3 different levels.
There are several ways to choose a Pod Security level for your namespace:
By security requirements for the namespace - If you are familiar with the expected access
level for the namespace, you can choose an appropriate level based on those requirements, similar
to how one might approach this on a new cluster.
By existing PodSecurityPolicies - Using the
Mapping PodSecurityPolicies to Pod Security Standards
reference you can map each
PSP to a Pod Security Standard level. If your PSPs aren't based on the Pod Security Standards, you
may need to decide between choosing a level that is at least as permissive as the PSP, and a
level that is at least as restrictive. You can see which PSPs are in use for pods in a given
namespace with this command:
By existing pods - Using the strategies under Verify the Pod Security level,
you can test out both the Baseline and Restricted levels to see
whether they are sufficiently permissive for existing workloads, and chose the least-privileged
valid level.
Caution: Options 2 & 3 above are based on existing pods, and may miss workloads that aren't currently
running, such as CronJobs, scale-to-zero workloads, or other workloads that haven't rolled out.
3.b. Verify the Pod Security level
Once you have selected a Pod Security level for the namespace (or if you're trying several), it's a
good idea to test it out first (you can skip this step if using the Privileged level). Pod Security
includes several tools to help test and safely roll out profiles.
First, you can dry-run the policy, which will evaluate pods currently running in the namespace
against the applied policy, without making the new policy take effect:
# $LEVEL is the level to dry-run, either "baseline" or "restricted".kubectl label --dry-run=server --overwrite ns $NAMESPACE pod-security.kubernetes.io/enforce=$LEVEL
This command will return a warning for any existing pods that are not valid under the proposed
level.
The second option is better for catching workloads that are not currently running: audit mode. When
running under audit-mode (as opposed to enforcing), pods that violate the policy level are recorded
in the audit logs, which can be reviewed later after some soak time, but are not forbidden. Warning
mode works similarly, but returns the warning to the user immediately. You can set the audit level
on a namespace with this command:
If either of these approaches yield unexpected violations, you will need to either update the
violating workloads to meet the policy requirements, or relax the namespace Pod Security level.
3.c. Enforce the Pod Security level
When you are satisfied that the chosen level can safely be enforced on the namespace, you can update
the namespace to enforce the desired level:
Finally, you can effectively bypass PodSecurityPolicy at the namespace level by binding the fully
privileged PSP
to all service
accounts in the namespace.
# The following cluster-scoped commands are only needed once.kubectl apply -f privileged-psp.yaml
kubectl create clusterrole privileged-psp --verb use --resource podsecuritypolicies.policy --resource-name privileged
# Per-namespace disablekubectl create -n $NAMESPACE rolebinding disable-psp --clusterrole privileged-psp --group system:serviceaccounts:$NAMESPACE
Since the privileged PSP is non-mutating, and the PSP admission controller always
prefers non-mutating PSPs, this will ensure that pods in this namespace are no longer being modified
or restricted by PodSecurityPolicy.
The advantage to disabling PodSecurityPolicy on a per-namespace basis like this is if a problem
arises you can easily roll the change back by deleting the RoleBinding. Just make sure the
pre-existing PodSecurityPolicies are still in place!
Now that existing namespaces have been updated to enforce Pod Security Admission, you should ensure
that your processes and/or policies for creating new namespaces are updated to ensure that an
appropriate Pod Security profile is applied to new namespaces.
You can also statically configure the Pod Security admission controller to set a default enforce,
audit, and/or warn level for unlabeled namespaces. See
Configure the Admission Controller
for more information.
5. Disable PodSecurityPolicy
Finally, you're ready to disable PodSecurityPolicy. To do so, you will need to modify the admission
configuration of the API server:
How do I turn off an admission controller?.
To verify that the PodSecurityPolicy admission controller is no longer enabled, you can manually run
a test by impersonating a user without access to any PodSecurityPolicies (see the
PodSecurityPolicy example), or by verifying in
the API server logs. At startup, the API server outputs log lines listing the loaded admission
controller plugins:
I0218 00:59:44.903329 13 plugins.go:158] Loaded 16 mutating admission controller(s) successfully in the following order: NamespaceLifecycle,LimitRanger,ServiceAccount,NodeRestriction,TaintNodesByCondition,Priority,DefaultTolerationSeconds,ExtendedResourceToleration,PersistentVolumeLabel,DefaultStorageClass,StorageObjectInUseProtection,RuntimeClass,DefaultIngressClass,MutatingAdmissionWebhook.
I0218 00:59:44.903350 13 plugins.go:161] Loaded 14 validating admission controller(s) successfully in the following order: LimitRanger,ServiceAccount,PodSecurity,Priority,PersistentVolumeClaimResize,RuntimeClass,CertificateApproval,CertificateSigning,CertificateSubjectRestriction,DenyServiceExternalIPs,ValidatingAdmissionWebhook,ResourceQuota.
You should see PodSecurity (in the validating admission controllers), and neither list should
contain PodSecurityPolicy.
Once you are certain the PSP admission controller is disabled (and after sufficient soak time to be
confident you won't need to roll back), you are free to delete your PodSecurityPolicies and any
associated Roles, ClusterRoles, RoleBindings and ClusterRoleBindings (just make sure they don't
grant any other unrelated permissions).
4 - Monitoring, Logging, and Debugging
Set up monitoring and logging to troubleshoot a cluster, or debug a containerized application.
Sometimes things go wrong. This guide is aimed at making them right. It has
two sections:
Debugging your application - Useful
for users who are deploying code into Kubernetes and wondering why it is not working.
Debugging your cluster - Useful
for cluster administrators and people whose Kubernetes cluster is unhappy.
You should also check the known issues for the release
you're using.
Getting help
If your problem isn't answered by any of the guides above, there are variety of
ways for you to get help from the Kubernetes community.
Questions
The documentation on this site has been structured to provide answers to a wide
range of questions. Concepts explain the Kubernetes
architecture and how each component works, while Setup provides
practical instructions for getting started. Tasks show how to
accomplish commonly used tasks, and Tutorials are more
comprehensive walkthroughs of real-world, industry-specific, or end-to-end
development scenarios. The Reference section provides
detailed documentation on the Kubernetes API
and command-line interfaces (CLIs), such as kubectl.
Help! My question isn't covered! I need help now!
Stack Exchange, Stack Overflow, or Server Fault
If you have questions related to software development for your containerized app,
you can ask those on Stack Overflow.
If you have Kubernetes questions related to cluster management or configuration,
you can ask those on
Server Fault.
There are also several more specific Stack Exchange network sites which might
be the right place to ask Kubernetes questions in areas such as
DevOps,
Software Engineering,
or InfoSec.
Someone else from the community may have already asked a similar question or
may be able to help with your problem.
Many people from the Kubernetes community hang out on Kubernetes Slack in the #kubernetes-users channel.
Slack requires registration; you can request an invitation,
and registration is open to everyone). Feel free to come and ask any and all questions.
Once registered, access the Kubernetes organisation in Slack
via your web browser or via Slack's own dedicated app.
Once you are registered, browse the growing list of channels for various subjects of
interest. For example, people new to Kubernetes may also want to join the
#kubernetes-novice channel. As another example, developers should join the
#kubernetes-contributors channel.
There are also many country specific / local language channels. Feel free to join
these channels for localized support and info:
If you have what looks like a bug, or you would like to make a feature request,
please use the GitHub issue tracking system.
Before you file an issue, please search existing issues to see if your issue is
already covered.
If filing a bug, please include detailed information about how to reproduce the
problem, such as:
Kubernetes version: kubectl version
Cloud provider, OS distro, network configuration, and container runtime version
Steps to reproduce the problem
4.1 - Troubleshooting Applications
Debugging common containerized application issues.
This doc contains a set of resources for fixing issues with containerized applications. It covers things like common issues with Kubernetes resources (like Pods, Services, or StatefulSets), advice on making sense of container termination messages, and ways to debug running containers.
4.1.1 - Debug Pods
This guide is to help users debug applications that are deployed into Kubernetes
and not behaving correctly. This is not a guide for people who want to debug their cluster.
For that you should check out this guide.
Diagnosing the problem
The first step in troubleshooting is triage. What is the problem?
Is it your Pods, your Replication Controller or your Service?
The first step in debugging a Pod is taking a look at it. Check the current
state of the Pod and recent events with the following command:
kubectl describe pods ${POD_NAME}
Look at the state of the containers in the pod. Are they all Running?
Have there been recent restarts?
Continue debugging depending on the state of the pods.
My pod stays pending
If a Pod is stuck in Pending it means that it can not be scheduled onto a node.
Generally this is because there are insufficient resources of one type or another
that prevent scheduling. Look at the output of the kubectl describe ... command above.
There should be messages from the scheduler about why it can not schedule your pod.
Reasons include:
You don't have enough resources: You may have exhausted the supply of CPU
or Memory in your cluster, in this case you need to delete Pods, adjust resource
requests, or add new nodes to your cluster. See Compute Resources document
for more information.
You are using hostPort: When you bind a Pod to a hostPort there are a
limited number of places that pod can be scheduled. In most cases, hostPort
is unnecessary, try using a Service object to expose your Pod. If you do require
hostPort then you can only schedule as many Pods as there are nodes in your Kubernetes cluster.
My pod stays waiting
If a Pod is stuck in the Waiting state, then it has been scheduled to a worker node,
but it can't run on that machine. Again, the information from kubectl describe ...
should be informative. The most common cause of Waiting pods is a failure to pull the image.
There are three things to check:
Make sure that you have the name of the image correct.
Have you pushed the image to the registry?
Try to manually pull the image to see if the image can be pulled. For example,
if you use Docker on your PC, run docker pull <image>.
My pod stays terminating
If a Pod is stuck in the Terminating state, it means that a deletion has been
issued for the Pod, but the control plane is unable to delete the Pod object.
This typically happens if the Pod has a finalizer
and there is an admission webhook
installed in the cluster that prevents the control plane from removing the
finalizer.
To identify this scenario, check if your cluster has any
ValidatingWebhookConfiguration or MutatingWebhookConfiguration that target
UPDATE operations for pods resources.
If the webhook is provided by a third-party:
Make sure you are using the latest version.
Disable the webhook for UPDATE operations.
Report an issue with the corresponding provider.
If you are the author of the webhook:
For a mutating webhook, make sure it never changes immutable fields on
UPDATE operations. For example, changes to containers are usually not allowed.
For a validating webhook, make sure that your validation policies only apply
to new changes. In other words, you should allow Pods with existing violations
to pass validation. This allows Pods that were created before the validating
webhook was installed to continue running.
My pod is crashing or otherwise unhealthy
Once your pod has been scheduled, the methods described in
Debug Running Pods
are available for debugging.
My pod is running but not doing what I told it to do
If your pod is not behaving as you expected, it may be that there was an error in your
pod description (e.g. mypod.yaml file on your local machine), and that the error
was silently ignored when you created the pod. Often a section of the pod description
is nested incorrectly, or a key name is typed incorrectly, and so the key is ignored.
For example, if you misspelled command as commnd then the pod will be created but
will not use the command line you intended it to use.
The first thing to do is to delete your pod and try creating it again with the --validate option.
For example, run kubectl apply --validate -f mypod.yaml.
If you misspelled command as commnd then will give an error like this:
I0805 10:43:25.129850 46757 schema.go:126] unknown field: commnd
I0805 10:43:25.129973 46757 schema.go:129] this may be a false alarm, see https://github.com/kubernetes/kubernetes/issues/6842
pods/mypod
The next thing to check is whether the pod on the apiserver
matches the pod you meant to create (e.g. in a yaml file on your local machine).
For example, run kubectl get pods/mypod -o yaml > mypod-on-apiserver.yaml and then
manually compare the original pod description, mypod.yaml with the one you got
back from apiserver, mypod-on-apiserver.yaml. There will typically be some
lines on the "apiserver" version that are not on the original version. This is
expected. However, if there are lines on the original that are not on the apiserver
version, then this may indicate a problem with your pod spec.
Debugging Replication Controllers
Replication controllers are fairly straightforward. They can either create Pods or they can't.
If they can't create pods, then please refer to the
instructions above to debug your pods.
You can also use kubectl describe rc ${CONTROLLER_NAME} to introspect events
related to the replication controller.
Debugging Services
Services provide load balancing across a set of pods. There are several common problems that can make Services
not work properly. The following instructions should help debug Service problems.
First, verify that there are endpoints for the service. For every Service object,
the apiserver makes an endpoints resource available.
You can view this resource with:
kubectl get endpoints ${SERVICE_NAME}
Make sure that the endpoints match up with the number of pods that you expect to be members of your service.
For example, if your Service is for an nginx container with 3 replicas, you would expect to see three different
IP addresses in the Service's endpoints.
My service is missing endpoints
If you are missing endpoints, try listing pods using the labels that Service uses.
Imagine that you have a Service where the labels are:
...spec:- selector:name:nginxtype:frontend
You can use:
kubectl get pods --selector=name=nginx,type=frontend
to list pods that match this selector. Verify that the list matches the Pods that you expect to provide your Service.
Verify that the pod's containerPort matches up with the Service's targetPort
If none of the above solves your problem, follow the instructions in
Debugging Service document
to make sure that your Service is running, has Endpoints, and your Pods are
actually serving; you have DNS working, iptables rules installed, and kube-proxy
does not seem to be misbehaving.
An issue that comes up rather frequently for new installations of Kubernetes is
that a Service is not working properly. You've run your Pods through a
Deployment (or other workload controller) and created a Service, but you
get no response when you try to access it. This document will hopefully help
you to figure out what's going wrong.
Running commands in a Pod
For many steps here you will want to see what a Pod running in the cluster
sees. The simplest way to do this is to run an interactive busybox Pod:
kubectl run -it --rm --restart=Never busybox --image=gcr.io/google-containers/busybox sh
Note: If you don't see a command prompt, try pressing enter.
If you already have a running Pod that you prefer to use, you can run a
command in it using:
For the purposes of this walk-through, let's run some Pods. Since you're
probably debugging your own Service you can substitute your own details, or you
can follow along and get a second data point.
The label "app" is automatically set by kubectl create deployment to the name of the
Deployment.
You can confirm your Pods are running:
kubectl get pods -l app=hostnames
NAME READY STATUS RESTARTS AGE
hostnames-632524106-bbpiw 1/1 Running 0 2m
hostnames-632524106-ly40y 1/1 Running 0 2m
hostnames-632524106-tlaok 1/1 Running 0 2m
You can also confirm that your Pods are serving. You can get the list of
Pod IP addresses and test them directly.
kubectl get pods -l app=hostnames \
-o go-template='{{range .items}}{{.status.podIP}}{{"\n"}}{{end}}'
10.244.0.5
10.244.0.6
10.244.0.7
The example container used for this walk-through serves its own hostname
via HTTP on port 9376, but if you are debugging your own app, you'll want to
use whatever port number your Pods are listening on.
From within a pod:
for ep in 10.244.0.5:9376 10.244.0.6:9376 10.244.0.7:9376; do wget -qO- $epdone
If you are not getting the responses you expect at this point, your Pods
might not be healthy or might not be listening on the port you think they are.
You might find kubectl logs to be useful for seeing what is happening, or
perhaps you need to kubectl exec directly into your Pods and debug from
there.
Assuming everything has gone to plan so far, you can start to investigate why
your Service doesn't work.
Does the Service exist?
The astute reader will have noticed that you did not actually create a Service
yet - that is intentional. This is a step that sometimes gets forgotten, and
is the first thing to check.
What would happen if you tried to access a non-existent Service? If
you have another Pod that consumes this Service by name you would get
something like:
wget -O- hostnames
Resolving hostnames (hostnames)... failed: Name or service not known.
wget: unable to resolve host address 'hostnames'
The first thing to check is whether that Service actually exists:
kubectl get svc hostnames
No resources found.
Error from server (NotFound): services "hostnames" not found
Let's create the Service. As before, this is for the walk-through - you can
use your own Service's details here.
In order to highlight the full range of configuration, the Service you created
here uses a different port number than the Pods. For many real-world
Services, these values might be the same.
Any Network Policy Ingress rules affecting the target Pods?
If you have deployed any Network Policy Ingress rules which may affect incoming
traffic to hostnames-* Pods, these need to be reviewed.
If this works, you'll need to adjust your app to use a cross-namespace name, or
run your app and Service in the same Namespace. If this still fails, try a
fully-qualified name:
Note the suffix here: "default.svc.cluster.local". The "default" is the
Namespace you're operating in. The "svc" denotes that this is a Service.
The "cluster.local" is your cluster domain, which COULD be different in your
own cluster.
You can also try this from a Node in the cluster:
Note: 10.0.0.10 is the cluster's DNS Service IP, yours might be different.
If you are able to do a fully-qualified name lookup but not a relative one, you
need to check that your /etc/resolv.conf file in your Pod is correct. From
within a Pod:
The nameserver line must indicate your cluster's DNS Service. This is
passed into kubelet with the --cluster-dns flag.
The search line must include an appropriate suffix for you to find the
Service name. In this case it is looking for Services in the local
Namespace ("default.svc.cluster.local"), Services in all Namespaces
("svc.cluster.local"), and lastly for names in the cluster ("cluster.local").
Depending on your own install you might have additional records after that (up
to 6 total). The cluster suffix is passed into kubelet with the
--cluster-domain flag. Throughout this document, the cluster suffix is
assumed to be "cluster.local". Your own clusters might be configured
differently, in which case you should change that in all of the previous
commands.
The options line must set ndots high enough that your DNS client library
considers search paths at all. Kubernetes sets this to 5 by default, which is
high enough to cover all of the DNS names it generates.
Does any Service work by DNS name?
If the above still fails, DNS lookups are not working for your Service. You
can take a step back and see what else is not working. The Kubernetes master
Service should always work. From within a Pod:
If this fails, please see the kube-proxy section
of this document, or even go back to the top of this document and start over,
but instead of debugging your own Service, debug the DNS Service.
Does the Service work by IP?
Assuming you have confirmed that DNS works, the next thing to test is whether your
Service works by its IP address. From a Pod in your cluster, access the
Service's IP (from kubectl get above).
for i in $(seq 1 3); do wget -qO- 10.0.1.175:80
done
If your Service is working, you should get correct responses. If not, there
are a number of things that could be going wrong. Read on.
Is the Service defined correctly?
It might sound silly, but you should really double and triple check that your
Service is correct and matches your Pod's port. Read back your Service
and verify it:
Is the Service port you are trying to access listed in spec.ports[]?
Is the targetPort correct for your Pods (some Pods use a different port than the Service)?
If you meant to use a numeric port, is it a number (9376) or a string "9376"?
If you meant to use a named port, do your Pods expose a port with the same name?
Is the port's protocol correct for your Pods?
Does the Service have any Endpoints?
If you got this far, you have confirmed that your Service is correctly
defined and is resolved by DNS. Now let's check that the Pods you ran are
actually being selected by the Service.
Earlier you saw that the Pods were running. You can re-check that:
kubectl get pods -l app=hostnames
NAME READY STATUS RESTARTS AGE
hostnames-632524106-bbpiw 1/1 Running 0 1h
hostnames-632524106-ly40y 1/1 Running 0 1h
hostnames-632524106-tlaok 1/1 Running 0 1h
The -l app=hostnames argument is a label selector configured on the Service.
The "AGE" column says that these Pods are about an hour old, which implies that
they are running fine and not crashing.
The "RESTARTS" column says that these pods are not crashing frequently or being
restarted. Frequent restarts could lead to intermittent connectivity issues.
If the restart count is high, read more about how to debug pods.
Inside the Kubernetes system is a control loop which evaluates the selector of
every Service and saves the results into a corresponding Endpoints object.
kubectl get endpoints hostnames
NAME ENDPOINTS
hostnames 10.244.0.5:9376,10.244.0.6:9376,10.244.0.7:9376
This confirms that the endpoints controller has found the correct Pods for
your Service. If the ENDPOINTS column is <none>, you should check that
the spec.selector field of your Service actually selects for
metadata.labels values on your Pods. A common mistake is to have a typo or
other error, such as the Service selecting for app=hostnames, but the
Deployment specifying run=hostnames, as in versions previous to 1.18, where
the kubectl run command could have been also used to create a Deployment.
Are the Pods working?
At this point, you know that your Service exists and has selected your Pods.
At the beginning of this walk-through, you verified the Pods themselves.
Let's check again that the Pods are actually working - you can bypass the
Service mechanism and go straight to the Pods, as listed by the Endpoints
above.
Note: These commands use the Pod port (9376), rather than the Service port (80).
From within a Pod:
for ep in 10.244.0.5:9376 10.244.0.6:9376 10.244.0.7:9376; do wget -qO- $epdone
You expect each Pod in the Endpoints list to return its own hostname. If
this is not what happens (or whatever the correct behavior is for your own
Pods), you should investigate what's happening there.
Is the kube-proxy working?
If you get here, your Service is running, has Endpoints, and your Pods
are actually serving. At this point, the whole Service proxy mechanism is
suspect. Let's confirm it, piece by piece.
The default implementation of Services, and the one used on most clusters, is
kube-proxy. This is a program that runs on every node and configures one of a
small set of mechanisms for providing the Service abstraction. If your
cluster does not use kube-proxy, the following sections will not apply, and you
will have to investigate whatever implementation of Services you are using.
Is kube-proxy running?
Confirm that kube-proxy is running on your Nodes. Running directly on a
Node, you should get something like the below:
Next, confirm that it is not failing something obvious, like contacting the
master. To do this, you'll have to look at the logs. Accessing the logs
depends on your Node OS. On some OSes it is a file, such as
/var/log/kube-proxy.log, while other OSes use journalctl to access logs. You
should see something like:
I1027 22:14:53.995134 5063 server.go:200] Running in resource-only container "/kube-proxy"
I1027 22:14:53.998163 5063 server.go:247] Using iptables Proxier.
I1027 22:14:54.038140 5063 proxier.go:352] Setting endpoints for "kube-system/kube-dns:dns-tcp" to [10.244.1.3:53]
I1027 22:14:54.038164 5063 proxier.go:352] Setting endpoints for "kube-system/kube-dns:dns" to [10.244.1.3:53]
I1027 22:14:54.038209 5063 proxier.go:352] Setting endpoints for "default/kubernetes:https" to [10.240.0.2:443]
I1027 22:14:54.038238 5063 proxier.go:429] Not syncing iptables until Services and Endpoints have been received from master
I1027 22:14:54.040048 5063 proxier.go:294] Adding new service "default/kubernetes:https" at 10.0.0.1:443/TCP
I1027 22:14:54.040154 5063 proxier.go:294] Adding new service "kube-system/kube-dns:dns" at 10.0.0.10:53/UDP
I1027 22:14:54.040223 5063 proxier.go:294] Adding new service "kube-system/kube-dns:dns-tcp" at 10.0.0.10:53/TCP
If you see error messages about not being able to contact the master, you
should double-check your Node configuration and installation steps.
One of the possible reasons that kube-proxy cannot run correctly is that the
required conntrack binary cannot be found. This may happen on some Linux
systems, depending on how you are installing the cluster, for example, you are
installing Kubernetes from scratch. If this is the case, you need to manually
install the conntrack package (e.g. sudo apt install conntrack on Ubuntu)
and then retry.
Kube-proxy can run in one of a few modes. In the log listed above, the
line Using iptables Proxier indicates that kube-proxy is running in
"iptables" mode. The most common other mode is "ipvs".
Iptables mode
In "iptables" mode, you should see something like the following on a Node:
iptables-save | grep hostnames
-A KUBE-SEP-57KPRZ3JQVENLNBR -s 10.244.3.6/32 -m comment --comment "default/hostnames:" -j MARK --set-xmark 0x00004000/0x00004000
-A KUBE-SEP-57KPRZ3JQVENLNBR -p tcp -m comment --comment "default/hostnames:" -m tcp -j DNAT --to-destination 10.244.3.6:9376
-A KUBE-SEP-WNBA2IHDGP2BOBGZ -s 10.244.1.7/32 -m comment --comment "default/hostnames:" -j MARK --set-xmark 0x00004000/0x00004000
-A KUBE-SEP-WNBA2IHDGP2BOBGZ -p tcp -m comment --comment "default/hostnames:" -m tcp -j DNAT --to-destination 10.244.1.7:9376
-A KUBE-SEP-X3P2623AGDH6CDF3 -s 10.244.2.3/32 -m comment --comment "default/hostnames:" -j MARK --set-xmark 0x00004000/0x00004000
-A KUBE-SEP-X3P2623AGDH6CDF3 -p tcp -m comment --comment "default/hostnames:" -m tcp -j DNAT --to-destination 10.244.2.3:9376
-A KUBE-SERVICES -d 10.0.1.175/32 -p tcp -m comment --comment "default/hostnames: cluster IP" -m tcp --dport 80 -j KUBE-SVC-NWV5X2332I4OT4T3
-A KUBE-SVC-NWV5X2332I4OT4T3 -m comment --comment "default/hostnames:" -m statistic --mode random --probability 0.33332999982 -j KUBE-SEP-WNBA2IHDGP2BOBGZ
-A KUBE-SVC-NWV5X2332I4OT4T3 -m comment --comment "default/hostnames:" -m statistic --mode random --probability 0.50000000000 -j KUBE-SEP-X3P2623AGDH6CDF3
-A KUBE-SVC-NWV5X2332I4OT4T3 -m comment --comment "default/hostnames:" -j KUBE-SEP-57KPRZ3JQVENLNBR
For each port of each Service, there should be 1 rule in KUBE-SERVICES and
one KUBE-SVC-<hash> chain. For each Pod endpoint, there should be a small
number of rules in that KUBE-SVC-<hash> and one KUBE-SEP-<hash> chain with
a small number of rules in it. The exact rules will vary based on your exact
config (including node-ports and load-balancers).
IPVS mode
In "ipvs" mode, you should see something like the following on a Node:
For each port of each Service, plus any NodePorts, external IPs, and
load-balancer IPs, kube-proxy will create a virtual server. For each Pod
endpoint, it will create corresponding real servers. In this example, service
hostnames(10.0.1.175:80) has 3 endpoints(10.244.0.5:9376,
10.244.0.6:9376, 10.244.0.7:9376).
Is kube-proxy proxying?
Assuming you do see one the above cases, try again to access your Service by
IP from one of your Nodes:
curl 10.0.1.175:80
hostnames-632524106-bbpiw
If this still fails, look at the kube-proxy logs for specific lines like:
Setting endpoints for default/hostnames:default to [10.244.0.5:9376 10.244.0.6:9376 10.244.0.7:9376]
If you don't see those, try restarting kube-proxy with the -v flag set to 4, and
then look at the logs again.
Edge case: A Pod fails to reach itself via the Service IP
This might sound unlikely, but it does happen and it is supposed to work.
This can happen when the network is not properly configured for "hairpin"
traffic, usually when kube-proxy is running in iptables mode and Pods
are connected with bridge network. The Kubelet exposes a hairpin-modeflag that allows endpoints of a Service to loadbalance
back to themselves if they try to access their own Service VIP. The
hairpin-mode flag must either be set to hairpin-veth or
promiscuous-bridge.
The common steps to trouble shoot this are as follows:
Confirm hairpin-mode is set to hairpin-veth or promiscuous-bridge.
You should see something like the below. hairpin-mode is set to
promiscuous-bridge in the following example.
Confirm the effective hairpin-mode. To do this, you'll have to look at
kubelet log. Accessing the logs depends on your Node OS. On some OSes it
is a file, such as /var/log/kubelet.log, while other OSes use journalctl
to access logs. Please be noted that the effective hairpin mode may not
match --hairpin-mode flag due to compatibility. Check if there is any log
lines with key word hairpin in kubelet.log. There should be log lines
indicating the effective hairpin mode, like something below.
I0629 00:51:43.648698 3252 kubelet.go:380] Hairpin mode set to "promiscuous-bridge"
If the effective hairpin mode is hairpin-veth, ensure the Kubelet has
the permission to operate in /sys on node. If everything works properly,
you should see something like:
for intf in /sys/devices/virtual/net/cbr0/brif/*; do cat $intf/hairpin_mode; done
1
1
1
1
If the effective hairpin mode is promiscuous-bridge, ensure Kubelet
has the permission to manipulate linux bridge on node. If cbr0 bridge is
used and configured properly, you should see:
ifconfig cbr0 |grep PROMISC
UP BROADCAST RUNNING PROMISC MULTICAST MTU:1460 Metric:1
Seek help if none of above works out.
Seek help
If you get this far, something very strange is happening. Your Service is
running, has Endpoints, and your Pods are actually serving. You have DNS
working, and kube-proxy does not seem to be misbehaving. And yet your
Service is not working. Please let us know what is going on, so we can help
investigate!
You need to have a Kubernetes cluster, and the kubectl command-line tool must be configured to communicate with your cluster.
You should have a StatefulSet running that you want to investigate.
Debugging a StatefulSet
In order to list all the pods which belong to a StatefulSet, which have a label app.kubernetes.io/name=MyApp set on them,
you can use the following:
kubectl get pods -l app.kubernetes.io/name=MyApp
If you find that any Pods listed are in Unknown or Terminating state for an extended period of time,
refer to the Deleting StatefulSet Pods task for
instructions on how to deal with them.
You can debug individual Pods in a StatefulSet using the
Debugging Pods guide.
This page shows how to write and read a Container termination message.
Termination messages provide a way for containers to write
information about fatal events to a location where it can
be easily retrieved and surfaced by tools like dashboards
and monitoring software. In most cases, information that you
put in a termination message should also be written to
the general
Kubernetes logs.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
In the YAML file, in the command and args fields, you can see that the
container sleeps for 10 seconds and then writes "Sleep expired" to
the /dev/termination-log file. After the container writes
the "Sleep expired" message, it terminates.
Display information about the Pod:
kubectl get pod termination-demo
Repeat the preceding command until the Pod is no longer running.
Use a Go template to filter the output so that it includes only the termination message:
kubectl get pod termination-demo -o go-template="{{range .status.containerStatuses}}{{.lastState.terminated.message}}{{end}}"
If you are running a multi-container Pod, you can use a Go template to include the container's name.
By doing so, you can discover which of the containers is failing:
kubectl get pod multi-container-pod -o go-template='{{range .status.containerStatuses}}{{printf "%s:\n%s\n\n" .name .lastState.terminated.message}}{{end}}'
Customizing the termination message
Kubernetes retrieves termination messages from the termination message file
specified in the terminationMessagePath field of a Container, which has a default
value of /dev/termination-log. By customizing this field, you can tell Kubernetes
to use a different file. Kubernetes use the contents from the specified file to
populate the Container's status message on both success and failure.
The termination message is intended to be brief final status, such as an assertion failure message.
The kubelet truncates messages that are longer than 4096 bytes.
The total message length across all containers is limited to 12KiB, divided equally among each container.
For example, if there are 12 containers (initContainers or containers), each has 1024 bytes of available termination message space.
The default termination message path is /dev/termination-log.
You cannot set the termination message path after a Pod is launched.
In the following example, the container writes termination messages to
/tmp/my-log for Kubernetes to retrieve:
Moreover, users can set the terminationMessagePolicy field of a Container for
further customization. This field defaults to "File" which means the termination
messages are retrieved only from the termination message file. By setting the
terminationMessagePolicy to "FallbackToLogsOnError", you can tell Kubernetes
to use the last chunk of container log output if the termination message file
is empty and the container exited with an error. The log output is limited to
2048 bytes or 80 lines, whichever is smaller.
What's next
See the terminationMessagePath field in
Container.
This page shows how to investigate problems related to the execution of
Init Containers. The example command lines below refer to the Pod as
<pod-name> and the Init Containers as <init-container-1> and
<init-container-2>.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
You can also access the Init Container statuses programmatically by reading the
status.initContainerStatuses field on the Pod Spec:
kubectl get pod nginx --template '{{.status.initContainerStatuses}}'
This command will return the same information as above in raw JSON.
Accessing logs from Init Containers
Pass the Init Container name along with the Pod name
to access its logs.
kubectl logs <pod-name> -c <init-container-2>
Init Containers that run a shell script print
commands as they're executed. For example, you can do this in Bash by running
set -x at the beginning of the script.
Understanding Pod status
A Pod status beginning with Init: summarizes the status of Init Container
execution. The table below describes some example status values that you might
see while debugging Init Containers.
Status
Meaning
Init:N/M
The Pod has M Init Containers, and N have completed so far.
Init:Error
An Init Container has failed to execute.
Init:CrashLoopBackOff
An Init Container has failed repeatedly.
Pending
The Pod has not yet begun executing Init Containers.
PodInitializing or Running
The Pod has already finished executing Init Containers.
4.1.6 - Debug Running Pods
This page explains how to debug Pods running (or crashing) on a Node.
Before you begin
Your Pod should already be
scheduled and running. If your Pod is not yet running, start with Debugging
Pods.
For some of the advanced debugging steps you need to know on which Node the
Pod is running and have shell access to run commands on that Node. You don't
need that access to run the standard debug steps that use kubectl.
Using kubectl describe pod to fetch details about pods
For this example we'll use a Deployment to create two pods, similar to the earlier example.
NAME READY STATUS RESTARTS AGE
nginx-deployment-67d4bdd6f5-cx2nz 1/1 Running 0 13s
nginx-deployment-67d4bdd6f5-w6kd7 1/1 Running 0 13s
We can retrieve a lot more information about each of these pods using kubectl describe pod. For example:
kubectl describe pod nginx-deployment-67d4bdd6f5-w6kd7
Name: nginx-deployment-67d4bdd6f5-w6kd7
Namespace: default
Priority: 0
Node: kube-worker-1/192.168.0.113
Start Time: Thu, 17 Feb 2022 16:51:01 -0500
Labels: app=nginx
pod-template-hash=67d4bdd6f5
Annotations: <none>
Status: Running
IP: 10.88.0.3
IPs:
IP: 10.88.0.3
IP: 2001:db8::1
Controlled By: ReplicaSet/nginx-deployment-67d4bdd6f5
Containers:
nginx:
Container ID: containerd://5403af59a2b46ee5a23fb0ae4b1e077f7ca5c5fb7af16e1ab21c00e0e616462a
Image: nginx
Image ID: docker.io/library/nginx@sha256:2834dc507516af02784808c5f48b7cbe38b8ed5d0f4837f16e78d00deb7e7767
Port: 80/TCP
Host Port: 0/TCP
State: Running
Started: Thu, 17 Feb 2022 16:51:05 -0500
Ready: True
Restart Count: 0
Limits:
cpu: 500m
memory: 128Mi
Requests:
cpu: 500m
memory: 128Mi
Environment: <none>
Mounts:
/var/run/secrets/kubernetes.io/serviceaccount from kube-api-access-bgsgp (ro)
Conditions:
Type Status
Initialized True
Ready True
ContainersReady True
PodScheduled True
Volumes:
kube-api-access-bgsgp:
Type: Projected (a volume that contains injected data from multiple sources)
TokenExpirationSeconds: 3607
ConfigMapName: kube-root-ca.crt
ConfigMapOptional: <nil>
DownwardAPI: true
QoS Class: Guaranteed
Node-Selectors: <none>
Tolerations: node.kubernetes.io/not-ready:NoExecute op=Exists for 300s
node.kubernetes.io/unreachable:NoExecute op=Exists for 300s
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal Scheduled 34s default-scheduler Successfully assigned default/nginx-deployment-67d4bdd6f5-w6kd7 to kube-worker-1
Normal Pulling 31s kubelet Pulling image "nginx"
Normal Pulled 30s kubelet Successfully pulled image "nginx" in 1.146417389s
Normal Created 30s kubelet Created container nginx
Normal Started 30s kubelet Started container nginx
Here you can see configuration information about the container(s) and Pod (labels, resource requirements, etc.), as well as status information about the container(s) and Pod (state, readiness, restart count, events, etc.).
The container state is one of Waiting, Running, or Terminated. Depending on the state, additional information will be provided -- here you can see that for a container in Running state, the system tells you when the container started.
Ready tells you whether the container passed its last readiness probe. (In this case, the container does not have a readiness probe configured; the container is assumed to be ready if no readiness probe is configured.)
Restart Count tells you how many times the container has been restarted; this information can be useful for detecting crash loops in containers that are configured with a restart policy of 'always.'
Currently the only Condition associated with a Pod is the binary Ready condition, which indicates that the pod is able to service requests and should be added to the load balancing pools of all matching services.
Lastly, you see a log of recent events related to your Pod. "From" indicates the component that is logging the event. "Reason" and "Message" tell you what happened.
Example: debugging Pending Pods
A common scenario that you can detect using events is when you've created a Pod that won't fit on any node. For example, the Pod might request more resources than are free on any node, or it might specify a label selector that doesn't match any nodes. Let's say we created the previous Deployment with 5 replicas (instead of 2) and requesting 600 millicores instead of 500, on a four-node cluster where each (virtual) machine has 1 CPU. In that case one of the Pods will not be able to schedule. (Note that because of the cluster addon pods such as fluentd, skydns, etc., that run on each node, if we requested 1000 millicores then none of the Pods would be able to schedule.)
kubectl get pods
NAME READY STATUS RESTARTS AGE
nginx-deployment-1006230814-6winp 1/1 Running 0 7m
nginx-deployment-1006230814-fmgu3 1/1 Running 0 7m
nginx-deployment-1370807587-6ekbw 1/1 Running 0 1m
nginx-deployment-1370807587-fg172 0/1 Pending 0 1m
nginx-deployment-1370807587-fz9sd 0/1 Pending 0 1m
To find out why the nginx-deployment-1370807587-fz9sd pod is not running, we can use kubectl describe pod on the pending Pod and look at its events:
kubectl describe pod nginx-deployment-1370807587-fz9sd
Name: nginx-deployment-1370807587-fz9sd
Namespace: default
Node: /
Labels: app=nginx,pod-template-hash=1370807587
Status: Pending
IP:
Controllers: ReplicaSet/nginx-deployment-1370807587
Containers:
nginx:
Image: nginx
Port: 80/TCP
QoS Tier:
memory: Guaranteed
cpu: Guaranteed
Limits:
cpu: 1
memory: 128Mi
Requests:
cpu: 1
memory: 128Mi
Environment Variables:
Volumes:
default-token-4bcbi:
Type: Secret (a volume populated by a Secret)
SecretName: default-token-4bcbi
Events:
FirstSeen LastSeen Count From SubobjectPath Type Reason Message
--------- -------- ----- ---- ------------- -------- ------ -------
1m 48s 7 {default-scheduler } Warning FailedScheduling pod (nginx-deployment-1370807587-fz9sd) failed to fit in any node
fit failure on node (kubernetes-node-6ta5): Node didn't have enough resource: CPU, requested: 1000, used: 1420, capacity: 2000
fit failure on node (kubernetes-node-wul5): Node didn't have enough resource: CPU, requested: 1000, used: 1100, capacity: 2000
Here you can see the event generated by the scheduler saying that the Pod failed to schedule for reason FailedScheduling (and possibly others). The message tells us that there were not enough resources for the Pod on any of the nodes.
To correct this situation, you can use kubectl scale to update your Deployment to specify four or fewer replicas. (Or you could leave the one Pod pending, which is harmless.)
Events such as the ones you saw at the end of kubectl describe pod are persisted in etcd and provide high-level information on what is happening in the cluster. To list all events you can use
kubectl get events
but you have to remember that events are namespaced. This means that if you're interested in events for some namespaced object (e.g. what happened with Pods in namespace my-namespace) you need to explicitly provide a namespace to the command:
kubectl get events --namespace=my-namespace
To see events from all namespaces, you can use the --all-namespaces argument.
In addition to kubectl describe pod, another way to get extra information about a pod (beyond what is provided by kubectl get pod) is to pass the -o yaml output format flag to kubectl get pod. This will give you, in YAML format, even more information than kubectl describe pod--essentially all of the information the system has about the Pod. Here you will see things like annotations (which are key-value metadata without the label restrictions, that is used internally by Kubernetes system components), restart policy, ports, and volumes.
kubectl get pod nginx-deployment-1006230814-6winp -o yaml
If the container image includes
debugging utilities, as is the case with images built from Linux and Windows OS
base images, you can run commands inside a specific container with
kubectl exec:
Ephemeral containers
are useful for interactive troubleshooting when kubectl exec is insufficient
because a container has crashed or a container image doesn't include debugging
utilities, such as with distroless images.
Example debugging using ephemeral containers
You can use the kubectl debug command to add ephemeral containers to a
running Pod. First, create a pod for the example:
kubectl run ephemeral-demo --image=registry.k8s.io/pause:3.1 --restart=Never
The examples in this section use the pause container image because it does not
contain debugging utilities, but this method works with all container
images.
If you attempt to use kubectl exec to create a shell you will see an error
because there is no shell in this container image.
kubectl exec -it ephemeral-demo -- sh
OCI runtime exec failed: exec failed: container_linux.go:346: starting container process caused "exec: \"sh\": executable file not found in $PATH": unknown
You can instead add a debugging container using kubectl debug. If you
specify the -i/--interactive argument, kubectl will automatically attach
to the console of the Ephemeral Container.
Defaulting debug container name to debugger-8xzrl.
If you don't see a command prompt, try pressing enter.
/ #
This command adds a new busybox container and attaches to it. The --target
parameter targets the process namespace of another container. It's necessary
here because kubectl run does not enable process namespace sharing in the pod it
creates.
Note: The --target parameter must be supported by the Container Runtime. When not supported,
the Ephemeral Container may not be started, or it may be started with an
isolated process namespace so that ps does not reveal processes in other
containers.
You can view the state of the newly created ephemeral container using kubectl describe:
Use kubectl delete to remove the Pod when you're finished:
kubectl delete pod ephemeral-demo
Debugging using a copy of the Pod
Sometimes Pod configuration options make it difficult to troubleshoot in certain
situations. For example, you can't run kubectl exec to troubleshoot your
container if your container image does not include a shell or if your application
crashes on startup. In these situations you can use kubectl debug to create a
copy of the Pod with configuration values changed to aid debugging.
Copying a Pod while adding a new container
Adding a new container can be useful when your application is running but not
behaving as you expect and you'd like to add additional troubleshooting
utilities to the Pod.
For example, maybe your application's container images are built on busybox
but you need debugging utilities not included in busybox. You can simulate
this scenario using kubectl run:
kubectl run myapp --image=busybox:1.28 --restart=Never -- sleep 1d
Run this command to create a copy of myapp named myapp-debug that adds a
new Ubuntu container for debugging:
Defaulting debug container name to debugger-w7xmf.
If you don't see a command prompt, try pressing enter.
root@myapp-debug:/#
Note:
kubectl debug automatically generates a container name if you don't choose
one using the --container flag.
The -i flag causes kubectl debug to attach to the new container by
default. You can prevent this by specifying --attach=false. If your session
becomes disconnected you can reattach using kubectl attach.
The --share-processes allows the containers in this Pod to see processes
from the other containers in the Pod. For more information about how this
works, see Share Process Namespace between Containers in a Pod.
Don't forget to clean up the debugging Pod when you're finished with it:
kubectl delete pod myapp myapp-debug
Copying a Pod while changing its command
Sometimes it's useful to change the command for a container, for example to
add a debugging flag or because the application is crashing.
To simulate a crashing application, use kubectl run to create a container
that immediately exits:
kubectl run --image=busybox:1.28 myapp -- false
You can see using kubectl describe pod myapp that this container is crashing:
You can use kubectl debug to create a copy of this Pod with the command
changed to an interactive shell:
kubectl debug myapp -it --copy-to=myapp-debug --container=myapp -- sh
If you don't see a command prompt, try pressing enter.
/ #
Now you have an interactive shell that you can use to perform tasks like
checking filesystem paths or running the container command manually.
Note:
To change the command of a specific container you must
specify its name using --container or kubectl debug will instead
create a new container to run the command you specified.
The -i flag causes kubectl debug to attach to the container by default.
You can prevent this by specifying --attach=false. If your session becomes
disconnected you can reattach using kubectl attach.
Don't forget to clean up the debugging Pod when you're finished with it:
kubectl delete pod myapp myapp-debug
Copying a Pod while changing container images
In some situations you may want to change a misbehaving Pod from its normal
production container images to an image containing a debugging build or
additional utilities.
As an example, create a Pod using kubectl run:
kubectl run myapp --image=busybox:1.28 --restart=Never -- sleep 1d
Now use kubectl debug to make a copy and change its container image
to ubuntu:
The syntax of --set-image uses the same container_name=image syntax as
kubectl set image. *=ubuntu means change the image of all containers
to ubuntu.
Don't forget to clean up the debugging Pod when you're finished with it:
kubectl delete pod myapp myapp-debug
Debugging via a shell on the node
If none of these approaches work, you can find the Node on which the Pod is
running and create a Pod running on the Node. To create
an interactive shell on a Node using kubectl debug, run:
kubectl debug node/mynode -it --image=ubuntu
Creating debugging pod node-debugger-mynode-pdx84 with container debugger on node mynode.
If you don't see a command prompt, try pressing enter.
root@ek8s:/#
When creating a debugging session on a node, keep in mind that:
kubectl debug automatically generates the name of the new Pod based on
the name of the Node.
The root filesystem of the Node will be mounted at /host.
The container runs in the host IPC, Network, and PID namespaces, although
the pod isn't privileged, so reading some process information may fail,
and chroot /host may fail.
If you need a privileged pod, create it manually.
Don't forget to clean up the debugging Pod when you're finished with it:
kubectl delete pod node-debugger-mynode-pdx84
4.1.7 - Get a Shell to a Running Container
This page shows how to use kubectl exec to get a shell to a
running container.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Note: The double dash (--) separates the arguments you want to pass to the command from the kubectl arguments.
In your shell, list the root directory:
# Run this inside the containerls /
In your shell, experiment with other commands. Here are
some examples:
# You can run these example commands inside the containerls /
cat /proc/mounts
cat /proc/1/maps
apt-get update
apt-get install -y tcpdump
tcpdump
apt-get install -y lsof
lsof
apt-get install -y procps
ps aux
ps aux | grep nginx
Writing the root page for nginx
Look again at the configuration file for your Pod. The Pod
has an emptyDir volume, and the container mounts the volume
at /usr/share/nginx/html.
In your shell, create an index.html file in the /usr/share/nginx/html
directory:
# Run this inside the containerecho'Hello shell demo' > /usr/share/nginx/html/index.html
In your shell, send a GET request to the nginx server:
# Run this in the shell inside your containerapt-get update
apt-get install curl
curl http://localhost/
The output shows the text that you wrote to the index.html file:
Hello shell demo
When you are finished with your shell, enter exit.
exit# To quit the shell in the container
Running individual commands in a container
In an ordinary command window, not your shell, list the environment
variables in the running container:
kubectl exec shell-demo -- env
Experiment with running other commands. Here are some examples:
kubectl exec shell-demo -- ps aux
kubectl exec shell-demo -- ls /
kubectl exec shell-demo -- cat /proc/1/mounts
Opening a shell when a Pod has more than one container
If a Pod has more than one container, use --container or -c to
specify a container in the kubectl exec command. For example,
suppose you have a Pod named my-pod, and the Pod has two containers
named main-app and helper-app. The following command would open a
shell to the main-app container.
This doc is about cluster troubleshooting; we assume you have already ruled out your application as the root cause of the
problem you are experiencing. See
the application troubleshooting guide for tips on application debugging.
You may also visit the troubleshooting overview document for more information.
The first thing to debug in your cluster is if your nodes are all registered correctly.
Run the following command:
kubectl get nodes
And verify that all of the nodes you expect to see are present and that they are all in the Ready state.
To get detailed information about the overall health of your cluster, you can run:
kubectl cluster-info dump
Example: debugging a down/unreachable node
Sometimes when debugging it can be useful to look at the status of a node -- for example, because
you've noticed strange behavior of a Pod that's running on the node, or to find out why a Pod
won't schedule onto the node. As with Pods, you can use kubectl describe node and kubectl get node -o yaml to retrieve detailed information about nodes. For example, here's what you'll see if
a node is down (disconnected from the network, or kubelet dies and won't restart, etc.). Notice
the events that show the node is NotReady, and also notice that the pods are no longer running
(they are evicted after five minutes of NotReady status).
kubectl get nodes
NAME STATUS ROLES AGE VERSION
kube-worker-1 NotReady <none> 1h v1.23.3
kubernetes-node-bols Ready <none> 1h v1.23.3
kubernetes-node-st6x Ready <none> 1h v1.23.3
kubernetes-node-unaj Ready <none> 1h v1.23.3
kubectl describe node kube-worker-1
Name: kube-worker-1
Roles: <none>
Labels: beta.kubernetes.io/arch=amd64
beta.kubernetes.io/os=linux
kubernetes.io/arch=amd64
kubernetes.io/hostname=kube-worker-1
kubernetes.io/os=linux
Annotations: kubeadm.alpha.kubernetes.io/cri-socket: /run/containerd/containerd.sock
node.alpha.kubernetes.io/ttl: 0
volumes.kubernetes.io/controller-managed-attach-detach: true
CreationTimestamp: Thu, 17 Feb 2022 16:46:30 -0500
Taints: node.kubernetes.io/unreachable:NoExecute
node.kubernetes.io/unreachable:NoSchedule
Unschedulable: false
Lease:
HolderIdentity: kube-worker-1
AcquireTime: <unset>
RenewTime: Thu, 17 Feb 2022 17:13:09 -0500
Conditions:
Type Status LastHeartbeatTime LastTransitionTime Reason Message
---- ------ ----------------- ------------------ ------ -------
NetworkUnavailable False Thu, 17 Feb 2022 17:09:13 -0500 Thu, 17 Feb 2022 17:09:13 -0500 WeaveIsUp Weave pod has set this
MemoryPressure Unknown Thu, 17 Feb 2022 17:12:40 -0500 Thu, 17 Feb 2022 17:13:52 -0500 NodeStatusUnknown Kubelet stopped posting node status.
DiskPressure Unknown Thu, 17 Feb 2022 17:12:40 -0500 Thu, 17 Feb 2022 17:13:52 -0500 NodeStatusUnknown Kubelet stopped posting node status.
PIDPressure Unknown Thu, 17 Feb 2022 17:12:40 -0500 Thu, 17 Feb 2022 17:13:52 -0500 NodeStatusUnknown Kubelet stopped posting node status.
Ready Unknown Thu, 17 Feb 2022 17:12:40 -0500 Thu, 17 Feb 2022 17:13:52 -0500 NodeStatusUnknown Kubelet stopped posting node status.
Addresses:
InternalIP: 192.168.0.113
Hostname: kube-worker-1
Capacity:
cpu: 2
ephemeral-storage: 15372232Ki
hugepages-2Mi: 0
memory: 2025188Ki
pods: 110
Allocatable:
cpu: 2
ephemeral-storage: 14167048988
hugepages-2Mi: 0
memory: 1922788Ki
pods: 110
System Info:
Machine ID: 9384e2927f544209b5d7b67474bbf92b
System UUID: aa829ca9-73d7-064d-9019-df07404ad448
Boot ID: 5a295a03-aaca-4340-af20-1327fa5dab5c
Kernel Version: 5.13.0-28-generic
OS Image: Ubuntu 21.10
Operating System: linux
Architecture: amd64
Container Runtime Version: containerd://1.5.9
Kubelet Version: v1.23.3
Kube-Proxy Version: v1.23.3
Non-terminated Pods: (4 in total)
Namespace Name CPU Requests CPU Limits Memory Requests Memory Limits Age
--------- ---- ------------ ---------- --------------- ------------- ---
default nginx-deployment-67d4bdd6f5-cx2nz 500m (25%) 500m (25%) 128Mi (6%) 128Mi (6%) 23m
default nginx-deployment-67d4bdd6f5-w6kd7 500m (25%) 500m (25%) 128Mi (6%) 128Mi (6%) 23m
kube-system kube-proxy-dnxbz 0 (0%) 0 (0%) 0 (0%) 0 (0%) 28m
kube-system weave-net-gjxxp 100m (5%) 0 (0%) 200Mi (10%) 0 (0%) 28m
Allocated resources:
(Total limits may be over 100 percent, i.e., overcommitted.)
Resource Requests Limits
-------- -------- ------
cpu 1100m (55%) 1 (50%)
memory 456Mi (24%) 256Mi (13%)
ephemeral-storage 0 (0%) 0 (0%)
hugepages-2Mi 0 (0%) 0 (0%)
Events:
...
kubectl get node kube-worker-1 -o yaml
apiVersion:v1kind:Nodemetadata:annotations:kubeadm.alpha.kubernetes.io/cri-socket:/run/containerd/containerd.socknode.alpha.kubernetes.io/ttl:"0"volumes.kubernetes.io/controller-managed-attach-detach:"true"creationTimestamp:"2022-02-17T21:46:30Z"labels:beta.kubernetes.io/arch:amd64beta.kubernetes.io/os:linuxkubernetes.io/arch:amd64kubernetes.io/hostname:kube-worker-1kubernetes.io/os:linuxname:kube-worker-1resourceVersion:"4026"uid:98efe7cb-2978-4a0b-842a-1a7bf12c05f8spec:{}status:addresses:- address:192.168.0.113type:InternalIP- address:kube-worker-1type:Hostnameallocatable:cpu:"2"ephemeral-storage:"14167048988"hugepages-2Mi:"0"memory:1922788Kipods:"110"capacity:cpu:"2"ephemeral-storage:15372232Kihugepages-2Mi:"0"memory:2025188Kipods:"110"conditions:- lastHeartbeatTime:"2022-02-17T22:20:32Z"lastTransitionTime:"2022-02-17T22:20:32Z"message:Weave pod has set thisreason:WeaveIsUpstatus:"False"type:NetworkUnavailable- lastHeartbeatTime:"2022-02-17T22:20:15Z"lastTransitionTime:"2022-02-17T22:13:25Z"message:kubelet has sufficient memory availablereason:KubeletHasSufficientMemorystatus:"False"type:MemoryPressure- lastHeartbeatTime:"2022-02-17T22:20:15Z"lastTransitionTime:"2022-02-17T22:13:25Z"message:kubelet has no disk pressurereason:KubeletHasNoDiskPressurestatus:"False"type:DiskPressure- lastHeartbeatTime:"2022-02-17T22:20:15Z"lastTransitionTime:"2022-02-17T22:13:25Z"message:kubelet has sufficient PID availablereason:KubeletHasSufficientPIDstatus:"False"type:PIDPressure- lastHeartbeatTime:"2022-02-17T22:20:15Z"lastTransitionTime:"2022-02-17T22:15:15Z"message:kubelet is posting ready status. AppArmor enabledreason:KubeletReadystatus:"True"type:ReadydaemonEndpoints:kubeletEndpoint:Port:10250nodeInfo:architecture:amd64bootID:22333234-7a6b-44d4-9ce1-67e31dc7e369containerRuntimeVersion:containerd://1.5.9kernelVersion:5.13.0-28-generickubeProxyVersion:v1.23.3kubeletVersion:v1.23.3machineID:9384e2927f544209b5d7b67474bbf92boperatingSystem:linuxosImage:Ubuntu 21.10systemUUID:aa829ca9-73d7-064d-9019-df07404ad448
Looking at logs
For now, digging deeper into the cluster requires logging into the relevant machines. Here are the locations
of the relevant log files. On systemd-based systems, you may need to use journalctl instead of examining log files.
Control Plane nodes
/var/log/kube-apiserver.log - API Server, responsible for serving the API
/var/log/kube-scheduler.log - Scheduler, responsible for making scheduling decisions
/var/log/kube-controller-manager.log - a component that runs most Kubernetes built-in
controllers, with the notable exception of scheduling
(the kube-scheduler handles scheduling).
Worker Nodes
/var/log/kubelet.log - logs from the kubelet, responsible for running containers on the node
/var/log/kube-proxy.log - logs from kube-proxy, which is responsible for directing traffic to Service endpoints
Cluster failure modes
This is an incomplete list of things that could go wrong, and how to adjust your cluster setup to mitigate the problems.
Contributing causes
VM(s) shutdown
Network partition within cluster, or between cluster and users
Crashes in Kubernetes software
Data loss or unavailability of persistent storage (e.g. GCE PD or AWS EBS volume)
Operator error, for example misconfigured Kubernetes software or application software
Specific scenarios
API server VM shutdown or apiserver crashing
Results
unable to stop, update, or start new pods, services, replication controller
existing pods and services should continue to work normally, unless they depend on the Kubernetes API
API server backing storage lost
Results
the kube-apiserver component fails to start successfully and become healthy
kubelets will not be able to reach it but will continue to run the same pods and provide the same service proxying
manual recovery or recreation of apiserver state necessary before apiserver is restarted
Supporting services (node controller, replication controller manager, scheduler, etc) VM shutdown or crashes
currently those are colocated with the apiserver, and their unavailability has similar consequences as apiserver
in future, these will be replicated as well and may not be co-located
they do not have their own persistent state
Individual node (VM or physical machine) shuts down
Results
pods on that Node stop running
Network partition
Results
partition A thinks the nodes in partition B are down; partition B thinks the apiserver is down.
(Assuming the master VM ends up in partition A.)
Kubelet software fault
Results
crashing kubelet cannot start new pods on the node
kubelet might delete the pods or not
node marked unhealthy
replication controllers start new pods elsewhere
Cluster operator error
Results
loss of pods, services, etc
lost of apiserver backing store
users unable to read API
etc.
Mitigations
Action: Use IaaS provider's automatic VM restarting feature for IaaS VMs
Mitigates: Apiserver VM shutdown or apiserver crashing
Mitigates: Supporting services VM shutdown or crashes
Action: Use IaaS providers reliable storage (e.g. GCE PD or AWS EBS volume) for VMs with apiserver+etcd
This documentation is about investigating and diagnosing
kubectl related issues.
If you encounter issues accessing kubectl or connecting to your cluster, this
document outlines various common scenarios and potential solutions to help
identify and address the likely cause.
Before you begin
You need to have a Kubernetes cluster.
You also need to have kubectl installed - see install tools
Verify kubectl setup
Make sure you have installed and configured kubectl correctly on your local machine.
Check the kubectl version to ensure it is up-to-date and compatible with your cluster.
If you see Unable to connect to the server: dial tcp <server-ip>:8443: i/o timeout,
instead of Server Version, you need to troubleshoot kubectl connectivity with your cluster.
The kubectl requires a kubeconfig file to connect to a Kubernetes cluster. The
kubeconfig file is usually located under the ~/.kube/config directory. Make sure
that you have a valid kubeconfig file. If you don't have a kubeconfig file, you can
obtain it from your Kubernetes administrator, or you can copy it from your Kubernetes
control plane's /etc/kubernetes/admin.conf directory. If you have deployed your
Kubernetes cluster on a cloud platform and lost your kubeconfig file, you can
re-generate it using your cloud provider's tools. Refer the cloud provider's
documentation for re-generating a kubeconfig file.
Check if the $KUBECONFIG environment variable is configured correctly. You can set
$KUBECONFIGenvironment variable or use the --kubeconfig parameter with the kubectl
to specify the directory of a kubeconfig file.
Check VPN connectivity
If you are using a Virtual Private Network (VPN) to access your Kubernetes cluster,
make sure that your VPN connection is active and stable. Sometimes, VPN disconnections
can lead to connection issues with the cluster. Reconnect to the VPN and try accessing
the cluster again.
Authentication and authorization
If you are using the token based authentication and the kubectl is returning an error
regarding the authentication token or authentication server address, validate the
Kubernetes authentication token and the authentication server address are configured
properly.
If kubectl is returning an error regarding the authorization, make sure that you are
using the valid user credentials. And you have the permission to access the resource
that you have requested.
Verify contexts
Kubernetes supports multiple clusters and contexts.
Ensure that you are using the correct context to interact with your cluster.
List available contexts:
kubectl config get-contexts
Switch to the appropriate context:
kubectl config use-context <context-name>
API server and load balancer
The kube-apiserver server is the
central component of a Kubernetes cluster. If the API server or the load balancer that
runs in front of your API servers is not reachable or not responding, you won't be able
to interact with the cluster.
Check the if the API server's host is reachable by using ping command. Check cluster's
network connectivity and firewall. If your are using a cloud provider for deploying
the cluster, check your cloud provider's health check status for the cluster's
API server.
Verify the status of the load balancer (if used) to ensure it is healthy and forwarding
traffic to the API server.
TLS problems
The Kubernetes API server only serves HTTPS requests by default. In that case TLS problems
may occur due to various reasons, such as certificate expiry or chain of trust validity.
You can find the TLS certificate in the kubeconfig file, located in the ~/.kube/config
directory. The certificate-authority attribute contains the CA certificate and the
client-certificate attribute contains the client certificate.
Some kubectl authentication helpers provide easy access to Kubernetes clusters. If you
have used such helpers and are facing connectivity issues, ensure that the necessary
configurations are still present.
Check kubectl configuration for authentication details:
kubectl config view
If you previously used a helper tool (for example, kubectl-oidc-login), ensure that it is still
installed and configured correctly.
4.2.2 - Resource metrics pipeline
For Kubernetes, the Metrics API offers a basic set of metrics to support automatic scaling and
similar use cases. This API makes information available about resource usage for node and pod,
including metrics for CPU and memory. If you deploy the Metrics API into your cluster, clients of
the Kubernetes API can then query for this information, and you can use Kubernetes' access control
mechanisms to manage permissions to do so.
You can also view the resource metrics using the
kubectl top
command.
Note: The Metrics API, and the metrics pipeline that it enables, only offers the minimum
CPU and memory metrics to enable automatic scaling using HPA and / or VPA.
If you would like to provide a more complete set of metrics, you can complement
the simpler Metrics API by deploying a second
metrics pipeline
that uses the Custom Metrics API.
Figure 1 illustrates the architecture of the resource metrics pipeline.
Figure 1. Resource Metrics Pipeline
The architecture components, from right to left in the figure, consist of the following:
cAdvisor: Daemon for collecting, aggregating and exposing
container metrics included in Kubelet.
kubelet: Node agent for managing container
resources. Resource metrics are accessible using the /metrics/resource and /stats kubelet
API endpoints.
node level resource metrics: API provided by the kubelet for discovering and retrieving
per-node summarized stats available through the /metrics/resource endpoint.
metrics-server: Cluster addon component that collects and aggregates resource
metrics pulled from each kubelet. The API server serves Metrics API for use by HPA, VPA, and by
the kubectl top command. Metrics Server is a reference implementation of the Metrics API.
Metrics API: Kubernetes API supporting access to CPU and memory used for
workload autoscaling. To make this work in your cluster, you need an API extension server that
provides the Metrics API.
Note: cAdvisor supports reading metrics from cgroups, which works with typical container runtimes on Linux.
If you use a container runtime that uses another resource isolation mechanism, for example
virtualization, then that container runtime must support
CRI Container Metrics
in order for metrics to be available to the kubelet.
Metrics API
FEATURE STATE:Kubernetes 1.8 [beta]
The metrics-server implements the Metrics API. This API allows you to access CPU and memory usage
for the nodes and pods in your cluster. Its primary role is to feed resource usage metrics to K8s
autoscaler components.
Here is an example of the Metrics API request for a minikube node piped through jq for easier
reading:
kubectl get --raw "/apis/metrics.k8s.io/v1beta1/nodes/minikube" | jq '.'
Here is an example of the Metrics API request for a kube-scheduler-minikube pod contained in the
kube-system namespace and piped through jq for easier reading:
kubectl get --raw "/apis/metrics.k8s.io/v1beta1/namespaces/kube-system/pods/kube-scheduler-minikube" | jq '.'
Note: You must deploy the metrics-server or alternative adapter that serves the Metrics API to be able
to access it.
Measuring resource usage
CPU
CPU is reported as the average core usage measured in cpu units. One cpu, in Kubernetes, is
equivalent to 1 vCPU/Core for cloud providers, and 1 hyper-thread on bare-metal Intel processors.
This value is derived by taking a rate over a cumulative CPU counter provided by the kernel (in
both Linux and Windows kernels). The time window used to calculate CPU is shown under window field
in Metrics API.
To learn more about how Kubernetes allocates and measures CPU resources, see
meaning of CPU.
Memory
Memory is reported as the working set, measured in bytes, at the instant the metric was collected.
In an ideal world, the "working set" is the amount of memory in-use that cannot be freed under
memory pressure. However, calculation of the working set varies by host OS, and generally makes
heavy use of heuristics to produce an estimate.
The Kubernetes model for a container's working set expects that the container runtime counts
anonymous memory associated with the container in question. The working set metric typically also
includes some cached (file-backed) memory, because the host OS cannot always reclaim pages.
To learn more about how Kubernetes allocates and measures memory resources, see
meaning of memory.
Metrics Server
The metrics-server fetches resource metrics from the kubelets and exposes them in the Kubernetes
API server through the Metrics API for use by the HPA and VPA. You can also view these metrics
using the kubectl top command.
The metrics-server uses the Kubernetes API to track nodes and pods in your cluster. The
metrics-server queries each node over HTTP to fetch metrics. The metrics-server also builds an
internal view of pod metadata, and keeps a cache of pod health. That cached pod health information
is available via the extension API that the metrics-server makes available.
For example with an HPA query, the metrics-server needs to identify which pods fulfill the label
selectors in the deployment.
The metrics-server calls the kubelet API
to collect metrics from each node. Depending on the metrics-server version it uses:
Metrics resource endpoint /metrics/resource in version v0.6.0+ or
Summary API endpoint /stats/summary in older versions
To learn about how the kubelet serves node metrics, and how you can access those via
the Kubernetes API, read Node Metrics Data.
4.2.3 - Tools for Monitoring Resources
To scale an application and provide a reliable service, you need to
understand how the application behaves when it is deployed. You can examine
application performance in a Kubernetes cluster by examining the containers,
pods,
services, and
the characteristics of the overall cluster. Kubernetes provides detailed
information about an application's resource usage at each of these levels.
This information allows you to evaluate your application's performance and
where bottlenecks can be removed to improve overall performance.
In Kubernetes, application monitoring does not depend on a single monitoring solution.
On new clusters, you can use resource metrics or
full metrics pipelines to collect monitoring statistics.
Resource metrics pipeline
The resource metrics pipeline provides a limited set of metrics related to
cluster components such as the
Horizontal Pod Autoscaler
controller, as well as the kubectl top utility.
These metrics are collected by the lightweight, short-term, in-memory
metrics-server and
are exposed via the metrics.k8s.io API.
metrics-server discovers all nodes on the cluster and
queries each node's
kubelet for CPU and
memory usage. The kubelet acts as a bridge between the Kubernetes master and
the nodes, managing the pods and containers running on a machine. The kubelet
translates each pod into its constituent containers and fetches individual
container usage statistics from the container runtime through the container
runtime interface. If you use a container runtime that uses Linux cgroups and
namespaces to implement containers, and the container runtime does not publish
usage statistics, then the kubelet can look up those statistics directly
(using code from cAdvisor).
No matter how those statistics arrive, the kubelet then exposes the aggregated pod
resource usage statistics through the metrics-server Resource Metrics API.
This API is served at /metrics/resource/v1beta1 on the kubelet's authenticated and
read-only ports.
Full metrics pipeline
A full metrics pipeline gives you access to richer metrics. Kubernetes can
respond to these metrics by automatically scaling or adapting the cluster
based on its current state, using mechanisms such as the Horizontal Pod
Autoscaler. The monitoring pipeline fetches metrics from the kubelet and
then exposes them to Kubernetes via an adapter by implementing either the
custom.metrics.k8s.io or external.metrics.k8s.io API.
If you glance over at the
CNCF Landscape,
you can see a number of monitoring projects that can work with Kubernetes by scraping
metric data and using that to help you observe your cluster. It is up to you to select the tool
or tools that suit your needs. The CNCF landscape for observability and analytics includes a
mix of open-source software, paid-for software-as-a-service, and other commercial products.
When you design and implement a full metrics pipeline you can make that monitoring data
available back to Kubernetes. For example, a HorizontalPodAutoscaler can use the processed
metrics to work out how many Pods to run for a component of your workload.
Integration of a full metrics pipeline into your Kubernetes implementation is outside
the scope of Kubernetes documentation because of the very wide scope of possible
solutions.
The choice of monitoring platform depends heavily on your needs, budget, and technical resources.
Kubernetes does not recommend any specific metrics pipeline; many options are available.
Your monitoring system should be capable of handling the OpenMetrics metrics
transmission standard, and needs to chosen to best fit in to your overall design and deployment of
your infrastructure platform.
What's next
Learn about additional debugging tools, including:
Node Problem Detector is a daemon for monitoring and reporting about a node's health.
You can run Node Problem Detector as a DaemonSet or as a standalone daemon.
Node Problem Detector collects information about node problems from various daemons
and reports these conditions to the API server as Node Conditions
or as Events.
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Node Problem Detector uses the kernel log format for reporting kernel issues.
To learn how to extend the kernel log format, see Add support for another log format.
Enabling Node Problem Detector
Some cloud providers enable Node Problem Detector as an Addon.
You can also enable Node Problem Detector with kubectl or by creating an Addon DaemonSet.
Using kubectl to enable Node Problem Detector
kubectl provides the most flexible management of Node Problem Detector.
You can overwrite the default configuration to fit it into your environment or
to detect customized node problems. For example:
Create a Node Problem Detector configuration similar to node-problem-detector.yaml:
Using an Addon pod to enable Node Problem Detector
If you are using a custom cluster bootstrap solution and don't need
to overwrite the default configuration, you can leverage the Addon pod to
further automate the deployment.
Create node-problem-detector.yaml, and save the configuration in the Addon pod's
directory /etc/kubernetes/addons/node-problem-detector on a control plane node.
Overwrite the configuration
The default configuration
is embedded when building the Docker image of Node Problem Detector.
However, you can use a ConfigMap
to overwrite the configuration:
apiVersion:apps/v1kind:DaemonSetmetadata:name:node-problem-detector-v0.1namespace:kube-systemlabels:k8s-app:node-problem-detectorversion:v0.1kubernetes.io/cluster-service:"true"spec:selector:matchLabels:k8s-app:node-problem-detector version:v0.1kubernetes.io/cluster-service:"true"template:metadata:labels:k8s-app:node-problem-detectorversion:v0.1kubernetes.io/cluster-service:"true"spec:hostNetwork:truecontainers:- name:node-problem-detectorimage:registry.k8s.io/node-problem-detector:v0.1securityContext:privileged:trueresources:limits:cpu:"200m"memory:"100Mi"requests:cpu:"20m"memory:"20Mi"volumeMounts:- name:logmountPath:/logreadOnly:true- name:config# Overwrite the config/ directory with ConfigMap volumemountPath:/configreadOnly:truevolumes:- name:loghostPath:path:/var/log/- name:config# Define ConfigMap volumeconfigMap:name:node-problem-detector-config
Recreate the Node Problem Detector with the new configuration file:
# If you have a node-problem-detector running, delete before recreatingkubectl delete -f https://k8s.io/examples/debug/node-problem-detector.yaml
kubectl apply -f https://k8s.io/examples/debug/node-problem-detector-configmap.yaml
Note: This approach only applies to a Node Problem Detector started with kubectl.
Overwriting a configuration is not supported if a Node Problem Detector runs as a cluster Addon.
The Addon manager does not support ConfigMap.
Problem Daemons
A problem daemon is a sub-daemon of the Node Problem Detector. It monitors specific kinds of node
problems and reports them to the Node Problem Detector.
There are several types of supported problem daemons.
A SystemLogMonitor type of daemon monitors the system logs and reports problems and metrics
according to predefined rules. You can customize the configurations for different log sources
such as filelog,
kmsg,
kernel,
abrt,
and systemd.
A SystemStatsMonitor type of daemon collects various health-related system stats as metrics.
You can customize its behavior by updating its
configuration file.
A CustomPluginMonitor type of daemon invokes and checks various node problems by running
user-defined scripts. You can use different custom plugin monitors to monitor different
problems and customize the daemon behavior by updating the
configuration file.
A HealthChecker type of daemon checks the health of the kubelet and container runtime on a node.
Adding support for other log format
The system log monitor currently supports file-based logs, journald, and kmsg.
Additional sources can be added by implementing a new
log watcher.
Adding custom plugin monitors
You can extend the Node Problem Detector to execute any monitor scripts written in any language by
developing a custom plugin. The monitor scripts must conform to the plugin protocol in exit code
and standard output. For more information, please refer to the
plugin interface proposal.
Exporter
An exporter reports the node problems and/or metrics to certain backends.
The following exporters are supported:
Kubernetes exporter: this exporter reports node problems to the Kubernetes API server.
Temporary problems are reported as Events and permanent problems are reported as Node Conditions.
Prometheus exporter: this exporter reports node problems and metrics locally as Prometheus
(or OpenMetrics) metrics. You can specify the IP address and port for the exporter using command
line arguments.
Stackdriver exporter: this exporter reports node problems and metrics to the Stackdriver
Monitoring API. The exporting behavior can be customized using a
configuration file.
Recommendations and restrictions
It is recommended to run the Node Problem Detector in your cluster to monitor node health.
When running the Node Problem Detector, you can expect extra resource overhead on each node.
Usually this is fine, because:
The kernel log grows relatively slowly.
A resource limit is set for the Node Problem Detector.
Even under high load, the resource usage is acceptable. For more information, see the Node Problem Detector
benchmark result.
4.2.5 - Debugging Kubernetes nodes with crictl
FEATURE STATE:Kubernetes v1.11 [stable]
crictl is a command-line interface for CRI-compatible container runtimes.
You can use it to inspect and debug container runtimes and applications on a
Kubernetes node. crictl and its source are hosted in the
cri-tools repository.
Before you begin
crictl requires a Linux operating system with a CRI runtime.
Installing crictl
You can download a compressed archive crictl from the cri-tools
release page, for several
different architectures. Download the version that corresponds to your version
of Kubernetes. Extract it and move it to a location on your system path, such as
/usr/local/bin/.
General usage
The crictl command has several subcommands and runtime flags. Use
crictl help or crictl <subcommand> help for more details.
You can set the endpoint for crictl by doing one of the following:
Set the --runtime-endpoint and --image-endpoint flags.
Set the CONTAINER_RUNTIME_ENDPOINT and IMAGE_SERVICE_ENDPOINT environment
variables.
Set the endpoint in the configuration file /etc/crictl.yaml. To specify a
different file, use the --config=PATH_TO_FILE flag when you run crictl.
Note: If you don't set an endpoint, crictl attempts to connect to a list of known
endpoints, which might result in an impact to performance.
You can also specify timeout values when connecting to the server and enable or
disable debugging, by specifying timeout or debug values in the configuration
file or using the --timeout and --debug command-line flags.
To view or edit the current configuration, view or edit the contents of
/etc/crictl.yaml. For example, the configuration when using the containerd
container runtime would be similar to this:
The following examples show some crictl commands and example output.
Warning: If you use crictl to create pod sandboxes or containers on a running
Kubernetes cluster, the Kubelet will eventually delete them. crictl is not a
general purpose workflow tool, but a tool that is useful for debugging.
List pods
List all pods:
crictl pods
The output is similar to this:
POD ID CREATED STATE NAME NAMESPACE ATTEMPT
926f1b5a1d33a About a minute ago Ready sh-84d7dcf559-4r2gq default 0
4dccb216c4adb About a minute ago Ready nginx-65899c769f-wv2gp default 0
a86316e96fa89 17 hours ago Ready kube-proxy-gblk4 kube-system 0
919630b8f81f1 17 hours ago Ready nvidia-device-plugin-zgbbv kube-system 0
List pods by name:
crictl pods --name nginx-65899c769f-wv2gp
The output is similar to this:
POD ID CREATED STATE NAME NAMESPACE ATTEMPT
4dccb216c4adb 2 minutes ago Ready nginx-65899c769f-wv2gp default 0
List pods by label:
crictl pods --label run=nginx
The output is similar to this:
POD ID CREATED STATE NAME NAMESPACE ATTEMPT
4dccb216c4adb 2 minutes ago Ready nginx-65899c769f-wv2gp default 0
CONTAINER ID IMAGE CREATED STATE NAME ATTEMPT
1f73f2d81bf98 busybox@sha256:141c253bc4c3fd0a201d32dc1f493bcf3fff003b6df416dea4f41046e0f37d47 7 minutes ago Running sh 1
9c5951df22c78 busybox@sha256:141c253bc4c3fd0a201d32dc1f493bcf3fff003b6df416dea4f41046e0f37d47 8 minutes ago Exited sh 0
87d3992f84f74 nginx@sha256:d0a8828cccb73397acb0073bf34f4d7d8aa315263f1e7806bf8c55d8ac139d5f 8 minutes ago Running nginx 0
1941fb4da154f k8s-gcrio.azureedge.net/hyperkube-amd64@sha256:00d814b1f7763f4ab5be80c58e98140dfc69df107f253d7fdd714b30a714260a 18 hours ago Running kube-proxy 0
List running containers:
crictl ps
The output is similar to this:
CONTAINER ID IMAGE CREATED STATE NAME ATTEMPT
1f73f2d81bf98 busybox@sha256:141c253bc4c3fd0a201d32dc1f493bcf3fff003b6df416dea4f41046e0f37d47 6 minutes ago Running sh 1
87d3992f84f74 nginx@sha256:d0a8828cccb73397acb0073bf34f4d7d8aa315263f1e7806bf8c55d8ac139d5f 7 minutes ago Running nginx 0
1941fb4da154f k8s-gcrio.azureedge.net/hyperkube-amd64@sha256:00d814b1f7763f4ab5be80c58e98140dfc69df107f253d7fdd714b30a714260a 17 hours ago Running kube-proxy 0
Using crictl to run a pod sandbox is useful for debugging container runtimes.
On a running Kubernetes cluster, the sandbox will eventually be stopped and
deleted by the Kubelet.
Use the crictl runp command to apply the JSON and run the sandbox.
crictl runp pod-config.json
The ID of the sandbox is returned.
Create a container
Using crictl to create a container is useful for debugging container runtimes.
On a running Kubernetes cluster, the sandbox will eventually be stopped and
deleted by the Kubelet.
Pull a busybox image
crictl pull busybox
Image is up to date for busybox@sha256:141c253bc4c3fd0a201d32dc1f493bcf3fff003b6df416dea4f41046e0f37d47
Create the container, passing the ID of the previously-created pod, the
container config file, and the pod config file. The ID of the container is
returned.
Kubernetes auditing provides a security-relevant, chronological set of records documenting
the sequence of actions in a cluster. The cluster audits the activities generated by users,
by applications that use the Kubernetes API, and by the control plane itself.
Auditing allows cluster administrators to answer the following questions:
what happened?
when did it happen?
who initiated it?
on what did it happen?
where was it observed?
from where was it initiated?
to where was it going?
Audit records begin their lifecycle inside the
kube-apiserver
component. Each request on each stage
of its execution generates an audit event, which is then pre-processed according to
a certain policy and written to a backend. The policy determines what's recorded
and the backends persist the records. The current backend implementations
include logs files and webhooks.
Each request can be recorded with an associated stage. The defined stages are:
RequestReceived - The stage for events generated as soon as the audit
handler receives the request, and before it is delegated down the handler
chain.
ResponseStarted - Once the response headers are sent, but before the
response body is sent. This stage is only generated for long-running requests
(e.g. watch).
ResponseComplete - The response body has been completed and no more bytes
will be sent.
The audit logging feature increases the memory consumption of the API server
because some context required for auditing is stored for each request.
Memory consumption depends on the audit logging configuration.
Audit policy
Audit policy defines rules about what events should be recorded and what data
they should include. The audit policy object structure is defined in the
audit.k8s.io API group.
When an event is processed, it's
compared against the list of rules in order. The first matching rule sets the
audit level of the event. The defined audit levels are:
None - don't log events that match this rule.
Metadata - log request metadata (requesting user, timestamp, resource,
verb, etc.) but not request or response body.
Request - log event metadata and request body but not response body.
This does not apply for non-resource requests.
RequestResponse - log event metadata, request and response bodies.
This does not apply for non-resource requests.
You can pass a file with the policy to kube-apiserver
using the --audit-policy-file flag. If the flag is omitted, no events are logged.
Note that the rules field must be provided in the audit policy file.
A policy with no (0) rules is treated as illegal.
apiVersion:audit.k8s.io/v1# This is required.kind:Policy# Don't generate audit events for all requests in RequestReceived stage.omitStages:- "RequestReceived"rules:# Log pod changes at RequestResponse level- level:RequestResponseresources:- group:""# Resource "pods" doesn't match requests to any subresource of pods,# which is consistent with the RBAC policy.resources:["pods"]# Log "pods/log", "pods/status" at Metadata level- level:Metadataresources:- group:""resources:["pods/log","pods/status"]# Don't log requests to a configmap called "controller-leader"- level:Noneresources:- group:""resources:["configmaps"]resourceNames:["controller-leader"]# Don't log watch requests by the "system:kube-proxy" on endpoints or services- level:Noneusers:["system:kube-proxy"]verbs:["watch"]resources:- group:""# core API groupresources:["endpoints","services"]# Don't log authenticated requests to certain non-resource URL paths.- level:NoneuserGroups:["system:authenticated"]nonResourceURLs:- "/api*"# Wildcard matching.- "/version"# Log the request body of configmap changes in kube-system.- level:Requestresources:- group:""# core API groupresources:["configmaps"]# This rule only applies to resources in the "kube-system" namespace.# The empty string "" can be used to select non-namespaced resources.namespaces:["kube-system"]# Log configmap and secret changes in all other namespaces at the Metadata level.- level:Metadataresources:- group:""# core API groupresources:["secrets","configmaps"]# Log all other resources in core and extensions at the Request level.- level:Requestresources:- group:""# core API group- group:"extensions"# Version of group should NOT be included.# A catch-all rule to log all other requests at the Metadata level.- level:Metadata# Long-running requests like watches that fall under this rule will not# generate an audit event in RequestReceived.omitStages:- "RequestReceived"
You can use a minimal audit policy file to log all requests at the Metadata level:
# Log all requests at the Metadata level.apiVersion:audit.k8s.io/v1kind:Policyrules:- level:Metadata
If you're crafting your own audit profile, you can use the audit profile for Google Container-Optimized OS as a starting point. You can check the
configure-helper.sh
script, which generates an audit policy file. You can see most of the audit policy file by looking directly at the script.
Audit backends persist audit events to an external storage.
Out of the box, the kube-apiserver provides two backends:
Log backend, which writes events into the filesystem
Webhook backend, which sends events to an external HTTP API
In all cases, audit events follow a structure defined by the Kubernetes API in the
audit.k8s.io API group.
Note:
In case of patches, request body is a JSON array with patch operations, not a JSON object
with an appropriate Kubernetes API object. For example, the following request body is a valid patch
request to /apis/batch/v1/namespaces/some-namespace/jobs/some-job-name:
The log backend writes audit events to a file in JSONlines format.
You can configure the log audit backend using the following kube-apiserver flags:
--audit-log-path specifies the log file path that log backend uses to write
audit events. Not specifying this flag disables log backend. - means standard out
--audit-log-maxage defined the maximum number of days to retain old audit log files
--audit-log-maxbackup defines the maximum number of audit log files to retain
--audit-log-maxsize defines the maximum size in megabytes of the audit log file before it gets rotated
If your cluster's control plane runs the kube-apiserver as a Pod, remember to mount the hostPath
to the location of the policy file and log file, so that audit records are persisted. For example:
The webhook audit backend sends audit events to a remote web API, which is assumed to
be a form of the Kubernetes API, including means of authentication. You can configure
a webhook audit backend using the following kube-apiserver flags:
--audit-webhook-config-file specifies the path to a file with a webhook
configuration. The webhook configuration is effectively a specialized
kubeconfig.
--audit-webhook-initial-backoff specifies the amount of time to wait after the first failed
request before retrying. Subsequent requests are retried with exponential backoff.
The webhook config file uses the kubeconfig format to specify the remote address of
the service and credentials used to connect to it.
Event batching
Both log and webhook backends support batching. Using webhook as an example, here's the list of
available flags. To get the same flag for log backend, replace webhook with log in the flag
name. By default, batching is enabled in webhook and disabled in log. Similarly, by default
throttling is enabled in webhook and disabled in log.
--audit-webhook-mode defines the buffering strategy. One of the following:
batch - buffer events and asynchronously process them in batches. This is the default.
blocking - block API server responses on processing each individual event.
blocking-strict - Same as blocking, but when there is a failure during audit logging at the
RequestReceived stage, the whole request to the kube-apiserver fails.
The following flags are used only in the batch mode:
--audit-webhook-batch-buffer-size defines the number of events to buffer before batching.
If the rate of incoming events overflows the buffer, events are dropped.
--audit-webhook-batch-max-size defines the maximum number of events in one batch.
--audit-webhook-batch-max-wait defines the maximum amount of time to wait before unconditionally
batching events in the queue.
--audit-webhook-batch-throttle-qps defines the maximum average number of batches generated
per second.
--audit-webhook-batch-throttle-burst defines the maximum number of batches generated at the same
moment if the allowed QPS was underutilized previously.
Parameter tuning
Parameters should be set to accommodate the load on the API server.
For example, if kube-apiserver receives 100 requests each second, and each request is audited only
on ResponseStarted and ResponseComplete stages, you should account for ≅200 audit
events being generated each second. Assuming that there are up to 100 events in a batch,
you should set throttling level at least 2 queries per second. Assuming that the backend can take up to
5 seconds to write events, you should set the buffer size to hold up to 5 seconds of events;
that is: 10 batches, or 1000 events.
In most cases however, the default parameters should be sufficient and you don't have to worry about
setting them manually. You can look at the following Prometheus metrics exposed by kube-apiserver
and in the logs to monitor the state of the auditing subsystem.
apiserver_audit_event_total metric contains the total number of audit events exported.
apiserver_audit_error_total metric contains the total number of events dropped due to an error
during exporting.
Log entry truncation
Both log and webhook backends support limiting the size of events that are logged.
As an example, the following is the list of flags available for the log backend:
audit-log-truncate-enabled whether event and batch truncating is enabled.
audit-log-truncate-max-batch-size maximum size in bytes of the batch sent to the underlying backend.
audit-log-truncate-max-event-size maximum size in bytes of the audit event sent to the underlying backend.
By default truncate is disabled in both webhook and log, a cluster administrator should set
audit-log-truncate-enabled or audit-webhook-truncate-enabled to enable the feature.
Learn more about Event
and the Policy
resource types by reading the Audit configuration reference.
4.2.7 - Debugging Kubernetes Nodes With Kubectl
This page shows how to debug a node
running on the Kubernetes cluster using kubectl debug command.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Your Kubernetes server must be at or later than version 1.2.
To check the version, enter kubectl version.
You need to have permission to create Pods and to assign those new Pods to arbitrary nodes.
You also need to be authorized to create Pods that access filesystems from the host.
Debugging a Node using kubectl debug node
Use the kubectl debug node command to deploy a Pod to a Node that you want to troubleshoot.
This command is helpful in scenarios where you can't access your Node by using an SSH connection.
When the Pod is created, the Pod opens an interactive shell on the Node.
To create an interactive shell on a Node named “mynode”, run:
kubectl debug node/mynode -it --image=ubuntu
Creating debugging pod node-debugger-mynode-pdx84 with container debugger on node mynode.
If you don't see a command prompt, try pressing enter.
root@mynode:/#
The debug command helps to gather information and troubleshoot issues. Commands
that you might use include ip, ifconfig, nc, ping, and ps and so on. You can also
install other tools, such as mtr, tcpdump, and curl, from the respective package manager.
Note: The debug commands may differ based on the image the debugging pod is using and
these commands might need to be installed.
The debugging Pod can access the root filesystem of the Node, mounted at /host in the Pod.
If you run your kubelet in a filesystem namespace,
the debugging Pod sees the root for that namespace, not for the entire node. For a typical Linux node,
you can look at the following paths to find relevant logs:
/host/var/log/kubelet.log
Logs from the kubelet, responsible for running containers on the node.
/host/var/log/kube-proxy.log
Logs from kube-proxy, which is responsible for directing traffic to Service endpoints.
/host/var/log/containerd.log
Logs from the containerd process running on the node.
/host/var/log/syslog
Shows general messages and information regarding the system.
/host/var/log/kern.log
Shows kernel logs.
When creating a debugging session on a Node, keep in mind that:
kubectl debug automatically generates the name of the new pod, based on
the name of the node.
The root filesystem of the Node will be mounted at /host.
Although the container runs in the host IPC, Network, and PID namespaces,
the pod isn't privileged. This means that reading some process information might fail
because access to that information is restricted to superusers. For example, chroot /host will fail.
If you need a privileged pod, create it manually.
Cleaning up
When you finish using the debugging Pod, delete it:
kubectl get pods
NAME READY STATUS RESTARTS AGE
node-debugger-mynode-pdx84 0/1 Completed 0 8m1s
# Change the pod name accordinglykubectl delete pod node-debugger-mynode-pdx84 --now
pod "node-debugger-mynode-pdx84" deleted
Note: The kubectl debug node command won't work if the Node is down (disconnected
from the network, or kubelet dies and won't restart, etc.).
Check debugging a down/unreachable node
in that case.
4.2.8 - Developing and debugging services locally using telepresence
Note:
This section links to third party projects that provide functionality required by Kubernetes. The Kubernetes project authors aren't responsible for these projects, which are listed alphabetically. To add a project to this list, read the content guide before submitting a change. More information.
Kubernetes applications usually consist of multiple, separate services,
each running in its own container. Developing and debugging these services
on a remote Kubernetes cluster can be cumbersome, requiring you to
get a shell on a running container
in order to run debugging tools.
telepresence is a tool to ease the process of developing and debugging
services locally while proxying the service to a remote Kubernetes cluster.
Using telepresence allows you to use custom tools, such as a debugger and
IDE, for a local service and provides the service full access to ConfigMap,
secrets, and the services running on the remote cluster.
This document describes using telepresence to develop and debug services
running on a remote cluster locally.
Before you begin
Kubernetes cluster is installed
kubectl is configured to communicate with the cluster
Connecting your local machine to a remote Kubernetes cluster
After installing telepresence, run telepresence connect to launch
its Daemon and connect your local workstation to the cluster.
$ telepresence connect
Launching Telepresence Daemon
...
Connected to context default (https://<cluster public IP>)
You can curl services using the Kubernetes syntax e.g. curl -ik https://kubernetes.default
Developing or debugging an existing service
When developing an application on Kubernetes, you typically program
or debug a single service. The service might require access to other
services for testing and debugging. One option is to use the continuous
deployment pipeline, but even the fastest deployment pipeline introduces
a delay in the program or debug cycle.
Use the telepresence intercept $SERVICE_NAME --port $LOCAL_PORT:$REMOTE_PORT
command to create an "intercept" for rerouting remote service traffic.
Where:
$SERVICE_NAME is the name of your local service
$LOCAL_PORT is the port that your service is running on your local workstation
And $REMOTE_PORT is the port your service listens to in the cluster
Running this command tells Telepresence to send remote traffic to your
local service instead of the service in the remote Kubernetes cluster.
Make edits to your service source code locally, save, and see the corresponding
changes when accessing your remote application take effect immediately.
You can also run your local service using a debugger or any other local development tool.
How does Telepresence work?
Telepresence installs a traffic-agent sidecar next to your existing
application's container running in the remote cluster. It then captures
all traffic requests going into the Pod, and instead of forwarding this
to the application in the remote cluster, it routes all traffic (when you
create a global intercept
or a subset of the traffic (when you create a
personal intercept)
to your local development environment.
What's next
If you're interested in a hands-on tutorial, check out
this tutorial
that walks through locally developing the Guestbook application on Google Kubernetes Engine.
My Pods are stuck at "Container Creating" or restarting over and over
Ensure that your pause image is compatible with your Windows OS version.
See Pause container
to see the latest / recommended pause image and/or get more information.
Note: If using containerd as your container runtime the pause image is specified in the
plugins.plugins.cri.sandbox_image field of the of config.toml configration file.
My pods show status as ErrImgPull or ImagePullBackOff
Ensure that your Pod is getting scheduled to a
compatible
Windows Node.
More information on how to specify a compatible node for your Pod can be found in
this guide.
Network troubleshooting
My Windows Pods do not have network connectivity
If you are using virtual machines, ensure that MAC spoofing is enabled on all
the VM network adapter(s).
My Windows Pods cannot ping external resources
Windows Pods do not have outbound rules programmed for the ICMP protocol. However,
TCP/UDP is supported. When trying to demonstrate connectivity to resources
outside of the cluster, substitute ping <IP> with corresponding
curl <IP> commands.
If you are still facing problems, most likely your network configuration in
cni.conf
deserves some extra attention. You can always edit this static file. The
configuration update will apply to any new Kubernetes resources.
One of the Kubernetes networking requirements
(see Kubernetes model) is
for cluster communication to occur without
NAT internally. To honor this requirement, there is an
ExceptionList
for all the communication where you do not want outbound NAT to occur. However,
this also means that you need to exclude the external IP you are trying to query
from the ExceptionList. Only then will the traffic originating from your Windows
pods be SNAT'ed correctly to receive a response from the outside world. In this
regard, your ExceptionList in cni.conf should look as follows:
My Windows node cannot access NodePort type Services
Local NodePort access from the node itself fails. This is a known
limitation. NodePort access works from other nodes or external clients.
vNICs and HNS endpoints of containers are being deleted
This issue can be caused when the hostname-override parameter is not passed to
kube-proxy. To resolve
it, users need to pass the hostname to kube-proxy as follows:
My Windows node cannot access my services using the service IP
This is a known limitation of the networking stack on Windows. However, Windows Pods can access the Service IP.
No network adapter is found when starting the kubelet
The Windows networking stack needs a virtual adapter for Kubernetes networking to work.
If the following commands return no results (in an admin shell),
virtual network creation — a necessary prerequisite for the kubelet to work — has failed:
Get-HnsNetwork | ? Name -ieq"cbr0"Get-NetAdapter | ? Name -Like"vEthernet (Ethernet*"
Often it is worthwhile to modify the InterfaceName
parameter of the start.ps1 script, in cases where the host's network adapter isn't "Ethernet".
Otherwise, consult the output of the start-kubelet.ps1 script to see if there are errors during virtual network creation.
DNS resolution is not properly working
Check the DNS limitations for Windows in this section.
kubectl port-forward fails with "unable to do port forwarding: wincat not found"
This was implemented in Kubernetes 1.15 by including wincat.exe in the pause infrastructure container
mcr.microsoft.com/oss/kubernetes/pause:3.6.
Be sure to use a supported version of Kubernetes.
If you would like to build your own pause infrastructure container be sure to include
wincat.
My Kubernetes installation is failing because my Windows Server node is behind a proxy
If you are behind a proxy, the following PowerShell environment variables must be defined:
With Flannel, my nodes are having issues after rejoining a cluster
Whenever a previously deleted node is being re-joined to the cluster, flannelD
tries to assign a new pod subnet to the node. Users should remove the old pod
subnet configuration files in the following paths:
Flanneld is stuck in "Waiting for the Network to be created"
There are numerous reports of this issue;
most likely it is a timing issue for when the management IP of the flannel network is set.
A workaround is to relaunch start.ps1 or relaunch it manually as follows:
My Windows Pods cannot launch because of missing /run/flannel/subnet.env
This indicates that Flannel didn't launch correctly. You can either try
to restart flanneld.exe or you can copy the files over manually from
/run/flannel/subnet.env on the Kubernetes master to C:\run\flannel\subnet.env
on the Windows worker node and modify the FLANNEL_SUBNET row to a different
number. For example, if node subnet 10.244.4.1/24 is desired:
Declarative and imperative paradigms for interacting with the Kubernetes API.
5.1 - Declarative Management of Kubernetes Objects Using Configuration Files
Kubernetes objects can be created, updated, and deleted by storing multiple
object configuration files in a directory and using kubectl apply to
recursively create and update those objects as needed. This method
retains writes made to live objects without merging the changes
back into the object configuration files. kubectl diff also gives you a
preview of what changes apply will make.
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
The kubectl tool supports three kinds of object management:
Imperative commands
Imperative object configuration
Declarative object configuration
See Kubernetes Object Management
for a discussion of the advantages and disadvantage of each kind of object management.
Overview
Declarative object configuration requires a firm understanding of
the Kubernetes object definitions and configuration. Read and complete
the following documents if you have not already:
Following are definitions for terms used in this document:
object configuration file / configuration file: A file that defines the
configuration for a Kubernetes object. This topic shows how to pass configuration
files to kubectl apply. Configuration files are typically stored in source control, such as Git.
live object configuration / live configuration: The live configuration
values of an object, as observed by the Kubernetes cluster. These are kept in the Kubernetes
cluster storage, typically etcd.
declarative configuration writer / declarative writer: A person or software component
that makes updates to a live object. The live writers referred to in this topic make changes
to object configuration files and run kubectl apply to write the changes.
How to create objects
Use kubectl apply to create all objects, except those that already exist,
defined by configuration files in a specified directory:
kubectl apply -f <directory>
This sets the kubectl.kubernetes.io/last-applied-configuration: '{...}'
annotation on each object. The annotation contains the contents of the object
configuration file that was used to create the object.
Note: Add the -R flag to recursively process directories.
Here's an example of an object configuration file:
Since diff performs a server-side apply request in dry-run mode,
it requires granting PATCH, CREATE, and UPDATE permissions.
See Dry-Run Authorization
for details.
kubectl get -f https://k8s.io/examples/application/simple_deployment.yaml -o yaml
The output shows that the kubectl.kubernetes.io/last-applied-configuration annotation
was written to the live configuration, and it matches the configuration file:
kind:Deploymentmetadata:annotations:# ...# This is the json representation of simple_deployment.yaml# It was written by kubectl apply when the object was createdkubectl.kubernetes.io/last-applied-configuration:| {"apiVersion":"apps/v1","kind":"Deployment",
"metadata":{"annotations":{},"name":"nginx-deployment","namespace":"default"},
"spec":{"minReadySeconds":5,"selector":{"matchLabels":{"app":nginx}},"template":{"metadata":{"labels":{"app":"nginx"}},
"spec":{"containers":[{"image":"nginx:1.14.2","name":"nginx",
"ports":[{"containerPort":80}]}]}}}}# ...spec:# ...minReadySeconds:5selector:matchLabels:# ...app:nginxtemplate:metadata:# ...labels:app:nginxspec:containers:- image:nginx:1.14.2# ...name:nginxports:- containerPort:80# ...# ...# ...# ...
How to update objects
You can also use kubectl apply to update all objects defined in a directory, even
if those objects already exist. This approach accomplishes the following:
Sets fields that appear in the configuration file in the live configuration.
Clears fields removed from the configuration file in the live configuration.
Note: For purposes of illustration, the preceding command refers to a single
configuration file instead of a directory.
Print the live configuration using kubectl get:
kubectl get -f https://k8s.io/examples/application/simple_deployment.yaml -o yaml
The output shows that the kubectl.kubernetes.io/last-applied-configuration annotation
was written to the live configuration, and it matches the configuration file:
kind:Deploymentmetadata:annotations:# ...# This is the json representation of simple_deployment.yaml# It was written by kubectl apply when the object was createdkubectl.kubernetes.io/last-applied-configuration:| {"apiVersion":"apps/v1","kind":"Deployment",
"metadata":{"annotations":{},"name":"nginx-deployment","namespace":"default"},
"spec":{"minReadySeconds":5,"selector":{"matchLabels":{"app":nginx}},"template":{"metadata":{"labels":{"app":"nginx"}},
"spec":{"containers":[{"image":"nginx:1.14.2","name":"nginx",
"ports":[{"containerPort":80}]}]}}}}# ...spec:# ...minReadySeconds:5selector:matchLabels:# ...app:nginxtemplate:metadata:# ...labels:app:nginxspec:containers:- image:nginx:1.14.2# ...name:nginxports:- containerPort:80# ...# ...# ...# ...
Directly update the replicas field in the live configuration by using kubectl scale.
This does not use kubectl apply:
The output shows that the replicas field has been set to 2, and the last-applied-configuration
annotation does not contain a replicas field:
apiVersion:apps/v1kind:Deploymentmetadata:annotations:# ...# note that the annotation does not contain replicas# because it was not updated through applykubectl.kubernetes.io/last-applied-configuration:| {"apiVersion":"apps/v1","kind":"Deployment",
"metadata":{"annotations":{},"name":"nginx-deployment","namespace":"default"},
"spec":{"minReadySeconds":5,"selector":{"matchLabels":{"app":nginx}},"template":{"metadata":{"labels":{"app":"nginx"}},
"spec":{"containers":[{"image":"nginx:1.14.2","name":"nginx",
"ports":[{"containerPort":80}]}]}}}}# ...spec:replicas:2# written by scale# ...minReadySeconds:5selector:matchLabels:# ...app:nginxtemplate:metadata:# ...labels:app:nginxspec:containers:- image:nginx:1.14.2# ...name:nginxports:- containerPort:80# ...
Update the simple_deployment.yaml configuration file to change the image from
nginx:1.14.2 to nginx:1.16.1, and delete the minReadySeconds field:
kubectl get -f https://k8s.io/examples/application/update_deployment.yaml -o yaml
The output shows the following changes to the live configuration:
The replicas field retains the value of 2 set by kubectl scale.
This is possible because it is omitted from the configuration file.
The image field has been updated to nginx:1.16.1 from nginx:1.14.2.
The last-applied-configuration annotation has been updated with the new image.
The minReadySeconds field has been cleared.
The last-applied-configuration annotation no longer contains the minReadySeconds field.
apiVersion:apps/v1kind:Deploymentmetadata:annotations:# ...# The annotation contains the updated image to nginx 1.16.1,# but does not contain the updated replicas to 2kubectl.kubernetes.io/last-applied-configuration:| {"apiVersion":"apps/v1","kind":"Deployment",
"metadata":{"annotations":{},"name":"nginx-deployment","namespace":"default"},
"spec":{"selector":{"matchLabels":{"app":nginx}},"template":{"metadata":{"labels":{"app":"nginx"}},
"spec":{"containers":[{"image":"nginx:1.16.1","name":"nginx",
"ports":[{"containerPort":80}]}]}}}}# ...spec:replicas:2# Set by `kubectl scale`. Ignored by `kubectl apply`.# minReadySeconds cleared by `kubectl apply`# ...selector:matchLabels:# ...app:nginxtemplate:metadata:# ...labels:app:nginxspec:containers:- image:nginx:1.16.1# Set by `kubectl apply`# ...name:nginxports:- containerPort:80# ...# ...# ...# ...
Warning: Mixing kubectl apply with the imperative object configuration commands
create and replace is not supported. This is because create
and replace do not retain the kubectl.kubernetes.io/last-applied-configuration
that kubectl apply uses to compute updates.
How to delete objects
There are two approaches to delete objects managed by kubectl apply.
Recommended: kubectl delete -f <filename>
Manually deleting objects using the imperative command is the recommended
approach, as it is more explicit about what is being deleted, and less likely
to result in the user deleting something unintentionally:
kubectl delete -f <filename>
Alternative: kubectl apply -f <directory> --prune
As an alternative to kubectl delete, you can use kubectl apply to identify objects to be deleted after
their manifests have been removed from a directory in the local filesystem.
In Kubernetes 1.28, there are two pruning modes available in kubectl apply:
Allowlist-based pruning: This mode has existed since kubectl v1.5 but is still
in alpha due to usability, correctness and performance issues with its design.
The ApplySet-based mode is designed to replace it.
ApplySet-based pruning: An apply set is a server-side object (by default, a Secret)
that kubectl can use to accurately and efficiently track set membership across apply
operations. This mode was introduced in alpha in kubectl v1.27 as a replacement for allowlist-based pruning.
Warning: Take care when using --prune with kubectl apply in allow list mode. Which
objects are pruned depends on the values of the --prune-allowlist, --selector
and --namespace flags, and relies on dynamic discovery of the objects in scope.
Especially if flag values are changed between invocations, this can lead to objects
being unexpectedly deleted or retained.
To use allowlist-based pruning, add the following flags to your kubectl apply invocation:
--prune: Delete previously applied objects that are not in the set passed to the current invocation.
--prune-allowlist: A list of group-version-kinds (GVKs) to consider for pruning.
This flag is optional but strongly encouraged, as its default value is a partial
list of both namespaced and cluster-scoped types, which can lead to surprising results.
--selector/-l: Use a label selector to constrain the set of objects selected
for pruning. This flag is optional but strongly encouraged.
--all: use instead of --selector/-l to explicitly select all previously
applied objects of the allowlisted types.
Allowlist-based pruning queries the API server for all objects of the allowlisted GVKs that match the given labels (if any), and attempts to match the returned live object configurations against the object
manifest files. If an object matches the query, and it does not have a
manifest in the directory, and it has a kubectl.kubernetes.io/last-applied-configuration annotation,
it is deleted.
Warning: Apply with prune should only be run against the root directory
containing the object manifests. Running against sub-directories
can cause objects to be unintentionally deleted if they were previously applied,
have the labels given (if any), and do not appear in the subdirectory.
FEATURE STATE:Kubernetes v1.27 [alpha]
Caution:kubectl apply --prune --applyset is in alpha, and backwards incompatible
changes might be introduced in subsequent releases.
To use ApplySet-based pruning, set the KUBECTL_APPLYSET=true environment variable,
and add the following flags to your kubectl apply invocation:
--prune: Delete previously applied objects that are not in the set passed
to the current invocation.
--applyset: The name of an object that kubectl can use to accurately and
efficiently track set membership across apply operations.
By default, the type of the ApplySet parent object used is a Secret. However,
ConfigMaps can also be used in the format: --applyset=configmaps/<name>.
When using a Secret or ConfigMap, kubectl will create the object if it does not already exist.
It is also possible to use custom resources as ApplySet parent objects. To enable
this, label the Custom Resource Definition (CRD) that defines the resource you want
to use with the following: applyset.kubernetes.io/is-parent-type: true. Then, create
the object you want to use as an ApplySet parent (kubectl does not do this automatically
for custom resources). Finally, refer to that object in the applyset flag as follows:
--applyset=<resource>.<group>/<name> (for example, widgets.custom.example.com/widget-name).
With ApplySet-based pruning, kubectl adds the applyset.kubernetes.io/part-of=<parentID>
label to each object in the set before they are sent to the server. For performance reasons,
it also collects the list of resource types and namespaces that the set contains and adds
these in annotations on the live parent object. Finally, at the end of the apply operation,
it queries the API server for objects of those types in those namespaces
(or in the cluster scope, as applicable) that belong to the set, as defined by the
applyset.kubernetes.io/part-of=<parentID> label.
Caveats and restrictions:
Each object may be a member of at most one set.
The --namespace flag is required when using any namespaced parent, including
the default Secret. This means that ApplySets spanning multiple namespaces must
use a cluster-scoped custom resource as the parent object.
To safely use ApplySet-based pruning with multiple directories,
use a unique ApplySet name for each.
How to view an object
You can use kubectl get with -o yaml to view the configuration of a live object:
kubectl get -f <filename|url> -o yaml
How apply calculates differences and merges changes
Caution: A patch is an update operation that is scoped to specific fields of an object
instead of the entire object. This enables updating only a specific set of fields
on an object without reading the object first.
When kubectl apply updates the live configuration for an object,
it does so by sending a patch request to the API server. The
patch defines updates scoped to specific fields of the live object
configuration. The kubectl apply command calculates this patch request
using the configuration file, the live configuration, and the
last-applied-configuration annotation stored in the live configuration.
Merge patch calculation
The kubectl apply command writes the contents of the configuration file to the
kubectl.kubernetes.io/last-applied-configuration annotation. This
is used to identify fields that have been removed from the configuration
file and need to be cleared from the live configuration. Here are the steps used
to calculate which fields should be deleted or set:
Calculate the fields to delete. These are the fields present in
last-applied-configuration and missing from the configuration file.
Calculate the fields to add or set. These are the fields present in
the configuration file whose values don't match the live configuration.
Here's an example. Suppose this is the configuration file for a Deployment object:
apiVersion:apps/v1kind:Deploymentmetadata:name:nginx-deploymentspec:selector:matchLabels:app:nginxtemplate:metadata:labels:app:nginxspec:containers:- name:nginximage:nginx:1.16.1# update the imageports:- containerPort:80
Also, suppose this is the live configuration for the same Deployment object:
apiVersion:apps/v1kind:Deploymentmetadata:annotations:# ...# note that the annotation does not contain replicas# because it was not updated through applykubectl.kubernetes.io/last-applied-configuration:| {"apiVersion":"apps/v1","kind":"Deployment",
"metadata":{"annotations":{},"name":"nginx-deployment","namespace":"default"},
"spec":{"minReadySeconds":5,"selector":{"matchLabels":{"app":nginx}},"template":{"metadata":{"labels":{"app":"nginx"}},
"spec":{"containers":[{"image":"nginx:1.14.2","name":"nginx",
"ports":[{"containerPort":80}]}]}}}}# ...spec:replicas:2# written by scale# ...minReadySeconds:5selector:matchLabels:# ...app:nginxtemplate:metadata:# ...labels:app:nginxspec:containers:- image:nginx:1.14.2# ...name:nginxports:- containerPort:80# ...
Here are the merge calculations that would be performed by kubectl apply:
Calculate the fields to delete by reading values from
last-applied-configuration and comparing them to values in the
configuration file.
Clear fields explicitly set to null in the local object configuration file
regardless of whether they appear in the last-applied-configuration.
In this example, minReadySeconds appears in the
last-applied-configuration annotation, but does not appear in the configuration file.
Action: Clear minReadySeconds from the live configuration.
Calculate the fields to set by reading values from the configuration
file and comparing them to values in the live configuration. In this example,
the value of image in the configuration file does not match
the value in the live configuration. Action: Set the value of image in the live configuration.
Set the last-applied-configuration annotation to match the value
of the configuration file.
Merge the results from 1, 2, 3 into a single patch request to the API server.
Here is the live configuration that is the result of the merge:
apiVersion:apps/v1kind:Deploymentmetadata:annotations:# ...# The annotation contains the updated image to nginx 1.16.1,# but does not contain the updated replicas to 2kubectl.kubernetes.io/last-applied-configuration:| {"apiVersion":"apps/v1","kind":"Deployment",
"metadata":{"annotations":{},"name":"nginx-deployment","namespace":"default"},
"spec":{"selector":{"matchLabels":{"app":nginx}},"template":{"metadata":{"labels":{"app":"nginx"}},
"spec":{"containers":[{"image":"nginx:1.16.1","name":"nginx",
"ports":[{"containerPort":80}]}]}}}}# ...spec:selector:matchLabels:# ...app:nginxreplicas:2# Set by `kubectl scale`. Ignored by `kubectl apply`.# minReadySeconds cleared by `kubectl apply`# ...template:metadata:# ...labels:app:nginxspec:containers:- image:nginx:1.16.1# Set by `kubectl apply`# ...name:nginxports:- containerPort:80# ...# ...# ...# ...
How different types of fields are merged
How a particular field in a configuration file is merged with
the live configuration depends on the
type of the field. There are several types of fields:
primitive: A field of type string, integer, or boolean.
For example, image and replicas are primitive fields. Action: Replace.
map, also called object: A field of type map or a complex type that contains subfields. For example, labels,
annotations,spec and metadata are all maps. Action: Merge elements or subfields.
list: A field containing a list of items that can be either primitive types or maps.
For example, containers, ports, and args are lists. Action: Varies.
When kubectl apply updates a map or list field, it typically does
not replace the entire field, but instead updates the individual subelements.
For instance, when merging the spec on a Deployment, the entire spec is
not replaced. Instead the subfields of spec, such as replicas, are compared
and merged.
Merging changes to primitive fields
Primitive fields are replaced or cleared.
Note:- is used for "not applicable" because the value is not used.
Field in object configuration file
Field in live object configuration
Field in last-applied-configuration
Action
Yes
Yes
-
Set live to configuration file value.
Yes
No
-
Set live to local configuration.
No
-
Yes
Clear from live configuration.
No
-
No
Do nothing. Keep live value.
Merging changes to map fields
Fields that represent maps are merged by comparing each of the subfields or elements of the map:
Note:- is used for "not applicable" because the value is not used.
Key in object configuration file
Key in live object configuration
Field in last-applied-configuration
Action
Yes
Yes
-
Compare sub fields values.
Yes
No
-
Set live to local configuration.
No
-
Yes
Delete from live configuration.
No
-
No
Do nothing. Keep live value.
Merging changes for fields of type list
Merging changes to a list uses one of three strategies:
Replace the list if all its elements are primitives.
Merge individual elements in a list of complex elements.
Merge a list of primitive elements.
The choice of strategy is made on a per-field basis.
Replace the list if all its elements are primitives
Treat the list the same as a primitive field. Replace or delete the
entire list. This preserves ordering.
Example: Use kubectl apply to update the args field of a Container in a Pod. This sets
the value of args in the live configuration to the value in the configuration file.
Any args elements that had previously been added to the live configuration are lost.
The order of the args elements defined in the configuration file is
retained in the live configuration.
# last-applied-configuration valueargs:["a","b"]# configuration file valueargs:["a","c"]# live configurationargs:["a","b","d"]# result after mergeargs:["a","c"]
Explanation: The merge used the configuration file value as the new list value.
Merge individual elements of a list of complex elements:
Treat the list as a map, and treat a specific field of each element as a key.
Add, delete, or update individual elements. This does not preserve ordering.
This merge strategy uses a special tag on each field called a patchMergeKey. The
patchMergeKey is defined for each field in the Kubernetes source code:
types.go
When merging a list of maps, the field specified as the patchMergeKey for a given element
is used like a map key for that element.
Example: Use kubectl apply to update the containers field of a PodSpec.
This merges the list as though it was a map where each element is keyed
by name.
# last-applied-configuration valuecontainers:- name:nginximage:nginx:1.16- name: nginx-helper-a # key:nginx-helper-a; will be deleted in resultimage:helper:1.3- name: nginx-helper-b # key:nginx-helper-b; will be retainedimage:helper:1.3# configuration file valuecontainers:- name:nginximage:nginx:1.16- name:nginx-helper-bimage:helper:1.3- name: nginx-helper-c # key:nginx-helper-c; will be added in resultimage:helper:1.3# live configurationcontainers:- name:nginximage:nginx:1.16- name:nginx-helper-aimage:helper:1.3- name:nginx-helper-bimage:helper:1.3args:["run"]# Field will be retained- name: nginx-helper-d # key:nginx-helper-d; will be retainedimage:helper:1.3# result after mergecontainers:- name:nginximage:nginx:1.16# Element nginx-helper-a was deleted- name:nginx-helper-bimage:helper:1.3args:["run"]# Field was retained- name:nginx-helper-c# Element was addedimage:helper:1.3- name:nginx-helper-d# Element was ignoredimage:helper:1.3
Explanation:
The container named "nginx-helper-a" was deleted because no container
named "nginx-helper-a" appeared in the configuration file.
The container named "nginx-helper-b" retained the changes to args
in the live configuration. kubectl apply was able to identify
that "nginx-helper-b" in the live configuration was the same
"nginx-helper-b" as in the configuration file, even though their fields
had different values (no args in the configuration file). This is
because the patchMergeKey field value (name) was identical in both.
The container named "nginx-helper-c" was added because no container
with that name appeared in the live configuration, but one with
that name appeared in the configuration file.
The container named "nginx-helper-d" was retained because
no element with that name appeared in the last-applied-configuration.
Merge a list of primitive elements
As of Kubernetes 1.5, merging lists of primitive elements is not supported.
Note: Which of the above strategies is chosen for a given field is controlled by
the patchStrategy tag in types.go
If no patchStrategy is specified for a field of type list, then
the list is replaced.
Default field values
The API server sets certain fields to default values in the live configuration if they are
not specified when the object is created.
Here's a configuration file for a Deployment. The file does not specify strategy:
kubectl get -f https://k8s.io/examples/application/simple_deployment.yaml -o yaml
The output shows that the API server set several fields to default values in the live
configuration. These fields were not specified in the configuration file.
apiVersion:apps/v1kind:Deployment# ...spec:selector:matchLabels:app:nginxminReadySeconds:5replicas:1# defaulted by apiserverstrategy:rollingUpdate:# defaulted by apiserver - derived from strategy.typemaxSurge:1maxUnavailable:1type:RollingUpdate# defaulted by apiservertemplate:metadata:creationTimestamp:nulllabels:app:nginxspec:containers:- image:nginx:1.14.2imagePullPolicy:IfNotPresent# defaulted by apiservername:nginxports:- containerPort:80protocol:TCP# defaulted by apiserverresources:{}# defaulted by apiserverterminationMessagePath:/dev/termination-log# defaulted by apiserverdnsPolicy:ClusterFirst# defaulted by apiserverrestartPolicy:Always# defaulted by apiserversecurityContext:{}# defaulted by apiserverterminationGracePeriodSeconds:30# defaulted by apiserver# ...
In a patch request, defaulted fields are not re-defaulted unless they are explicitly cleared
as part of a patch request. This can cause unexpected behavior for
fields that are defaulted based
on the values of other fields. When the other fields are later changed,
the values defaulted from them will not be updated unless they are
explicitly cleared.
For this reason, it is recommended that certain fields defaulted
by the server are explicitly defined in the configuration file, even
if the desired values match the server defaults. This makes it
easier to recognize conflicting values that will not be re-defaulted
by the server.
Example:
# last-applied-configurationspec:template:metadata:labels:app:nginxspec:containers:- name:nginximage:nginx:1.14.2ports:- containerPort:80# configuration filespec:strategy:type:Recreate# updated valuetemplate:metadata:labels:app:nginxspec:containers:- name:nginximage:nginx:1.14.2ports:- containerPort:80# live configurationspec:strategy:type:RollingUpdate# defaulted valuerollingUpdate:# defaulted value derived from typemaxSurge :1maxUnavailable:1template:metadata:labels:app:nginxspec:containers:- name:nginximage:nginx:1.14.2ports:- containerPort:80# result after merge - ERROR!spec:strategy:type: Recreate # updated value:incompatible with rollingUpdaterollingUpdate: # defaulted value:incompatible with "type: Recreate"maxSurge :1maxUnavailable:1template:metadata:labels:app:nginxspec:containers:- name:nginximage:nginx:1.14.2ports:- containerPort:80
Explanation:
The user creates a Deployment without defining strategy.type.
The server defaults strategy.type to RollingUpdate and defaults the
strategy.rollingUpdate values.
The user changes strategy.type to Recreate. The strategy.rollingUpdate
values remain at their defaulted values, though the server expects them to be cleared.
If the strategy.rollingUpdate values had been defined initially in the configuration file,
it would have been more clear that they needed to be deleted.
Apply fails because strategy.rollingUpdate is not cleared. The strategy.rollingupdate
field cannot be defined with a strategy.type of Recreate.
Recommendation: These fields should be explicitly defined in the object configuration file:
Selectors and PodTemplate labels on workloads, such as Deployment, StatefulSet, Job, DaemonSet,
ReplicaSet, and ReplicationController
Deployment rollout strategy
How to clear server-defaulted fields or fields set by other writers
Fields that do not appear in the configuration file can be cleared by
setting their values to null and then applying the configuration file.
For fields defaulted by the server, this triggers re-defaulting
the values.
How to change ownership of a field between the configuration file and direct imperative writers
These are the only methods you should use to change an individual object field:
Use kubectl apply.
Write directly to the live configuration without modifying the configuration file:
for example, use kubectl scale.
Changing the owner from a direct imperative writer to a configuration file
Add the field to the configuration file. For the field, discontinue direct updates to
the live configuration that do not go through kubectl apply.
Changing the owner from a configuration file to a direct imperative writer
As of Kubernetes 1.5, changing ownership of a field from a configuration file to
an imperative writer requires manual steps:
Remove the field from the configuration file.
Remove the field from the kubectl.kubernetes.io/last-applied-configuration annotation on the live object.
Changing management methods
Kubernetes objects should be managed using only one method at a time.
Switching from one method to another is possible, but is a manual process.
Note: It is OK to use imperative deletion with declarative management.
Migrating from imperative command management to declarative object configuration
Migrating from imperative command management to declarative object
configuration involves several manual steps:
Export the live object to a local configuration file:
kubectl get <kind>/<name> -o yaml > <kind>_<name>.yaml
Manually remove the status field from the configuration file.
Note: This step is optional, as kubectl apply does not update the status field
even if it is present in the configuration file.
Set the kubectl.kubernetes.io/last-applied-configuration annotation on the object:
Since 1.14, Kubectl also
supports the management of Kubernetes objects using a kustomization file.
To view Resources found in a directory containing a kustomization file, run the following command:
kubectl kustomize <kustomization_directory>
To apply those Resources, run kubectl apply with --kustomize or -k flag:
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Kustomize is a tool for customizing Kubernetes configurations. It has the following features to manage application configuration files:
generating resources from other sources
setting cross-cutting fields for resources
composing and customizing collections of resources
Generating Resources
ConfigMaps and Secrets hold configuration or sensitive data that are used by other Kubernetes objects, such as Pods. The source of truth of ConfigMaps or Secrets are usually external to a cluster, such as a .properties file or an SSH keyfile.
Kustomize has secretGenerator and configMapGenerator, which generate Secret and ConfigMap from files or literals.
configMapGenerator
To generate a ConfigMap from a file, add an entry to the files list in configMapGenerator. Here is an example of generating a ConfigMap with a data item from a .properties file:
To generate a ConfigMap from an env file, add an entry to the envs list in configMapGenerator. Here is an example of generating a ConfigMap with a data item from a .env file:
Note: Each variable in the .env file becomes a separate key in the ConfigMap that you generate. This is different from the previous example which embeds a file named application.properties (and all its entries) as the value for a single key.
ConfigMaps can also be generated from literal key-value pairs. To generate a ConfigMap from a literal key-value pair, add an entry to the literals list in configMapGenerator. Here is an example of generating a ConfigMap with a data item from a key-value pair:
To use a generated ConfigMap in a Deployment, reference it by the name of the configMapGenerator. Kustomize will automatically replace this name with the generated name.
This is an example deployment that uses a generated ConfigMap:
You can generate Secrets from files or literal key-value pairs. To generate a Secret from a file, add an entry to the files list in secretGenerator. Here is an example of generating a Secret with a data item from a file:
To generate a Secret from a literal key-value pair, add an entry to literals list in secretGenerator. Here is an example of generating a Secret with a data item from a key-value pair:
The generated ConfigMaps and Secrets have a content hash suffix appended. This ensures that a new ConfigMap or Secret is generated when the contents are changed. To disable the behavior of appending a suffix, one can use generatorOptions. Besides that, it is also possible to specify cross-cutting options for generated ConfigMaps and Secrets.
It is common to compose a set of Resources in a project and manage them inside
the same file or directory.
Kustomize offers composing Resources from different files and applying patches or other customization to them.
Composing
Kustomize supports composition of different resources. The resources field, in the kustomization.yaml file, defines the list of resources to include in a configuration. Set the path to a resource's configuration file in the resources list.
Here is an example of an NGINX application comprised of a Deployment and a Service:
The Resources from kubectl kustomize ./ contain both the Deployment and the Service objects.
Customizing
Patches can be used to apply different customizations to Resources. Kustomize supports different patching
mechanisms through patchesStrategicMerge and patchesJson6902. patchesStrategicMerge is a list of file paths. Each file should be resolved to a strategic merge patch. The names inside the patches must match Resource names that are already loaded. Small patches that do one thing are recommended. For example, create one patch for increasing the deployment replica number and another patch for setting the memory limit.
Not all Resources or fields support strategic merge patches. To support modifying arbitrary fields in arbitrary Resources,
Kustomize offers applying JSON patch through patchesJson6902.
To find the correct Resource for a Json patch, the group, version, kind and name of that Resource need to be
specified in kustomization.yaml. For example, increasing the replica number of a Deployment object can also be done
through patchesJson6902.
In addition to patches, Kustomize also offers customizing container images or injecting field values from other objects into containers
without creating patches. For example, you can change the image used inside containers by specifying the new image in images field in kustomization.yaml.
Sometimes, the application running in a Pod may need to use configuration values from other objects. For example,
a Pod from a Deployment object need to read the corresponding Service name from Env or as a command argument.
Since the Service name may change as namePrefix or nameSuffix is added in the kustomization.yaml file. It is
not recommended to hard code the Service name in the command argument. For this usage, Kustomize can inject the Service name into containers through vars.
Kustomize has the concepts of bases and overlays. A base is a directory with a kustomization.yaml, which contains a
set of resources and associated customization. A base could be either a local directory or a directory from a remote repo,
as long as a kustomization.yaml is present inside. An overlay is a directory with a kustomization.yaml that refers to other
kustomization directories as its bases. A base has no knowledge of an overlay and can be used in multiple overlays.
An overlay may have multiple bases and it composes all resources
from bases and may also have customization on top of them.
Here is an example of a base:
# Create a directory to hold the basemkdir base
# Create a base/deployment.yamlcat <<EOF > base/deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: my-nginx
spec:
selector:
matchLabels:
run: my-nginx
replicas: 2
template:
metadata:
labels:
run: my-nginx
spec:
containers:
- name: my-nginx
image: nginx
EOF# Create a base/service.yaml filecat <<EOF > base/service.yaml
apiVersion: v1
kind: Service
metadata:
name: my-nginx
labels:
run: my-nginx
spec:
ports:
- port: 80
protocol: TCP
selector:
run: my-nginx
EOF# Create a base/kustomization.yamlcat <<EOF > base/kustomization.yaml
resources:
- deployment.yaml
- service.yaml
EOF
This base can be used in multiple overlays. You can add different namePrefix or other cross-cutting fields
in different overlays. Here are two overlays using the same base.
Use --kustomize or -k in kubectl commands to recognize Resources managed by kustomization.yaml.
Note that -k should point to a kustomization directory, such as
5.3 - Managing Kubernetes Objects Using Imperative Commands
Kubernetes objects can quickly be created, updated, and deleted directly using
imperative commands built into the kubectl command-line tool. This document
explains how those commands are organized and how to use them to manage live objects.
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
The kubectl tool supports three kinds of object management:
Imperative commands
Imperative object configuration
Declarative object configuration
See Kubernetes Object Management
for a discussion of the advantages and disadvantage of each kind of object management.
How to create objects
The kubectl tool supports verb-driven commands for creating some of the most common
object types. The commands are named to be recognizable to users unfamiliar with
the Kubernetes object types.
run: Create a new Pod to run a Container.
expose: Create a new Service object to load balance traffic across Pods.
autoscale: Create a new Autoscaler object to automatically horizontally scale a controller, such as a Deployment.
The kubectl tool also supports creation commands driven by object type.
These commands support more object types and are more explicit about
their intent, but require users to know the type of objects they intend
to create.
create <objecttype> [<subtype>] <instancename>
Some objects types have subtypes that you can specify in the create command.
For example, the Service object has several subtypes including ClusterIP,
LoadBalancer, and NodePort. Here's an example that creates a Service with
subtype NodePort:
kubectl create service nodeport <myservicename>
In the preceding example, the create service nodeport command is called
a subcommand of the create service command.
You can use the -h flag to find the arguments and flags supported by
a subcommand:
kubectl create service nodeport -h
How to update objects
The kubectl command supports verb-driven commands for some common update operations.
These commands are named to enable users unfamiliar with Kubernetes
objects to perform updates without knowing the specific fields
that must be set:
scale: Horizontally scale a controller to add or remove Pods by updating the replica count of the controller.
annotate: Add or remove an annotation from an object.
label: Add or remove a label from an object.
The kubectl command also supports update commands driven by an aspect of the object.
Setting this aspect may set different fields for different object types:
set<field>: Set an aspect of an object.
Note: In Kubernetes version 1.5, not every verb-driven command has an associated aspect-driven command.
The kubectl tool supports these additional ways to update a live object directly,
however they require a better understanding of the Kubernetes object schema.
edit: Directly edit the raw configuration of a live object by opening its configuration in an editor.
patch: Directly modify specific fields of a live object by using a patch string.
For more details on patch strings, see the patch section in
API Conventions.
How to delete objects
You can use the delete command to delete an object from a cluster:
delete <type>/<name>
Note: You can use kubectl delete for both imperative commands and imperative object
configuration. The difference is in the arguments passed to the command. To use
kubectl delete as an imperative command, pass the object to be deleted as
an argument. Here's an example that passes a Deployment object named nginx:
kubectl delete deployment/nginx
How to view an object
There are several commands for printing information about an object:
get: Prints basic information about matching objects. Use get -h to see a list of options.
describe: Prints aggregated detailed information about matching objects.
logs: Prints the stdout and stderr for a container running in a Pod.
Using set commands to modify objects before creation
There are some object fields that don't have a flag you can use
in a create command. In some of those cases, you can use a combination of
set and create to specify a value for the field before object
creation. This is done by piping the output of the create command to the
set command, and then back to the create command. Here's an example:
The kubectl create service -o yaml --dry-run=client command creates the configuration for the Service, but prints it to stdout as YAML instead of sending it to the Kubernetes API server.
The kubectl set selector --local -f - -o yaml command reads the configuration from stdin, and writes the updated configuration to stdout as YAML.
The kubectl create -f - command creates the object using the configuration provided via stdin.
Using --edit to modify objects before creation
You can use kubectl create --edit to make arbitrary changes to an object
before it is created. Here's an example:
5.4 - Imperative Management of Kubernetes Objects Using Configuration Files
Kubernetes objects can be created, updated, and deleted by using the kubectl
command-line tool along with an object configuration file written in YAML or JSON.
This document explains how to define and manage objects using configuration files.
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
The kubectl tool supports three kinds of object management:
Imperative commands
Imperative object configuration
Declarative object configuration
See Kubernetes Object Management
for a discussion of the advantages and disadvantage of each kind of object management.
How to create objects
You can use kubectl create -f to create an object from a configuration file.
Refer to the kubernetes API reference
for details.
kubectl create -f <filename|url>
How to update objects
Warning: Updating objects with the replace command drops all
parts of the spec not specified in the configuration file. This
should not be used with objects whose specs are partially managed
by the cluster, such as Services of type LoadBalancer, where
the externalIPs field is managed independently from the configuration
file. Independently managed fields must be copied to the configuration
file to prevent replace from dropping them.
You can use kubectl replace -f to update a live object according to a
configuration file.
kubectl replace -f <filename|url>
How to delete objects
You can use kubectl delete -f to delete an object that is described in a
configuration file.
kubectl delete -f <filename|url>
Note:
If configuration file has specified the generateName field in the metadata
section instead of the name field, you cannot delete the object using
kubectl delete -f <filename|url>.
You will have to use other flags for deleting the object. For example:
You can use kubectl get -f to view information about an object that is
described in a configuration file.
kubectl get -f <filename|url> -o yaml
The -o yaml flag specifies that the full object configuration is printed.
Use kubectl get -h to see a list of options.
Limitations
The create, replace, and delete commands work well when each object's
configuration is fully defined and recorded in its configuration
file. However when a live object is updated, and the updates are not merged
into its configuration file, the updates will be lost the next time a replace
is executed. This can happen if a controller, such as
a HorizontalPodAutoscaler, makes updates directly to a live object. Here's
an example:
You create an object from a configuration file.
Another source updates the object by changing some field.
You replace the object from the configuration file. Changes made by
the other source in step 2 are lost.
If you need to support multiple writers to the same object, you can use
kubectl apply to manage the object.
Creating and editing an object from a URL without saving the configuration
Suppose you have the URL of an object configuration file. You can use
kubectl create --edit to make changes to the configuration before the
object is created. This is particularly useful for tutorials and tasks
that point to a configuration file that could be modified by the reader.
kubectl create -f <url> --edit
Migrating from imperative commands to imperative object configuration
Migrating from imperative commands to imperative object configuration involves
several manual steps.
Export the live object to a local object configuration file:
kubectl get <kind>/<name> -o yaml > <kind>_<name>.yaml
Manually remove the status field from the object configuration file.
For subsequent object management, use replace exclusively.
kubectl replace -f <kind>_<name>.yaml
Defining controller selectors and PodTemplate labels
Warning: Updating selectors on controllers is strongly discouraged.
The recommended approach is to define a single, immutable PodTemplate label
used only by the controller selector with no other semantic meaning.
5.5 - Update API Objects in Place Using kubectl patch
Use kubectl patch to update Kubernetes API objects in place. Do a strategic merge patch or a JSON merge patch.
This task shows how to use kubectl patch to update an API object in place. The exercises
in this task demonstrate a strategic merge patch and a JSON merge patch.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
The output shows that the Deployment has two Pods. The 1/1 indicates that
each Pod has one container:
NAME READY STATUS RESTARTS AGE
patch-demo-28633765-670qr 1/1 Running 0 23s
patch-demo-28633765-j5qs3 1/1 Running 0 23s
Make a note of the names of the running Pods. Later, you will see that these Pods
get terminated and replaced by new ones.
At this point, each Pod has one Container that runs the nginx image. Now suppose
you want each Pod to have two containers: one that runs nginx and one that runs redis.
Create a file named patch-file.yaml that has this content:
View the Pods associated with your patched Deployment:
kubectl get pods
The output shows that the running Pods have different names from the Pods that
were running previously. The Deployment terminated the old Pods and created two
new Pods that comply with the updated Deployment spec. The 2/2 indicates that
each Pod has two Containers:
NAME READY STATUS RESTARTS AGE
patch-demo-1081991389-2wrn5 2/2 Running 0 1m
patch-demo-1081991389-jmg7b 2/2 Running 0 1m
Take a closer look at one of the patch-demo Pods:
kubectl get pod <your-pod-name> --output yaml
The output shows that the Pod has two Containers: one running nginx and one running redis:
containers:
- image: redis
...
- image: nginx
...
Notes on the strategic merge patch
The patch you did in the preceding exercise is called a strategic merge patch.
Notice that the patch did not replace the containers list. Instead it added a new
Container to the list. In other words, the list in the patch was merged with the
existing list. This is not always what happens when you use a strategic merge patch on a list.
In some cases, the list is replaced, not merged.
With a strategic merge patch, a list is either replaced or merged depending on its
patch strategy. The patch strategy is specified by the value of the patchStrategy key
in a field tag in the Kubernetes source code. For example, the Containers field of PodSpec
struct has a patchStrategy of merge:
You can also see the patch strategy in the
OpenApi spec:
"io.k8s.api.core.v1.PodSpec": {...,"containers": {"description": "List of containers belonging to the pod. ...."},"x-kubernetes-patch-merge-key": "name","x-kubernetes-patch-strategy": "merge"}
Notice that the tolerations list in the PodSpec was replaced, not merged. This is because
the Tolerations field of PodSpec does not have a patchStrategy key in its field tag. So the
strategic merge patch uses the default patch strategy, which is replace.
type PodSpec struct {
... Tolerations []Toleration `json:"tolerations,omitempty" protobuf:"bytes,22,opt,name=tolerations"`...}
Use a JSON merge patch to update a Deployment
A strategic merge patch is different from a
JSON merge patch.
With a JSON merge patch, if you
want to update a list, you have to specify the entire new list. And the new list completely
replaces the existing list.
The kubectl patch command has a type parameter that you can set to one of these values:
The containers list that you specified in the patch has only one Container.
The output shows that your list of one Container replaced the existing containers list.
In the output, you can see that the existing Pods were terminated, and new Pods
were created. The 1/1 indicates that each new Pod is running only one Container.
NAME READY STATUS RESTARTS AGE
patch-demo-1307768864-69308 1/1 Running 0 1m
patch-demo-1307768864-c86dc 1/1 Running 0 1m
Use strategic merge patch to update a Deployment using the retainKeys strategy
Here's the configuration file for a Deployment that uses the RollingUpdate strategy:
In the output, you can see that it is not possible to set type as Recreate when a value is defined for spec.strategy.rollingUpdate:
The Deployment "retainkeys-demo" is invalid: spec.strategy.rollingUpdate: Forbidden: may not be specified when strategy `type` is 'Recreate'
The way to remove the value for spec.strategy.rollingUpdate when updating the value for type is to use the retainKeys strategy for the strategic merge.
Create another file named patch-file-retainkeys.yaml that has this content:
spec:strategy:$retainKeys:- typetype:Recreate
With this patch, we indicate that we want to retain only the type key of the strategy object. Thus, the rollingUpdate will be removed during the patch operation.
kubectl get deployment retainkeys-demo --output yaml
The output shows that the strategy object in the Deployment does not contain the rollingUpdate key anymore:
spec:strategy:type:Recreatetemplate:
Notes on the strategic merge patch using the retainKeys strategy
The patch you did in the preceding exercise is called a strategic merge patch with retainKeys strategy. This method introduces a new directive $retainKeys that has the following strategies:
It contains a list of strings.
All fields needing to be preserved must be present in the $retainKeys list.
The fields that are present will be merged with live object.
All of the missing fields will be cleared when patching.
All fields in the $retainKeys list must be a superset or the same as the fields present in the patch.
The retainKeys strategy does not work for all objects. It only works when the value of the patchStrategy key in a field tag in the Kubernetes source code contains retainKeys. For example, the Strategy field of the DeploymentSpec struct has a patchStrategy of retainKeys:
You can also see the retainKeys strategy in the OpenApi spec:
"io.k8s.api.apps.v1.DeploymentSpec": {...,"strategy": {"$ref": "#/definitions/io.k8s.api.apps.v1.DeploymentStrategy","description": "The deployment strategy to use to replace existing pods with new ones.","x-kubernetes-patch-strategy": "retainKeys"},....}
Update an object's replica count using kubectl patch with --subresource
FEATURE STATE:Kubernetes v1.24 [alpha]
The flag --subresource=[subresource-name] is used with kubectl commands like get, patch,
edit and replace to fetch and update status and scale subresources of the resources
(applicable for kubectl version v1.24 or more). This flag is used with all the API resources
(built-in and CRs) that have status or scale subresource. Deployment is one of the
examples which supports these subresources.
Here's a manifest for a Deployment that has two replicas:
apiVersion:apps/v1kind:Deploymentmetadata:name:nginx-deploymentspec:selector:matchLabels:app:nginxreplicas:2# tells deployment to run 2 pods matching the templatetemplate:metadata:labels:app:nginxspec:containers:- name:nginximage:nginx:1.14.2ports:- containerPort:80
Note: If you run kubectl patch and specify --subresource flag for resource that doesn't support that
particular subresource, the API server returns a 404 Not Found error.
Summary
In this exercise, you used kubectl patch to change the live configuration
of a Deployment object. You did not change the configuration file that you originally used to
create the Deployment object. Other commands for updating API objects include
kubectl annotate,
kubectl edit,
kubectl replace,
kubectl scale,
and
kubectl apply.
Note: Strategic merge patch is not supported for custom resources.
Managing confidential settings data using Secrets.
6.1 - Managing Secrets using kubectl
Creating Secret objects using kubectl command line.
This page shows you how to create, edit, manage, and delete Kubernetes
Secrets using the kubectl
command-line tool.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
A Secret object stores sensitive data such as credentials
used by Pods to access services. For example, you might need a Secret to store
the username and password needed to access a database.
You can create the Secret by passing the raw data in the command, or by storing
the credentials in files that you pass in the command. The following commands
create a Secret that stores the username admin and the password S!B\*d$zDsb=.
You must use single quotes '' to escape special characters such as $, \,
*, =, and ! in your strings. If you don't, your shell will interpret these
characters.
The -n flag ensures that the generated files do not have an extra newline
character at the end of the text. This is important because when kubectl
reads a file and encodes the content into a base64 string, the extra
newline character gets encoded too. You do not need to escape special
characters in strings that you include in a file.
The commands kubectl get and kubectl describe avoid showing the contents
of a Secret by default. This is to protect the Secret from being exposed
accidentally, or from being stored in a terminal log.
Decode the Secret
View the contents of the Secret you created:
kubectl get secret db-user-pass -o jsonpath='{.data}'
Caution: This is an example for documentation purposes. In practice,
this method could cause the command with the encoded data to be stored in
your shell history. Anyone with access to your computer could find the
command and decode the secret. A better approach is to combine the view and
decode commands.
kubectl get secret db-user-pass -o jsonpath='{.data.password}' | base64 --decode
Edit a Secret
You can edit an existing Secret object unless it is
immutable. To edit a
Secret, run the following command:
kubectl edit secrets <secret-name>
This opens your default editor and allows you to update the base64 encoded
Secret values in the data field, such as in the following example:
# Please edit the object below. Lines beginning with a '#' will be ignored,# and an empty file will abort the edit. If an error occurs while saving this file, it will be# reopened with the relevant failures.#apiVersion:v1data:password:UyFCXCpkJHpEc2I9username:YWRtaW4=kind:Secretmetadata:creationTimestamp:"2022-06-28T17:44:13Z"name:db-user-passnamespace:defaultresourceVersion:"12708504"uid:91becd59-78fa-4c85-823f-6d44436242actype:Opaque
Creating Secret objects using resource configuration file.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
You can define the Secret object in a manifest first, in JSON or YAML format,
and then create that object. The
Secret
resource contains two maps: data and stringData.
The data field is used to store arbitrary data, encoded using base64. The
stringData field is provided for convenience, and it allows you to provide
the same data as unencoded strings.
The keys of data and stringData must consist of alphanumeric characters,
-, _ or ..
The following example stores two strings in a Secret using the data field.
Note: The serialized JSON and YAML values of Secret data are encoded as base64 strings. Newlines are not valid within these strings and must be omitted. When using the base64 utility on Darwin/macOS, users should avoid using the -b option to split long lines. Conversely, Linux users should add the option -w 0 to base64 commands or the pipeline base64 | tr -d '\n' if the -w option is not available.
For certain scenarios, you may wish to use the stringData field instead. This
field allows you to put a non-base64 encoded string directly into the Secret,
and the string will be encoded for you when the Secret is created or updated.
A practical example of this might be where you are deploying an application
that uses a Secret to store a configuration file, and you want to populate
parts of that configuration file during your deployment process.
For example, if your application uses the following configuration file:
To edit the data in the Secret you created using a manifest, modify the data
or stringData field in your manifest and apply the file to your
cluster. You can edit an existing Secret object unless it is
immutable.
For example, if you want to change the password from the previous example to
birdsarentreal, do the following:
Encode the new password string:
echo -n 'birdsarentreal' | base64
The output is similar to:
YmlyZHNhcmVudHJlYWw=
Update the data field with your new password string:
Kubernetes updates the existing Secret object. In detail, the kubectl tool
notices that there is an existing Secret object with the same name. kubectl
fetches the existing object, plans changes to it, and submits the changed
Secret object to your cluster control plane.
If you specified kubectl apply --server-side instead, kubectl uses
Server Side Apply instead.
Creating Secret objects using kustomization.yaml file.
kubectl supports using the Kustomize object management tool to manage Secrets
and ConfigMaps. You create a resource generator using Kustomize, which
generates a Secret that you can apply to the API server using kubectl.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
You can generate a Secret by defining a secretGenerator in a
kustomization.yaml file that references other existing files, .env files, or
literal values. For example, the following instructions create a Kustomization
file for the username admin and the password 1f2d1e2e67df.
You can also define the secretGenerator in the kustomization.yaml file by
providing .env files. For example, the following kustomization.yaml file
pulls in data from an .env.secret file:
In all cases, you don't need to base64 encode the values. The name of the YAML
file must be kustomization.yaml or kustomization.yml.
Apply the kustomization file
To create the Secret, apply the directory that contains the kustomization file:
kubectl apply -k <directory-path>
The output is similar to:
secret/database-creds-5hdh7hhgfk created
When a Secret is generated, the Secret name is created by hashing
the Secret data and appending the hash value to the name. This ensures that
a new Secret is generated each time the data is modified.
To verify that the Secret was created and to decode the Secret data,
kubectl get -k <directory-path> -o jsonpath='{.data}'
In your kustomization.yaml file, modify the data, such as the password.
Apply the directory that contains the kustomization file:
kubectl apply -k <directory-path>
The output is similar to:
secret/db-user-pass-6f24b56cc8 created
The edited Secret is created as a new Secret object, instead of updating the
existing Secret object. You might need to update references to the Secret in
your Pods.
Specify configuration and other data for the Pods that run your workload.
7.1 - Define a Command and Arguments for a Container
This page shows how to define commands and arguments when you run a container
in a Pod.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Define a command and arguments when you create a Pod
When you create a Pod, you can define a command and arguments for the
containers that run in the Pod. To define a command, include the command
field in the configuration file. To define arguments for the command, include
the args field in the configuration file. The command and arguments that
you define cannot be changed after the Pod is created.
The command and arguments that you define in the configuration file
override the default command and arguments provided by the container image.
If you define args, but do not define a command, the default command is used
with your new arguments.
Note: The command field corresponds to entrypoint in some container runtimes.
In this exercise, you create a Pod that runs one container. The configuration
file for the Pod defines a command and two arguments:
The output shows that the container that ran in the command-demo Pod has
completed.
To see the output of the command that ran in the container, view the logs
from the Pod:
kubectl logs command-demo
The output shows the values of the HOSTNAME and KUBERNETES_PORT environment
variables:
command-demo
tcp://10.3.240.1:443
Use environment variables to define arguments
In the preceding example, you defined the arguments directly by
providing strings. As an alternative to providing strings directly,
you can define arguments by using environment variables:
This means you can define an argument for a Pod using any of
the techniques available for defining environment variables, including
ConfigMaps
and
Secrets.
Note: The environment variable appears in parentheses, "$(VAR)". This is
required for the variable to be expanded in the command or args field.
Run a command in a shell
In some cases, you need your command to run in a shell. For example, your
command might consist of several commands piped together, or it might be a shell
script. To run your command in a shell, wrap it like this:
command: ["/bin/sh"]args: ["-c", "while true; do echo hello; sleep 10;done"]
This page shows how to define dependent environment variables for a container
in a Kubernetes Pod.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Define an environment dependent variable for a container
When you create a Pod, you can set dependent environment variables for the containers that run in the Pod. To set dependent environment variables, you can use $(VAR_NAME) in the value of env in the configuration file.
In this exercise, you create a Pod that runs one container. The configuration
file for the Pod defines a dependent environment variable with common usage defined. Here is the configuration manifest for the
Pod:
As shown above, you have defined the correct dependency reference of SERVICE_ADDRESS, bad dependency reference of UNCHANGED_REFERENCE and skip dependent references of ESCAPED_REFERENCE.
When an environment variable is already defined when being referenced,
the reference can be correctly resolved, such as in the SERVICE_ADDRESS case.
Note that order matters in the env list. An environment variable is not considered
"defined" if it is specified further down the list. That is why UNCHANGED_REFERENCE
fails to resolve $(PROTOCOL) in the example above.
When the environment variable is undefined or only includes some variables, the undefined environment variable is treated as a normal string, such as UNCHANGED_REFERENCE. Note that incorrectly parsed environment variables, in general, will not block the container from starting.
The $(VAR_NAME) syntax can be escaped with a double $, ie: $$(VAR_NAME).
Escaped references are never expanded, regardless of whether the referenced variable
is defined or not. This can be seen from the ESCAPED_REFERENCE case above.
7.3 - Define Environment Variables for a Container
This page shows how to define environment variables for a container
in a Kubernetes Pod.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
When you create a Pod, you can set environment variables for the containers
that run in the Pod. To set environment variables, include the env or
envFrom field in the configuration file.
The env and envFrom fields have different effects.
env
allows you to set environment variables for a container, specifying a value directly for each variable that you name.
envFrom
allows you to set environment variables for a container by referencing either a ConfigMap or a Secret.
When you use envFrom, all the key-value pairs in the referenced ConfigMap or Secret
are set as environment variables for the container.
You can also specify a common prefix string.
In this exercise, you create a Pod that runs one container. The configuration
file for the Pod defines an environment variable with name DEMO_GREETING and
value "Hello from the environment". Here is the configuration manifest for the
Pod:
apiVersion:v1kind:Podmetadata:name:envar-demolabels:purpose:demonstrate-envarsspec:containers:- name:envar-demo-containerimage:gcr.io/google-samples/node-hello:1.0env:- name:DEMO_GREETINGvalue:"Hello from the environment"- name:DEMO_FAREWELLvalue:"Such a sweet sorrow"
NAME READY STATUS RESTARTS AGE
envar-demo 1/1 Running 0 9s
List the Pod's container environment variables:
kubectl exec envar-demo -- printenv
The output is similar to this:
NODE_VERSION=4.4.2
EXAMPLE_SERVICE_PORT_8080_TCP_ADDR=10.3.245.237
HOSTNAME=envar-demo
...
DEMO_GREETING=Hello from the environment
DEMO_FAREWELL=Such a sweet sorrow
Note: The environment variables set using the env or envFrom field
override any environment variables specified in the container image.
Note: Environment variables may reference each other, however ordering is important.
Variables making use of others defined in the same context must come later in
the list. Similarly, avoid circular references.
Using environment variables inside of your config
Environment variables that you define in a Pod's configuration under
.spec.containers[*].env[*] can be used elsewhere in the configuration, for
example in commands and arguments that you set for the Pod's containers.
In the example configuration below, the GREETING, HONORIFIC, and
NAME environment variables are set to Warm greetings to, The Most Honorable, and Kubernetes, respectively. The environment variable
MESSAGE combines the set of all these environment variables and then uses it
as a CLI argument passed to the env-print-demo container.
7.4 - Expose Pod Information to Containers Through Environment Variables
This page shows how a Pod can use environment variables to expose information
about itself to containers running in the Pod, using the downward API.
You can use environment variables to expose Pod fields, container fields, or both.
In Kubernetes, there are two ways to expose Pod and container fields to a running container:
Together, these two ways of exposing Pod and container fields are called the
downward API.
As Services are the primary mode of communication between containerized applications managed by Kubernetes,
it is helpful to be able to discover them at runtime.
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Use Pod fields as values for environment variables
In this part of exercise, you create a Pod that has one container, and you
project Pod-level fields into the running container as environment variables.
In that manifest, you can see five environment variables. The env
field is an array of
environment variable definitions.
The first element in the array specifies that the MY_NODE_NAME environment
variable gets its value from the Pod's spec.nodeName field. Similarly, the
other environment variables get their names from Pod fields.
Note: The fields in this example are Pod fields. They are not fields of the
container in the Pod.
To see why these values are in the log, look at the command and args fields
in the configuration file. When the container starts, it writes the values of
five environment variables to stdout. It repeats this every ten seconds.
Next, get a shell into the container that is running in your Pod:
kubectl exec -it dapi-envars-fieldref -- sh
In your shell, view the environment variables:
# Run this in a shell inside the containerprintenv
The output shows that certain environment variables have been assigned the
values of Pod fields:
Use container fields as values for environment variables
In the preceding exercise, you used information from Pod-level fields as the values
for environment variables.
In this next exercise, you are going to pass fields that are part of the Pod
definition, but taken from the specific
container
rather than from the Pod overall.
Here is a manifest for another Pod that again has just one container:
In this manifest, you can see four environment variables. The env
field is an array of
environment variable definitions.
The first element in the array specifies that the MY_CPU_REQUEST environment
variable gets its value from the requests.cpu field of a container named
test-container. Similarly, the other environment variables get their values
from fields that are specific to this container.
7.5 - Expose Pod Information to Containers Through Files
This page shows how a Pod can use a
downwardAPI volume,
to expose information about itself to containers running in the Pod.
A downwardAPI volume can expose Pod fields and container fields.
In Kubernetes, there are two ways to expose Pod and container fields to a running container:
Together, these two ways of exposing Pod and container fields are called the
downward API.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
In this part of exercise, you create a Pod that has one container, and you
project Pod-level fields into the running container as files.
Here is the manifest for the Pod:
In the manifest, you can see that the Pod has a downwardAPI Volume,
and the container mounts the volume at /etc/podinfo.
Look at the items array under downwardAPI. Each element of the array
defines a downwardAPI volume.
The first element specifies that the value of the Pod's
metadata.labels field should be stored in a file named labels.
The second element specifies that the value of the Pod's annotations
field should be stored in a file named annotations.
Note: The fields in this example are Pod fields. They are not
fields of the container in the Pod.
In the output, you can see that the labels and annotations files
are in a temporary subdirectory: in this example,
..2982_06_02_21_47_53.299460680. In the /etc/podinfo directory, ..data is
a symbolic link to the temporary subdirectory. Also in the /etc/podinfo directory,
labels and annotations are symbolic links.
drwxr-xr-x ... Feb 6 21:47 ..2982_06_02_21_47_53.299460680
lrwxrwxrwx ... Feb 6 21:47 ..data -> ..2982_06_02_21_47_53.299460680
lrwxrwxrwx ... Feb 6 21:47 annotations -> ..data/annotations
lrwxrwxrwx ... Feb 6 21:47 labels -> ..data/labels
/etc/..2982_06_02_21_47_53.299460680:
total 8
-rw-r--r-- ... Feb 6 21:47 annotations
-rw-r--r-- ... Feb 6 21:47 labels
Using symbolic links enables dynamic atomic refresh of the metadata; updates are
written to a new temporary directory, and the ..data symlink is updated
atomically using rename(2).
Note: A container using Downward API as a
subPath volume mount will not
receive Downward API updates.
Exit the shell:
/# exit
Store container fields
The preceding exercise, you made Pod-level fields accessible using the
downward API.
In this next exercise, you are going to pass fields that are part of the Pod
definition, but taken from the specific
container
rather than from the Pod overall. Here is a manifest for a Pod that again has
just one container:
In the manifest, you can see that the Pod has a
downwardAPI volume,
and that the single container in that Pod mounts the volume at /etc/podinfo.
Look at the items array under downwardAPI. Each element of the array
defines a file in the downward API volume.
The first element specifies that in the container named client-container,
the value of the limits.cpu field in the format specified by 1m should be
published as a file named cpu_limit. The divisor field is optional and has the
default value of 1. A divisor of 1 means cores for cpu resources, or
bytes for memory resources.
Get a shell into the container that is running in your Pod:
kubectl exec -it kubernetes-downwardapi-volume-example-2 -- sh
In your shell, view the cpu_limit file:
# Run this in a shell inside the containercat /etc/podinfo/cpu_limit
You can use similar commands to view the cpu_request, mem_limit and
mem_request files.
Project keys to specific paths and file permissions
You can project keys to specific paths and specific permissions on a per-file
basis. For more information, see
Secrets.
What's next
Read the spec
API definition for Pod. This includes the definition of Container (part of Pod).
Read the list of available fields that you
can expose using the downward API.
Read about volumes in the legacy API reference:
Check the Volume
API definition which defines a generic volume in a Pod for containers to access.
Check the DownwardAPIVolumeSource
API definition which defines a volume that contains Downward API information.
Check the DownwardAPIVolumeFile
API definition which contains references to object or resource fields for
populating a file in the Downward API volume.
Check the ResourceFieldSelector
API definition which specifies the container resources and their output format.
7.6 - Distribute Credentials Securely Using Secrets
This page shows how to securely inject sensitive data, such as passwords and
encryption keys, into Pods.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Convert your secret data to a base-64 representation
Suppose you want to have two pieces of secret data: a username my-app and a password
39528$vdg7Jb. First, use a base64 encoding tool to convert your username and password to a base64 representation. Here's an example using the commonly available base64 program:
apiVersion:v1kind:Podmetadata:name:secret-test-podspec:containers:- name:test-containerimage:nginxvolumeMounts:# name must match the volume name below- name:secret-volumemountPath:/etc/secret-volumereadOnly:true# The secret data is exposed to Containers in the Pod through a Volume.volumes:- name:secret-volumesecret:secretName:test-secret
NAME READY STATUS RESTARTS AGE
secret-test-pod 1/1 Running 0 42m
Get a shell into the Container that is running in your Pod:
kubectl exec -i -t secret-test-pod -- /bin/bash
The secret data is exposed to the Container through a Volume mounted under
/etc/secret-volume.
In your shell, list the files in the /etc/secret-volume directory:
# Run this in the shell inside the containerls /etc/secret-volume
The output shows two files, one for each piece of secret data:
password username
In your shell, display the contents of the username and password files:
# Run this in the shell inside the containerecho"$( cat /etc/secret-volume/username )"echo"$( cat /etc/secret-volume/password )"
The output is your username and password:
my-app
39528$vdg7Jb
Modify your image or command line so that the program looks for files in the
mountPath directory. Each key in the Secret data map becomes a file name
in this directory.
Project Secret keys to specific file paths
You can also control the paths within the volume where Secret keys are projected. Use the
.spec.volumes[].secret.items field to change the target path of each key:
The username key from mysecret is available to the container at the path
/etc/foo/my-group/my-username instead of at /etc/foo/username.
The password key from that Secret object is not projected.
If you list keys explicitly using .spec.volumes[].secret.items, consider the
following:
Only keys specified in items are projected.
To consume all keys from the Secret, all of them must be listed in the
items field.
All listed keys must exist in the corresponding Secret. Otherwise, the volume
is not created.
Set POSIX permissions for Secret keys
You can set the POSIX file access permission bits for a single Secret key.
If you don't specify any permissions, 0644 is used by default.
You can also set a default POSIX file mode for the entire Secret volume, and
you can override per key if needed.
For example, you can specify a default mode like this:
The Secret is mounted on /etc/foo; all the files created by the
secret volume mount have permission 0400.
Note: If you're defining a Pod or a Pod template using JSON, beware that the JSON
specification doesn't support octal literals for numbers because JSON considers
0400 to be the decimal value 400. In JSON, use decimal values for the
defaultMode instead. If you're writing YAML, you can write the defaultMode
in octal.
Define container environment variables using Secret data
You can consume the data in Secrets as environment variables in your
containers.
If a container already consumes a Secret in an environment variable,
a Secret update will not be seen by the container unless it is
restarted. There are third party solutions for triggering restarts when
secrets change.
Define a container environment variable with data from a single Secret
Define an environment variable as a key-value pair in a Secret:
Use envFrom to define all of the Secret's data as container environment variables.
The key from the Secret becomes the environment variable name in the Pod.
Example: Provide prod/test credentials to Pods using Secrets
This example illustrates a Pod which consumes a secret containing production credentials and
another Pod which consumes a secret with test environment credentials.
Special characters such as $, \, *, =, and ! will be interpreted by your
shell and require escaping.
In most shells, the easiest way to escape the password is to surround it with single quotes (').
For example, if your actual password is S!B\*d$zDsb=, you should execute the command as follows:
Run and manage both stateless and stateful applications.
8.1 - Run a Stateless Application Using a Deployment
This page shows how to run an application using a Kubernetes Deployment object.
Objectives
Create an nginx deployment.
Use kubectl to list information about the deployment.
Update the deployment.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Your Kubernetes server must be at or later than version v1.9.
To check the version, enter kubectl version.
Creating and exploring an nginx deployment
You can run an application by creating a Kubernetes Deployment object, and you
can describe a Deployment in a YAML file. For example, this YAML file describes
a Deployment that runs the nginx:1.14.2 Docker image:
apiVersion:apps/v1kind:Deploymentmetadata:name:nginx-deploymentspec:selector:matchLabels:app:nginxreplicas:2# tells deployment to run 2 pods matching the templatetemplate:metadata:labels:app:nginxspec:containers:- name:nginximage:nginx:1.14.2ports:- containerPort:80
apiVersion:apps/v1kind:Deploymentmetadata:name:nginx-deploymentspec:selector:matchLabels:app:nginxreplicas:2template:metadata:labels:app:nginxspec:containers:- name:nginximage:nginx:1.16.1# Update the version of nginx from 1.14.2 to 1.16.1ports:- containerPort:80
Watch the deployment create pods with new names and delete the old pods:
kubectl get pods -l app=nginx
Scaling the application by increasing the replica count
You can increase the number of Pods in your Deployment by applying a new YAML
file. This YAML file sets replicas to 4, which specifies that the Deployment
should have four Pods:
apiVersion:apps/v1kind:Deploymentmetadata:name:nginx-deploymentspec:selector:matchLabels:app:nginxreplicas:4# Update the replicas from 2 to 4template:metadata:labels:app:nginxspec:containers:- name:nginximage:nginx:1.16.1ports:- containerPort:80
NAME READY STATUS RESTARTS AGE
nginx-deployment-148880595-4zdqq 1/1 Running 0 25s
nginx-deployment-148880595-6zgi1 1/1 Running 0 25s
nginx-deployment-148880595-fxcez 1/1 Running 0 2m
nginx-deployment-148880595-rwovn 1/1 Running 0 2m
Deleting a deployment
Delete the deployment by name:
kubectl delete deployment nginx-deployment
ReplicationControllers -- the Old Way
The preferred way to create a replicated application is to use a Deployment,
which in turn uses a ReplicaSet. Before the Deployment and ReplicaSet were
added to Kubernetes, replicated applications were configured using a
ReplicationController.
This page shows you how to run a single-instance stateful application
in Kubernetes using a PersistentVolume and a Deployment. The
application is MySQL.
Objectives
Create a PersistentVolume referencing a disk in your environment.
Create a MySQL Deployment.
Expose MySQL to other pods in the cluster at a known DNS name.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
You can run a stateful application by creating a Kubernetes Deployment
and connecting it to an existing PersistentVolume using a
PersistentVolumeClaim. For example, this YAML file describes a
Deployment that runs MySQL and references the PersistentVolumeClaim. The file
defines a volume mount for /var/lib/mysql, and then creates a
PersistentVolumeClaim that looks for a 20G volume. This claim is
satisfied by any existing volume that meets the requirements,
or by a dynamic provisioner.
Note: The password is defined in the config yaml, and this is insecure. See
Kubernetes Secrets
for a secure solution.
apiVersion:v1kind:Servicemetadata:name:mysqlspec:ports:- port:3306selector:app:mysqlclusterIP:None---apiVersion:apps/v1kind:Deploymentmetadata:name:mysqlspec:selector:matchLabels:app:mysqlstrategy:type:Recreatetemplate:metadata:labels:app:mysqlspec:containers:- image:mysql:5.6name:mysqlenv:# Use secret in real usage- name:MYSQL_ROOT_PASSWORDvalue:passwordports:- containerPort:3306name:mysqlvolumeMounts:- name:mysql-persistent-storagemountPath:/var/lib/mysqlvolumes:- name:mysql-persistent-storagepersistentVolumeClaim:claimName:mysql-pv-claim
The preceding YAML file creates a service that
allows other Pods in the cluster to access the database. The Service option
clusterIP: None lets the Service DNS name resolve directly to the
Pod's IP address. This is optimal when you have only one Pod
behind a Service and you don't intend to increase the number of Pods.
Run a MySQL client to connect to the server:
kubectl run -it --rm --image=mysql:5.6 --restart=Never mysql-client -- mysql -h mysql -ppassword
This command creates a new Pod in the cluster running a MySQL client
and connects it to the server through the Service. If it connects, you
know your stateful MySQL database is up and running.
Waiting for pod default/mysql-client-274442439-zyp6i to be running, status is Pending, pod ready: false
If you don't see a command prompt, try pressing enter.
mysql>
Updating
The image or any other part of the Deployment can be updated as usual
with the kubectl apply command. Here are some precautions that are
specific to stateful apps:
Don't scale the app. This setup is for single-instance apps
only. The underlying PersistentVolume can only be mounted to one
Pod. For clustered stateful apps, see the
StatefulSet documentation.
Use strategy:type: Recreate in the Deployment configuration
YAML file. This instructs Kubernetes to not use rolling
updates. Rolling updates will not work, as you cannot have more than
one Pod running at a time. The Recreate strategy will stop the
first pod before creating a new one with the updated configuration.
If you manually provisioned a PersistentVolume, you also need to manually
delete it, as well as release the underlying resource.
If you used a dynamic provisioner, it automatically deletes the
PersistentVolume when it sees that you deleted the PersistentVolumeClaim.
Some dynamic provisioners (such as those for EBS and PD) also release the
underlying resource upon deleting the PersistentVolume.
This page shows how to run a replicated stateful application using a
StatefulSet.
This application is a replicated MySQL database. The example topology has a
single primary server and multiple replicas, using asynchronous row-based
replication.
Note:This is not a production configuration. MySQL settings remain on insecure defaults to keep the focus
on general patterns for running stateful applications in Kubernetes.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
apiVersion:v1kind:ConfigMapmetadata:name:mysqllabels:app:mysqlapp.kubernetes.io/name:mysqldata:primary.cnf:| # Apply this config only on the primary.
[mysqld]
log-binreplica.cnf:| # Apply this config only on replicas.
[mysqld]
super-read-only
This ConfigMap provides my.cnf overrides that let you independently control
configuration on the primary MySQL server and its replicas.
In this case, you want the primary server to be able to serve replication logs to replicas
and you want replicas to reject any writes that don't come via replication.
There's nothing special about the ConfigMap itself that causes different
portions to apply to different Pods.
Each Pod decides which portion to look at as it's initializing,
based on information provided by the StatefulSet controller.
Create Services
Create the Services from the following YAML configuration file:
# Headless service for stable DNS entries of StatefulSet members.apiVersion:v1kind:Servicemetadata:name:mysqllabels:app:mysqlapp.kubernetes.io/name:mysqlspec:ports:- name:mysqlport:3306clusterIP:Noneselector:app:mysql---# Client service for connecting to any MySQL instance for reads.# For writes, you must instead connect to the primary: mysql-0.mysql.apiVersion:v1kind:Servicemetadata:name:mysql-readlabels:app:mysqlapp.kubernetes.io/name:mysqlreadonly:"true"spec:ports:- name:mysqlport:3306selector:app:mysql
The headless Service provides a home for the DNS entries that the StatefulSet
controllers creates for each
Pod that's part of the set.
Because the headless Service is named mysql, the Pods are accessible by
resolving <pod-name>.mysql from within any other Pod in the same Kubernetes
cluster and namespace.
The client Service, called mysql-read, is a normal Service with its own
cluster IP that distributes connections across all MySQL Pods that report
being Ready. The set of potential endpoints includes the primary MySQL server and all
replicas.
Note that only read queries can use the load-balanced client Service.
Because there is only one primary MySQL server, clients should connect directly to the
primary MySQL Pod (through its DNS entry within the headless Service) to execute
writes.
Create the StatefulSet
Finally, create the StatefulSet from the following YAML configuration file:
After a while, you should see all 3 Pods become Running:
NAME READY STATUS RESTARTS AGE
mysql-0 2/2 Running 0 2m
mysql-1 2/2 Running 0 1m
mysql-2 2/2 Running 0 1m
Press Ctrl+C to cancel the watch.
Note: If you don't see any progress, make sure you have a dynamic PersistentVolume
provisioner enabled, as mentioned in the prerequisites.
This manifest uses a variety of techniques for managing stateful Pods as part of
a StatefulSet. The next section highlights some of these techniques to explain
what happens as the StatefulSet creates Pods.
Understanding stateful Pod initialization
The StatefulSet controller starts Pods one at a time, in order by their
ordinal index.
It waits until each Pod reports being Ready before starting the next one.
In addition, the controller assigns each Pod a unique, stable name of the form
<statefulset-name>-<ordinal-index>, which results in Pods named mysql-0,
mysql-1, and mysql-2.
The Pod template in the above StatefulSet manifest takes advantage of these
properties to perform orderly startup of MySQL replication.
Generating configuration
Before starting any of the containers in the Pod spec, the Pod first runs any
init containers
in the order defined.
The first init container, named init-mysql, generates special MySQL config
files based on the ordinal index.
The script determines its own ordinal index by extracting it from the end of
the Pod name, which is returned by the hostname command.
Then it saves the ordinal (with a numeric offset to avoid reserved values)
into a file called server-id.cnf in the MySQL conf.d directory.
This translates the unique, stable identity provided by the StatefulSet
into the domain of MySQL server IDs, which require the same properties.
The script in the init-mysql container also applies either primary.cnf or
replica.cnf from the ConfigMap by copying the contents into conf.d.
Because the example topology consists of a single primary MySQL server and any number of
replicas, the script assigns ordinal 0 to be the primary server, and everyone
else to be replicas.
Combined with the StatefulSet controller's
deployment order guarantee,
this ensures the primary MySQL server is Ready before creating replicas, so they can begin
replicating.
Cloning existing data
In general, when a new Pod joins the set as a replica, it must assume the primary MySQL
server might already have data on it. It also must assume that the replication
logs might not go all the way back to the beginning of time.
These conservative assumptions are the key to allow a running StatefulSet
to scale up and down over time, rather than being fixed at its initial size.
The second init container, named clone-mysql, performs a clone operation on
a replica Pod the first time it starts up on an empty PersistentVolume.
That means it copies all existing data from another running Pod,
so its local state is consistent enough to begin replicating from the primary server.
MySQL itself does not provide a mechanism to do this, so the example uses a
popular open-source tool called Percona XtraBackup.
During the clone, the source MySQL server might suffer reduced performance.
To minimize impact on the primary MySQL server, the script instructs each Pod to clone
from the Pod whose ordinal index is one lower.
This works because the StatefulSet controller always ensures Pod N is
Ready before starting Pod N+1.
Starting replication
After the init containers complete successfully, the regular containers run.
The MySQL Pods consist of a mysql container that runs the actual mysqld
server, and an xtrabackup container that acts as a
sidecar.
The xtrabackup sidecar looks at the cloned data files and determines if
it's necessary to initialize MySQL replication on the replica.
If so, it waits for mysqld to be ready and then executes the
CHANGE MASTER TO and START SLAVE commands with replication parameters
extracted from the XtraBackup clone files.
Once a replica begins replication, it remembers its primary MySQL server and
reconnects automatically if the server restarts or the connection dies.
Also, because replicas look for the primary server at its stable DNS name
(mysql-0.mysql), they automatically find the primary server even if it gets a new
Pod IP due to being rescheduled.
Lastly, after starting replication, the xtrabackup container listens for
connections from other Pods requesting a data clone.
This server remains up indefinitely in case the StatefulSet scales up, or in
case the next Pod loses its PersistentVolumeClaim and needs to redo the clone.
Sending client traffic
You can send test queries to the primary MySQL server (hostname mysql-0.mysql)
by running a temporary container with the mysql:5.7 image and running the
mysql client binary.
kubectl run mysql-client --image=mysql:5.7 -i --rm --restart=Never --\
mysql -h mysql-0.mysql <<EOF
CREATE DATABASE test;
CREATE TABLE test.messages (message VARCHAR(250));
INSERT INTO test.messages VALUES ('hello');
EOF
Use the hostname mysql-read to send test queries to any server that reports
being Ready:
kubectl run mysql-client --image=mysql:5.7 -i -t --rm --restart=Never --\
mysql -h mysql-read -e "SELECT * FROM test.messages"
You should get output like this:
Waiting for pod default/mysql-client to be running, status is Pending, pod ready: false
+---------+
| message |
+---------+
| hello |
+---------+
pod "mysql-client" deleted
To demonstrate that the mysql-read Service distributes connections across
servers, you can run SELECT @@server_id in a loop:
kubectl run mysql-client-loop --image=mysql:5.7 -i -t --rm --restart=Never --\
bash -ic "while sleep 1; do mysql -h mysql-read -e 'SELECT @@server_id,NOW()'; done"
You should see the reported @@server_id change randomly, because a different
endpoint might be selected upon each connection attempt:
You can press Ctrl+C when you want to stop the loop, but it's useful to keep
it running in another window so you can see the effects of the following steps.
Simulate Pod and Node failure
To demonstrate the increased availability of reading from the pool of replicas
instead of a single server, keep the SELECT @@server_id loop from above
running while you force a Pod out of the Ready state.
Break the Readiness probe
The readiness probe
for the mysql container runs the command mysql -h 127.0.0.1 -e 'SELECT 1'
to make sure the server is up and able to execute queries.
One way to force this readiness probe to fail is to break that command:
kubectl exec mysql-2 -c mysql -- mv /usr/bin/mysql /usr/bin/mysql.off
This reaches into the actual container's filesystem for Pod mysql-2 and
renames the mysql command so the readiness probe can't find it.
After a few seconds, the Pod should report one of its containers as not Ready,
which you can check by running:
kubectl get pod mysql-2
Look for 1/2 in the READY column:
NAME READY STATUS RESTARTS AGE
mysql-2 1/2 Running 0 3m
At this point, you should see your SELECT @@server_id loop continue to run,
although it never reports 102 anymore.
Recall that the init-mysql script defined server-id as 100 + $ordinal,
so server ID 102 corresponds to Pod mysql-2.
Now repair the Pod and it should reappear in the loop output
after a few seconds:
kubectl exec mysql-2 -c mysql -- mv /usr/bin/mysql.off /usr/bin/mysql
Delete Pods
The StatefulSet also recreates Pods if they're deleted, similar to what a
ReplicaSet does for stateless Pods.
kubectl delete pod mysql-2
The StatefulSet controller notices that no mysql-2 Pod exists anymore,
and creates a new one with the same name and linked to the same
PersistentVolumeClaim.
You should see server ID 102 disappear from the loop output for a while
and then return on its own.
Drain a Node
If your Kubernetes cluster has multiple Nodes, you can simulate Node downtime
(such as when Nodes are upgraded) by issuing a
drain.
First determine which Node one of the MySQL Pods is on:
kubectl get pod mysql-2 -o wide
The Node name should show up in the last column:
NAME READY STATUS RESTARTS AGE IP NODE
mysql-2 2/2 Running 0 15m 10.244.5.27 kubernetes-node-9l2t
Then, drain the Node by running the following command, which cordons it so
no new Pods may schedule there, and then evicts any existing Pods.
Replace <node-name> with the name of the Node you found in the last step.
Caution: Draining a Node can impact other workloads and applications
running on the same node. Only perform the following step in a test
cluster.
# See above advice about impact on other workloadskubectl drain <node-name> --force --delete-emptydir-data --ignore-daemonsets
Now you can watch as the Pod reschedules on a different Node:
And again, you should see server ID 102 disappear from the
SELECT @@server_id loop output for a while and then return.
Now uncordon the Node to return it to a normal state:
kubectl uncordon <node-name>
Scaling the number of replicas
When you use MySQL replication, you can scale your read query capacity by
adding replicas.
For a StatefulSet, you can achieve this with a single command:
kubectl scale statefulset mysql --replicas=5
Watch the new Pods come up by running:
kubectl get pods -l app=mysql --watch
Once they're up, you should see server IDs 103 and 104 start appearing in
the SELECT @@server_id loop output.
You can also verify that these new servers have the data you added before they
existed:
kubectl run mysql-client --image=mysql:5.7 -i -t --rm --restart=Never --\
mysql -h mysql-3.mysql -e "SELECT * FROM test.messages"
Waiting for pod default/mysql-client to be running, status is Pending, pod ready: false
+---------+
| message |
+---------+
| hello |
+---------+
pod "mysql-client" deleted
Scaling back down is also seamless:
kubectl scale statefulset mysql --replicas=3
Note:
Although scaling up creates new PersistentVolumeClaims
automatically, scaling down does not automatically delete these PVCs.
This gives you the choice to keep those initialized PVCs around to make
scaling back up quicker, or to extract data before deleting them.
You can see this by running:
kubectl get pvc -l app=mysql
Which shows that all 5 PVCs still exist, despite having scaled the
StatefulSet down to 3:
Cancel the SELECT @@server_id loop by pressing Ctrl+C in its terminal,
or running the following from another terminal:
kubectl delete pod mysql-client-loop --now
Delete the StatefulSet. This also begins terminating the Pods.
kubectl delete statefulset mysql
Verify that the Pods disappear.
They might take some time to finish terminating.
kubectl get pods -l app=mysql
You'll know the Pods have terminated when the above returns:
No resources found.
Delete the ConfigMap, Services, and PersistentVolumeClaims.
kubectl delete configmap,service,pvc -l app=mysql
If you manually provisioned PersistentVolumes, you also need to manually
delete them, as well as release the underlying resources.
If you used a dynamic provisioner, it automatically deletes the
PersistentVolumes when it sees that you deleted the PersistentVolumeClaims.
Some dynamic provisioners (such as those for EBS and PD) also release the
underlying resources upon deleting the PersistentVolumes.
This task shows how to scale a StatefulSet. Scaling a StatefulSet refers to
increasing or decreasing the number of replicas.
Before you begin
StatefulSets are only available in Kubernetes version 1.5 or later.
To check your version of Kubernetes, run kubectl version.
Not all stateful applications scale nicely. If you are unsure about whether
to scale your StatefulSets, see StatefulSet concepts
or StatefulSet tutorial for further information.
You should perform scaling only when you are confident that your stateful application
cluster is completely healthy.
Scaling StatefulSets
Use kubectl to scale StatefulSets
First, find the StatefulSet you want to scale.
kubectl get statefulsets <stateful-set-name>
Change the number of replicas of your StatefulSet:
You cannot scale down a StatefulSet when any of the stateful Pods it manages is
unhealthy. Scaling down only takes place after those stateful Pods become running and ready.
If spec.replicas > 1, Kubernetes cannot determine the reason for an unhealthy Pod.
It might be the result of a permanent fault or of a transient fault. A transient
fault can be caused by a restart required by upgrading or maintenance.
If the Pod is unhealthy due to a permanent fault, scaling
without correcting the fault may lead to a state where the StatefulSet membership
drops below a certain minimum number of replicas that are needed to function
correctly. This may cause your StatefulSet to become unavailable.
If the Pod is unhealthy due to a transient fault and the Pod might become available again,
the transient error may interfere with your scale-up or scale-down operation. Some distributed
databases have issues when nodes join and leave at the same time. It is better
to reason about scaling operations at the application level in these cases, and
perform scaling only when you are sure that your stateful application cluster is
completely healthy.
This task assumes you have an application running on your cluster represented by a StatefulSet.
Deleting a StatefulSet
You can delete a StatefulSet in the same way you delete other resources in Kubernetes:
use the kubectl delete command, and specify the StatefulSet either by file or by name.
kubectl delete -f <file.yaml>
kubectl delete statefulsets <statefulset-name>
You may need to delete the associated headless service separately after the StatefulSet itself is deleted.
kubectl delete service <service-name>
When deleting a StatefulSet through kubectl, the StatefulSet scales down to 0.
All Pods that are part of this workload are also deleted. If you want to delete
only the StatefulSet and not the Pods, use --cascade=orphan. For example:
kubectl delete -f <file.yaml> --cascade=orphan
By passing --cascade=orphan to kubectl delete, the Pods managed by the StatefulSet
are left behind even after the StatefulSet object itself is deleted. If the pods have
a label app.kubernetes.io/name=MyApp, you can then delete them as follows:
Deleting the Pods in a StatefulSet will not delete the associated volumes.
This is to ensure that you have the chance to copy data off the volume before
deleting it. Deleting the PVC after the pods have terminated might trigger
deletion of the backing Persistent Volumes depending on the storage class
and reclaim policy. You should never assume ability to access a volume
after claim deletion.
Note: Use caution when deleting a PVC, as it may lead to data loss.
Complete deletion of a StatefulSet
To delete everything in a StatefulSet, including the associated pods,
you can run a series of commands similar to the following:
In the example above, the Pods have the label app.kubernetes.io/name=MyApp;
substitute your own label as appropriate.
Force deletion of StatefulSet pods
If you find that some pods in your StatefulSet are stuck in the 'Terminating'
or 'Unknown' states for an extended period of time, you may need to manually
intervene to forcefully delete the pods from the apiserver.
This is a potentially dangerous task. Refer to
Force Delete StatefulSet Pods
for details.
This page shows how to delete Pods which are part of a
stateful set,
and explains the considerations to keep in mind when doing so.
Before you begin
This is a fairly advanced task and has the potential to violate some of the properties
inherent to StatefulSet.
Before proceeding, make yourself familiar with the considerations enumerated below.
StatefulSet considerations
In normal operation of a StatefulSet, there is never a need to force delete a StatefulSet Pod.
The StatefulSet controller is responsible for
creating, scaling and deleting members of the StatefulSet. It tries to ensure that the specified
number of Pods from ordinal 0 through N-1 are alive and ready. StatefulSet ensures that, at any time,
there is at most one Pod with a given identity running in a cluster. This is referred to as
at most one semantics provided by a StatefulSet.
Manual force deletion should be undertaken with caution, as it has the potential to violate the
at most one semantics inherent to StatefulSet. StatefulSets may be used to run distributed and
clustered applications which have a need for a stable network identity and stable storage.
These applications often have configuration which relies on an ensemble of a fixed number of
members with fixed identities. Having multiple members with the same identity can be disastrous
and may lead to data loss (e.g. split brain scenario in quorum-based systems).
Delete Pods
You can perform a graceful pod deletion with the following command:
kubectl delete pods <pod>
For the above to lead to graceful termination, the Pod must not specify a
pod.Spec.TerminationGracePeriodSeconds of 0. The practice of setting a
pod.Spec.TerminationGracePeriodSeconds of 0 seconds is unsafe and strongly discouraged
for StatefulSet Pods. Graceful deletion is safe and will ensure that the Pod
shuts down gracefully
before the kubelet deletes the name from the apiserver.
A Pod is not deleted automatically when a node is unreachable.
The Pods running on an unreachable Node enter the 'Terminating' or 'Unknown' state after a
timeout.
Pods may also enter these states when the user attempts graceful deletion of a Pod
on an unreachable Node.
The only ways in which a Pod in such a state can be removed from the apiserver are as follows:
The Node object is deleted (either by you, or by the
Node Controller).
The kubelet on the unresponsive Node starts responding, kills the Pod and removes the entry
from the apiserver.
Force deletion of the Pod by the user.
The recommended best practice is to use the first or second approach. If a Node is confirmed
to be dead (e.g. permanently disconnected from the network, powered down, etc), then delete
the Node object. If the Node is suffering from a network partition, then try to resolve this
or wait for it to resolve. When the partition heals, the kubelet will complete the deletion
of the Pod and free up its name in the apiserver.
Normally, the system completes the deletion once the Pod is no longer running on a Node, or
the Node is deleted by an administrator. You may override this by force deleting the Pod.
Force Deletion
Force deletions do not wait for confirmation from the kubelet that the Pod has been terminated.
Irrespective of whether a force deletion is successful in killing a Pod, it will immediately
free up the name from the apiserver. This would let the StatefulSet controller create a replacement
Pod with that same identity; this can lead to the duplication of a still-running Pod,
and if said Pod can still communicate with the other members of the StatefulSet,
will violate the at most one semantics that StatefulSet is designed to guarantee.
When you force delete a StatefulSet pod, you are asserting that the Pod in question will never
again make contact with other Pods in the StatefulSet and its name can be safely freed up for a
replacement to be created.
If you want to delete a Pod forcibly using kubectl version >= 1.5, do the following:
In Kubernetes, a HorizontalPodAutoscaler automatically updates a workload resource (such as
a Deployment or
StatefulSet), with the
aim of automatically scaling the workload to match demand.
Horizontal scaling means that the response to increased load is to deploy more
Pods.
This is different from vertical scaling, which for Kubernetes would mean
assigning more resources (for example: memory or CPU) to the Pods that are already
running for the workload.
If the load decreases, and the number of Pods is above the configured minimum,
the HorizontalPodAutoscaler instructs the workload resource (the Deployment, StatefulSet,
or other similar resource) to scale back down.
Horizontal pod autoscaling does not apply to objects that can't be scaled (for example:
a DaemonSet.)
The HorizontalPodAutoscaler is implemented as a Kubernetes API resource and a
controller.
The resource determines the behavior of the controller.
The horizontal pod autoscaling controller, running within the Kubernetes
control plane, periodically adjusts the
desired scale of its target (for example, a Deployment) to match observed metrics such as average
CPU utilization, average memory utilization, or any other custom metric you specify.
Figure 1. HorizontalPodAutoscaler controls the scale of a Deployment and its ReplicaSet
Kubernetes implements horizontal pod autoscaling as a control loop that runs intermittently
(it is not a continuous process). The interval is set by the
--horizontal-pod-autoscaler-sync-period parameter to the
kube-controller-manager
(and the default interval is 15 seconds).
Once during each period, the controller manager queries the resource utilization against the
metrics specified in each HorizontalPodAutoscaler definition. The controller manager
finds the target resource defined by the scaleTargetRef,
then selects the pods based on the target resource's .spec.selector labels,
and obtains the metrics from either the resource metrics API (for per-pod resource metrics),
or the custom metrics API (for all other metrics).
For per-pod resource metrics (like CPU), the controller fetches the metrics
from the resource metrics API for each Pod targeted by the HorizontalPodAutoscaler.
Then, if a target utilization value is set, the controller calculates the utilization
value as a percentage of the equivalent
resource request
on the containers in each Pod. If a target raw value is set, the raw metric values are used directly.
The controller then takes the mean of the utilization or the raw value (depending on the type
of target specified) across all targeted Pods, and produces a ratio used to scale
the number of desired replicas.
Please note that if some of the Pod's containers do not have the relevant resource request set,
CPU utilization for the Pod will not be defined and the autoscaler will
not take any action for that metric. See the algorithm details section below
for more information about how the autoscaling algorithm works.
For per-pod custom metrics, the controller functions similarly to per-pod resource metrics,
except that it works with raw values, not utilization values.
For object metrics and external metrics, a single metric is fetched, which describes
the object in question. This metric is compared to the target
value, to produce a ratio as above. In the autoscaling/v2 API
version, this value can optionally be divided by the number of Pods before the
comparison is made.
The common use for HorizontalPodAutoscaler is to configure it to fetch metrics from
aggregated APIs
(metrics.k8s.io, custom.metrics.k8s.io, or external.metrics.k8s.io). The metrics.k8s.io API is
usually provided by an add-on named Metrics Server, which needs to be launched separately.
For more information about resource metrics, see
Metrics Server.
Support for metrics APIs explains the stability guarantees and support status for these
different APIs.
The HorizontalPodAutoscaler controller accesses corresponding workload resources that support scaling (such as Deployments
and StatefulSet). These resources each have a subresource named scale, an interface that allows you to dynamically set the
number of replicas and examine each of their current states.
For general information about subresources in the Kubernetes API, see
Kubernetes API Concepts.
Algorithm details
From the most basic perspective, the HorizontalPodAutoscaler controller
operates on the ratio between desired metric value and current metric
value:
For example, if the current metric value is 200m, and the desired value
is 100m, the number of replicas will be doubled, since 200.0 / 100.0 == 2.0 If the current value is instead 50m, you'll halve the number of
replicas, since 50.0 / 100.0 == 0.5. The control plane skips any scaling
action if the ratio is sufficiently close to 1.0 (within a globally-configurable
tolerance, 0.1 by default).
When a targetAverageValue or targetAverageUtilization is specified,
the currentMetricValue is computed by taking the average of the given
metric across all Pods in the HorizontalPodAutoscaler's scale target.
Before checking the tolerance and deciding on the final values, the control
plane also considers whether any metrics are missing, and how many Pods
are Ready.
All Pods with a deletion timestamp set (objects with a deletion timestamp are
in the process of being shut down / removed) are ignored, and all failed Pods
are discarded.
If a particular Pod is missing metrics, it is set aside for later; Pods
with missing metrics will be used to adjust the final scaling amount.
When scaling on CPU, if any pod has yet to become ready (it's still
initializing, or possibly is unhealthy) or the most recent metric point for
the pod was before it became ready, that pod is set aside as well.
Due to technical constraints, the HorizontalPodAutoscaler controller
cannot exactly determine the first time a pod becomes ready when
determining whether to set aside certain CPU metrics. Instead, it
considers a Pod "not yet ready" if it's unready and transitioned to
ready within a short, configurable window of time since it started.
This value is configured with the --horizontal-pod-autoscaler-initial-readiness-delay
flag, and its default is 30 seconds.
Once a pod has become ready, it considers any transition to
ready to be the first if it occurred within a longer, configurable time
since it started. This value is configured with the
--horizontal-pod-autoscaler-cpu-initialization-period flag, and its
default is 5 minutes.
The currentMetricValue / desiredMetricValue base scale ratio is then
calculated using the remaining pods not set aside or discarded from above.
If there were any missing metrics, the control plane recomputes the average more
conservatively, assuming those pods were consuming 100% of the desired
value in case of a scale down, and 0% in case of a scale up. This dampens
the magnitude of any potential scale.
Furthermore, if any not-yet-ready pods were present, and the workload would have
scaled up without factoring in missing metrics or not-yet-ready pods,
the controller conservatively assumes that the not-yet-ready pods are consuming 0%
of the desired metric, further dampening the magnitude of a scale up.
After factoring in the not-yet-ready pods and missing metrics, the
controller recalculates the usage ratio. If the new ratio reverses the scale
direction, or is within the tolerance, the controller doesn't take any scaling
action. In other cases, the new ratio is used to decide any change to the
number of Pods.
Note that the original value for the average utilization is reported
back via the HorizontalPodAutoscaler status, without factoring in the
not-yet-ready pods or missing metrics, even when the new usage ratio is
used.
If multiple metrics are specified in a HorizontalPodAutoscaler, this
calculation is done for each metric, and then the largest of the desired
replica counts is chosen. If any of these metrics cannot be converted
into a desired replica count (e.g. due to an error fetching the metrics
from the metrics APIs) and a scale down is suggested by the metrics which
can be fetched, scaling is skipped. This means that the HPA is still capable
of scaling up if one or more metrics give a desiredReplicas greater than
the current value.
Finally, right before HPA scales the target, the scale recommendation is recorded. The
controller considers all recommendations within a configurable window choosing the
highest recommendation from within that window. This value can be configured using the
--horizontal-pod-autoscaler-downscale-stabilization flag, which defaults to 5 minutes.
This means that scaledowns will occur gradually, smoothing out the impact of rapidly
fluctuating metric values.
API Object
The Horizontal Pod Autoscaler is an API resource in the Kubernetes
autoscaling API group. The current stable version can be found in
the autoscaling/v2 API version which includes support for scaling on
memory and custom metrics. The new fields introduced in
autoscaling/v2 are preserved as annotations when working with
autoscaling/v1.
When you create a HorizontalPodAutoscaler API object, make sure the name specified is a valid
DNS subdomain name.
More details about the API object can be found at
HorizontalPodAutoscaler Object.
Stability of workload scale
When managing the scale of a group of replicas using the HorizontalPodAutoscaler,
it is possible that the number of replicas keeps fluctuating frequently due to the
dynamic nature of the metrics evaluated. This is sometimes referred to as thrashing,
or flapping. It's similar to the concept of hysteresis in cybernetics.
Autoscaling during rolling update
Kubernetes lets you perform a rolling update on a Deployment. In that
case, the Deployment manages the underlying ReplicaSets for you.
When you configure autoscaling for a Deployment, you bind a
HorizontalPodAutoscaler to a single Deployment. The HorizontalPodAutoscaler
manages the replicas field of the Deployment. The deployment controller is responsible
for setting the replicas of the underlying ReplicaSets so that they add up to a suitable
number during the rollout and also afterwards.
If you perform a rolling update of a StatefulSet that has an autoscaled number of
replicas, the StatefulSet directly manages its set of Pods (there is no intermediate resource
similar to ReplicaSet).
Support for resource metrics
Any HPA target can be scaled based on the resource usage of the pods in the scaling target.
When defining the pod specification the resource requests like cpu and memory should
be specified. This is used to determine the resource utilization and used by the HPA controller
to scale the target up or down. To use resource utilization based scaling specify a metric source
like this:
With this metric the HPA controller will keep the average utilization of the pods in the scaling
target at 60%. Utilization is the ratio between the current usage of resource to the requested
resources of the pod. See Algorithm for more details about how the utilization
is calculated and averaged.
Note: Since the resource usages of all the containers are summed up the total pod utilization may not
accurately represent the individual container resource usage. This could lead to situations where
a single container might be running with high usage and the HPA will not scale out because the overall
pod usage is still within acceptable limits.
Container resource metrics
FEATURE STATE:Kubernetes v1.27 [beta]
The HorizontalPodAutoscaler API also supports a container metric source where the HPA can track the
resource usage of individual containers across a set of Pods, in order to scale the target resource.
This lets you configure scaling thresholds for the containers that matter most in a particular Pod.
For example, if you have a web application and a logging sidecar, you can scale based on the resource
use of the web application, ignoring the sidecar container and its resource use.
If you revise the target resource to have a new Pod specification with a different set of containers,
you should revise the HPA spec if that newly added container should also be used for
scaling. If the specified container in the metric source is not present or only present in a subset
of the pods then those pods are ignored and the recommendation is recalculated. See Algorithm
for more details about the calculation. To use container resources for autoscaling define a metric
source as follows:
In the above example the HPA controller scales the target such that the average utilization of the cpu
in the application container of all the pods is 60%.
Note:
If you change the name of a container that a HorizontalPodAutoscaler is tracking, you can
make that change in a specific order to ensure scaling remains available and effective
whilst the change is being applied. Before you update the resource that defines the container
(such as a Deployment), you should update the associated HPA to track both the new and
old container names. This way, the HPA is able to calculate a scaling recommendation
throughout the update process.
Once you have rolled out the container name change to the workload resource, tidy up by removing
the old container name from the HPA specification.
Scaling on custom metrics
FEATURE STATE:Kubernetes v1.23 [stable]
(the autoscaling/v2beta2 API version previously provided this ability as a beta feature)
Provided that you use the autoscaling/v2 API version, you can configure a HorizontalPodAutoscaler
to scale based on a custom metric (that is not built in to Kubernetes or any Kubernetes component).
The HorizontalPodAutoscaler controller then queries for these custom metrics from the Kubernetes
API.
(the autoscaling/v2beta2 API version previously provided this ability as a beta feature)
Provided that you use the autoscaling/v2 API version, you can specify multiple metrics for a
HorizontalPodAutoscaler to scale on. Then, the HorizontalPodAutoscaler controller evaluates each metric,
and proposes a new scale based on that metric. The HorizontalPodAutoscaler takes the maximum scale
recommended for each metric and sets the workload to that size (provided that this isn't larger than the
overall maximum that you configured).
Support for metrics APIs
By default, the HorizontalPodAutoscaler controller retrieves metrics from a series of APIs.
In order for it to access these APIs, cluster administrators must ensure that:
For resource metrics, this is the metrics.k8s.ioAPI,
generally provided by metrics-server.
It can be launched as a cluster add-on.
For custom metrics, this is the custom.metrics.k8s.ioAPI.
It's provided by "adapter" API servers provided by metrics solution vendors.
Check with your metrics pipeline to see if there is a Kubernetes metrics adapter available.
For external metrics, this is the external.metrics.k8s.ioAPI.
It may be provided by the custom metrics adapters provided above.
(the autoscaling/v2beta2 API version previously provided this ability as a beta feature)
If you use the v2 HorizontalPodAutoscaler API, you can use the behavior field
(see the API reference)
to configure separate scale-up and scale-down behaviors.
You specify these behaviours by setting scaleUp and / or scaleDown
under the behavior field.
You can specify a stabilization window that prevents flapping
the replica count for a scaling target. Scaling policies also let you control the
rate of change of replicas while scaling.
Scaling policies
One or more scaling policies can be specified in the behavior section of the spec.
When multiple policies are specified the policy which allows the highest amount of
change is the policy which is selected by default. The following example shows this behavior
while scaling down:
periodSeconds indicates the length of time in the past for which the policy must hold true.
The maximum value that you can set for periodSeconds is 1800 (half an hour).
The first policy (Pods) allows at most 4 replicas to be scaled down in one minute. The second policy
(Percent) allows at most 10% of the current replicas to be scaled down in one minute.
Since by default the policy which allows the highest amount of change is selected, the second policy will
only be used when the number of pod replicas is more than 40. With 40 or less replicas, the first policy will be applied.
For instance if there are 80 replicas and the target has to be scaled down to 10 replicas
then during the first step 8 replicas will be reduced. In the next iteration when the number
of replicas is 72, 10% of the pods is 7.2 but the number is rounded up to 8. On each loop of
the autoscaler controller the number of pods to be change is re-calculated based on the number
of current replicas. When the number of replicas falls below 40 the first policy (Pods) is applied
and 4 replicas will be reduced at a time.
The policy selection can be changed by specifying the selectPolicy field for a scaling
direction. By setting the value to Min which would select the policy which allows the
smallest change in the replica count. Setting the value to Disabled completely disables
scaling in that direction.
Stabilization window
The stabilization window is used to restrict the flapping of
replica count when the metrics used for scaling keep fluctuating. The autoscaling algorithm
uses this window to infer a previous desired state and avoid unwanted changes to workload
scale.
For example, in the following example snippet, a stabilization window is specified for scaleDown.
behavior:scaleDown:stabilizationWindowSeconds:300
When the metrics indicate that the target should be scaled down the algorithm looks
into previously computed desired states, and uses the highest value from the specified
interval. In the above example, all desired states from the past 5 minutes will be considered.
This approximates a rolling maximum, and avoids having the scaling algorithm frequently
remove Pods only to trigger recreating an equivalent Pod just moments later.
Default Behavior
To use the custom scaling not all fields have to be specified. Only values which need to be
customized can be specified. These custom values are merged with default values. The default values
match the existing behavior in the HPA algorithm.
For scaling down the stabilization window is 300 seconds (or the value of the
--horizontal-pod-autoscaler-downscale-stabilization flag if provided). There is only a single policy
for scaling down which allows a 100% of the currently running replicas to be removed which
means the scaling target can be scaled down to the minimum allowed replicas.
For scaling up there is no stabilization window. When the metrics indicate that the target should be
scaled up the target is scaled up immediately. There are 2 policies where 4 pods or a 100% of the currently
running replicas may at most be added every 15 seconds till the HPA reaches its steady state.
Example: change downscale stabilization window
To provide a custom downscale stabilization window of 1 minute, the following
behavior would be added to the HPA:
behavior:scaleDown:stabilizationWindowSeconds:60
Example: limit scale down rate
To limit the rate at which pods are removed by the HPA to 10% per minute, the
following behavior would be added to the HPA:
To ensure that no more than 5 Pods are removed per minute, you can add a second scale-down
policy with a fixed size of 5, and set selectPolicy to minimum. Setting selectPolicy to Min means
that the autoscaler chooses the policy that affects the smallest number of Pods:
The selectPolicy value of Disabled turns off scaling the given direction.
So to prevent downscaling the following policy would be used:
behavior:scaleDown:selectPolicy:Disabled
Support for HorizontalPodAutoscaler in kubectl
HorizontalPodAutoscaler, like every API resource, is supported in a standard way by kubectl.
You can create a new autoscaler using kubectl create command.
You can list autoscalers by kubectl get hpa or get detailed description by kubectl describe hpa.
Finally, you can delete an autoscaler using kubectl delete hpa.
In addition, there is a special kubectl autoscale command for creating a HorizontalPodAutoscaler object.
For instance, executing kubectl autoscale rs foo --min=2 --max=5 --cpu-percent=80
will create an autoscaler for ReplicaSet foo, with target CPU utilization set to 80%
and the number of replicas between 2 and 5.
Implicit maintenance-mode deactivation
You can implicitly deactivate the HPA for a target without the
need to change the HPA configuration itself. If the target's desired replica count
is set to 0, and the HPA's minimum replica count is greater than 0, the HPA
stops adjusting the target (and sets the ScalingActive Condition on itself
to false) until you reactivate it by manually adjusting the target's desired
replica count or HPA's minimum replica count.
Migrating Deployments and StatefulSets to horizontal autoscaling
When an HPA is enabled, it is recommended that the value of spec.replicas of
the Deployment and / or StatefulSet be removed from their
manifest(s). If this isn't done, any time
a change to that object is applied, for example via kubectl apply -f deployment.yaml, this will instruct Kubernetes to scale the current number of Pods
to the value of the spec.replicas key. This may not be
desired and could be troublesome when an HPA is active.
Keep in mind that the removal of spec.replicas may incur a one-time
degradation of Pod counts as the default value of this key is 1 (reference
Deployment Replicas).
Upon the update, all Pods except 1 will begin their termination procedures. Any
deployment application afterwards will behave as normal and respect a rolling
update configuration as desired. You can avoid this degradation by choosing one of the following two
methods based on how you are modifying your deployments:
In the editor, remove spec.replicas. When you save and exit the editor, kubectl
applies the update. No changes to Pod counts happen at this step.
You can now remove spec.replicas from the manifest. If you use source code management,
also commit your changes or take whatever other steps for revising the source code
are appropriate for how you track updates.
From here on out you can run kubectl apply -f deployment.yaml
A HorizontalPodAutoscaler
(HPA for short)
automatically updates a workload resource (such as
a Deployment or
StatefulSet), with the
aim of automatically scaling the workload to match demand.
Horizontal scaling means that the response to increased load is to deploy more
Pods.
This is different from vertical scaling, which for Kubernetes would mean
assigning more resources (for example: memory or CPU) to the Pods that are already
running for the workload.
If the load decreases, and the number of Pods is above the configured minimum,
the HorizontalPodAutoscaler instructs the workload resource (the Deployment, StatefulSet,
or other similar resource) to scale back down.
This document walks you through an example of enabling HorizontalPodAutoscaler to
automatically manage scale for an example web app. This example workload is Apache
httpd running some PHP code.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Your Kubernetes server must be at or later than version 1.23.
To check the version, enter kubectl version.
If you're running an older
release of Kubernetes, refer to the version of the documentation for that release (see
available documentation versions).
To follow this walkthrough, you also need to use a cluster that has a
Metrics Server deployed and configured.
The Kubernetes Metrics Server collects resource metrics from
the kubelets in your cluster, and exposes those metrics
through the Kubernetes API,
using an APIService to add
new kinds of resource that represent metric readings.
To demonstrate a HorizontalPodAutoscaler, you will first start a Deployment that runs a container using the
hpa-example image, and expose it as a Service
using the following manifest:
deployment.apps/php-apache created
service/php-apache created
Create the HorizontalPodAutoscaler
Now that the server is running, create the autoscaler using kubectl. There is
kubectl autoscale subcommand,
part of kubectl, that helps you do this.
You will shortly run a command that creates a HorizontalPodAutoscaler that maintains
between 1 and 10 replicas of the Pods controlled by the php-apache Deployment that
you created in the first step of these instructions.
Roughly speaking, the HPA controller will increase and decrease
the number of replicas (by updating the Deployment) to maintain an average CPU utilization across all Pods of 50%.
The Deployment then updates the ReplicaSet - this is part of how all Deployments work in Kubernetes -
and then the ReplicaSet either adds or removes Pods based on the change to its .spec.
Since each pod requests 200 milli-cores by kubectl run, this means an average CPU usage of 100 milli-cores.
See Algorithm details for more details
on the algorithm.
You can check the current status of the newly-made HorizontalPodAutoscaler, by running:
# You can use "hpa" or "horizontalpodautoscaler"; either name works OK.kubectl get hpa
The output is similar to:
NAME REFERENCE TARGET MINPODS MAXPODS REPLICAS AGE
php-apache Deployment/php-apache/scale 0% / 50% 1 10 1 18s
(if you see other HorizontalPodAutoscalers with different names, that means they already existed,
and isn't usually a problem).
Please note that the current CPU consumption is 0% as there are no clients sending requests to the server
(the TARGET column shows the average across all the Pods controlled by the corresponding deployment).
Increase the load
Next, see how the autoscaler reacts to increased load.
To do this, you'll start a different Pod to act as a client. The container within the client Pod
runs in an infinite loop, sending queries to the php-apache service.
# Run this in a separate terminal# so that the load generation continues and you can carry on with the rest of the stepskubectl run -i --tty load-generator --rm --image=busybox:1.28 --restart=Never -- /bin/sh -c "while sleep 0.01; do wget -q -O- http://php-apache; done"
Now run:
# type Ctrl+C to end the watch when you're readykubectl get hpa php-apache --watch
Within a minute or so, you should see the higher CPU load; for example:
NAME REFERENCE TARGET MINPODS MAXPODS REPLICAS AGE
php-apache Deployment/php-apache/scale 305% / 50% 1 10 1 3m
and then, more replicas. For example:
NAME REFERENCE TARGET MINPODS MAXPODS REPLICAS AGE
php-apache Deployment/php-apache/scale 305% / 50% 1 10 7 3m
Here, CPU consumption has increased to 305% of the request.
As a result, the Deployment was resized to 7 replicas:
kubectl get deployment php-apache
You should see the replica count matching the figure from the HorizontalPodAutoscaler
NAME READY UP-TO-DATE AVAILABLE AGE
php-apache 7/7 7 7 19m
Note: It may take a few minutes to stabilize the number of replicas. Since the amount
of load is not controlled in any way it may happen that the final number of replicas
will differ from this example.
Stop generating load
To finish the example, stop sending the load.
In the terminal where you created the Pod that runs a busybox image, terminate
the load generation by typing <Ctrl> + C.
Then verify the result state (after a minute or so):
# type Ctrl+C to end the watch when you're readykubectl get hpa php-apache --watch
The output is similar to:
NAME REFERENCE TARGET MINPODS MAXPODS REPLICAS AGE
php-apache Deployment/php-apache/scale 0% / 50% 1 10 1 11m
and the Deployment also shows that it has scaled down:
kubectl get deployment php-apache
NAME READY UP-TO-DATE AVAILABLE AGE
php-apache 1/1 1 1 27m
Once CPU utilization dropped to 0, the HPA automatically scaled the number of replicas back down to 1.
Autoscaling the replicas may take a few minutes.
Autoscaling on multiple metrics and custom metrics
You can introduce additional metrics to use when autoscaling the php-apache Deployment
by making use of the autoscaling/v2 API version.
First, get the YAML of your HorizontalPodAutoscaler in the autoscaling/v2 form:
kubectl get hpa php-apache -o yaml > /tmp/hpa-v2.yaml
Open the /tmp/hpa-v2.yaml file in an editor, and you should see YAML which looks like this:
Notice that the targetCPUUtilizationPercentage field has been replaced with an array called metrics.
The CPU utilization metric is a resource metric, since it is represented as a percentage of a resource
specified on pod containers. Notice that you can specify other resource metrics besides CPU. By default,
the only other supported resource metric is memory. These resources do not change names from cluster
to cluster, and should always be available, as long as the metrics.k8s.io API is available.
You can also specify resource metrics in terms of direct values, instead of as percentages of the
requested value, by using a target.type of AverageValue instead of Utilization, and
setting the corresponding target.averageValue field instead of the target.averageUtilization.
There are two other types of metrics, both of which are considered custom metrics: pod metrics and
object metrics. These metrics may have names which are cluster specific, and require a more
advanced cluster monitoring setup.
The first of these alternative metric types is pod metrics. These metrics describe Pods, and
are averaged together across Pods and compared with a target value to determine the replica count.
They work much like resource metrics, except that they only support a target type of AverageValue.
Pod metrics are specified using a metric block like this:
The second alternative metric type is object metrics. These metrics describe a different
object in the same namespace, instead of describing Pods. The metrics are not necessarily
fetched from the object; they only describe it. Object metrics support target types of
both Value and AverageValue. With Value, the target is compared directly to the returned
metric from the API. With AverageValue, the value returned from the custom metrics API is divided
by the number of Pods before being compared to the target. The following example is the YAML
representation of the requests-per-second metric.
If you provide multiple such metric blocks, the HorizontalPodAutoscaler will consider each metric in turn.
The HorizontalPodAutoscaler will calculate proposed replica counts for each metric, and then choose the
one with the highest replica count.
For example, if you had your monitoring system collecting metrics about network traffic,
you could update the definition above using kubectl edit to look like this:
Then, your HorizontalPodAutoscaler would attempt to ensure that each pod was consuming roughly
50% of its requested CPU, serving 1000 packets per second, and that all pods behind the main-route
Ingress were serving a total of 10000 requests per second.
Autoscaling on more specific metrics
Many metrics pipelines allow you to describe metrics either by name or by a set of additional
descriptors called labels. For all non-resource metric types (pod, object, and external,
described below), you can specify an additional label selector which is passed to your metric
pipeline. For instance, if you collect a metric http_requests with the verb
label, you can specify the following metric block to scale only on GET requests:
This selector uses the same syntax as the full Kubernetes label selectors. The monitoring pipeline
determines how to collapse multiple series into a single value, if the name and selector
match multiple series. The selector is additive, and cannot select metrics
that describe objects that are not the target object (the target pods in the case of the Pods
type, and the described object in the case of the Object type).
Autoscaling on metrics not related to Kubernetes objects
Applications running on Kubernetes may need to autoscale based on metrics that don't have an obvious
relationship to any object in the Kubernetes cluster, such as metrics describing a hosted service with
no direct correlation to Kubernetes namespaces. In Kubernetes 1.10 and later, you can address this use case
with external metrics.
Using external metrics requires knowledge of your monitoring system; the setup is
similar to that required when using custom metrics. External metrics allow you to autoscale your cluster
based on any metric available in your monitoring system. Provide a metric block with a
name and selector, as above, and use the External metric type instead of Object.
If multiple time series are matched by the metricSelector,
the sum of their values is used by the HorizontalPodAutoscaler.
External metrics support both the Value and AverageValue target types, which function exactly the same
as when you use the Object type.
For example if your application processes tasks from a hosted queue service, you could add the following
section to your HorizontalPodAutoscaler manifest to specify that you need one worker per 30 outstanding tasks.
When possible, it's preferable to use the custom metric target types instead of external metrics, since it's
easier for cluster administrators to secure the custom metrics API. The external metrics API potentially allows
access to any metric, so cluster administrators should take care when exposing it.
Appendix: Horizontal Pod Autoscaler Status Conditions
When using the autoscaling/v2 form of the HorizontalPodAutoscaler, you will be able to see
status conditions set by Kubernetes on the HorizontalPodAutoscaler. These status conditions indicate
whether or not the HorizontalPodAutoscaler is able to scale, and whether or not it is currently restricted
in any way.
The conditions appear in the status.conditions field. To see the conditions affecting a HorizontalPodAutoscaler,
we can use kubectl describe hpa:
kubectl describe hpa cm-test
Name: cm-test
Namespace: prom
Labels: <none>
Annotations: <none>
CreationTimestamp: Fri, 16 Jun 2017 18:09:22 +0000
Reference: ReplicationController/cm-test
Metrics: ( current / target )
"http_requests" on pods: 66m / 500m
Min replicas: 1
Max replicas: 4
ReplicationController pods: 1 current / 1 desired
Conditions:
Type Status Reason Message
---- ------ ------ -------
AbleToScale True ReadyForNewScale the last scale time was sufficiently old as to warrant a new scale
ScalingActive True ValidMetricFound the HPA was able to successfully calculate a replica count from pods metric http_requests
ScalingLimited False DesiredWithinRange the desired replica count is within the acceptable range
Events:
For this HorizontalPodAutoscaler, you can see several conditions in a healthy state. The first,
AbleToScale, indicates whether or not the HPA is able to fetch and update scales, as well as
whether or not any backoff-related conditions would prevent scaling. The second, ScalingActive,
indicates whether or not the HPA is enabled (i.e. the replica count of the target is not zero) and
is able to calculate desired scales. When it is False, it generally indicates problems with
fetching metrics. Finally, the last condition, ScalingLimited, indicates that the desired scale
was capped by the maximum or minimum of the HorizontalPodAutoscaler. This is an indication that
you may wish to raise or lower the minimum or maximum replica count constraints on your
HorizontalPodAutoscaler.
Quantities
All metrics in the HorizontalPodAutoscaler and metrics APIs are specified using
a special whole-number notation known in Kubernetes as a
quantity. For example,
the quantity 10500m would be written as 10.5 in decimal notation. The metrics APIs
will return whole numbers without a suffix when possible, and will generally return
quantities in milli-units otherwise. This means you might see your metric value fluctuate
between 1 and 1500m, or 1 and 1.5 when written in decimal notation.
Other possible scenarios
Creating the autoscaler declaratively
Instead of using kubectl autoscale command to create a HorizontalPodAutoscaler imperatively we
can use the following manifest to create it declaratively:
horizontalpodautoscaler.autoscaling/php-apache created
8.9 - Specifying a Disruption Budget for your Application
FEATURE STATE:Kubernetes v1.21 [stable]
This page shows how to limit the number of concurrent disruptions
that your application experiences, allowing for higher availability
while permitting the cluster administrator to manage the clusters
nodes.
Before you begin
Your Kubernetes server must be at or later than version v1.21.
To check the version, enter kubectl version.
You are the owner of an application running on a Kubernetes cluster that requires
high availability.
You should confirm with your cluster owner or service provider that they respect
Pod Disruption Budgets.
Protecting an Application with a PodDisruptionBudget
Identify what application you want to protect with a PodDisruptionBudget (PDB).
Think about how your application reacts to disruptions.
Create a PDB definition as a YAML file.
Create the PDB object from the YAML file.
Identify an Application to Protect
The most common use case when you want to protect an application
specified by one of the built-in Kubernetes controllers:
Deployment
ReplicationController
ReplicaSet
StatefulSet
In this case, make a note of the controller's .spec.selector; the same
selector goes into the PDBs .spec.selector.
From version 1.15 PDBs support custom controllers where the
scale subresource
is enabled.
You can also use PDBs with pods which are not controlled by one of the above
controllers, or arbitrary groups of pods, but there are some restrictions,
described in Arbitrary workloads and arbitrary selectors.
Think about how your application reacts to disruptions
Decide how many instances can be down at the same time for a short period
due to a voluntary disruption.
Stateless frontends:
Concern: don't reduce serving capacity by more than 10%.
Solution: use PDB with minAvailable 90% for example.
Single-instance Stateful Application:
Concern: do not terminate this application without talking to me.
Possible Solution 1: Do not use a PDB and tolerate occasional downtime.
Possible Solution 2: Set PDB with maxUnavailable=0. Have an understanding
(outside of Kubernetes) that the cluster operator needs to consult you before
termination. When the cluster operator contacts you, prepare for downtime,
and then delete the PDB to indicate readiness for disruption. Recreate afterwards.
Multiple-instance Stateful application such as Consul, ZooKeeper, or etcd:
Concern: Do not reduce number of instances below quorum, otherwise writes fail.
Possible Solution 1: set maxUnavailable to 1 (works with varying scale of application).
Possible Solution 2: set minAvailable to quorum-size (e.g. 3 when scale is 5).
(Allows more disruptions at once).
Restartable Batch Job:
Concern: Job needs to complete in case of voluntary disruption.
Possible solution: Do not create a PDB. The Job controller will create a replacement pod.
Rounding logic when specifying percentages
Values for minAvailable or maxUnavailable can be expressed as integers or as a percentage.
When you specify an integer, it represents a number of Pods. For instance, if you set
minAvailable to 10, then 10 Pods must always be available, even during a disruption.
When you specify a percentage by setting the value to a string representation of a
percentage (eg. "50%"), it represents a percentage of total Pods. For instance, if
you set minAvailable to "50%", then at least 50% of the Pods remain available
during a disruption.
When you specify the value as a percentage, it may not map to an exact number of Pods.
For example, if you have 7 Pods and you set minAvailable to "50%", it's not
immediately obvious whether that means 3 Pods or 4 Pods must be available. Kubernetes
rounds up to the nearest integer, so in this case, 4 Pods must be available. When you
specify the value maxUnavailable as a percentage, Kubernetes rounds up the number of
Pods that may be disrupted. Thereby a disruption can exceed your defined
maxUnavailable percentage. You can examine the
code
that controls this behavior.
Specifying a PodDisruptionBudget
A PodDisruptionBudget has three fields:
A label selector .spec.selector to specify the set of
pods to which it applies. This field is required.
.spec.minAvailable which is a description of the number of pods from that
set that must still be available after the eviction, even in the absence
of the evicted pod. minAvailable can be either an absolute number or a percentage.
.spec.maxUnavailable (available in Kubernetes 1.7 and higher) which is a description
of the number of pods from that set that can be unavailable after the eviction.
It can be either an absolute number or a percentage.
Note: The behavior for an empty selector differs between the policy/v1beta1 and policy/v1 APIs for
PodDisruptionBudgets. For policy/v1beta1 an empty selector matches zero pods, while
for policy/v1 an empty selector matches every pod in the namespace.
You can specify only one of maxUnavailable and minAvailable in a single PodDisruptionBudget.
maxUnavailable can only be used to control the eviction of pods
that have an associated controller managing them. In the examples below, "desired replicas"
is the scale of the controller managing the pods being selected by the
PodDisruptionBudget.
Example 1: With a minAvailable of 5, evictions are allowed as long as they leave behind
5 or more healthy pods among those selected by the PodDisruptionBudget's selector.
Example 2: With a minAvailable of 30%, evictions are allowed as long as at least 30%
of the number of desired replicas are healthy.
Example 3: With a maxUnavailable of 5, evictions are allowed as long as there are at most 5
unhealthy replicas among the total number of desired replicas.
Example 4: With a maxUnavailable of 30%, evictions are allowed as long as the number of
unhealthy replicas does not exceed 30% of the total number of desired replica rounded up to
the nearest integer. If the total number of desired replicas is just one, that single replica
is still allowed for disruption, leading to an effective unavailability of 100%.
In typical usage, a single budget would be used for a collection of pods managed by
a controller—for example, the pods in a single ReplicaSet or StatefulSet.
Note: A disruption budget does not truly guarantee that the specified
number/percentage of pods will always be up. For example, a node that hosts a
pod from the collection may fail when the collection is at the minimum size
specified in the budget, thus bringing the number of available pods from the
collection below the specified size. The budget can only protect against
voluntary evictions, not all causes of unavailability.
If you set maxUnavailable to 0% or 0, or you set minAvailable to 100% or the number of replicas,
you are requiring zero voluntary evictions. When you set zero voluntary evictions for a workload
object such as ReplicaSet, then you cannot successfully drain a Node running one of those Pods.
If you try to drain a Node where an unevictable Pod is running, the drain never completes.
This is permitted as per the semantics of PodDisruptionBudget.
You can find examples of pod disruption budgets defined below. They match pods with the label
app: zookeeper.
For example, if the above zk-pdb object selects the pods of a StatefulSet of size 3, both
specifications have the exact same meaning. The use of maxUnavailable is recommended as it
automatically responds to changes in the number of replicas of the corresponding controller.
Create the PDB object
You can create or update the PDB object using kubectl.
kubectl apply -f mypdb.yaml
Check the status of the PDB
Use kubectl to check that your PDB is created.
Assuming you don't actually have pods matching app: zookeeper in your namespace,
then you'll see something like this:
kubectl get poddisruptionbudgets
NAME MIN AVAILABLE MAX UNAVAILABLE ALLOWED DISRUPTIONS AGE
zk-pdb 2 N/A 0 7s
If there are matching pods (say, 3), then you would see something like this:
kubectl get poddisruptionbudgets
NAME MIN AVAILABLE MAX UNAVAILABLE ALLOWED DISRUPTIONS AGE
zk-pdb 2 N/A 1 7s
The non-zero value for ALLOWED DISRUPTIONS means that the disruption controller has seen the pods,
counted the matching pods, and updated the status of the PDB.
You can get more information about the status of a PDB with this command:
The current implementation considers healthy pods, as pods that have .status.conditions
item with type="Ready" and status="True".
These pods are tracked via .status.currentHealthy field in the PDB status.
Unhealthy Pod Eviction Policy
FEATURE STATE:Kubernetes v1.27 [beta]
Note: This feature is enabled by default. You can disable it by disabling the PDBUnhealthyPodEvictionPolicyfeature gate
on the API server.
PodDisruptionBudget guarding an application ensures that .status.currentHealthy number of pods
does not fall below the number specified in .status.desiredHealthy by disallowing eviction of healthy pods.
By using .spec.unhealthyPodEvictionPolicy, you can also define the criteria when unhealthy pods
should be considered for eviction. The default behavior when no policy is specified corresponds
to the IfHealthyBudget policy.
Policies:
IfHealthyBudget
Running pods (.status.phase="Running"), but not yet healthy can be evicted only
if the guarded application is not disrupted (.status.currentHealthy is at least
equal to .status.desiredHealthy).
This policy ensures that running pods of an already disrupted application have
the best chance to become healthy. This has negative implications for draining
nodes, which can be blocked by misbehaving applications that are guarded by a PDB.
More specifically applications with pods in CrashLoopBackOff state
(due to a bug or misconfiguration), or pods that are just failing to report the
Ready condition.
AlwaysAllow
Running pods (.status.phase="Running"), but not yet healthy are considered
disrupted and can be evicted regardless of whether the criteria in a PDB is met.
This means prospective running pods of a disrupted application might not get a
chance to become healthy. By using this policy, cluster managers can easily evict
misbehaving applications that are guarded by a PDB. More specifically applications
with pods in CrashLoopBackOff state (due to a bug or misconfiguration), or pods
that are just failing to report the Ready condition.
Note: Pods in Pending, Succeeded or Failed phase are always considered for eviction.
Arbitrary workloads and arbitrary selectors
You can skip this section if you only use PDBs with the built-in
workload resources (Deployment, ReplicaSet, StatefulSet and ReplicationController)
or with custom resources
that implement a scalesubresource,
and where the PDB selector exactly matches the selector of the Pod's owning resource.
You can use a PDB with pods controlled by another resource, by an
"operator", or bare pods, but with these restrictions:
only .spec.minAvailable can be used, not .spec.maxUnavailable.
only an integer value can be used with .spec.minAvailable, not a percentage.
It is not possible to use other availability configurations,
because Kubernetes cannot derive a total number of pods without a supported owning resource.
You can use a selector which selects a subset or superset of the pods belonging to a
workload resource. The eviction API will disallow eviction of any pod covered by multiple PDBs,
so most users will want to avoid overlapping selectors. One reasonable use of overlapping
PDBs is when pods are being transitioned from one PDB to another.
8.10 - Accessing the Kubernetes API from a Pod
This guide demonstrates how to access the Kubernetes API from within a pod.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
When accessing the API from within a Pod, locating and authenticating
to the API server are slightly different to the external client case.
The easiest way to use the Kubernetes API from a Pod is to use
one of the official client libraries. These
libraries can automatically discover the API server and authenticate.
Using Official Client Libraries
From within a Pod, the recommended ways to connect to the Kubernetes API are:
For a Go client, use the official
Go client library.
The rest.InClusterConfig() function handles API host discovery and authentication automatically.
See an example here.
For a Python client, use the official
Python client library.
The config.load_incluster_config() function handles API host discovery and authentication automatically.
See an example here.
There are a number of other libraries available, please refer to the
Client Libraries page.
In each case, the service account credentials of the Pod are used to communicate
securely with the API server.
Directly accessing the REST API
While running in a Pod, your container can create an HTTPS URL for the Kubernetes API
server by fetching the KUBERNETES_SERVICE_HOST and KUBERNETES_SERVICE_PORT_HTTPS
environment variables. The API server's in-cluster address is also published to a
Service named kubernetes in the default namespace so that pods may reference
kubernetes.default.svc as a DNS name for the local API server.
Note: Kubernetes does not guarantee that the API server has a valid certificate for
the hostname kubernetes.default.svc;
however, the control plane is expected to present a valid certificate for the
hostname or IP address that $KUBERNETES_SERVICE_HOST represents.
The recommended way to authenticate to the API server is with a
service account
credential. By default, a Pod
is associated with a service account, and a credential (token) for that
service account is placed into the filesystem tree of each container in that Pod,
at /var/run/secrets/kubernetes.io/serviceaccount/token.
If available, a certificate bundle is placed into the filesystem tree of each
container at /var/run/secrets/kubernetes.io/serviceaccount/ca.crt, and should be
used to verify the serving certificate of the API server.
Finally, the default namespace to be used for namespaced API operations is placed in a file
at /var/run/secrets/kubernetes.io/serviceaccount/namespace in each container.
Using kubectl proxy
If you would like to query the API without an official client library, you can run kubectl proxy
as the command
of a new sidecar container in the Pod. This way, kubectl proxy will authenticate
to the API and expose it on the localhost interface of the Pod, so that other containers
in the Pod can use it directly.
Without using a proxy
It is possible to avoid using the kubectl proxy by passing the authentication token
directly to the API server. The internal certificate secures the connection.
# Point to the internal API server hostnameAPISERVER=https://kubernetes.default.svc
# Path to ServiceAccount tokenSERVICEACCOUNT=/var/run/secrets/kubernetes.io/serviceaccount
# Read this Pod's namespaceNAMESPACE=$(cat ${SERVICEACCOUNT}/namespace)# Read the ServiceAccount bearer tokenTOKEN=$(cat ${SERVICEACCOUNT}/token)# Reference the internal certificate authority (CA)CACERT=${SERVICEACCOUNT}/ca.crt
# Explore the API with TOKENcurl --cacert ${CACERT} --header "Authorization: Bearer ${TOKEN}" -X GET ${APISERVER}/api
This page shows how to run automated tasks using Kubernetes CronJob object.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
After creating the cron job, get its status using this command:
kubectl get cronjob hello
The output is similar to this:
NAME SCHEDULE SUSPEND ACTIVE LAST SCHEDULE AGE
hello */1 * * * * False 0 <none> 10s
As you can see from the results of the command, the cron job has not scheduled or run any jobs yet.
Watch for the job to be created in around one minute:
kubectl get jobs --watch
The output is similar to this:
NAME COMPLETIONS DURATION AGE
hello-4111706356 0/1 0s
hello-4111706356 0/1 0s 0s
hello-4111706356 1/1 5s 5s
Now you've seen one running job scheduled by the "hello" cron job.
You can stop watching the job and view the cron job again to see that it scheduled the job:
kubectl get cronjob hello
The output is similar to this:
NAME SCHEDULE SUSPEND ACTIVE LAST SCHEDULE AGE
hello */1 * * * * False 0 50s 75s
You should see that the cron job hello successfully scheduled a job at the time specified in
LAST SCHEDULE. There are currently 0 active jobs, meaning that the job has completed or failed.
Now, find the pods that the last scheduled job created and view the standard output of one of the pods.
Note: The job name is different from the pod name.
# Replace "hello-4111706356" with the job name in your systempods=$(kubectl get pods --selector=job-name=hello-4111706356 --output=jsonpath={.items[*].metadata.name})
Show the pod log:
kubectl logs $pods
The output is similar to this:
Fri Feb 22 11:02:09 UTC 2019
Hello from the Kubernetes cluster
Deleting a CronJob
When you don't need a cron job any more, delete it with kubectl delete cronjob <cronjob name>:
kubectl delete cronjob hello
Deleting the cron job removes all the jobs and pods it created and stops it from creating additional jobs.
You can read more about removing jobs in garbage collection.
9.2 - Coarse Parallel Processing Using a Work Queue
In this example, we will run a Kubernetes Job with multiple parallel
worker processes.
In this example, as each pod is created, it picks up one unit of work
from a task queue, completes it, deletes it from the queue, and exits.
Here is an overview of the steps in this example:
Start a message queue service. In this example, we use RabbitMQ, but you could use another
one. In practice you would set up a message queue service once and reuse it for many jobs.
Create a queue, and fill it with messages. Each message represents one task to be done. In
this example, a message is an integer that we will do a lengthy computation on.
Start a Job that works on tasks from the queue. The Job starts several pods. Each pod takes
one task from the message queue, processes it, and exits.
Before you begin
Be familiar with the basic,
non-parallel, use of Job.
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Now, we can experiment with accessing the message queue. We will
create a temporary interactive pod, install some tools on it,
and experiment with queues.
First create a temporary interactive Pod.
# Create a temporary interactive containerkubectl run -i --tty temp --image ubuntu:18.04
Waiting for pod default/temp-loe07 to be running, status is Pending, pod ready: false
... [ previous line repeats several times .. hit return when it stops ] ...
Note that your pod name and command prompt will be different.
Next install the amqp-tools so we can work with message queues.
# Install some toolsroot@temp-loe07:/# apt-get update
.... [ lots of output ] ....
root@temp-loe07:/# apt-get install -y curl ca-certificates amqp-tools python dnsutils
.... [ lots of output ] ....
Later, we will make a docker image that includes these packages.
Next, we will check that we can discover the rabbitmq service:
# Note the rabbitmq-service has a DNS name, provided by Kubernetes:
root@temp-loe07:/# nslookup rabbitmq-service
Server: 10.0.0.10
Address: 10.0.0.10#53
Name: rabbitmq-service.default.svc.cluster.local
Address: 10.0.147.152
# Your address will vary.
If Kube-DNS is not set up correctly, the previous step may not work for you.
You can also find the service IP in an env var:
# env | grep RABBIT | grep HOST
RABBITMQ_SERVICE_SERVICE_HOST=10.0.147.152
# Your address will vary.
Next we will verify we can create a queue, and publish and consume messages.
# In the next line, rabbitmq-service is the hostname where the rabbitmq-service# can be reached. 5672 is the standard port for rabbitmq.root@temp-loe07:/# exportBROKER_URL=amqp://guest:guest@rabbitmq-service:5672
# If you could not resolve "rabbitmq-service" in the previous step,# then use this command instead:# root@temp-loe07:/# BROKER_URL=amqp://guest:guest@$RABBITMQ_SERVICE_SERVICE_HOST:5672# Now create a queue:root@temp-loe07:/# /usr/bin/amqp-declare-queue --url=$BROKER_URL -q foo -d
foo
# Publish one message to it:root@temp-loe07:/# /usr/bin/amqp-publish --url=$BROKER_URL -r foo -p -b Hello
# And get it back.root@temp-loe07:/# /usr/bin/amqp-consume --url=$BROKER_URL -q foo -c 1 cat &&echoHello
root@temp-loe07:/#
In the last command, the amqp-consume tool takes one message (-c 1)
from the queue, and passes that message to the standard input of an arbitrary command. In this case, the program cat prints out the characters read from standard input, and the echo adds a carriage
return so the example is readable.
Filling the Queue with tasks
Now let's fill the queue with some "tasks". In our example, our tasks are strings to be
printed.
In a practice, the content of the messages might be:
names of files to that need to be processed
extra flags to the program
ranges of keys in a database table
configuration parameters to a simulation
frame numbers of a scene to be rendered
In practice, if there is large data that is needed in a read-only mode by all pods
of the Job, you will typically put that in a shared file system like NFS and mount
that readonly on all the pods, or the program in the pod will natively read data from
a cluster file system like HDFS.
For our example, we will create the queue and fill it using the amqp command line tools.
In practice, you might write a program to fill the queue using an amqp client library.
#!/usr/bin/env python# Just prints standard out and sleeps for 10 seconds.importsysimporttimeprint("Processing "+ sys.stdin.readlines()[0])
time.sleep(10)
Give the script execution permission:
chmod +x worker.py
Now, build an image. If you are working in the source
tree, then change directory to examples/job/work-queue-1.
Otherwise, make a temporary directory, change to it,
download the Dockerfile,
and worker.py. In either case,
build the image with this command:
docker build -t job-wq-1 .
For the Docker Hub, tag your app image with
your username and push to the Hub with the below commands. Replace
<username> with your Hub username.
docker tag job-wq-1 <username>/job-wq-1
docker push <username>/job-wq-1
If you are using Google Container
Registry, tag
your app image with your project ID, and push to GCR. Replace
<project> with your project ID.
docker tag job-wq-1 gcr.io/<project>/job-wq-1
gcloud docker -- push gcr.io/<project>/job-wq-1
Defining a Job
Here is a job definition. You'll need to make a copy of the Job and edit the
image to match the name you used, and call it ./job.yaml.
In this example, each pod works on one item from the queue and then exits.
So, the completion count of the Job corresponds to the number of work items
done. So we set, .spec.completions: 8 for the example, since we put 8 items in the queue.
Running the Job
So, now run the Job:
kubectl apply -f ./job.yaml
You can wait for the Job to succeed, with a timeout:
# The check for condition name is case insensitivekubectl wait --for=condition=complete --timeout=300s job/job-wq-1
Next, check on the Job:
kubectl describe jobs/job-wq-1
Name: job-wq-1
Namespace: default
Selector: controller-uid=41d75705-92df-11e7-b85e-fa163ee3c11f
Labels: controller-uid=41d75705-92df-11e7-b85e-fa163ee3c11f
job-name=job-wq-1
Annotations: <none>
Parallelism: 2
Completions: 8
Start Time: Wed, 06 Sep 2017 16:42:02 +0800
Pods Statuses: 0 Running / 8 Succeeded / 0 Failed
Pod Template:
Labels: controller-uid=41d75705-92df-11e7-b85e-fa163ee3c11f
job-name=job-wq-1
Containers:
c:
Image: gcr.io/causal-jigsaw-637/job-wq-1
Port:
Environment:
BROKER_URL: amqp://guest:guest@rabbitmq-service:5672
QUEUE: job1
Mounts: <none>
Volumes: <none>
Events:
FirstSeen LastSeen Count From SubobjectPath Type Reason Message
───────── ──────── ───── ──── ───────────── ────── ────── ───────
27s 27s 1 {job } Normal SuccessfulCreate Created pod: job-wq-1-hcobb
27s 27s 1 {job } Normal SuccessfulCreate Created pod: job-wq-1-weytj
27s 27s 1 {job } Normal SuccessfulCreate Created pod: job-wq-1-qaam5
27s 27s 1 {job } Normal SuccessfulCreate Created pod: job-wq-1-b67sr
26s 26s 1 {job } Normal SuccessfulCreate Created pod: job-wq-1-xe5hj
15s 15s 1 {job } Normal SuccessfulCreate Created pod: job-wq-1-w2zqe
14s 14s 1 {job } Normal SuccessfulCreate Created pod: job-wq-1-d6ppa
14s 14s 1 {job } Normal SuccessfulCreate Created pod: job-wq-1-p17e0
All the pods for that Job succeeded. Yay.
Alternatives
This approach has the advantage that you
do not need to modify your "worker" program to be aware that there is a work queue.
It does require that you run a message queue service.
If running a queue service is inconvenient, you may
want to consider one of the other job patterns.
This approach creates a pod for every work item. If your work items only take a few seconds,
though, creating a Pod for every work item may add a lot of overhead. Consider another
example, that executes multiple work items per Pod.
In this example, we use the amqp-consume utility to read the message
from the queue and run our actual program. This has the advantage that you
do not need to modify your program to be aware of the queue.
A different example, shows how to
communicate with the work queue using a client library.
Caveats
If the number of completions is set to less than the number of items in the queue, then
not all items will be processed.
If the number of completions is set to more than the number of items in the queue,
then the Job will not appear to be completed, even though all items in the queue
have been processed. It will start additional pods which will block waiting
for a message.
There is an unlikely race with this pattern. If the container is killed in between the time
that the message is acknowledged by the amqp-consume command and the time that the container
exits with success, or if the node crashes before the kubelet is able to post the success of the pod
back to the api-server, then the Job will not appear to be complete, even though all items
in the queue have been processed.
9.3 - Fine Parallel Processing Using a Work Queue
In this example, we will run a Kubernetes Job with multiple parallel
worker processes in a given pod.
In this example, as each pod is created, it picks up one unit of work
from a task queue, processes it, and repeats until the end of the queue is reached.
Here is an overview of the steps in this example:
Start a storage service to hold the work queue. In this example, we use Redis to store
our work items. In the previous example, we used RabbitMQ. In this example, we use Redis and
a custom work-queue client library because AMQP does not provide a good way for clients to
detect when a finite-length work queue is empty. In practice you would set up a store such
as Redis once and reuse it for the work queues of many jobs, and other things.
Create a queue, and fill it with messages. Each message represents one task to be done. In
this example, a message is an integer that we will do a lengthy computation on.
Start a Job that works on tasks from the queue. The Job starts several pods. Each pod takes
one task from the message queue, processes it, and repeats until the end of the queue is reached.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Be familiar with the basic,
non-parallel, use of Job.
Starting Redis
For this example, for simplicity, we will start a single instance of Redis.
See the Redis Example for an example
of deploying Redis scalably and redundantly.
You could also download the following files directly:
Now let's fill the queue with some "tasks". In our example, our tasks are strings to be
printed.
Start a temporary interactive pod for running the Redis CLI.
kubectl run -i --tty temp --image redis --command "/bin/sh"Waiting for pod default/redis2-c7h78 to be running, status is Pending, pod ready: falseHit enter forcommand prompt
Now hit enter, start the redis CLI, and create a list with some work items in it.
#!/usr/bin/env pythonimporttimeimportrediswqhost="redis"# Uncomment next two lines if you do not have Kube-DNS working.# import os# host = os.getenv("REDIS_SERVICE_HOST")q = rediswq.RedisWQ(name="job2", host=host)
print("Worker with sessionID: "+ q.sessionID())
print("Initial queue state: empty="+str(q.empty()))
whilenot q.empty():
item = q.lease(lease_secs=10, block=True, timeout=2)
if item isnotNone:
itemstr = item.decode("utf-8")
print("Working on "+ itemstr)
time.sleep(10) # Put your actual work here instead of sleep. q.complete(item)
else:
print("Waiting for work")
print("Queue empty, exiting")
Be sure to edit the job template to
change gcr.io/myproject to your own path.
In this example, each pod works on several items from the queue and then exits when there are no more items.
Since the workers themselves detect when the workqueue is empty, and the Job controller does not
know about the workqueue, it relies on the workers to signal when they are done working.
The workers signal that the queue is empty by exiting with success. So, as soon as any worker
exits with success, the controller knows the work is done, and the Pods will exit soon.
So, we set the completion count of the Job to 1. The job controller will wait for the other pods to complete
too.
You can wait for the Job to succeed, with a timeout:
# The check for condition name is case insensitivekubectl wait --for=condition=complete --timeout=300s job/job-wq-2
kubectl logs pods/job-wq-2-7r7b2
Worker with sessionID: bbd72d0a-9e5c-4dd6-abf6-416cc267991f
Initial queue state: empty=False
Working on banana
Working on date
Working on lemon
As you can see, one of our pods worked on several work units.
Alternatives
If running a queue service or modifying your containers to use a work queue is inconvenient, you may
want to consider one of the other
job patterns.
If you have a continuous stream of background processing work to run, then
consider running your background workers with a ReplicaSet instead,
and consider running a background processing library such as
https://github.com/resque/resque.
9.4 - Indexed Job for Parallel Processing with Static Work Assignment
FEATURE STATE:Kubernetes v1.24 [stable]
In this example, you will run a Kubernetes Job that uses multiple parallel
worker processes.
Each worker is a different container running in its own Pod. The Pods have an
index number that the control plane sets automatically, which allows each Pod
to identify which part of the overall task to work on.
The pod index is available in the annotationbatch.kubernetes.io/job-completion-index as a string representing its
decimal value. In order for the containerized task process to obtain this index,
you can publish the value of the annotation using the downward API
mechanism.
For convenience, the control plane automatically sets the downward API to
expose the index in the JOB_COMPLETION_INDEX environment variable.
Here is an overview of the steps in this example:
Define a Job manifest using indexed completion.
The downward API allows you to pass the pod index annotation as an
environment variable or file to the container.
Start an Indexed Job based on that manifest.
Before you begin
You should already be familiar with the basic,
non-parallel, use of Job.
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Your Kubernetes server must be at or later than version v1.21.
To check the version, enter kubectl version.
Choose an approach
To access the work item from the worker program, you have a few options:
Read the JOB_COMPLETION_INDEX environment variable. The Job
controller
automatically links this variable to the annotation containing the completion
index.
Read a file that contains the completion index.
Assuming that you can't modify the program, you can wrap it with a script
that reads the index using any of the methods above and converts it into
something that the program can use as input.
For this example, imagine that you chose option 3 and you want to run the
rev utility. This
program accepts a file as an argument and prints its content reversed.
rev data.txt
You'll use the rev tool from the
busybox container image.
As this is only an example, each Pod only does a tiny piece of work (reversing a short
string). In a real workload you might, for example, create a Job that represents
the
task of producing 60 seconds of video based on scene data.
Each work item in the video rendering Job would be to render a particular
frame of that video clip. Indexed completion would mean that each Pod in
the Job knows which frame to render and publish, by counting frames from
the start of the clip.
Define an Indexed Job
Here is a sample Job manifest that uses Indexed completion mode:
# This uses the first approach (relying on $JOB_COMPLETION_INDEX)kubectl apply -f https://kubernetes.io/examples/application/job/indexed-job.yaml
When you create this Job, the control plane creates a series of Pods, one for each index you specified. The value of .spec.parallelism determines how many can run at once whereas .spec.completions determines how many Pods the Job creates in total.
Because .spec.parallelism is less than .spec.completions, the control plane waits for some of the first Pods to complete before starting more of them.
You can wait for the Job to succeed, with a timeout:
# The check for condition name is case insensitivekubectl wait --for=condition=complete --timeout=300s job/indexed-job
Now, describe the Job and check that it was successful.
kubectl describe jobs/indexed-job
The output is similar to:
Name: indexed-job
Namespace: default
Selector: controller-uid=bf865e04-0b67-483b-9a90-74cfc4c3e756
Labels: controller-uid=bf865e04-0b67-483b-9a90-74cfc4c3e756
job-name=indexed-job
Annotations: <none>
Parallelism: 3
Completions: 5
Start Time: Thu, 11 Mar 2021 15:47:34 +0000
Pods Statuses: 2 Running / 3 Succeeded / 0 Failed
Completed Indexes: 0-2
Pod Template:
Labels: controller-uid=bf865e04-0b67-483b-9a90-74cfc4c3e756
job-name=indexed-job
Init Containers:
input:
Image: docker.io/library/bash
Port: <none>
Host Port: <none>
Command:
bash
-c
items=(foo bar baz qux xyz)
echo ${items[$JOB_COMPLETION_INDEX]} > /input/data.txt
Environment: <none>
Mounts:
/input from input (rw)
Containers:
worker:
Image: docker.io/library/busybox
Port: <none>
Host Port: <none>
Command:
rev
/input/data.txt
Environment: <none>
Mounts:
/input from input (rw)
Volumes:
input:
Type: EmptyDir (a temporary directory that shares a pod's lifetime)
Medium:
SizeLimit: <unset>
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal SuccessfulCreate 4s job-controller Created pod: indexed-job-njkjj
Normal SuccessfulCreate 4s job-controller Created pod: indexed-job-9kd4h
Normal SuccessfulCreate 4s job-controller Created pod: indexed-job-qjwsz
Normal SuccessfulCreate 1s job-controller Created pod: indexed-job-fdhq5
Normal SuccessfulCreate 1s job-controller Created pod: indexed-job-ncslj
In this example, you run the Job with custom values for each index. You can
inspect the output of one of the pods:
kubectl logs indexed-job-fdhq5 # Change this to match the name of a Pod from that Job
The output is similar to:
xuq
9.5 - Job with Pod-to-Pod Communication
In this example, you will run a Job in Indexed completion mode configured such that
the pods created by the Job can communicate with each other using pod hostnames rather than pod IP addresses.
Pods within a Job might need to communicate among themselves. The user workload running in each pod could query the Kubernetes API server
to learn the IPs of the other Pods, but it's much simpler to rely on Kubernetes' built-in DNS resolution.
Jobs in Indexed completion mode automatically set the pods' hostname to be in the format of
${jobName}-${completionIndex}. You can use this format to deterministically build
pod hostnames and enable pod communication without needing to create a client connection to
the Kubernetes control plane to obtain pod hostnames/IPs via API requests.
This configuration is useful
for use cases where pod networking is required but you don't want to depend on a network
connection with the Kubernetes API server.
Before you begin
You should already be familiar with the basic use of Job.
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Your Kubernetes server must be at or later than version v1.21.
To check the version, enter kubectl version.
Note: If you are using MiniKube or a similar tool, you may need to take
extra steps
to ensure you have DNS.
Starting a Job with Pod-to-Pod Communication
To enable pod-to-pod communication using pod hostnames in a Job, you must do the following:
Set up a headless Service
with a valid label selector for the pods created by your Job. The headless service must be in the same namespace as
the Job. One easy way to do this is to use the job-name: <your-job-name> selector, since the job-name label will be automatically added by Kubernetes. This configuration will trigger the DNS system to create records of the hostnames of
the pods running your Job.
Configure the headless service as subdomain service for the Job pods by including the following value in your Job template spec:
subdomain:<headless-svc-name>
Example
Below is a working example of a Job with pod-to-pod communication via pod hostnames enabled.
The Job is completed only after all pods successfully ping each other using hostnames.
Note: In the Bash script executed on each pod in the example below, the pod hostnames can be prefixed
by the namespace as well if the pod needs to be reached from outside the namespace.
apiVersion:v1kind:Servicemetadata:name:headless-svcspec:clusterIP:None# clusterIP must be None to create a headless serviceselector:job-name:example-job# must match Job name---apiVersion:batch/v1kind:Jobmetadata:name:example-jobspec:completions:3parallelism:3completionMode:Indexedtemplate:spec:subdomain:headless-svc# has to match Service namerestartPolicy:Nevercontainers:- name:example-workloadimage:bash:latestcommand:- bash- -c- | for i in 0 1 2
do
gotStatus="-1"
wantStatus="0"
while [ $gotStatus -ne $wantStatus ]
do
ping -c 1 example-job-${i}.headless-svc > /dev/null 2>&1
gotStatus=$?
if [ $gotStatus -ne $wantStatus ]; then
echo "Failed to ping pod example-job-${i}.headless-svc, retrying in 1 second..."
sleep 1
fi
done
echo "Successfully pinged pod: example-job-${i}.headless-svc"
done
After applying the example above, reach each other over the network
using: <pod-hostname>.<headless-service-name>. You should see output similar to the following:
kubectl logs example-job-0-qws42
Failed to ping pod example-job-0.headless-svc, retrying in 1 second...
Successfully pinged pod: example-job-0.headless-svc
Successfully pinged pod: example-job-1.headless-svc
Successfully pinged pod: example-job-2.headless-svc
Note: Keep in mind that the <pod-hostname>.<headless-service-name> name format used
in this example would not work with DNS policy set to None or Default.
You can learn more about pod DNS policies here.
9.6 - Parallel Processing using Expansions
This task demonstrates running multiple Jobs
based on a common template. You can use this approach to process batches of work in
parallel.
For this example there are only three items: apple, banana, and cherry.
The sample Jobs process each item by printing a string then pausing.
You should be familiar with the basic,
non-parallel, use of Job.
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
# Use curl to download job-tmpl.yamlcurl -L -s -O https://k8s.io/examples/application/job/job-tmpl.yaml
The file you downloaded is not yet a valid Kubernetes
manifest.
Instead that template is a YAML representation of a Job object with some placeholders
that need to be filled in before it can be used. The $ITEM syntax is not meaningful to Kubernetes.
Create manifests from the template
The following shell snippet uses sed to replace the string $ITEM with the loop
variable, writing into a temporary directory named jobs. Run this now:
# Expand the template into multiple files, one for each item to be processed.mkdir ./jobs
for i in apple banana cherry
do cat job-tmpl.yaml | sed "s/\$ITEM/$i/" > ./jobs/job-$i.yaml
done
Check if it worked:
ls jobs/
The output is similar to this:
job-apple.yaml
job-banana.yaml
job-cherry.yaml
You could use any type of template language (for example: Jinja2; ERB), or
write a program to generate the Job manifests.
Create Jobs from the manifests
Next, create all the Jobs with one kubectl command:
kubectl create -f ./jobs
The output is similar to this:
job.batch/process-item-apple created
job.batch/process-item-banana created
job.batch/process-item-cherry created
Now, check on the jobs:
kubectl get jobs -l jobgroup=jobexample
The output is similar to this:
NAME COMPLETIONS DURATION AGE
process-item-apple 1/1 14s 22s
process-item-banana 1/1 12s 21s
process-item-cherry 1/1 12s 20s
Using the -l option to kubectl selects only the Jobs that are part
of this group of jobs (there might be other unrelated jobs in the system).
You can check on the Pods as well using the same
label selector:
kubectl get pods -l jobgroup=jobexample
The output is similar to:
NAME READY STATUS RESTARTS AGE
process-item-apple-kixwv 0/1 Completed 0 4m
process-item-banana-wrsf7 0/1 Completed 0 4m
process-item-cherry-dnfu9 0/1 Completed 0 4m
We can use this single command to check on the output of all jobs at once:
kubectl logs -f -l jobgroup=jobexample
The output should be:
Processing item apple
Processing item banana
Processing item cherry
Clean up
# Remove the Jobs you created# Your cluster automatically cleans up their Podskubectl delete job -l jobgroup=jobexample
Use advanced template parameters
In the first example, each instance of the template had one
parameter, and that parameter was also used in the Job's name. However,
names are restricted
to contain only certain characters.
This slightly more complex example uses the
Jinja template language to generate manifests
and then objects from those manifests, with a multiple parameters for each Job.
For this part of the task, you are going to use a one-line Python script to
convert the template to a set of manifests.
First, copy and paste the following template of a Job object, into a file called job.yaml.jinja2:
{% set params = [{ "name": "apple", "url": "http://dbpedia.org/resource/Apple", },
{ "name": "banana", "url": "http://dbpedia.org/resource/Banana", },
{ "name": "cherry", "url": "http://dbpedia.org/resource/Cherry" }]
%}
{% for p in params %}
{% set name = p["name"] %}
{% set url = p["url"] %}
---
apiVersion: batch/v1
kind: Job
metadata:
name: jobexample-{{ name }}
labels:
jobgroup: jobexample
spec:
template:
metadata:
name: jobexample
labels:
jobgroup: jobexample
spec:
containers:
- name: c
image: busybox:1.28
command: ["sh", "-c", "echo Processing URL {{ url }} && sleep 5"]
restartPolicy: Never
{% endfor %}
The above template defines two parameters for each Job object using a list of
python dicts (lines 1-4). A for loop emits one Job manifest for each
set of parameters (remaining lines).
This example relies on a feature of YAML. One YAML file can contain multiple
documents (Kubernetes manifests, in this case), separated by --- on a line
by itself.
You can pipe the output directly to kubectl to create the Jobs.
Next, use this one-line Python program to expand the template:
# Remove the Jobs you created# Your cluster automatically cleans up their Podskubectl delete job -l jobgroup=jobexample
Using Jobs in real workloads
In a real use case, each Job performs some substantial computation, such as rendering a frame
of a movie, or processing a range of rows in a database. If you were rendering a movie
you would set $ITEM to the frame number. If you were processing rows from a database
table, you would set $ITEM to represent the range of database rows to process.
In the task, you ran a command to collect the output from Pods by fetching
their logs. In a real use case, each Pod for a Job writes its output to
durable storage before completing. You can use a PersistentVolume for each Job,
or an external storage service. For example, if you are rendering frames for a movie,
use HTTP to PUT the rendered frame data to a URL, using a different URL for each
frame.
Labels on Jobs and Pods
After you create a Job, Kubernetes automatically adds additional
labels that
distinguish one Job's pods from another Job's pods.
In this example, each Job and its Pod template have a label:
jobgroup=jobexample.
Kubernetes itself pays no attention to labels named jobgroup. Setting a label
for all the Jobs you create from a template makes it convenient to operate on all
those Jobs at once.
In the first example you used a template to
create several Jobs. The template ensures that each Pod also gets the same label, so
you can check on all Pods for these templated Jobs with a single command.
Note: The label key jobgroup is not special or reserved.
You can pick your own labelling scheme.
There are recommended labels
that you can use if you wish.
Alternatives
If you plan to create a large number of Job objects, you may find that:
Even using labels, managing so many Jobs is cumbersome.
If you create many Jobs in a batch, you might place high load
on the Kubernetes control plane. Alternatively, the Kubernetes API
server could rate limit you, temporarily rejecting your requests with a 429 status.
You are limited by a resource quota
on Jobs: the API server permanently rejects some of your requests
when you create a great deal of work in one batch.
There are other job patterns
that you can use to process large amounts of work without creating very many Job
objects.
You could also consider writing your own controller
to manage Job objects automatically.
9.7 - Handling retriable and non-retriable pod failures with Pod failure policy
FEATURE STATE:Kubernetes v1.26 [beta]
This document shows you how to use the
Pod failure policy,
in combination with the default
Pod backoff failure policy,
to improve the control over the handling of container- or Pod-level failure
within a Job.
The definition of Pod failure policy may help you to:
better utilize the computational resources by avoiding unnecessary Pod retries.
You should already be familiar with the basic use of Job.
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Your Kubernetes server must be at or later than version v1.25.
To check the version, enter kubectl version.
Ensure that the feature gatesPodDisruptionConditions and JobPodFailurePolicy are both enabled in your cluster.
Using Pod failure policy to avoid unnecessary Pod retries
With the following example, you can learn how to use Pod failure policy to
avoid unnecessary Pod restarts when a Pod failure indicates a non-retriable
software bug.
After around 30s the entire Job should be terminated. Inspect the status of the Job by running:
kubectl get jobs -l job-name=job-pod-failure-policy-failjob -o yaml
In the Job status, see a job Failed condition with the field reason
equal PodFailurePolicy. Additionally, the message field contains a
more detailed information about the Job termination, such as:
Container main for pod default/job-pod-failure-policy-failjob-8ckj8 failed with exit code 42 matching FailJob rule at index 0.
For comparison, if the Pod failure policy was disabled it would take 6 retries
of the Pod, taking at least 2 minutes.
Using Pod failure policy to ignore Pod disruptions
With the following example, you can learn how to use Pod failure policy to
ignore Pod disruptions from incrementing the Pod retry counter towards the
.spec.backoffLimit limit.
Caution: Timing is important for this example, so you may want to read the steps before
execution. In order to trigger a Pod disruption it is important to drain the
node while the Pod is running on it (within 90s since the Pod is scheduled).
Inspect the .status.failed to check the counter for the Job is not incremented:
kubectl get jobs -l job-name=job-pod-failure-policy-ignore -o yaml
Uncordon the node:
kubectl uncordon nodes/$nodeName
The Job resumes and succeeds.
For comparison, if the Pod failure policy was disabled the Pod disruption would
result in terminating the entire Job (as the .spec.backoffLimit is set to 0).
Cleaning up
Delete the Job you created:
kubectl delete jobs/job-pod-failure-policy-ignore
The cluster automatically cleans up the Pods.
Using Pod failure policy to avoid unnecessary Pod retries based on custom Pod Conditions
With the following example, you can learn how to use Pod failure policy to
avoid unnecessary Pod restarts based on custom Pod Conditions.
Note: The example below works since version 1.27 as it relies on transitioning of
deleted pods, in the Pending phase, to a terminal phase
(see: Pod Phase).
Note that the pod remains in the Pending phase as it fails to pull the
misconfigured image. This, in principle, could be a transient issue and the
image could get pulled. However, in this case, the image does not exist so
we indicate this fact by a custom condition.
Add the custom condition. First prepare the patch by running:
Delete the pod to transition it to Failed phase, by running the command:
kubectl delete pods/$podName
Inspect the status of the Job by running:
kubectl get jobs -l job-name=job-pod-failure-policy-config-issue -o yaml
In the Job status, see a job Failed condition with the field reason
equal PodFailurePolicy. Additionally, the message field contains a
more detailed information about the Job termination, such as:
Pod default/job-pod-failure-policy-config-issue-k6pvp has condition ConfigIssue matching FailJob rule at index 0.
Note: In a production environment, the steps 3 and 4 should be automated by a
user-provided controller.
You could rely solely on the
Pod backoff failure policy,
by specifying the Job's .spec.backoffLimit field. However, in many situations
it is problematic to find a balance between setting a low value for .spec.backoffLimit
to avoid unnecessary Pod retries, yet high enough to make sure the Job would
not be terminated by Pod disruptions.
10 - Access Applications in a Cluster
Configure load balancing, port forwarding, or setup firewall or DNS configurations to access applications in a cluster.
10.1 - Deploy and Access the Kubernetes Dashboard
Deploy the web UI (Kubernetes Dashboard) and access it.
Dashboard is a web-based Kubernetes user interface.
You can use Dashboard to deploy containerized applications to a Kubernetes cluster,
troubleshoot your containerized application, and manage the cluster resources.
You can use Dashboard to get an overview of applications running on your cluster,
as well as for creating or modifying individual Kubernetes resources
(such as Deployments, Jobs, DaemonSets, etc).
For example, you can scale a Deployment, initiate a rolling update, restart a pod
or deploy new applications using a deploy wizard.
Dashboard also provides information on the state of Kubernetes resources in your cluster and on any errors that may have occurred.
Deploying the Dashboard UI
The Dashboard UI is not deployed by default. To deploy it, run the following command:
To protect your cluster data, Dashboard deploys with a minimal RBAC configuration by default.
Currently, Dashboard only supports logging in with a Bearer Token.
To create a token for this demo, you can follow our guide on
creating a sample user.
Warning: The sample user created in the tutorial will have administrative privileges and is for educational purposes only.
Command line proxy
You can enable access to the Dashboard using the kubectl command-line tool,
by running the following command:
The UI can only be accessed from the machine where the command is executed. See kubectl proxy --help for more options.
Note: The kubeconfig authentication method does not support external identity providers
or X.509 certificate-based authentication.
Welcome view
When you access Dashboard on an empty cluster, you'll see the welcome page.
This page contains a link to this document as well as a button to deploy your first application.
In addition, you can view which system applications are running by default in the kube-systemnamespace of your cluster, for example the Dashboard itself.
Deploying containerized applications
Dashboard lets you create and deploy a containerized application as a Deployment and optional Service with a simple wizard.
You can either manually specify application details, or upload a YAML or JSON manifest file containing application configuration.
Click the CREATE button in the upper right corner of any page to begin.
Specifying application details
The deploy wizard expects that you provide the following information:
App name (mandatory): Name for your application.
A label with the name will be
added to the Deployment and Service, if any, that will be deployed.
The application name must be unique within the selected Kubernetes namespace.
It must start with a lowercase character, and end with a lowercase character or a number,
and contain only lowercase letters, numbers and dashes (-). It is limited to 24 characters.
Leading and trailing spaces are ignored.
Container image (mandatory):
The URL of a public Docker container image on any registry,
or a private image (commonly hosted on the Google Container Registry or Docker Hub).
The container image specification must end with a colon.
Number of pods (mandatory): The target number of Pods you want your application to be deployed in.
The value must be a positive integer.
A Deployment will be created to
maintain the desired number of Pods across your cluster.
Service (optional): For some parts of your application (e.g. frontends) you may want to expose a
Service onto an external,
maybe public IP address outside of your cluster (external Service).
Note: For external Services, you may need to open up one or more ports to do so.
Other Services that are only visible from inside the cluster are called internal Services.
Irrespective of the Service type, if you choose to create a Service and your container listens
on a port (incoming), you need to specify two ports.
The Service will be created mapping the port (incoming) to the target port seen by the container.
This Service will route to your deployed Pods. Supported protocols are TCP and UDP.
The internal DNS name for this Service will be the value you specified as application name above.
If needed, you can expand the Advanced options section where you can specify more settings:
Description: The text you enter here will be added as an
annotation
to the Deployment and displayed in the application's details.
Labels: Default labels to be used
for your application are application name and version.
You can specify additional labels to be applied to the Deployment, Service (if any), and Pods,
such as release, environment, tier, partition, and release track.
Namespace: Kubernetes supports multiple virtual clusters backed by the same physical cluster.
These virtual clusters are called namespaces.
They let you partition resources into logically named groups.
Dashboard offers all available namespaces in a dropdown list, and allows you to create a new namespace.
The namespace name may contain a maximum of 63 alphanumeric characters and dashes (-) but can not contain capital letters.
Namespace names should not consist of only numbers.
If the name is set as a number, such as 10, the pod will be put in the default namespace.
In case the creation of the namespace is successful, it is selected by default.
If the creation fails, the first namespace is selected.
Image Pull Secret:
In case the specified Docker container image is private, it may require
pull secret credentials.
Dashboard offers all available secrets in a dropdown list, and allows you to create a new secret.
The secret name must follow the DNS domain name syntax, for example new.image-pull.secret.
The content of a secret must be base64-encoded and specified in a
.dockercfg file.
The secret name may consist of a maximum of 253 characters.
In case the creation of the image pull secret is successful, it is selected by default. If the creation fails, no secret is applied.
CPU requirement (cores) and Memory requirement (MiB):
You can specify the minimum resource limits
for the container. By default, Pods run with unbounded CPU and memory limits.
Run command and Run command arguments:
By default, your containers run the specified Docker image's default
entrypoint command.
You can use the command options and arguments to override the default.
Run as privileged: This setting determines whether processes in
privileged containers
are equivalent to processes running as root on the host.
Privileged containers can make use of capabilities like manipulating the network stack and accessing devices.
Environment variables: Kubernetes exposes Services through
environment variables.
You can compose environment variable or pass arguments to your commands using the values of environment variables.
They can be used in applications to find a Service.
Values can reference other variables using the $(VAR_NAME) syntax.
Uploading a YAML or JSON file
Kubernetes supports declarative configuration.
In this style, all configuration is stored in manifests (YAML or JSON configuration files).
The manifests use Kubernetes API resource schemas.
As an alternative to specifying application details in the deploy wizard,
you can define your application in one or more manifests, and upload the files using Dashboard.
Using Dashboard
Following sections describe views of the Kubernetes Dashboard UI; what they provide and how can they be used.
Navigation
When there are Kubernetes objects defined in the cluster, Dashboard shows them in the initial view.
By default only objects from the default namespace are shown and
this can be changed using the namespace selector located in the navigation menu.
Dashboard shows most Kubernetes object kinds and groups them in a few menu categories.
Admin overview
For cluster and namespace administrators, Dashboard lists Nodes, Namespaces and PersistentVolumes and has detail views for them.
Node list view contains CPU and memory usage metrics aggregated across all Nodes.
The details view shows the metrics for a Node, its specification, status,
allocated resources, events and pods running on the node.
Workloads
Shows all applications running in the selected namespace.
The view lists applications by workload kind (for example: Deployments, ReplicaSets, StatefulSets).
Each workload kind can be viewed separately.
The lists summarize actionable information about the workloads,
such as the number of ready pods for a ReplicaSet or current memory usage for a Pod.
Detail views for workloads show status and specification information and
surface relationships between objects.
For example, Pods that ReplicaSet is controlling or new ReplicaSets and HorizontalPodAutoscalers for Deployments.
Services
Shows Kubernetes resources that allow for exposing services to external world and
discovering them within a cluster.
For that reason, Service and Ingress views show Pods targeted by them,
internal endpoints for cluster connections and external endpoints for external users.
Storage
Storage view shows PersistentVolumeClaim resources which are used by applications for storing data.
ConfigMaps and Secrets
Shows all Kubernetes resources that are used for live configuration of applications running in clusters.
The view allows for editing and managing config objects and displays secrets hidden by default.
Logs viewer
Pod lists and detail pages link to a logs viewer that is built into Dashboard.
The viewer allows for drilling down logs from containers belonging to a single Pod.
This topic discusses multiple ways to interact with clusters.
Accessing for the first time with kubectl
When accessing the Kubernetes API for the first time, we suggest using the
Kubernetes CLI, kubectl.
To access a cluster, you need to know the location of the cluster and have credentials
to access it. Typically, this is automatically set-up when you work through
a Getting started guide,
or someone else set up the cluster and provided you with credentials and a location.
Check the location and credentials that kubectl knows about with this command:
kubectl config view
Many of the examples provide an introduction to using
kubectl, and complete documentation is found in the
kubectl reference.
Directly accessing the REST API
Kubectl handles locating and authenticating to the apiserver.
If you want to directly access the REST API with an http client like
curl or wget, or a browser, there are several ways to locate and authenticate:
Run kubectl in proxy mode.
Recommended approach.
Uses stored apiserver location.
Verifies identity of apiserver using self-signed cert. No MITM possible.
Authenticates to apiserver.
In future, may do intelligent client-side load-balancing and failover.
Provide the location and credentials directly to the http client.
Alternate approach.
Works with some types of client code that are confused by using a proxy.
Need to import a root cert into your browser to protect against MITM.
Using kubectl proxy
The following command runs kubectl in a mode where it acts as a reverse proxy. It handles
locating the apiserver and authenticating.
Run it like this:
The above examples use the --insecure flag. This leaves it subject to MITM
attacks. When kubectl accesses the cluster it uses a stored root certificate
and client certificates to access the server. (These are installed in the
~/.kube directory). Since cluster certificates are typically self-signed, it
may take special configuration to get your http client to use root
certificate.
On some clusters, the apiserver does not require authentication; it may serve
on localhost, or be protected by a firewall. There is not a standard
for this. Controlling Access to the API
describes how a cluster admin can configure this.
Programmatic access to the API
Kubernetes officially supports Go and Python
client libraries.
Go client
To get the library, run the following command: go get k8s.io/client-go@kubernetes-<kubernetes-version-number>, see INSTALL.md for detailed installation instructions. See https://github.com/kubernetes/client-go to see which versions are supported.
Write an application atop of the client-go clients. Note that client-go defines its own API objects, so if needed, please import API definitions from client-go rather than from the main repository, e.g., import "k8s.io/client-go/kubernetes" is correct.
The Go client can use the same kubeconfig file
as the kubectl CLI does to locate and authenticate to the apiserver. See this example.
If the application is deployed as a Pod in the cluster, please refer to the next section.
The previous section describes how to connect to the Kubernetes API server.
For information about connecting to other services running on a Kubernetes cluster, see
Access Cluster Services.
Requesting redirects
The redirect capabilities have been deprecated and removed. Please use a proxy (see below) instead.
So Many Proxies
There are several different proxies you may encounter when using Kubernetes:
existence and implementation varies from cluster to cluster (e.g. nginx)
sits between all clients and one or more apiservers
acts as load balancer if there are several apiservers.
Cloud Load Balancers on external services:
are provided by some cloud providers (e.g. AWS ELB, Google Cloud Load Balancer)
are created automatically when the Kubernetes service has type LoadBalancer
use UDP/TCP only
implementation varies by cloud provider.
Kubernetes users will typically not need to worry about anything other than the first two types. The cluster admin
will typically ensure that the latter types are set up correctly.
10.3 - Configure Access to Multiple Clusters
This page shows how to configure access to multiple clusters by using
configuration files. After your clusters, users, and contexts are defined in
one or more configuration files, you can quickly switch between clusters by using the
kubectl config use-context command.
Note: A file that is used to configure access to a cluster is sometimes called
a kubeconfig file. This is a generic way of referring to configuration files.
It does not mean that there is a file named kubeconfig.
Warning: Only use kubeconfig files from trusted sources. Using a specially-crafted kubeconfig
file could result in malicious code execution or file exposure.
If you must use an untrusted kubeconfig file, inspect it carefully first, much as you would a shell script.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
To check that kubectl is installed,
run kubectl version --client. The kubectl version should be
within one minor version of your
cluster's API server.
Define clusters, users, and contexts
Suppose you have two clusters, one for development work and one for test work.
In the development cluster, your frontend developers work in a namespace called frontend,
and your storage developers work in a namespace called storage. In your test cluster,
developers work in the default namespace, or they create auxiliary namespaces as they
see fit. Access to the development cluster requires authentication by certificate. Access
to the test cluster requires authentication by username and password.
Create a directory named config-exercise. In your
config-exercise directory, create a file named config-demo with this content:
A configuration file describes clusters, users, and contexts. Your config-demo file
has the framework to describe two clusters, two users, and three contexts.
Go to your config-exercise directory. Enter these commands to add cluster details to
your configuration file:
kubectl config --kubeconfig=config-demo set-cluster development --server=https://1.2.3.4 --certificate-authority=fake-ca-file
kubectl config --kubeconfig=config-demo set-cluster test --server=https://5.6.7.8 --insecure-skip-tls-verify
Add user details to your configuration file:
Caution: Storing passwords in Kubernetes client config is risky. A better alternative would be to use a credential plugin and store them separately. See: client-go credential plugins
Open your config-demo file to see the added details. As an alternative to opening the
config-demo file, you can use the config view command.
kubectl config --kubeconfig=config-demo view
The output shows the two clusters, two users, and three contexts:
apiVersion:v1clusters:- cluster:certificate-authority:fake-ca-fileserver:https://1.2.3.4name:development- cluster:insecure-skip-tls-verify:trueserver:https://5.6.7.8name:testcontexts:- context:cluster:developmentnamespace:frontenduser:developername:dev-frontend- context:cluster:developmentnamespace:storageuser:developername:dev-storage- context:cluster:testnamespace:defaultuser:experimentername:exp-testcurrent-context:""kind:Configpreferences:{}users:- name:developeruser:client-certificate:fake-cert-fileclient-key:fake-key-file- name:experimenteruser:# Documentation note (this comment is NOT part of the command output).# Storing passwords in Kubernetes client config is risky.# A better alternative would be to use a credential plugin# and store the credentials separately.# See https://kubernetes.io/docs/reference/access-authn-authz/authentication/#client-go-credential-pluginspassword:some-passwordusername:exp
The fake-ca-file, fake-cert-file and fake-key-file above are the placeholders
for the pathnames of the certificate files. You need to change these to the actual pathnames
of certificate files in your environment.
Sometimes you may want to use Base64-encoded data embedded here instead of separate
certificate files; in that case you need to add the suffix -data to the keys, for example,
certificate-authority-data, client-certificate-data, client-key-data.
Each context is a triple (cluster, user, namespace). For example, the
dev-frontend context says, "Use the credentials of the developer
user to access the frontend namespace of the development cluster".
Now whenever you enter a kubectl command, the action will apply to the cluster,
and namespace listed in the dev-frontend context. And the command will use
the credentials of the user listed in the dev-frontend context.
To see only the configuration information associated with
the current context, use the --minify flag.
Now any kubectl command you give will apply to the default namespace of
the test cluster. And the command will use the credentials of the user
listed in the exp-test context.
View configuration associated with the new current context, exp-test.
The preceding configuration file defines a new context named dev-ramp-up.
Set the KUBECONFIG environment variable
See whether you have an environment variable named KUBECONFIG. If so, save the
current value of your KUBECONFIG environment variable, so you can restore it later.
For example:
Linux
exportKUBECONFIG_SAVED="$KUBECONFIG"
Windows PowerShell
$Env:KUBECONFIG_SAVED=$ENV:KUBECONFIG
The KUBECONFIG environment variable is a list of paths to configuration files. The list is
colon-delimited for Linux and Mac, and semicolon-delimited for Windows. If you have
a KUBECONFIG environment variable, familiarize yourself with the configuration files
in the list.
Temporarily append two paths to your KUBECONFIG environment variable. For example:
In your config-exercise directory, enter this command:
kubectl config view
The output shows merged information from all the files listed in your KUBECONFIG
environment variable. In particular, notice that the merged information has the
dev-ramp-up context from the config-demo-2 file and the three contexts from
the config-demo file:
If you already have a cluster, and you can use kubectl to interact with
the cluster, then you probably have a file named config in the $HOME/.kube
directory.
Go to $HOME/.kube, and see what files are there. Typically, there is a file named
config. There might also be other configuration files in this directory. Briefly
familiarize yourself with the contents of these files.
Append $HOME/.kube/config to your KUBECONFIG environment variable
If you have a $HOME/.kube/config file, and it's not already listed in your
KUBECONFIG environment variable, append it to your KUBECONFIG environment variable now.
For example:
View configuration information merged from all the files that are now listed
in your KUBECONFIG environment variable. In your config-exercise directory, enter:
kubectl config view
Clean up
Return your KUBECONFIG environment variable to its original value. For example:
Linux
exportKUBECONFIG="$KUBECONFIG_SAVED"
Windows PowerShell
$Env:KUBECONFIG=$ENV:KUBECONFIG_SAVED
Check the subject represented by the kubeconfig
It is not always obvious what attributes (username, groups) you will get after authenticating to the cluster.
It can be even more challenging if you are managing more than one cluster at the same time.
There is a kubectl subcommand to check subject attributes, such as username, for your selected Kubernetes
client context: kubectl auth whoami.
10.4 - Use Port Forwarding to Access Applications in a Cluster
This page shows how to use kubectl port-forward to connect to a MongoDB
server running in a Kubernetes cluster. This type of connection can be useful
for database debugging.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
The output of a successful command verifies that the Service was created:
service/mongo created
Check the Service created:
kubectl get service mongo
The output displays the service created:
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
mongo ClusterIP 10.96.41.183 <none> 27017/TCP 11s
Verify that the MongoDB server is running in the Pod, and listening on port 27017:
# Change mongo-75f59d57f4-4nd6q to the name of the Podkubectl get pod mongo-75f59d57f4-4nd6q --template='{{(index (index .spec.containers 0).ports 0).containerPort}}{{"\n"}}'
The output displays the port for MongoDB in that Pod:
27017
27017 is the TCP port allocated to MongoDB on the internet.
Forward a local port to a port on the Pod
kubectl port-forward allows using resource name, such as a pod name, to select a matching pod to port forward to.
# Change mongo-75f59d57f4-4nd6q to the name of the Podkubectl port-forward mongo-75f59d57f4-4nd6q 28015:27017
Any of the above commands works. The output is similar to this:
Forwarding from 127.0.0.1:28015 -> 27017
Forwarding from [::1]:28015 -> 27017
Note:kubectl port-forward does not return. To continue with the exercises, you will need to open another terminal.
Start the MongoDB command line interface:
mongosh --port 28015
At the MongoDB command line prompt, enter the ping command:
db.runCommand( { ping: 1 } )
A successful ping request returns:
{ ok: 1 }
Optionally let kubectl choose the local port
If you don't need a specific local port, you can let kubectl choose and allocate
the local port and thus relieve you from having to manage local port conflicts, with
the slightly simpler syntax:
kubectl port-forward deployment/mongo :27017
The kubectl tool finds a local port number that is not in use (avoiding low ports numbers,
because these might be used by other applications). The output is similar to:
Forwarding from 127.0.0.1:63753 -> 27017
Forwarding from [::1]:63753 -> 27017
Discussion
Connections made to local port 28015 are forwarded to port 27017 of the Pod that
is running the MongoDB server. With this connection in place, you can use your
local workstation to debug the database that is running in the Pod.
Note:kubectl port-forward is implemented for TCP ports only.
The support for UDP protocol is tracked in
issue 47862.
10.5 - Use a Service to Access an Application in a Cluster
This page shows how to create a Kubernetes Service object that external
clients can use to access an application running in a cluster. The Service
provides load balancing for an application that has two running instances.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Make a note of the NodePort value for the service. For example,
in the preceding output, the NodePort value is 31496.
List the pods that are running the Hello World application:
kubectl get pods --selector="run=load-balancer-example" --output=wide
The output is similar to this:
NAME READY STATUS ... IP NODE
hello-world-2895499144-bsbk5 1/1 Running ... 10.200.1.4 worker1
hello-world-2895499144-m1pwt 1/1 Running ... 10.200.2.5 worker2
Get the public IP address of one of your nodes that is running
a Hello World pod. How you get this address depends on how you set
up your cluster. For example, if you are using Minikube, you can
see the node address by running kubectl cluster-info. If you are
using Google Compute Engine instances, you can use the
gcloud compute instances list command to see the public addresses of your
nodes.
On your chosen node, create a firewall rule that allows TCP traffic
on your node port. For example, if your Service has a NodePort value of
31568, create a firewall rule that allows TCP traffic on port 31568. Different
cloud providers offer different ways of configuring firewall rules.
Use the node address and node port to access the Hello World application:
curl http://<public-node-ip>:<node-port>
where <public-node-ip> is the public IP address of your node,
and <node-port> is the NodePort value for your service. The
response to a successful request is a hello message:
Hello Kubernetes!
Using a service configuration file
As an alternative to using kubectl expose, you can use a
service configuration file
to create a Service.
Cleaning up
To delete the Service, enter this command:
kubectl delete services example-service
To delete the Deployment, the ReplicaSet, and the Pods that are running
the Hello World application, enter this command:
10.6 - Connect a Frontend to a Backend Using Services
This task shows how to create a frontend and a backend microservice. The backend
microservice is a hello greeter. The frontend exposes the backend using nginx and a
Kubernetes Service object.
Objectives
Create and run a sample hello backend microservice using a
Deployment object.
Use a Service object to send traffic to the backend microservice's multiple replicas.
Create and run a nginx frontend microservice, also using a Deployment object.
Configure the frontend microservice to send traffic to the backend microservice.
Use a Service object of type=LoadBalancer to expose the frontend microservice
outside the cluster.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
This task uses
Services with external load balancers, which
require a supported environment. If your environment does not support this, you can use a Service of type
NodePort instead.
Creating the backend using a Deployment
The backend is a simple hello greeter microservice. Here is the configuration
file for the backend Deployment:
Name: backend
Namespace: default
CreationTimestamp: Mon, 24 Oct 2016 14:21:02 -0700
Labels: app=hello
tier=backend
track=stable
Annotations: deployment.kubernetes.io/revision=1
Selector: app=hello,tier=backend,track=stable
Replicas: 3 desired | 3 updated | 3 total | 3 available | 0 unavailable
StrategyType: RollingUpdate
MinReadySeconds: 0
RollingUpdateStrategy: 1 max unavailable, 1 max surge
Pod Template:
Labels: app=hello
tier=backend
track=stable
Containers:
hello:
Image: "gcr.io/google-samples/hello-go-gke:1.0"
Port: 80/TCP
Environment: <none>
Mounts: <none>
Volumes: <none>
Conditions:
Type Status Reason
---- ------ ------
Available True MinimumReplicasAvailable
Progressing True NewReplicaSetAvailable
OldReplicaSets: <none>
NewReplicaSet: hello-3621623197 (3/3 replicas created)
Events:
...
Creating the hello Service object
The key to sending requests from a frontend to a backend is the backend
Service. A Service creates a persistent IP address and DNS name entry
so that the backend microservice can always be reached. A Service uses
selectors to find
the Pods that it routes traffic to.
At this point, you have a backend Deployment running three replicas of your hello
application, and you have a Service that can route traffic to them. However, this
service is neither available nor resolvable outside the cluster.
Creating the frontend
Now that you have your backend running, you can create a frontend that is accessible
outside the cluster, and connects to the backend by proxying requests to it.
The frontend sends requests to the backend worker Pods by using the DNS name
given to the backend Service. The DNS name is hello, which is the value
of the name field in the examples/service/access/backend-service.yaml
configuration file.
The Pods in the frontend Deployment run a nginx image that is configured
to proxy requests to the hello backend Service. Here is the nginx configuration file:
# The identifier Backend is internal to nginx, and used to name this specific upstream
upstream Backend {
# hello is the internal DNS name used by the backend Service inside Kubernetes
server hello;
}
server {
listen 80;
location / {
# The following statement will proxy traffic to the upstream named Backend
proxy_pass http://Backend;
}
}
Similar to the backend, the frontend has a Deployment and a Service. An important
difference to notice between the backend and frontend services, is that the
configuration for the frontend Service has type: LoadBalancer, which means that
the Service uses a load balancer provisioned by your cloud provider and will be
accessible from outside the cluster.
The output verifies that both resources were created:
deployment.apps/frontend created
service/frontend created
Note: The nginx configuration is baked into the
container image. A better way to do this would
be to use a
ConfigMap,
so that you can change the configuration more easily.
Interact with the frontend Service
Once you've created a Service of type LoadBalancer, you can use this
command to find the external IP:
kubectl get service frontend --watch
This displays the configuration for the frontend Service and watches for
changes. Initially, the external IP is listed as <pending>:
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
frontend LoadBalancer 10.51.252.116 <pending> 80/TCP 10s
As soon as an external IP is provisioned, however, the configuration updates
to include the new IP under the EXTERNAL-IP heading:
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
frontend LoadBalancer 10.51.252.116 XXX.XXX.XXX.XXX 80/TCP 1m
That IP can now be used to interact with the frontend service from outside the
cluster.
Send traffic through the frontend
The frontend and backend are now connected. You can hit the endpoint
by using the curl command on the external IP of your frontend Service.
curl http://${EXTERNAL_IP}# replace this with the EXTERNAL-IP you saw earlier
The output shows the message generated by the backend:
{"message":"Hello"}
Cleaning up
To delete the Services, enter this command:
kubectl delete services frontend backend
To delete the Deployments, the ReplicaSets and the Pods that are running the backend and frontend applications, enter this command:
This page shows how to create an external load balancer.
When creating a Service, you have
the option of automatically creating a cloud load balancer. This provides an
externally-accessible IP address that sends traffic to the correct port on your cluster
nodes,
provided your cluster runs in a supported environment and is configured with
the correct cloud load balancer provider package.
You can also use an Ingress in place of Service.
For more information, check the Ingress
documentation.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
The load balancer's IP address is listed next to LoadBalancer Ingress.
Note:
If you are running your service on Minikube, you can find the assigned IP address and port with:
minikube service example-service --url
Preserving the client source IP
By default, the source IP seen in the target container is not the original
source IP of the client. To enable preservation of the client IP, the following
fields can be configured in the .spec of the Service:
.spec.externalTrafficPolicy - denotes if this Service desires to route
external traffic to node-local or cluster-wide endpoints. There are two available
options: Cluster (default) and Local. Cluster obscures the client source
IP and may cause a second hop to another node, but should have good overall
load-spreading. Local preserves the client source IP and avoids a second hop
for LoadBalancer and NodePort type Services, but risks potentially imbalanced
traffic spreading.
.spec.healthCheckNodePort - specifies the health check node port
(numeric port number) for the service. If you don't specify
healthCheckNodePort, the service controller allocates a port from your
cluster's NodePort range.
You can configure that range by setting an API server command line option,
--service-node-port-range. The Service will use the user-specified
healthCheckNodePort value if you specify it, provided that the
Service type is set to LoadBalancer and externalTrafficPolicy is set
to Local.
Setting externalTrafficPolicy to Local in the Service manifest
activates this feature. For example:
Caveats and limitations when preserving source IPs
Load balancing services from some cloud providers do not let you configure different weights for each target.
With each target weighted equally in terms of sending traffic to Nodes, external
traffic is not equally load balanced across different Pods. The external load balancer
is unaware of the number of Pods on each node that are used as a target.
Where NumServicePods << _NumNodes or NumServicePods >> NumNodes, a fairly close-to-equal
distribution will be seen, even without weights.
Internal pod to pod traffic should behave similar to ClusterIP services, with equal probability across all pods.
Garbage collecting load balancers
FEATURE STATE:Kubernetes v1.17 [stable]
In usual case, the correlating load balancer resources in cloud provider should
be cleaned up soon after a LoadBalancer type Service is deleted. But it is known
that there are various corner cases where cloud resources are orphaned after the
associated Service is deleted. Finalizer Protection for Service LoadBalancers was
introduced to prevent this from happening. By using finalizers, a Service resource
will never be deleted until the correlating load balancer resources are also deleted.
Specifically, if a Service has type LoadBalancer, the service controller will attach
a finalizer named service.kubernetes.io/load-balancer-cleanup.
The finalizer will only be removed after the load balancer resource is cleaned up.
This prevents dangling load balancer resources even in corner cases such as the
service controller crashing.
External load balancer providers
It is important to note that the datapath for this functionality is provided by a load balancer external to the Kubernetes cluster.
When the Service type is set to LoadBalancer, Kubernetes provides functionality equivalent to type equals ClusterIP to pods
within the cluster and extends it by programming the (external to Kubernetes) load balancer with entries for the nodes
hosting the relevant Kubernetes pods. The Kubernetes control plane automates the creation of the external load balancer,
health checks (if needed), and packet filtering rules (if needed). Once the cloud provider allocates an IP address for the load
balancer, the control plane looks up that external IP address and populates it into the Service object.
10.8 - List All Container Images Running in a Cluster
This page shows how to use kubectl to list all of the Container images
for Pods running in a cluster.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
In this exercise you will use kubectl to fetch all of the Pods
running in a cluster, and format the output to pull out the list
of Containers for each.
List all Container images in all namespaces
Fetch all Pods in all namespaces using kubectl get pods --all-namespaces
Format the output to include only the list of Container image names
using -o jsonpath={.items[*].spec['initContainers', 'containers'][*].image}. This will recursively parse out the
image field from the returned json.
See the jsonpath reference
for further information on how to use jsonpath.
Format the output using standard tools: tr, sort, uniq
['initContainers', 'containers'][*]: for each container
.image: get the image
Note: When fetching a single Pod by name, for example kubectl get pod nginx,
the .items[*] portion of the path should be omitted because a single
Pod is returned instead of a list of items.
List Container images by Pod
The formatting can be controlled further by using the range operation to
iterate over elements individually.
10.9 - Set up Ingress on Minikube with the NGINX Ingress Controller
An Ingress is an API object that defines rules
which allow external access to services in a cluster. An
Ingress controller
fulfills the rules set in the Ingress.
This page shows you how to set up a simple Ingress which routes requests to Service 'web' or
'web2' depending on the HTTP URI.
Before you begin
This tutorial assumes that you are using minikube to run a local Kubernetes cluster.
Visit Install tools to learn how to install minikube.
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Your Kubernetes server must be at or later than version 1.19.
To check the version, enter kubectl version.
If you are using an older Kubernetes version, switch to the documentation for that version.
Create a minikube cluster
If you haven't already set up a cluster locally, run minikube start to create a cluster.
Enable the Ingress controller
To enable the NGINX Ingress controller, run the following command:
minikube addons enable ingress
Verify that the NGINX Ingress controller is running
kubectl get pods -n ingress-nginx
Note: It can take up to a minute before you see these pods running OK.
The output is similar to:
NAME READY STATUS RESTARTS AGE
ingress-nginx-admission-create-g9g49 0/1 Completed 0 11m
ingress-nginx-admission-patch-rqp78 0/1 Completed 1 11m
ingress-nginx-controller-59b45fb494-26npt 1/1 Running 0 11m
Deploy a hello, world app
Create a Deployment using the following command:
kubectl create deployment web --image=gcr.io/google-samples/hello-app:1.0
The output should be:
deployment.apps/web created
Expose the Deployment:
kubectl expose deployment web --type=NodePort --port=8080
The output should be:
service/web exposed
Verify the Service is created and is available on a node port:
kubectl get service web
The output is similar to:
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
web NodePort 10.104.133.249 <none> 8080:31637/TCP 12m
You can now access the sample application via the Minikube IP address and NodePort.
The next step lets you access the application using the Ingress resource.
Create an Ingress
The following manifest defines an Ingress that sends traffic to your Service via
hello-world.info.
Create example-ingress.yaml from the following file:
10.10 - Communicate Between Containers in the Same Pod Using a Shared Volume
This page shows how to use a Volume to communicate between two Containers running
in the same Pod. See also how to allow processes to communicate by
sharing process namespace
between containers.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
In this exercise, you create a Pod that runs two Containers. The two containers
share a Volume that they can use to communicate. Here is the configuration file
for the Pod:
apiVersion:v1kind:Podmetadata:name:two-containersspec:restartPolicy:Nevervolumes:- name:shared-dataemptyDir:{}containers:- name:nginx-containerimage:nginxvolumeMounts:- name:shared-datamountPath:/usr/share/nginx/html- name:debian-containerimage:debianvolumeMounts:- name:shared-datamountPath:/pod-datacommand:["/bin/sh"]args:["-c","echo Hello from the debian container > /pod-data/index.html"]
In the configuration file, you can see that the Pod has a Volume named
shared-data.
The first container listed in the configuration file runs an nginx server. The
mount path for the shared Volume is /usr/share/nginx/html.
The second container is based on the debian image, and has a mount path of
/pod-data. The second container runs the following command and then terminates.
echo Hello from the debian container > /pod-data/index.html
Notice that the second container writes the index.html file in the root
directory of the nginx server.
USER PID ... STAT START TIME COMMAND
root 1 ... Ss 21:12 0:00 nginx: master process nginx -g daemon off;
Recall that the debian Container created the index.html file in the nginx root
directory. Use curl to send a GET request to the nginx server:
root@two-containers:/# curl localhost
The output shows that nginx serves a web page written by the debian container:
Hello from the debian container
Discussion
The primary reason that Pods can have multiple containers is to support
helper applications that assist a primary application. Typical examples of
helper applications are data pullers, data pushers, and proxies.
Helper and primary applications often need to communicate with each other.
Typically this is done through a shared filesystem, as shown in this exercise,
or through the loopback network interface, localhost. An example of this pattern is a
web server along with a helper program that polls a Git repository for new updates.
The Volume in this exercise provides a way for Containers to communicate during
the life of the Pod. If the Pod is deleted and recreated, any data stored in
the shared Volume is lost.
Kubernetes offers a DNS cluster addon, which most of the supported environments enable by default. In Kubernetes version 1.11 and later, CoreDNS is recommended and is installed by default with kubeadm.
For more information on how to configure CoreDNS for a Kubernetes cluster, see the Customizing DNS Service. An example demonstrating how to use Kubernetes DNS with kube-dns, see the Kubernetes DNS sample plugin.
10.12 - Access Services Running on Clusters
This page shows how to connect to services running on the Kubernetes cluster.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
In Kubernetes, nodes,
pods and services all have
their own IPs. In many cases, the node IPs, pod IPs, and some service IPs on a cluster will not be
routable, so they will not be reachable from a machine outside the cluster,
such as your desktop machine.
Ways to connect
You have several options for connecting to nodes, pods and services from outside the cluster:
Access services through public IPs.
Use a service with type NodePort or LoadBalancer to make the service reachable outside
the cluster. See the services and
kubectl expose documentation.
Depending on your cluster environment, this may only expose the service to your corporate network,
or it may expose it to the internet. Think about whether the service being exposed is secure.
Does it do its own authentication?
Place pods behind services. To access one specific pod from a set of replicas, such as for debugging,
place a unique label on the pod and create a new service which selects this label.
In most cases, it should not be necessary for application developer to directly access
nodes via their nodeIPs.
Access services, nodes, or pods using the Proxy Verb.
Does apiserver authentication and authorization prior to accessing the remote service.
Use this if the services are not secure enough to expose to the internet, or to gain
access to ports on the node IP, or for debugging.
Proxies may cause problems for some web applications.
Run a pod, and then connect to a shell in it using kubectl exec.
Connect to other nodes, pods, and services from that shell.
Some clusters may allow you to ssh to a node in the cluster. From there you may be able to
access cluster services. This is a non-standard method, and will work on some clusters but
not others. Browsers and other tools may or may not be installed. Cluster DNS may not work.
Discovering builtin services
Typically, there are several services which are started on a cluster by kube-system. Get a list of these
with the kubectl cluster-info command:
kubectl cluster-info
The output is similar to this:
Kubernetes master is running at https://192.0.2.1
elasticsearch-logging is running at https://192.0.2.1/api/v1/namespaces/kube-system/services/elasticsearch-logging/proxy
kibana-logging is running at https://192.0.2.1/api/v1/namespaces/kube-system/services/kibana-logging/proxy
kube-dns is running at https://192.0.2.1/api/v1/namespaces/kube-system/services/kube-dns/proxy
grafana is running at https://192.0.2.1/api/v1/namespaces/kube-system/services/monitoring-grafana/proxy
heapster is running at https://192.0.2.1/api/v1/namespaces/kube-system/services/monitoring-heapster/proxy
This shows the proxy-verb URL for accessing each service.
For example, this cluster has cluster-level logging enabled (using Elasticsearch), which can be reached
at https://192.0.2.1/api/v1/namespaces/kube-system/services/elasticsearch-logging/proxy/
if suitable credentials are passed, or through a kubectl proxy at, for example:
http://localhost:8080/api/v1/namespaces/kube-system/services/elasticsearch-logging/proxy/.
As mentioned above, you use the kubectl cluster-info command to retrieve the service's proxy URL. To create
proxy URLs that include service endpoints, suffixes, and parameters, you append to the service's proxy URL:
http://kubernetes_master_address/api/v1/namespaces/namespace_name/services/[https:]service_name[:port_name]/proxy
If you haven't specified a name for your port, you don't have to specify port_name in the URL. You can also
use the port number in place of the port_name for both named and unnamed ports.
By default, the API server proxies to your service using HTTP. To use HTTPS, prefix the service name with https::
http://<kubernetes_master_address>/api/v1/namespaces/<namespace_name>/services/<service_name>/proxy
The supported formats for the <service_name> segment of the URL are:
<service_name> - proxies to the default or unnamed port using http
<service_name>:<port_name> - proxies to the specified port name or port number using http
https:<service_name>: - proxies to the default or unnamed port using https (note the trailing colon)
https:<service_name>:<port_name> - proxies to the specified port name or port number using https
Examples
To access the Elasticsearch service endpoint _search?q=user:kimchy, you would use:
Using web browsers to access services running on the cluster
You may be able to put an apiserver proxy URL into the address bar of a browser. However:
Web browsers cannot usually pass tokens, so you may need to use basic (password) auth.
Apiserver can be configured to accept basic auth,
but your cluster may not be configured to accept basic auth.
Some web apps may not work, particularly those with client side javascript that construct URLs in a
way that is unaware of the proxy path prefix.
11 - Extend Kubernetes
Understand advanced ways to adapt your Kubernetes cluster to the needs of your work environment.
11.1 - Configure the Aggregation Layer
Configuring the aggregation layer
allows the Kubernetes apiserver to be extended with additional APIs, which are not
part of the core Kubernetes APIs.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Note: There are a few setup requirements for getting the aggregation layer working in
your environment to support mutual TLS auth between the proxy and extension apiservers.
Kubernetes and the kube-apiserver have multiple CAs, so make sure that the proxy is
signed by the aggregation layer CA and not by something else, like the Kubernetes general CA.
Caution: Reusing the same CA for different client types can negatively impact the cluster's
ability to function. For more information, see CA Reusage and Conflicts.
Authentication Flow
Unlike Custom Resource Definitions (CRDs), the Aggregation API involves
another server - your Extension apiserver - in addition to the standard Kubernetes apiserver.
The Kubernetes apiserver will need to communicate with your extension apiserver,
and your extension apiserver will need to communicate with the Kubernetes apiserver.
In order for this communication to be secured, the Kubernetes apiserver uses x509
certificates to authenticate itself to the extension apiserver.
This section describes how the authentication and authorization flows work,
and how to configure them.
The high-level flow is as follows:
Kubernetes apiserver: authenticate the requesting user and authorize their
rights to the requested API path.
Kubernetes apiserver: proxy the request to the extension apiserver
Extension apiserver: authenticate the request from the Kubernetes apiserver
Extension apiserver: authorize the request from the original user
Extension apiserver: execute
The rest of this section describes these steps in detail.
The flow can be seen in the following diagram.
The source for the above swimlanes can be found in the source of this document.
Kubernetes Apiserver Authentication and Authorization
A request to an API path that is served by an extension apiserver begins
the same way as all API requests: communication to the Kubernetes apiserver.
This path already has been registered with the Kubernetes apiserver by the extension apiserver.
The user communicates with the Kubernetes apiserver, requesting access to the path.
The Kubernetes apiserver uses standard authentication and authorization configured
with the Kubernetes apiserver to authenticate the user and authorize access to the specific path.
Everything to this point has been standard Kubernetes API requests, authentication and authorization.
The Kubernetes apiserver now is prepared to send the request to the extension apiserver.
Kubernetes Apiserver Proxies the Request
The Kubernetes apiserver now will send, or proxy, the request to the extension
apiserver that registered to handle the request. In order to do so,
it needs to know several things:
How should the Kubernetes apiserver authenticate to the extension apiserver,
informing the extension apiserver that the request, which comes over the network,
is coming from a valid Kubernetes apiserver?
How should the Kubernetes apiserver inform the extension apiserver of the
username and group for which the original request was authenticated?
In order to provide for these two, you must configure the Kubernetes apiserver using several flags.
Kubernetes Apiserver Client Authentication
The Kubernetes apiserver connects to the extension apiserver over TLS,
authenticating itself using a client certificate. You must provide the
following to the Kubernetes apiserver upon startup, using the provided flags:
private key file via --proxy-client-key-file
signed client certificate file via --proxy-client-cert-file
certificate of the CA that signed the client certificate file via --requestheader-client-ca-file
valid Common Name values (CNs) in the signed client certificate via --requestheader-allowed-names
The Kubernetes apiserver will use the files indicated by --proxy-client-*-file
to authenticate to the extension apiserver. In order for the request to be considered
valid by a compliant extension apiserver, the following conditions must be met:
The connection must be made using a client certificate that is signed by
the CA whose certificate is in --requestheader-client-ca-file.
The connection must be made using a client certificate whose CN is one of
those listed in --requestheader-allowed-names.
Note: You can set this option to blank as --requestheader-allowed-names="".
This will indicate to an extension apiserver that any CN is acceptable.
When started with these options, the Kubernetes apiserver will:
Use them to authenticate to the extension apiserver.
Create a configmap in the kube-system namespace called extension-apiserver-authentication,
in which it will place the CA certificate and the allowed CNs. These in turn can be retrieved
by extension apiservers to validate requests.
Note that the same client certificate is used by the Kubernetes apiserver to authenticate
against all extension apiservers. It does not create a client certificate per extension
apiserver, but rather a single one to authenticate as the Kubernetes apiserver.
This same one is reused for all extension apiserver requests.
Original Request Username and Group
When the Kubernetes apiserver proxies the request to the extension apiserver,
it informs the extension apiserver of the username and group with which the
original request successfully authenticated. It provides these in http headers
of its proxied request. You must inform the Kubernetes apiserver of the names
of the headers to be used.
the header in which to store the username via --requestheader-username-headers
the header in which to store the group via --requestheader-group-headers
the prefix to append to all extra headers via --requestheader-extra-headers-prefix
These header names are also placed in the extension-apiserver-authentication configmap,
so they can be retrieved and used by extension apiservers.
Extension Apiserver Authenticates the Request
The extension apiserver, upon receiving a proxied request from the Kubernetes apiserver,
must validate that the request actually did come from a valid authenticating proxy,
which role the Kubernetes apiserver is fulfilling. The extension apiserver validates it via:
Retrieve the following from the configmap in kube-system, as described above:
Client CA certificate
List of allowed names (CNs)
Header names for username, group and extra info
Check that the TLS connection was authenticated using a client certificate which:
Was signed by the CA whose certificate matches the retrieved CA certificate.
Has a CN in the list of allowed CNs, unless the list is blank, in which case all CNs are allowed.
Extract the username and group from the appropriate headers
If the above passes, then the request is a valid proxied request from a legitimate
authenticating proxy, in this case the Kubernetes apiserver.
Note that it is the responsibility of the extension apiserver implementation to provide
the above. Many do it by default, leveraging the k8s.io/apiserver/ package.
Others may provide options to override it using command-line options.
In order to have permission to retrieve the configmap, an extension apiserver
requires the appropriate role. There is a default role named extension-apiserver-authentication-reader
in the kube-system namespace which can be assigned.
Extension Apiserver Authorizes the Request
The extension apiserver now can validate that the user/group retrieved from
the headers are authorized to execute the given request. It does so by sending
a standard SubjectAccessReview
request to the Kubernetes apiserver.
In order for the extension apiserver to be authorized itself to submit the
SubjectAccessReview request to the Kubernetes apiserver, it needs the correct permissions.
Kubernetes includes a default ClusterRole named system:auth-delegator that
has the appropriate permissions. It can be granted to the extension apiserver's service account.
Extension Apiserver Executes
If the SubjectAccessReview passes, the extension apiserver executes the request.
Enable Kubernetes Apiserver flags
Enable the aggregation layer via the following kube-apiserver flags.
They may have already been taken care of by your provider.
--requestheader-client-ca-file=<path to aggregator CA cert>
--requestheader-allowed-names=front-proxy-client
--requestheader-extra-headers-prefix=X-Remote-Extra-
--requestheader-group-headers=X-Remote-Group
--requestheader-username-headers=X-Remote-User
--proxy-client-cert-file=<path to aggregator proxy cert>
--proxy-client-key-file=<path to aggregator proxy key>
CA Reusage and Conflicts
The Kubernetes apiserver has two client CA options:
--client-ca-file
--requestheader-client-ca-file
Each of these functions independently and can conflict with each other,
if not used correctly.
--client-ca-file: When a request arrives to the Kubernetes apiserver,
if this option is enabled, the Kubernetes apiserver checks the certificate
of the request. If it is signed by one of the CA certificates in the file referenced by
--client-ca-file, then the request is treated as a legitimate request,
and the user is the value of the common name CN=, while the group is the organization O=.
See the documentation on TLS authentication.
--requestheader-client-ca-file: When a request arrives to the Kubernetes apiserver,
if this option is enabled, the Kubernetes apiserver checks the certificate of the request.
If it is signed by one of the CA certificates in the file reference by --requestheader-client-ca-file,
then the request is treated as a potentially legitimate request. The Kubernetes apiserver then
checks if the common name CN= is one of the names in the list provided by --requestheader-allowed-names.
If the name is allowed, the request is approved; if it is not, the request is not.
If both--client-ca-file and --requestheader-client-ca-file are provided,
then the request first checks the --requestheader-client-ca-file CA and then the
--client-ca-file. Normally, different CAs, either root CAs or intermediate CAs,
are used for each of these options; regular client requests match against --client-ca-file,
while aggregation requests match against --requestheader-client-ca-file. However,
if both use the same CA, then client requests that normally would pass via --client-ca-file
will fail, because the CA will match the CA in --requestheader-client-ca-file,
but the common name CN= will not match one of the acceptable common names in
--requestheader-allowed-names. This can cause your kubelets and other control plane components,
as well as end-users, to be unable to authenticate to the Kubernetes apiserver.
For this reason, use different CA certs for the --client-ca-file
option - to authorize control plane components and end-users - and the --requestheader-client-ca-file option - to authorize aggregation apiserver requests.
Warning: Do not reuse a CA that is used in a different context unless you understand
the risks and the mechanisms to protect the CA's usage.
If you are not running kube-proxy on a host running the API server,
then you must make sure that the system is enabled with the following
kube-apiserver flag:
--enable-aggregator-routing=true
Register APIService objects
You can dynamically configure what client requests are proxied to extension
apiserver. The following is an example registration:
apiVersion:apiregistration.k8s.io/v1kind:APIServicemetadata:name:<name of the registration object>spec:group:<API group name this extension apiserver hosts>version:<API version this extension apiserver hosts>groupPriorityMinimum:<priority this APIService for this group, see API documentation>versionPriority:<prioritizes ordering of this version within a group, see API documentation>service:namespace:<namespace of the extension apiserver service>name:<name of the extension apiserver service>caBundle:<pem encoded ca cert that signs the server cert used by the webhook>
Once the Kubernetes apiserver has determined a request should be sent to an extension apiserver,
it needs to know how to contact it.
The service stanza is a reference to the service for an extension apiserver.
The service namespace and name are required. The port is optional and defaults to 443.
Here is an example of an extension apiserver that is configured to be called on port "1234",
and to verify the TLS connection against the ServerName
my-service-name.my-service-namespace.svc using a custom CA bundle.
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Your Kubernetes server must be at or later than version 1.16.
To check the version, enter kubectl version.
If you are using an older version of Kubernetes that is still supported, switch to
the documentation for that version to see advice that is relevant for your cluster.
Create a CustomResourceDefinition
When you create a new CustomResourceDefinition (CRD), the Kubernetes API Server
creates a new RESTful resource path for each version you specify. The custom
resource created from a CRD object can be either namespaced or cluster-scoped,
as specified in the CRD's spec.scope field. As with existing built-in
objects, deleting a namespace deletes all custom objects in that namespace.
CustomResourceDefinitions themselves are non-namespaced and are available to
all namespaces.
For example, if you save the following CustomResourceDefinition to resourcedefinition.yaml:
apiVersion:apiextensions.k8s.io/v1kind:CustomResourceDefinitionmetadata:# name must match the spec fields below, and be in the form: <plural>.<group>name:crontabs.stable.example.comspec:# group name to use for REST API: /apis/<group>/<version>group:stable.example.com# list of versions supported by this CustomResourceDefinitionversions:- name:v1# Each version can be enabled/disabled by Served flag.served:true# One and only one version must be marked as the storage version.storage:trueschema:openAPIV3Schema:type:objectproperties:spec:type:objectproperties:cronSpec:type:stringimage:type:stringreplicas:type:integer# either Namespaced or Clusterscope:Namespacednames:# plural name to be used in the URL: /apis/<group>/<version>/<plural>plural:crontabs# singular name to be used as an alias on the CLI and for displaysingular:crontab# kind is normally the CamelCased singular type. Your resource manifests use this.kind:CronTab# shortNames allow shorter string to match your resource on the CLIshortNames:- ct
and create it:
kubectl apply -f resourcedefinition.yaml
Then a new namespaced RESTful API endpoint is created at:
This endpoint URL can then be used to create and manage custom objects.
The kind of these objects will be CronTab from the spec of the
CustomResourceDefinition object you created above.
It might take a few seconds for the endpoint to be created.
You can watch the Established condition of your CustomResourceDefinition
to be true or watch the discovery information of the API server for your
resource to show up.
Create custom objects
After the CustomResourceDefinition object has been created, you can create
custom objects. Custom objects can contain custom fields. These fields can
contain arbitrary JSON.
In the following example, the cronSpec and image custom fields are set in a
custom object of kind CronTab. The kind CronTab comes from the spec of the
CustomResourceDefinition object you created above.
If you save the following YAML to my-crontab.yaml:
You can then manage your CronTab objects using kubectl. For example:
kubectl get crontab
Should print a list like this:
NAME AGE
my-new-cron-object 6s
Resource names are not case-sensitive when using kubectl, and you can use either
the singular or plural forms defined in the CRD, as well as any short names.
You can also view the raw YAML data:
kubectl get ct -o yaml
You should see that it contains the custom cronSpec and image fields
from the YAML you used to create it:
When you delete a CustomResourceDefinition, the server will uninstall the RESTful API endpoint
and delete all custom objects stored in it.
kubectl delete -f resourcedefinition.yaml
kubectl get crontabs
Error from server (NotFound): Unable to list {"stable.example.com" "v1" "crontabs"}: the server could not
find the requested resource (get crontabs.stable.example.com)
If you later recreate the same CustomResourceDefinition, it will start out empty.
Specifying a structural schema
CustomResources store structured data in custom fields (alongside the built-in
fields apiVersion, kind and metadata, which the API server validates
implicitly). With OpenAPI v3.0 validation a schema can be
specified, which is validated during creation and updates, compare below for
details and limits of such a schema.
With apiextensions.k8s.io/v1 the definition of a structural schema is
mandatory for CustomResourceDefinitions. In the beta version of
CustomResourceDefinition, the structural schema was optional.
specifies a non-empty type (via type in OpenAPI) for the root, for each specified field of an object node
(via properties or additionalProperties in OpenAPI) and for each item in an array node
(via items in OpenAPI), with the exception of:
a node with x-kubernetes-int-or-string: true
a node with x-kubernetes-preserve-unknown-fields: true
for each field in an object and each item in an array which is specified within any of allOf, anyOf,
oneOf or not, the schema also specifies the field/item outside of those logical junctors (compare example 1 and 2).
does not set description, type, default, additionalProperties, nullable within an allOf, anyOf,
oneOf or not, with the exception of the two pattern for x-kubernetes-int-or-string: true (see below).
if metadata is specified, then only restrictions on metadata.name and metadata.generateName are allowed.
Non-structural example 1:
allOf:
- properties:
foo:
...
conflicts with rule 2. The following would be correct:
is not a structural schema because of the following violations:
the type at the root is missing (rule 1).
the type of foo is missing (rule 1).
bar inside of anyOf is not specified outside (rule 2).
bar's type is within anyOf (rule 3).
the description is set within anyOf (rule 3).
metadata.finalizers might not be restricted (rule 4).
In contrast, the following, corresponding schema is structural:
type:objectdescription:"foo bar object"properties:foo:type:stringpattern:"abc"bar:type:integermetadata:type:objectproperties:name:type:stringpattern:"^a"anyOf:- properties:bar:minimum:42required:["bar"]
Violations of the structural schema rules are reported in the NonStructural condition in the
CustomResourceDefinition.
Field pruning
CustomResourceDefinitions store validated resource data in the cluster's persistence store, etcd.
As with native Kubernetes resources such as ConfigMap,
if you specify a field that the API server does not recognize, the unknown field is pruned (removed) before being persisted.
CRDs converted from apiextensions.k8s.io/v1beta1 to apiextensions.k8s.io/v1 might lack
structural schemas, and spec.preserveUnknownFields might be true.
For legacy CustomResourceDefinition objects created as
apiextensions.k8s.io/v1beta1 with spec.preserveUnknownFields set to
true, the following is also true:
Pruning is not enabled.
You can store arbitrary data.
For compatibility with apiextensions.k8s.io/v1, update your custom
resource definitions to:
Use a structural OpenAPI schema.
Set spec.preserveUnknownFields to false.
If you save the following YAML to my-crontab.yaml:
This example turned off client-side validation to demonstrate the API server's behavior, by adding
the --validate=false command line option.
Because the OpenAPI validation schemas are also published
to clients, kubectl also checks for unknown fields and rejects those objects well before they
would be sent to the API server.
Controlling pruning
By default, all unspecified fields for a custom resource, across all versions, are pruned. It is possible though to
opt-out of that for specifc sub-trees of fields by adding x-kubernetes-preserve-unknown-fields: true in the
structural OpenAPI v3 validation schema.
Also those nodes are partially excluded from rule 3 in the sense that the following two patterns are allowed
(exactly those, without variations in order to additional fields):
x-kubernetes-int-or-string: true
allOf:
- anyOf:
- type: integer
- type: string
- ... # zero or more
...
With one of those specification, both an integer and a string validate.
In Validation Schema Publishing,
x-kubernetes-int-or-string: true is unfolded to one of the two patterns shown above.
RawExtension
RawExtensions (as in runtime.RawExtension)
holds complete Kubernetes objects, i.e. with apiVersion and kind fields.
It is possible to specify those embedded objects (both completely without constraints or partially specified)
by setting x-kubernetes-embedded-resource: true. For example:
Here, the field foo holds a complete object, e.g.:
foo:
apiVersion: v1
kind: Pod
spec:
...
Because x-kubernetes-preserve-unknown-fields: true is specified alongside, nothing is pruned.
The use of x-kubernetes-preserve-unknown-fields: true is optional though.
With x-kubernetes-embedded-resource: true, the apiVersion, kind and metadata are implicitly specified and validated.
Serving multiple versions of a CRD
See Custom resource definition versioning
for more information about serving multiple versions of your
CustomResourceDefinition and migrating your objects from one version to another.
Advanced topics
Finalizers
Finalizers allow controllers to implement asynchronous pre-delete hooks.
Custom objects support finalizers similar to built-in objects.
You can add a finalizer to a custom object like this:
Identifiers of custom finalizers consist of a domain name, a forward slash and the name of
the finalizer. Any controller can add a finalizer to any object's list of finalizers.
The first delete request on an object with finalizers sets a value for the
metadata.deletionTimestamp field but does not delete it. Once this value is set,
entries in the finalizers list can only be removed. While any finalizers remain it is also
impossible to force the deletion of an object.
When the metadata.deletionTimestamp field is set, controllers watching the object execute any
finalizers they handle and remove the finalizer from the list after they are done. It is the
responsibility of each controller to remove its finalizer from the list.
The value of metadata.deletionGracePeriodSeconds controls the interval between polling updates.
Once the list of finalizers is empty, meaning all finalizers have been executed, the resource is
deleted by Kubernetes.
Refer to the structural schemas section for other
restrictions and CustomResourceDefinition features.
The schema is defined in the CustomResourceDefinition. In the following example, the
CustomResourceDefinition applies the following validations on the custom object:
spec.cronSpec must be a string and must be of the form described by the regular expression.
spec.replicas must be an integer and must have a minimum value of 1 and a maximum value of 10.
Save the CustomResourceDefinition to resourcedefinition.yaml:
apiVersion:apiextensions.k8s.io/v1kind:CustomResourceDefinitionmetadata:name:crontabs.stable.example.comspec:group:stable.example.comversions:- name:v1served:truestorage:trueschema:# openAPIV3Schema is the schema for validating custom objects.openAPIV3Schema:type:objectproperties:spec:type:objectproperties:cronSpec:type:stringpattern:'^(\d+|\*)(/\d+)?(\s+(\d+|\*)(/\d+)?){4}$'image:type:stringreplicas:type:integerminimum:1maximum:10scope:Namespacednames:plural:crontabssingular:crontabkind:CronTabshortNames:- ct
and create it:
kubectl apply -f resourcedefinition.yaml
A request to create a custom object of kind CronTab is rejected if there are invalid values in its fields.
In the following example, the custom object contains fields with invalid values:
spec.cronSpec does not match the regular expression.
spec.replicas is greater than 10.
If you save the following YAML to my-crontab.yaml:
The CronTab "my-new-cron-object" is invalid: []: Invalid value: map[string]interface {}{"apiVersion":"stable.example.com/v1", "kind":"CronTab", "metadata":map[string]interface {}{"name":"my-new-cron-object", "namespace":"default", "deletionTimestamp":interface {}(nil), "deletionGracePeriodSeconds":(*int64)(nil), "creationTimestamp":"2017-09-05T05:20:07Z", "uid":"e14d79e7-91f9-11e7-a598-f0761cb232d1", "clusterName":""}, "spec":map[string]interface {}{"cronSpec":"* * * *", "image":"my-awesome-cron-image", "replicas":15}}:
validation failure list:
spec.cronSpec in body should match '^(\d+|\*)(/\d+)?(\s+(\d+|\*)(/\d+)?){4}$'
spec.replicas in body should be less than or equal to 10
If the fields contain valid values, the object creation request is accepted.
kubectl apply -f my-crontab.yaml
crontab "my-new-cron-object" created
Validation ratcheting
FEATURE STATE:Kubernetes v1.28 [alpha]
You need to enable the CRDValidationRatchetingfeature gate to
use this behavior, which then applies to all CustomResourceDefinitions in your
cluster.
Provided you enabled the feature gate, Kubernetes implements validation racheting
for CustomResourceDefinitions. The API server is willing to accept updates to resources that
are not valid after the update, provided that each part of the resource that failed to validate
was not changed by the update operation. In other words, any invalid part of the resource
that remains invalid must have already been wrong.
You cannot use this mechanism to update a valid resource so that it becomes invalid.
This feature allows authors of CRDs to confidently add new validations to the
OpenAPIV3 schema under certain conditions. Users can update to the new schema
safely without bumping the version of the object or breaking workflows.
While most validations placed in the OpenAPIV3 schema of a CRD support
ratcheting, there are a few exceptions. The following OpenAPIV3 schema
validations are not supported by ratcheting under the implementation in Kubernetes
1.28 and if violated will continue to throw an error as normally:
Quantors
allOf
oneOf
anyOf
not
any validations in a descendent of one of these fields
x-kubernetes-validations
For Kubernetes 1.28, CRD validation rules](#validation-rules) are ignored by
ratcheting. This may change in later Kubernetes releases.
x-kubernetes-list-type
Errors arising from changing the list type of a subschema will not be
ratcheted. For example adding set onto a list with duplicates will always
result in an error.
x-kubernetes-map-keys
Errors arising from changing the map keys of a list schema will not be
ratcheted.
required
Errors arising from changing the list of required fields will not be ratcheted.
properties
Adding/removing/modifying the names of properties is not ratcheted, but
changes to validations in each properties' schemas and subschemas may be ratcheted
if the name of the property stays the same.
additionalProperties
To remove a previously specified additionalProperties validation will not be
ratcheted.
Validation rules
FEATURE STATE:Kubernetes v1.25 [beta]
Validation rules are in beta since 1.25 and the CustomResourceValidationExpressionsfeature gate is enabled by default to
validate custom resource based on validation rules. You can disable this feature by explicitly
setting the CustomResourceValidationExpressions feature gate to false, for the
kube-apiserver component. This
feature is only available if the schema is a structural schema.
Validation rules use the Common Expression Language (CEL)
to validate custom resource values. Validation rules are included in
CustomResourceDefinition schemas using the x-kubernetes-validations extension.
The Rule is scoped to the location of the x-kubernetes-validations extension in the schema.
And self variable in the CEL expression is bound to the scoped value.
All validation rules are scoped to the current object: no cross-object or stateful validation
rules are supported.
For example:
...
openAPIV3Schema:
type: object
properties:
spec:
type: object
x-kubernetes-validations:
- rule: "self.minReplicas <= self.replicas"
message: "replicas should be greater than or equal to minReplicas."
- rule: "self.replicas <= self.maxReplicas"
message: "replicas should be smaller than or equal to maxReplicas."
properties:
...
minReplicas:
type: integer
replicas:
type: integer
maxReplicas:
type: integer
required:
- minReplicas
- replicas
- maxReplicas
will reject a request to create this custom resource:
The CronTab "my-new-cron-object" is invalid:
* spec: Invalid value: map[string]interface {}{"maxReplicas":10, "minReplicas":0, "replicas":20}: replicas should be smaller than or equal to maxReplicas.
x-kubernetes-validations could have multiple rules.
The rule under x-kubernetes-validations represents the expression which will be evaluated by CEL.
The message represents the message displayed when validation fails. If message is unset, the
above response would be:
The CronTab "my-new-cron-object" is invalid:
* spec: Invalid value: map[string]interface {}{"maxReplicas":10, "minReplicas":0, "replicas":20}: failed rule: self.replicas <= self.maxReplicas
Note: You can quickly test CEL expressions in CEL Playground.
Validation rules are compiled when CRDs are created/updated.
The request of CRDs create/update will fail if compilation of validation rules fail.
Compilation process includes type checking as well.
The compilation failure:
no_matching_overload: this function has no overload for the types of the arguments.
For example, a rule like self == true against a field of integer type will get error:
Invalid value: apiextensions.ValidationRule{Rule:"self == true", Message:""}: compilation failed: ERROR: \<input>:1:6: found no matching overload for '_==_' applied to '(int, bool)'
no_such_field: does not contain the desired field.
For example, a rule like self.nonExistingField > 0 against a non-existing field will return
the following error:
If the Rule is scoped to the root of a resource, it may make field selection into any fields
declared in the OpenAPIv3 schema of the CRD as well as apiVersion, kind, metadata.name and
metadata.generateName. This includes selection of fields in both the spec and status in the
same expression:
If the Rule is scoped to an object with properties, the accessible properties of the object are field selectable
via self.field and field presence can be checked via has(self.field). Null valued fields are treated as
absent fields in CEL expressions.
If the Rule is scoped to an object with additionalProperties (i.e. a map) the value of the map
are accessible via self[mapKey], map containment can be checked via mapKey in self and all
entries of the map are accessible via CEL macros and functions such as self.all(...).
The apiVersion, kind, metadata.name and metadata.generateName are always accessible from
the root of the object and from any x-kubernetes-embedded-resource annotated objects. No other
metadata properties are accessible.
Unknown data preserved in custom resources via x-kubernetes-preserve-unknown-fields is not
accessible in CEL expressions. This includes:
Unknown field values that are preserved by object schemas with x-kubernetes-preserve-unknown-fields.
Object properties where the property schema is of an "unknown type". An "unknown type" is
recursively defined as:
A schema with no type and x-kubernetes-preserve-unknown-fields set to true
An array where the items schema is of an "unknown type"
An object where the additionalProperties schema is of an "unknown type"
Only property names of the form [a-zA-Z_.-/][a-zA-Z0-9_.-/]* are accessible.
Accessible property names are escaped according to the following rules when accessed in the expression:
Note: CEL RESERVED keyword needs to match the exact property name to be escaped (e.g. int in the word sprint would not be escaped).
Examples on escaping:
property name
rule with escaped property name
namespace
self.__namespace__ > 0
x-prop
self.x__dash__prop > 0
redact__d
self.redact__underscores__d > 0
string
self.startsWith('kube')
Equality on arrays with x-kubernetes-list-type of set or map ignores element order,
i.e., [1, 2] == [2, 1]. Concatenation on arrays with x-kubernetes-list-type use the semantics of
the list type:
set: X + Y performs a union where the array positions of all elements in X are preserved
and non-intersecting elements in Y are appended, retaining their partial order.
map: X + Y performs a merge where the array positions of all keys in X are preserved but
the values are overwritten by values in Y when the key sets of X and Y intersect. Elements
in Y with non-intersecting keys are appended, retaining their partial order.
Here is the declarations type mapping between OpenAPIv3 and CEL type:
OpenAPIv3 type
CEL type
'object' with Properties
object / "message type"
'object' with AdditionalProperties
map
'object' with x-kubernetes-embedded-type
object / "message type", 'apiVersion', 'kind', 'metadata.name' and 'metadata.generateName' are implicitly included in schema
'object' with x-kubernetes-preserve-unknown-fields
object / "message type", unknown fields are NOT accessible in CEL expression
x-kubernetes-int-or-string
dynamic object that is either an int or a string, type(value) can be used to check the type
'array
list
'array' with x-kubernetes-list-type=map
list with map based Equality & unique key guarantees
'array' with x-kubernetes-list-type=set
list with set based Equality & unique entry guarantees
Similar to the message field, which defines the string reported for a validation rule failure,
messageExpression allows you to use a CEL expression to construct the message string.
This allows you to insert more descriptive information into the validation failure message.
messageExpression must evaluate a string and may use the same variables that are available to the rule
field. For example:
x-kubernetes-validations:- rule:"self.x <= self.maxLimit"messageExpression:'"x exceeded max limit of " + string(self.maxLimit)'
Keep in mind that CEL string concatenation (+ operator) does not auto-cast to string. If
you have a non-string scalar, use the string(<value>) function to cast the scalar to a string
like shown in the above example.
messageExpression must evaluate to a string, and this is checked while the CRD is being written. Note that it is possible
to set message and messageExpression on the same rule, and if both are present, messageExpression
will be used. However, if messageExpression evaluates to an error, the string defined in message
will be used instead, and the messageExpression error will be logged. This fallback will also occur if
the CEL expression defined in messageExpression generates an empty string, or a string containing line
breaks.
If one of the above conditions are met and no message has been set, then the default validation failure
message will be used instead.
messageExpression is a CEL expression, so the restrictions listed in Resource use by validation functions apply. If evaluation halts due to resource constraints
during messageExpression execution, then no further validation rules will be executed.
Setting messageExpression is optional.
The message field
If you want to set a static message, you can supply message rather than messageExpression.
The value of message is used as an opaque error string if validation fails.
Setting message is optional.
The reason field
You can add a machine-readable validation failure reason within a validation, to be returned
whenever a request fails this validation rule.
The HTTP status code returned to the caller will match the reason of the first failed validation rule.
The currently supported reasons are: "FieldValueInvalid", "FieldValueForbidden", "FieldValueRequired", "FieldValueDuplicate".
If not set or unknown reasons, default to use "FieldValueInvalid".
Setting reason is optional.
The fieldPath field
You can specify the field path returned when the validation fails.
In the example above, the validation checks the value of field x should be less than the value of maxLimit.
If no fieldPath specified, when validation fails, the fieldPath would be default to wherever self scoped.
With fieldPath specified, the returned error will have fieldPath properly refer to the location of field x.
The fieldPath value must be a relative JSON path that is scoped to the location of this x-kubernetes-validations extension in the schema.
Additionally, it should refer to an existing field within the schema.
For example when validation checks if a specific attribute foo under a map testMap, you could set
fieldPath to ".testMap.foo" or .testMap['foo']'.
If the validation requires checking for unique attributes in two lists, the fieldPath can be set to either of the lists.
For example, it can be set to .testList1 or .testList2.
It supports child operation to refer to an existing field currently.
Refer to JSONPath support in Kubernetes for more info.
The fieldPath field does not support indexing arrays numerically.
A rule that contains an expression referencing the identifier oldSelf is implicitly considered a
transition rule. Transition rules allow schema authors to prevent certain transitions between two
otherwise valid states. For example:
Unlike other rules, transition rules apply only to operations meeting the following criteria:
The operation updates an existing object. Transition rules never apply to create operations.
Both an old and a new value exist. It remains possible to check if a value has been added or
removed by placing a transition rule on the parent node. Transition rules are never applied to
custom resource creation. When placed on an optional field, a transition rule will not apply to
update operations that set or unset the field.
The path to the schema node being validated by a transition rule must resolve to a node that is
comparable between the old object and the new object. For example, list items and their
descendants (spec.foo[10].bar) can't necessarily be correlated between an existing object and a
later update to the same object.
Errors will be generated on CRD writes if a schema node contains a transition rule that can never be
applied, e.g. "path: update rule rule cannot be set on schema because the schema or its parent
schema is not mergeable".
Transition rules are only allowed on correlatable portions of a schema.
A portion of the schema is correlatable if all array parent schemas are of type x-kubernetes-list-type=map;
any setor atomicarray parent schemas make it impossible to unambiguously correlate a self with oldSelf.
If previous value was X, new value can only be A or B, not Y or Z
oldSelf != 'X' || self in ['A', 'B']
Monotonic (non-decreasing) counters
self >= oldSelf
Resource use by validation functions
When you create or update a CustomResourceDefinition that uses validation rules,
the API server checks the likely impact of running those validation rules. If a rule is
estimated to be prohibitively expensive to execute, the API server rejects the create
or update operation, and returns an error message.
A similar system is used at runtime that observes the actions the interpreter takes. If the interpreter executes
too many instructions, execution of the rule will be halted, and an error will result.
Each CustomResourceDefinition is also allowed a certain amount of resources to finish executing all of
its validation rules. If the sum total of its rules are estimated at creation time to go over that limit,
then a validation error will also occur.
You are unlikely to encounter issues with the resource budget for validation if you only
specify rules that always take the same amount of time regardless of how large their input is.
For example, a rule that asserts that self.foo == 1 does not by itself have any
risk of rejection on validation resource budget groups.
But if foo is a string and you define a validation rule self.foo.contains("someString"), that rule takes
longer to execute depending on how long foo is.
Another example would be if foo were an array, and you specified a validation rule self.foo.all(x, x > 5).
The cost system always assumes the worst-case scenario if a limit on the length of foo is not
given, and this will happen for anything that can be iterated over (lists, maps, etc.).
Because of this, it is considered best practice to put a limit via maxItems, maxProperties, and
maxLength for anything that will be processed in a validation rule in order to prevent validation
errors during cost estimation. For example, given this schema with one rule:
then the API server rejects this rule on validation budget grounds with error:
spec.validation.openAPIV3Schema.properties[spec].properties[foo].x-kubernetes-validations[0].rule: Forbidden:
CEL rule exceeded budget by more than 100x (try simplifying the rule, or adding maxItems, maxProperties, and
maxLength where arrays, maps, and strings are used)
The rejection happens because self.all implies calling contains() on every string in foo,
which in turn will check the given string to see if it contains 'a string'. Without limits, this
is a very expensive rule.
If you do not specify any validation limit, the estimated cost of this rule will exceed the
per-rule cost limit. But if you add limits in the appropriate places, the rule will be allowed:
The cost estimation system takes into account how many times the rule will be executed in addition to the
estimated cost of the rule itself. For instance, the following rule will have the same estimated cost as the
previous example (despite the rule now being defined on the individual array items):
If a list inside of a list has a validation rule that uses self.all, that is significantly more expensive
than a non-nested list with the same rule. A rule that would have been allowed on a non-nested list might need
lower limits set on both nested lists in order to be allowed. For example, even without having limits set,
the following rule is allowed:
openAPIV3Schema:type:objectproperties:foo:type:arrayitems:type:integerx-kubernetes-validations:- rule:"self.all(x, x == 5)"
But the same rule on the following schema (with a nested array added) produces a validation error:
openAPIV3Schema:type:objectproperties:foo:type:arrayitems:type:arrayitems:type:integerx-kubernetes-validations:- rule:"self.all(x, x == 5)"
This is because each item of foo is itself an array, and each subarray in turn calls self.all.
Avoid nested lists and maps if possible where validation rules are used.
Defaulting
Note: To use defaulting, your CustomResourceDefinition must use API version apiextensions.k8s.io/v1.
apiVersion:apiextensions.k8s.io/v1kind:CustomResourceDefinitionmetadata:name:crontabs.stable.example.comspec:group:stable.example.comversions:- name:v1served:truestorage:trueschema:# openAPIV3Schema is the schema for validating custom objects.openAPIV3Schema:type:objectproperties:spec:type:objectproperties:cronSpec:type:stringpattern:'^(\d+|\*)(/\d+)?(\s+(\d+|\*)(/\d+)?){4}$'default:"5 0 * * *"image:type:stringreplicas:type:integerminimum:1maximum:10default:1scope:Namespacednames:plural:crontabssingular:crontabkind:CronTabshortNames:- ct
With this both cronSpec and replicas are defaulted:
in the request to the API server using the request version defaults,
when reading from etcd using the storage version defaults,
after mutating admission plugins with non-empty patches using the admission webhook object version defaults.
Defaults applied when reading data from etcd are not automatically written back to etcd.
An update request via the API is required to persist those defaults back into etcd.
Default values must be pruned (with the exception of defaults for metadata fields) and must
validate against a provided schema.
Default values for metadata fields of x-kubernetes-embedded-resources: true nodes (or parts of
a default value covering metadata) are not pruned during CustomResourceDefinition creation, but
through the pruning step during handling of requests.
Defaulting and Nullable
Null values for fields that either don't specify the nullable flag, or give it a
false value, will be pruned before defaulting happens. If a default is present, it will be
applied. When nullable is true, null values will be conserved and won't be defaulted.
creating an object with null values for foo and bar and baz
spec:foo:nullbar:nullbaz:null
leads to
spec:foo:"default"bar:null
with foo pruned and defaulted because the field is non-nullable, bar maintaining the null
value due to nullable: true, and baz pruned because the field is non-nullable and has no
default.
Publish Validation Schema in OpenAPI
CustomResourceDefinition OpenAPI v3 validation schemas which are
structural and enable pruning are published
as OpenAPI v3 and
OpenAPI v2 from Kubernetes API server. It is recommended to use the OpenAPI v3 document
as it is a lossless representation of the CustomResourceDefinition OpenAPI v3 validation schema
while OpenAPI v2 represents a lossy conversion.
The kubectl command-line tool consumes the published schema to perform
client-side validation (kubectl create and kubectl apply), schema explanation (kubectl explain)
on custom resources. The published schema can be consumed for other purposes as well, like client generation or documentation.
Compatibility with OpenAPI V2
For compatibility with OpenAPI V2, the OpenAPI v3 validation schema performs a lossy conversion
to the OpenAPI v2 schema. The schema show up in definitions and paths fields in the
OpenAPI v2 spec.
The following modifications are applied during the conversion to keep backwards compatibility with
kubectl in previous 1.13 version. These modifications prevent kubectl from being over-strict and rejecting
valid OpenAPI schemas that it doesn't understand. The conversion won't modify the validation schema defined in CRD,
and therefore won't affect validation in the API server.
The following fields are removed as they aren't supported by OpenAPI v2.
The fields allOf, anyOf, oneOf and not are removed
If nullable: true is set, we drop type, nullable, items and properties because OpenAPI v2 is
not able to express nullable. To avoid kubectl to reject good objects, this is necessary.
Additional printer columns
The kubectl tool relies on server-side output formatting. Your cluster's API server decides which
columns are shown by the kubectl get command. You can customize these columns for a
CustomResourceDefinition. The following example adds the Spec, Replicas, and Age
columns.
Save the CustomResourceDefinition to resourcedefinition.yaml:
apiVersion:apiextensions.k8s.io/v1kind:CustomResourceDefinitionmetadata:name:crontabs.stable.example.comspec:group:stable.example.comscope:Namespacednames:plural:crontabssingular:crontabkind:CronTabshortNames:- ctversions:- name:v1served:truestorage:trueschema:openAPIV3Schema:type:objectproperties:spec:type:objectproperties:cronSpec:type:stringimage:type:stringreplicas:type:integeradditionalPrinterColumns:- name:Spectype:stringdescription:The cron spec defining the interval a CronJob is runjsonPath:.spec.cronSpec- name:Replicastype:integerdescription:The number of jobs launched by the CronJobjsonPath:.spec.replicas- name:Agetype:datejsonPath:.metadata.creationTimestamp
Create the CustomResourceDefinition:
kubectl apply -f resourcedefinition.yaml
Create an instance using the my-crontab.yaml from the previous section.
Invoke the server-side printing:
kubectl get crontab my-new-cron-object
Notice the NAME, SPEC, REPLICAS, and AGE columns in the output:
NAME SPEC REPLICAS AGE
my-new-cron-object * * * * * 1 7s
Note: The NAME column is implicit and does not need to be defined in the CustomResourceDefinition.
Priority
Each column includes a priority field. Currently, the priority
differentiates between columns shown in standard view or wide view (using the -o wide flag).
Columns with priority 0 are shown in standard view.
Columns with priority greater than 0 are shown only in wide view.
date – rendered differentially as time since this timestamp.
If the value inside a CustomResource does not match the type specified for the column,
the value is omitted. Use CustomResource validation to ensure that the value
types are correct.
Format
A column's format field can be any of the following:
int32
int64
float
double
byte
date
date-time
password
The column's format controls the style used when kubectl prints the value.
Subresources
Custom resources support /status and /scale subresources.
The status and scale subresources can be optionally enabled by
defining them in the CustomResourceDefinition.
Status subresource
When the status subresource is enabled, the /status subresource for the custom resource is exposed.
The status and the spec stanzas are represented by the .status and .spec JSONPaths
respectively inside of a custom resource.
PUT requests to the /status subresource take a custom resource object and ignore changes to
anything except the status stanza.
PUT requests to the /status subresource only validate the status stanza of the custom
resource.
PUT/POST/PATCH requests to the custom resource ignore changes to the status stanza.
The .metadata.generation value is incremented for all changes, except for changes to
.metadata or .status.
Only the following constructs are allowed at the root of the CRD OpenAPI validation schema:
description
example
exclusiveMaximum
exclusiveMinimum
externalDocs
format
items
maximum
maxItems
maxLength
minimum
minItems
minLength
multipleOf
pattern
properties
required
title
type
uniqueItems
Scale subresource
When the scale subresource is enabled, the /scale subresource for the custom resource is exposed.
The autoscaling/v1.Scale object is sent as the payload for /scale.
To enable the scale subresource, the following fields are defined in the CustomResourceDefinition.
specReplicasPath defines the JSONPath inside of a custom resource that corresponds to scale.spec.replicas.
It is a required value.
Only JSONPaths under .spec and with the dot notation are allowed.
If there is no value under the specReplicasPath in the custom resource,
the /scale subresource will return an error on GET.
statusReplicasPath defines the JSONPath inside of a custom resource that corresponds to scale.status.replicas.
It is a required value.
Only JSONPaths under .status and with the dot notation are allowed.
If there is no value under the statusReplicasPath in the custom resource,
the status replica value in the /scale subresource will default to 0.
labelSelectorPath defines the JSONPath inside of a custom resource that corresponds to
Scale.Status.Selector.
It is an optional value.
It must be set to work with HPA and VPA.
Only JSONPaths under .status or .spec and with the dot notation are allowed.
If there is no value under the labelSelectorPath in the custom resource,
the status selector value in the /scale subresource will default to the empty string.
The field pointed by this JSON path must be a string field (not a complex selector struct)
which contains a serialized label selector in string form.
In the following example, both status and scale subresources are enabled.
Save the CustomResourceDefinition to resourcedefinition.yaml:
apiVersion:apiextensions.k8s.io/v1kind:CustomResourceDefinitionmetadata:name:crontabs.stable.example.comspec:group:stable.example.comversions:- name:v1served:truestorage:trueschema:openAPIV3Schema:type:objectproperties:spec:type:objectproperties:cronSpec:type:stringimage:type:stringreplicas:type:integerstatus:type:objectproperties:replicas:type:integerlabelSelector:type:string# subresources describes the subresources for custom resources.subresources:# status enables the status subresource.status:{}# scale enables the scale subresource.scale:# specReplicasPath defines the JSONPath inside of a custom resource that corresponds to Scale.Spec.Replicas.specReplicasPath:.spec.replicas# statusReplicasPath defines the JSONPath inside of a custom resource that corresponds to Scale.Status.Replicas.statusReplicasPath:.status.replicas# labelSelectorPath defines the JSONPath inside of a custom resource that corresponds to Scale.Status.Selector.labelSelectorPath:.status.labelSelectorscope:Namespacednames:plural:crontabssingular:crontabkind:CronTabshortNames:- ct
And create it:
kubectl apply -f resourcedefinition.yaml
After the CustomResourceDefinition object has been created, you can create custom objects.
If you save the following YAML to my-crontab.yaml:
A custom resource can be scaled using the kubectl scale command.
For example, the following command sets .spec.replicas of the
custom resource created above to 5:
You can use a PodDisruptionBudget to protect custom
resources that have the scale subresource enabled.
Categories
Categories is a list of grouped resources the custom resource belongs to (eg. all).
You can use kubectl get <category-name> to list the resources belonging to the category.
The following example adds all in the list of categories in the CustomResourceDefinition
and illustrates how to output the custom resource using kubectl get all.
Save the following CustomResourceDefinition to resourcedefinition.yaml:
apiVersion:apiextensions.k8s.io/v1kind:CustomResourceDefinitionmetadata:name:crontabs.stable.example.comspec:group:stable.example.comversions:- name:v1served:truestorage:trueschema:openAPIV3Schema:type:objectproperties:spec:type:objectproperties:cronSpec:type:stringimage:type:stringreplicas:type:integerscope:Namespacednames:plural:crontabssingular:crontabkind:CronTabshortNames:- ct# categories is a list of grouped resources the custom resource belongs to.categories:- all
and create it:
kubectl apply -f resourcedefinition.yaml
After the CustomResourceDefinition object has been created, you can create custom objects.
This page explains how to add versioning information to
CustomResourceDefinitions, to indicate the stability
level of your CustomResourceDefinitions or advance your API to a new version with conversion between API representations. It also describes how to upgrade an object from one version to another.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Your Kubernetes server must be at or later than version v1.16.
To check the version, enter kubectl version.
Overview
The CustomResourceDefinition API provides a workflow for introducing and upgrading
to new versions of a CustomResourceDefinition.
When a CustomResourceDefinition is created, the first version is set in the
CustomResourceDefinition spec.versions list to an appropriate stability level
and a version number. For example v1beta1 would indicate that the first
version is not yet stable. All custom resource objects will initially be stored
at this version.
Once the CustomResourceDefinition is created, clients may begin using the
v1beta1 API.
Later it might be necessary to add new version such as v1.
Adding a new version:
Pick a conversion strategy. Since custom resource objects need the ability to
be served at both versions, that means they will sometimes be served in a
different version than the one stored. To make this possible, the custom resource objects must sometimes be converted between the
version they are stored at and the version they are served at. If the
conversion involves schema changes and requires custom logic, a conversion
webhook should be used. If there are no schema changes, the default None
conversion strategy may be used and only the apiVersion field will be
modified when serving different versions.
If using conversion webhooks, create and deploy the conversion webhook. See
the Webhook conversion for more details.
Update the CustomResourceDefinition to include the new version in the
spec.versions list with served:true. Also, set spec.conversion field
to the selected conversion strategy. If using a conversion webhook, configure
spec.conversion.webhookClientConfig field to call the webhook.
Once the new version is added, clients may incrementally migrate to the new
version. It is perfectly safe for some clients to use the old version while
others use the new version.
It is safe for clients to use both the old and new version before, during and
after upgrading the objects to a new stored version.
Removing an old version:
Ensure all clients are fully migrated to the new version. The kube-apiserver
logs can be reviewed to help identify any clients that are still accessing via
the old version.
Set served to false for the old version in the spec.versions list. If
any clients are still unexpectedly using the old version they may begin reporting
errors attempting to access the custom resource objects at the old version.
If this occurs, switch back to using served:true on the old version, migrate the
remaining clients to the new version and repeat this step.
Verify that the storage is set to true for the new version in the spec.versions list in the CustomResourceDefinition.
Verify that the old version is no longer listed in the CustomResourceDefinition status.storedVersions.
Remove the old version from the CustomResourceDefinition spec.versions list.
Drop conversion support for the old version in conversion webhooks.
Specify multiple versions
The CustomResourceDefinition API versions field can be used to support multiple versions of custom resources that you
have developed. Versions can have different schemas, and conversion webhooks can convert custom resources between versions.
Webhook conversions should follow the Kubernetes API conventions wherever applicable.
Specifically, See the API change documentation for a set of useful gotchas and suggestions.
Note: In apiextensions.k8s.io/v1beta1, there was a version field instead of versions. The
version field is deprecated and optional, but if it is not empty, it must
match the first item in the versions field.
This example shows a CustomResourceDefinition with two versions. For the first
example, the assumption is all versions share the same schema with no conversion
between them. The comments in the YAML provide more context.
apiVersion:apiextensions.k8s.io/v1kind:CustomResourceDefinitionmetadata:# name must match the spec fields below, and be in the form: <plural>.<group>name:crontabs.example.comspec:# group name to use for REST API: /apis/<group>/<version>group:example.com# list of versions supported by this CustomResourceDefinitionversions:- name:v1beta1# Each version can be enabled/disabled by Served flag.served:true# One and only one version must be marked as the storage version.storage:true# A schema is requiredschema:openAPIV3Schema:type:objectproperties:host:type:stringport:type:string- name:v1served:truestorage:falseschema:openAPIV3Schema:type:objectproperties:host:type:stringport:type:string# The conversion section is introduced in Kubernetes 1.13+ with a default value of# None conversion (strategy sub-field set to None).conversion:# None conversion assumes the same schema for all versions and only sets the apiVersion# field of custom resources to the proper valuestrategy:None# either Namespaced or Clusterscope:Namespacednames:# plural name to be used in the URL: /apis/<group>/<version>/<plural>plural:crontabs# singular name to be used as an alias on the CLI and for displaysingular:crontab# kind is normally the CamelCased singular type. Your resource manifests use this.kind:CronTab# shortNames allow shorter string to match your resource on the CLIshortNames:- ct
# Deprecated in v1.16 in favor of apiextensions.k8s.io/v1apiVersion:apiextensions.k8s.io/v1beta1kind:CustomResourceDefinitionmetadata:# name must match the spec fields below, and be in the form: <plural>.<group>name:crontabs.example.comspec:# group name to use for REST API: /apis/<group>/<version>group:example.com# list of versions supported by this CustomResourceDefinitionversions:- name:v1beta1# Each version can be enabled/disabled by Served flag.served:true# One and only one version must be marked as the storage version.storage:true- name:v1served:truestorage:falsevalidation:openAPIV3Schema:type:objectproperties:host:type:stringport:type:string# The conversion section is introduced in Kubernetes 1.13+ with a default value of# None conversion (strategy sub-field set to None).conversion:# None conversion assumes the same schema for all versions and only sets the apiVersion# field of custom resources to the proper valuestrategy:None# either Namespaced or Clusterscope:Namespacednames:# plural name to be used in the URL: /apis/<group>/<version>/<plural>plural:crontabs# singular name to be used as an alias on the CLI and for displaysingular:crontab# kind is normally the PascalCased singular type. Your resource manifests use this.kind:CronTab# shortNames allow shorter string to match your resource on the CLIshortNames:- ct
You can save the CustomResourceDefinition in a YAML file, then use
kubectl apply to create it.
kubectl apply -f my-versioned-crontab.yaml
After creation, the API server starts to serve each enabled version at an HTTP
REST endpoint. In the above example, the API versions are available at
/apis/example.com/v1beta1 and /apis/example.com/v1.
Version priority
Regardless of the order in which versions are defined in a
CustomResourceDefinition, the version with the highest priority is used by
kubectl as the default version to access objects. The priority is determined
by parsing the name field to determine the version number, the stability
(GA, Beta, or Alpha), and the sequence within that stability level.
The algorithm used for sorting the versions is designed to sort versions in the
same way that the Kubernetes project sorts Kubernetes versions. Versions start with a
v followed by a number, an optional beta or alpha designation, and
optional additional numeric versioning information. Broadly, a version string might look
like v2 or v2beta1. Versions are sorted using the following algorithm:
Entries that follow Kubernetes version patterns are sorted before those that
do not.
For entries that follow Kubernetes version patterns, the numeric portions of
the version string is sorted largest to smallest.
If the strings beta or alpha follow the first numeric portion, they sorted
in that order, after the equivalent string without the beta or alpha
suffix (which is presumed to be the GA version).
If another number follows the beta, or alpha, those numbers are also
sorted from largest to smallest.
Strings that don't fit the above format are sorted alphabetically and the
numeric portions are not treated specially. Notice that in the example below,
foo1 is sorted above foo10. This is different from the sorting of the
numeric portion of entries that do follow the Kubernetes version patterns.
This might make sense if you look at the following sorted version list:
For the example in Specify multiple versions, the
version sort order is v1, followed by v1beta1. This causes the kubectl
command to use v1 as the default version unless the provided object specifies
the version.
Version deprecation
FEATURE STATE:Kubernetes v1.19 [stable]
Starting in v1.19, a CustomResourceDefinition can indicate a particular version of the resource it defines is deprecated.
When API requests to a deprecated version of that resource are made, a warning message is returned in the API response as a header.
The warning message for each deprecated version of the resource can be customized if desired.
A customized warning message should indicate the deprecated API group, version, and kind,
and should indicate what API group, version, and kind should be used instead, if applicable.
apiVersion:apiextensions.k8s.io/v1kind:CustomResourceDefinitionname:crontabs.example.comspec:group:example.comnames:plural:crontabssingular:crontabkind:CronTabscope:Namespacedversions:- name:v1alpha1served:truestorage:false# This indicates the v1alpha1 version of the custom resource is deprecated.# API requests to this version receive a warning header in the server response.deprecated:true# This overrides the default warning returned to API clients making v1alpha1 API requests.deprecationWarning:"example.com/v1alpha1 CronTab is deprecated; see http://example.com/v1alpha1-v1 for instructions to migrate to example.com/v1 CronTab"schema:...- name:v1beta1served:true# This indicates the v1beta1 version of the custom resource is deprecated.# API requests to this version receive a warning header in the server response.# A default warning message is returned for this version.deprecated:trueschema:...- name:v1served:truestorage:trueschema:...
# Deprecated in v1.16 in favor of apiextensions.k8s.io/v1apiVersion:apiextensions.k8s.io/v1beta1kind:CustomResourceDefinitionmetadata:name:crontabs.example.comspec:group:example.comnames:plural:crontabssingular:crontabkind:CronTabscope:Namespacedvalidation:...versions:- name:v1alpha1served:truestorage:false# This indicates the v1alpha1 version of the custom resource is deprecated.# API requests to this version receive a warning header in the server response.deprecated:true# This overrides the default warning returned to API clients making v1alpha1 API requests.deprecationWarning:"example.com/v1alpha1 CronTab is deprecated; see http://example.com/v1alpha1-v1 for instructions to migrate to example.com/v1 CronTab"- name:v1beta1served:true# This indicates the v1beta1 version of the custom resource is deprecated.# API requests to this version receive a warning header in the server response.# A default warning message is returned for this version.deprecated:true- name:v1served:truestorage:true
Version removal
An older API version cannot be dropped from a CustomResourceDefinition manifest until existing stored data has been migrated to the newer API version for all clusters that served the older version of the custom resource, and the old version is removed from the status.storedVersions of the CustomResourceDefinition.
apiVersion:apiextensions.k8s.io/v1kind:CustomResourceDefinitionname:crontabs.example.comspec:group:example.comnames:plural:crontabssingular:crontabkind:CronTabscope:Namespacedversions:- name:v1beta1# This indicates the v1beta1 version of the custom resource is no longer served.# API requests to this version receive a not found error in the server response.served:falseschema:...- name:v1served:true# The new served version should be set as the storage versionstorage:trueschema:...
Webhook conversion
FEATURE STATE:Kubernetes v1.16 [stable]
Note: Webhook conversion is available as beta since 1.15, and as alpha since Kubernetes 1.13. The
CustomResourceWebhookConversion feature must be enabled, which is the case automatically for many clusters for beta features. Please refer to the feature gate documentation for more information.
The above example has a None conversion between versions which only sets the apiVersion field
on conversion and does not change the rest of the object. The API server also supports webhook
conversions that call an external service in case a conversion is required. For example when:
custom resource is requested in a different version than stored version.
Watch is created in one version but the changed object is stored in another version.
custom resource PUT request is in a different version than storage version.
To cover all of these cases and to optimize conversion by the API server,
the conversion requests may contain multiple objects in order to minimize the external calls.
The webhook should perform these conversions independently.
Write a conversion webhook server
Please refer to the implementation of the custom resource conversion webhook
server
that is validated in a Kubernetes e2e test. The webhook handles the
ConversionReview requests sent by the API servers, and sends back conversion
results wrapped in ConversionResponse. Note that the request
contains a list of custom resources that need to be converted independently without
changing the order of objects.
The example server is organized in a way to be reused for other conversions.
Most of the common code are located in the
framework file
that leaves only
one function
to be implemented for different conversions.
Note: The example conversion webhook server leaves the ClientAuth field
empty,
which defaults to NoClientCert. This means that the webhook server does not
authenticate the identity of the clients, supposedly API servers. If you need
mutual TLS or other ways to authenticate the clients, see
how to authenticate API servers.
Permissible mutations
A conversion webhook must not mutate anything inside of metadata of the converted object
other than labels and annotations.
Attempted changes to name, UID and namespace are rejected and fail the request
which caused the conversion. All other changes are ignored.
Deploy the conversion webhook service
Documentation for deploying the conversion webhook is the same as for the
admission webhook example service.
The assumption for next sections is that the conversion webhook server is deployed to a service
named example-conversion-webhook-server in default namespace and serving traffic on path /crdconvert.
Note: When the webhook server is deployed into the Kubernetes cluster as a
service, it has to be exposed via a service on port 443 (The server
itself can have an arbitrary port but the service object should map it to port 443).
The communication between the API server and the webhook service may fail
if a different port is used for the service.
Configure CustomResourceDefinition to use conversion webhooks
The None conversion example can be extended to use the conversion webhook by modifying conversion
section of the spec:
apiVersion:apiextensions.k8s.io/v1kind:CustomResourceDefinitionmetadata:# name must match the spec fields below, and be in the form: <plural>.<group>name:crontabs.example.comspec:# group name to use for REST API: /apis/<group>/<version>group:example.com# list of versions supported by this CustomResourceDefinitionversions:- name:v1beta1# Each version can be enabled/disabled by Served flag.served:true# One and only one version must be marked as the storage version.storage:true# Each version can define its own schema when there is no top-level# schema is defined.schema:openAPIV3Schema:type:objectproperties:hostPort:type:string- name:v1served:truestorage:falseschema:openAPIV3Schema:type:objectproperties:host:type:stringport:type:stringconversion:# a Webhook strategy instruct API server to call an external webhook for any conversion between custom resources.strategy:Webhook# webhook is required when strategy is `Webhook` and it configures the webhook endpoint to be called by API server.webhook:# conversionReviewVersions indicates what ConversionReview versions are understood/preferred by the webhook.# The first version in the list understood by the API server is sent to the webhook.# The webhook must respond with a ConversionReview object in the same version it received.conversionReviewVersions:["v1","v1beta1"]clientConfig:service:namespace:defaultname:example-conversion-webhook-serverpath:/crdconvertcaBundle:"Ci0tLS0tQk...<base64-encoded PEM bundle>...tLS0K"# either Namespaced or Clusterscope:Namespacednames:# plural name to be used in the URL: /apis/<group>/<version>/<plural>plural:crontabs# singular name to be used as an alias on the CLI and for displaysingular:crontab# kind is normally the CamelCased singular type. Your resource manifests use this.kind:CronTab# shortNames allow shorter string to match your resource on the CLIshortNames:- ct
# Deprecated in v1.16 in favor of apiextensions.k8s.io/v1apiVersion:apiextensions.k8s.io/v1beta1kind:CustomResourceDefinitionmetadata:# name must match the spec fields below, and be in the form: <plural>.<group>name:crontabs.example.comspec:# group name to use for REST API: /apis/<group>/<version>group:example.com# prunes object fields that are not specified in OpenAPI schemas below.preserveUnknownFields:false# list of versions supported by this CustomResourceDefinitionversions:- name:v1beta1# Each version can be enabled/disabled by Served flag.served:true# One and only one version must be marked as the storage version.storage:true# Each version can define its own schema when there is no top-level# schema is defined.schema:openAPIV3Schema:type:objectproperties:hostPort:type:string- name:v1served:truestorage:falseschema:openAPIV3Schema:type:objectproperties:host:type:stringport:type:stringconversion:# a Webhook strategy instruct API server to call an external webhook for any conversion between custom resources.strategy:Webhook# webhookClientConfig is required when strategy is `Webhook` and it configures the webhook endpoint to be called by API server.webhookClientConfig:service:namespace:defaultname:example-conversion-webhook-serverpath:/crdconvertcaBundle:"Ci0tLS0tQk...<base64-encoded PEM bundle>...tLS0K"# either Namespaced or Clusterscope:Namespacednames:# plural name to be used in the URL: /apis/<group>/<version>/<plural>plural:crontabs# singular name to be used as an alias on the CLI and for displaysingular:crontab# kind is normally the CamelCased singular type. Your resource manifests use this.kind:CronTab# shortNames allow shorter string to match your resource on the CLIshortNames:- ct
You can save the CustomResourceDefinition in a YAML file, then use
kubectl apply to apply it.
Make sure the conversion service is up and running before applying new changes.
Contacting the webhook
Once the API server has determined a request should be sent to a conversion webhook,
it needs to know how to contact the webhook. This is specified in the webhookClientConfig
stanza of the webhook configuration.
Conversion webhooks can either be called via a URL or a service reference,
and can optionally include a custom CA bundle to use to verify the TLS connection.
URL
url gives the location of the webhook, in standard URL form
(scheme://host:port/path).
The host should not refer to a service running in the cluster; use
a service reference by specifying the service field instead.
The host might be resolved via external DNS in some apiservers
(i.e., kube-apiserver cannot resolve in-cluster DNS as that would
be a layering violation). host may also be an IP address.
Please note that using localhost or 127.0.0.1 as a host is
risky unless you take great care to run this webhook on all hosts
which run an apiserver which might need to make calls to this
webhook. Such installations are likely to be non-portable or not readily run in a new cluster.
The scheme must be "https"; the URL must begin with "https://".
Attempting to use a user or basic auth (for example "user:password@") is not allowed.
Fragments ("#...") and query parameters ("?...") are also not allowed.
Here is an example of a conversion webhook configured to call a URL
(and expects the TLS certificate to be verified using system trust roots, so does not specify a caBundle):
# Deprecated in v1.16 in favor of apiextensions.k8s.io/v1apiVersion:apiextensions.k8s.io/v1beta1kind:CustomResourceDefinition...spec:...conversion:strategy:WebhookwebhookClientConfig:url:"https://my-webhook.example.com:9443/my-webhook-path"...
Service Reference
The service stanza inside webhookClientConfig is a reference to the service for a conversion webhook.
If the webhook is running within the cluster, then you should use service instead of url.
The service namespace and name are required. The port is optional and defaults to 443.
The path is optional and defaults to "/".
Here is an example of a webhook that is configured to call a service on port "1234"
at the subpath "/my-path", and to verify the TLS connection against the ServerName
my-service-name.my-service-namespace.svc using a custom CA bundle.
# Deprecated in v1.16 in favor of apiextensions.k8s.io/v1apiVersion:apiextensions.k8s.io/v1beta1kind:CustomResourceDefinition...spec:...conversion:strategy:WebhookwebhookClientConfig:service:namespace:my-service-namespacename:my-service-namepath:/my-pathport:1234caBundle:"Ci0tLS0tQk...<base64-encoded PEM bundle>...tLS0K"...
Webhook request and response
Request
Webhooks are sent a POST request, with Content-Type: application/json,
with a ConversionReview API object in the apiextensions.k8s.io API group
serialized to JSON as the body.
Webhooks can specify what versions of ConversionReview objects they accept
with the conversionReviewVersions field in their CustomResourceDefinition:
conversionReviewVersions is a required field when creating
apiextensions.k8s.io/v1 custom resource definitions.
Webhooks are required to support at least one ConversionReview
version understood by the current and previous API server.
# Deprecated in v1.16 in favor of apiextensions.k8s.io/v1apiVersion:apiextensions.k8s.io/v1beta1kind:CustomResourceDefinition...spec:...conversion:strategy:WebhookconversionReviewVersions:["v1","v1beta1"]...
If no conversionReviewVersions are specified, the default when creating
apiextensions.k8s.io/v1beta1 custom resource definitions is v1beta1.
API servers send the first ConversionReview version in the conversionReviewVersions list they support.
If none of the versions in the list are supported by the API server, the custom resource definition will not be allowed to be created.
If an API server encounters a conversion webhook configuration that was previously created and does not support any of the ConversionReview
versions the API server knows how to send, attempts to call to the webhook will fail.
This example shows the data contained in an ConversionReview object
for a request to convert CronTab objects to example.com/v1:
{"apiVersion": "apiextensions.k8s.io/v1","kind": "ConversionReview","request": {# Random uid uniquely identifying this conversion call"uid": "705ab4f5-6393-11e8-b7cc-42010a800002",# The API group and version the objects should be converted to"desiredAPIVersion": "example.com/v1",# The list of objects to convert.# May contain one or more objects, in one or more versions."objects": [{"kind": "CronTab","apiVersion": "example.com/v1beta1","metadata": {"creationTimestamp": "2019-09-04T14:03:02Z","name": "local-crontab","namespace": "default","resourceVersion": "143","uid": "3415a7fc-162b-4300-b5da-fd6083580d66"},"hostPort": "localhost:1234"},{"kind": "CronTab","apiVersion": "example.com/v1beta1","metadata": {"creationTimestamp": "2019-09-03T13:02:01Z","name": "remote-crontab","resourceVersion": "12893","uid": "359a83ec-b575-460d-b553-d859cedde8a0"},"hostPort": "example.com:2345"}]}}
{# Deprecated in v1.16 in favor of apiextensions.k8s.io/v1"apiVersion": "apiextensions.k8s.io/v1beta1","kind": "ConversionReview","request": {# Random uid uniquely identifying this conversion call"uid": "705ab4f5-6393-11e8-b7cc-42010a800002",# The API group and version the objects should be converted to"desiredAPIVersion": "example.com/v1",# The list of objects to convert.# May contain one or more objects, in one or more versions."objects": [{"kind": "CronTab","apiVersion": "example.com/v1beta1","metadata": {"creationTimestamp": "2019-09-04T14:03:02Z","name": "local-crontab","namespace": "default","resourceVersion": "143","uid": "3415a7fc-162b-4300-b5da-fd6083580d66"},"hostPort": "localhost:1234"},{"kind": "CronTab","apiVersion": "example.com/v1beta1","metadata": {"creationTimestamp": "2019-09-03T13:02:01Z","name": "remote-crontab","resourceVersion": "12893","uid": "359a83ec-b575-460d-b553-d859cedde8a0"},"hostPort": "example.com:2345"}]}}
Response
Webhooks respond with a 200 HTTP status code, Content-Type: application/json,
and a body containing a ConversionReview object (in the same version they were sent),
with the response stanza populated, serialized to JSON.
If conversion succeeds, a webhook should return a response stanza containing the following fields:
uid, copied from the request.uid sent to the webhook
result, set to {"status":"Success"}
convertedObjects, containing all of the objects from request.objects, converted to request.desiredVersion
Example of a minimal successful response from a webhook:
{"apiVersion": "apiextensions.k8s.io/v1","kind": "ConversionReview","response": {# must match <request.uid>"uid": "705ab4f5-6393-11e8-b7cc-42010a800002","result": {"status": "Success"},# Objects must match the order of request.objects, and have apiVersion set to <request.desiredAPIVersion>.# kind, metadata.uid, metadata.name, and metadata.namespace fields must not be changed by the webhook.# metadata.labels and metadata.annotations fields may be changed by the webhook.# All other changes to metadata fields by the webhook are ignored."convertedObjects": [{"kind": "CronTab","apiVersion": "example.com/v1","metadata": {"creationTimestamp": "2019-09-04T14:03:02Z","name": "local-crontab","namespace": "default","resourceVersion": "143","uid": "3415a7fc-162b-4300-b5da-fd6083580d66"},"host": "localhost","port": "1234"},{"kind": "CronTab","apiVersion": "example.com/v1","metadata": {"creationTimestamp": "2019-09-03T13:02:01Z","name": "remote-crontab","resourceVersion": "12893","uid": "359a83ec-b575-460d-b553-d859cedde8a0"},"host": "example.com","port": "2345"}]}}
{# Deprecated in v1.16 in favor of apiextensions.k8s.io/v1"apiVersion": "apiextensions.k8s.io/v1beta1","kind": "ConversionReview","response": {# must match <request.uid>"uid": "705ab4f5-6393-11e8-b7cc-42010a800002","result": {"status": "Failed"},# Objects must match the order of request.objects, and have apiVersion set to <request.desiredAPIVersion>.# kind, metadata.uid, metadata.name, and metadata.namespace fields must not be changed by the webhook.# metadata.labels and metadata.annotations fields may be changed by the webhook.# All other changes to metadata fields by the webhook are ignored."convertedObjects": [{"kind": "CronTab","apiVersion": "example.com/v1","metadata": {"creationTimestamp": "2019-09-04T14:03:02Z","name": "local-crontab","namespace": "default","resourceVersion": "143","uid": "3415a7fc-162b-4300-b5da-fd6083580d66"},"host": "localhost","port": "1234"},{"kind": "CronTab","apiVersion": "example.com/v1","metadata": {"creationTimestamp": "2019-09-03T13:02:01Z","name": "remote-crontab","resourceVersion": "12893","uid": "359a83ec-b575-460d-b553-d859cedde8a0"},"host": "example.com","port": "2345"}]}}
If conversion fails, a webhook should return a response stanza containing the following fields:
uid, copied from the request.uid sent to the webhook
result, set to {"status":"Failed"}
Warning: Failing conversion can disrupt read and write access to the custom resources,
including the ability to update or delete the resources. Conversion failures
should be avoided whenever possible, and should not be used to enforce validation
constraints (use validation schemas or webhook admission instead).
Example of a response from a webhook indicating a conversion request failed, with an optional message:
{"apiVersion": "apiextensions.k8s.io/v1","kind": "ConversionReview","response": {"uid": "<value from request.uid>","result": {"status": "Failed","message": "hostPort could not be parsed into a separate host and port"}}}
{# Deprecated in v1.16 in favor of apiextensions.k8s.io/v1"apiVersion": "apiextensions.k8s.io/v1beta1","kind": "ConversionReview","response": {"uid": "<value from request.uid>","result": {"status": "Failed","message": "hostPort could not be parsed into a separate host and port"}}}
Writing, reading, and updating versioned CustomResourceDefinition objects
When an object is written, it is stored at the version designated as the
storage version at the time of the write. If the storage version changes,
existing objects are never converted automatically. However, newly-created
or updated objects are written at the new storage version. It is possible for an
object to have been written at a version that is no longer served.
When you read an object, you specify the version as part of the path.
You can request an object at any version that is currently served.
If you specify a version that is different from the object's stored version,
Kubernetes returns the object to you at the version you requested, but the
stored object is not changed on disk.
What happens to the object that is being returned while serving the read
request depends on what is specified in the CRD's spec.conversion:
if the default strategy value None is specified, the only modifications
to the object are changing the apiVersion string and perhaps pruning
unknown fields
(depending on the configuration). Note that this is unlikely to lead to good
results if the schemas differ between the storage and requested version.
In particular, you should not use this strategy if the same data is
represented in different fields between versions.
if webhook conversion is specified, then this
mechanism controls the conversion.
If you update an existing object, it is rewritten at the version that is
currently the storage version. This is the only way that objects can change from
one version to another.
To illustrate this, consider the following hypothetical series of events:
The storage version is v1beta1. You create an object. It is stored at version v1beta1
You add version v1 to your CustomResourceDefinition and designate it as
the storage version. Here the schemas for v1 and v1beta1 are identical,
which is typically the case when promoting an API to stable in the
Kubernetes ecosystem.
You read your object at version v1beta1, then you read the object again at
version v1. Both returned objects are identical except for the apiVersion
field.
You create a new object. It is stored at version v1. You now
have two objects, one of which is at v1beta1, and the other of which is at
v1.
You update the first object. It is now stored at version v1 since that
is the current storage version.
Previous storage versions
The API server records each version which has ever been marked as the storage
version in the status field storedVersions. Objects may have been stored
at any version that has ever been designated as a storage version. No objects
can exist in storage at a version that has never been a storage version.
Upgrade existing objects to a new stored version
When deprecating versions and dropping support, select a storage upgrade
procedure.
Remove the old version from the CustomResourceDefinition status.storedVersions field.
Option 2: Manually upgrade the existing objects to a new stored version
The following is an example procedure to upgrade from v1beta1 to v1.
Set v1 as the storage in the CustomResourceDefinition file and apply it
using kubectl. The storedVersions is now v1beta1, v1.
Write an upgrade procedure to list all existing objects and write them with
the same content. This forces the backend to write objects in the current
storage version, which is v1.
Remove v1beta1 from the CustomResourceDefinition status.storedVersions field.
Note:
The flag --subresource is used with the kubectl get, patch, edit, and replace commands to
fetch and update the subresources, status and scale, for all the API resources that
support them. This flag is available starting from kubectl version v1.24. Previously, reading
subresources (like status) via kubectl involved using kubectl --raw, and updating
subresources using kubectl was not possible at all. Starting from v1.24, the kubectl tool
can be used to edit or patch the status subresource on a CRD object. See How to patch a Deployment using the subresource flag.
This page is part of the documentation for Kubernetes v1.28.
If you are running a different version of Kubernetes, consult the documentation for that release.
Here is an example of how to patch the status subresource for a CRD object using kubectl:
Setting up an extension API server to work with the aggregation layer allows the Kubernetes apiserver to be extended with additional APIs, which are not part of the core Kubernetes APIs.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Set up an extension api-server to work with the aggregation layer
The following steps describe how to set up an extension-apiserver at a high level. These steps apply regardless if you're using YAML configs or using APIs. An attempt is made to specifically identify any differences between the two. For a concrete example of how they can be implemented using YAML configs, you can look at the sample-apiserver in the Kubernetes repo.
Alternatively, you can use an existing 3rd party solution, such as apiserver-builder, which should generate a skeleton and automate all of the following steps for you.
Make sure the APIService API is enabled (check --runtime-config). It should be on by default, unless it's been deliberately turned off in your cluster.
You may need to make an RBAC rule allowing you to add APIService objects, or get your cluster administrator to make one. (Since API extensions affect the entire cluster, it is not recommended to do testing/development/debug of an API extension in a live cluster.)
Create the Kubernetes namespace you want to run your extension api-service in.
Create/get a CA cert to be used to sign the server cert the extension api-server uses for HTTPS.
Create a server cert/key for the api-server to use for HTTPS. This cert should be signed by the above CA. It should also have a CN of the Kube DNS name. This is derived from the Kubernetes service and be of the form <service name>.<service name namespace>.svc
Create a Kubernetes secret with the server cert/key in your namespace.
Create a Kubernetes deployment for the extension api-server and make sure you are loading the secret as a volume. It should contain a reference to a working image of your extension api-server. The deployment should also be in your namespace.
Make sure that your extension-apiserver loads those certs from that volume and that they are used in the HTTPS handshake.
Create a Kubernetes service account in your namespace.
Create a Kubernetes cluster role for the operations you want to allow on your resources.
Create a Kubernetes cluster role binding from the service account in your namespace to the cluster role you created.
Create a Kubernetes cluster role binding from the service account in your namespace to the system:auth-delegator cluster role to delegate auth decisions to the Kubernetes core API server.
Create a Kubernetes role binding from the service account in your namespace to the extension-apiserver-authentication-reader role. This allows your extension api-server to access the extension-apiserver-authentication configmap.
Create a Kubernetes apiservice. The CA cert above should be base64 encoded, stripped of new lines and used as the spec.caBundle in the apiservice. This should not be namespaced. If using the kube-aggregator API, only pass in the PEM encoded CA bundle because the base 64 encoding is done for you.
Use kubectl to get your resource. When run, kubectl should return "No resources found.". This message
indicates that everything worked but you currently have no objects of that resource type created.
Kubernetes ships with a default scheduler that is described
here.
If the default scheduler does not suit your needs you can implement your own scheduler.
Moreover, you can even run multiple schedulers simultaneously alongside the default
scheduler and instruct Kubernetes what scheduler to use for each of your pods. Let's
learn how to run multiple schedulers in Kubernetes with an example.
A detailed description of how to implement a scheduler is outside the scope of this
document. Please refer to the kube-scheduler implementation in
pkg/scheduler
in the Kubernetes source directory for a canonical example.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Package your scheduler binary into a container image. For the purposes of this example,
you can use the default scheduler (kube-scheduler) as your second scheduler.
Clone the Kubernetes source code from GitHub
and build the source.
git clone https://github.com/kubernetes/kubernetes.git
cd kubernetes
make
Create a container image containing the kube-scheduler binary. Here is the Dockerfile
to build the image:
FROM busyboxADD ./_output/local/bin/linux/amd64/kube-scheduler /usr/local/bin/kube-scheduler
Save the file as Dockerfile, build the image and push it to a registry. This example
pushes the image to
Google Container Registry (GCR).
For more details, please read the GCR
documentation. Alternatively
you can also use the docker hub. For more details
refer to the docker hub documentation.
docker build -t gcr.io/my-gcp-project/my-kube-scheduler:1.0 . # The image name and the repositorygcloud docker -- push gcr.io/my-gcp-project/my-kube-scheduler:1.0 # used in here is just an example
Define a Kubernetes Deployment for the scheduler
Now that you have your scheduler in a container image, create a pod
configuration for it and run it in your Kubernetes cluster. But instead of creating a pod
directly in the cluster, you can use a Deployment
for this example. A Deployment manages a
Replica Set which in turn manages the pods,
thereby making the scheduler resilient to failures. Here is the deployment
config. Save it as my-scheduler.yaml:
In the above manifest, you use a KubeSchedulerConfiguration
to customize the behavior of your scheduler implementation. This configuration has been passed to
the kube-scheduler during initialization with the --config option. The my-scheduler-config ConfigMap stores the configuration file. The Pod of themy-scheduler Deployment mounts the my-scheduler-config ConfigMap as a volume.
In the aforementioned Scheduler Configuration, your scheduler implementation is represented via
a KubeSchedulerProfile.
Note: To determine if a scheduler is responsible for scheduling a specific Pod, the spec.schedulerName field in a
PodTemplate or Pod manifest must match the schedulerName field of the KubeSchedulerProfile.
All schedulers running in the cluster must have unique names.
Also, note that you create a dedicated service account my-scheduler and bind the ClusterRole
system:kube-scheduler to it so that it can acquire the same privileges as kube-scheduler.
In order to run your scheduler in a Kubernetes cluster, create the deployment
specified in the config above in a Kubernetes cluster:
kubectl create -f my-scheduler.yaml
Verify that the scheduler pod is running:
kubectl get pods --namespace=kube-system
NAME READY STATUS RESTARTS AGE
....
my-scheduler-lnf4s-4744f 1/1 Running 0 2m
...
You should see a "Running" my-scheduler pod, in addition to the default kube-scheduler
pod in this list.
Enable leader election
To run multiple-scheduler with leader election enabled, you must do the following:
Update the following fields for the KubeSchedulerConfiguration in the my-scheduler-config ConfigMap in your YAML file:
leaderElection.leaderElect to true
leaderElection.resourceNamespace to <lock-object-namespace>
leaderElection.resourceName to <lock-object-name>
Note: The control plane creates the lock objects for you, but the namespace must already exist.
You can use the kube-system namespace.
If RBAC is enabled on your cluster, you must update the system:kube-scheduler cluster role.
Add your scheduler name to the resourceNames of the rule applied for endpoints and leases resources, as in the following example:
Now that your second scheduler is running, create some pods, and direct them
to be scheduled by either the default scheduler or the one you deployed.
In order to schedule a given pod using a specific scheduler, specify the name of the
scheduler in that pod spec. Let's look at three examples.
A scheduler is specified by supplying the scheduler name as a value to spec.schedulerName. In this case, we supply the name of the
default scheduler which is default-scheduler.
Save this file as pod2.yaml and submit it to the Kubernetes cluster.
In this case, we specify that this pod should be scheduled using the scheduler that we
deployed - my-scheduler. Note that the value of spec.schedulerName should match the name supplied for the scheduler
in the schedulerName field of the mapping KubeSchedulerProfile.
Save this file as pod3.yaml and submit it to the Kubernetes cluster.
kubectl create -f pod3.yaml
Verify that all three pods are running.
kubectl get pods
Verifying that the pods were scheduled using the desired schedulers
In order to make it easier to work through these examples, we did not verify that the
pods were actually scheduled using the desired schedulers. We can verify that by
changing the order of pod and deployment config submissions above. If we submit all the
pod configs to a Kubernetes cluster before submitting the scheduler deployment config,
we see that the pod annotation-second-scheduler remains in "Pending" state forever
while the other two pods get scheduled. Once we submit the scheduler deployment config
and our new scheduler starts running, the annotation-second-scheduler pod gets
scheduled as well.
Alternatively, you can look at the "Scheduled" entries in the event logs to
verify that the pods were scheduled by the desired schedulers.
kubectl get events
You can also use a custom scheduler configuration
or a custom container image for the cluster's main scheduler by modifying its static pod manifest
on the relevant control plane nodes.
11.5 - Use an HTTP Proxy to Access the Kubernetes API
This page shows how to use an HTTP proxy to access the Kubernetes API.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
11.6 - Use a SOCKS5 Proxy to Access the Kubernetes API
FEATURE STATE:Kubernetes v1.24 [stable]
This page shows how to use a SOCKS5 proxy to access the API of a remote Kubernetes cluster.
This is useful when the cluster you want to access does not expose its API directly on the public internet.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Your Kubernetes server must be at or later than version v1.24.
To check the version, enter kubectl version.
You need SSH client software (the ssh tool), and an SSH service running on the remote server.
You must be able to log in to the SSH service on the remote server.
Task context
Note: This example tunnels traffic using SSH, with the SSH client and server acting as a SOCKS proxy.
You can instead use any other kind of SOCKS5 proxies.
Figure 1 represents what you're going to achieve in this task.
You have a client computer, referred to as local in the steps ahead, from where you're going to create requests to talk to the Kubernetes API.
The Kubernetes server/API is hosted on a remote server.
You will use SSH client and server software to create a secure SOCKS5 tunnel between the local and
the remote server. The HTTPS traffic between the client and the Kubernetes API will flow over the SOCKS5
tunnel, which is itself tunnelled over SSH.
Figure 1. SOCKS5 tutorial components
Using ssh to create a SOCKS5 proxy
The following command starts a SOCKS5 proxy between your client machine and the remote SOCKS server:
# The SSH tunnel continues running in the foreground after you run thisssh -D 1080 -q -N username@kubernetes-remote-server.example
The SOCKS5 proxy lets you connect to your cluster's API server based on the following configuration:
-D 1080: opens a SOCKS proxy on local port :1080.
-q: quiet mode. Causes most warning and diagnostic messages to be suppressed.
-N: Do not execute a remote command. Useful for just forwarding ports.
username@kubernetes-remote-server.example: the remote SSH server behind which the Kubernetes cluster
is running (eg: a bastion host).
Client configuration
To access the Kubernetes API server through the proxy you must instruct kubectl to send queries through
the SOCKS proxy we created earlier. Do this by either setting the appropriate environment variable,
or via the proxy-url attribute in the kubeconfig file. Using an environment variable:
exportHTTPS_PROXY=socks5://localhost:1080
To always use this setting on a specific kubectl context, specify the proxy-url attribute in the relevant
cluster entry within the ~/.kube/config file. For example:
apiVersion:v1clusters:- cluster:certificate-authority-data:LRMEMMW2# shortened for readability server:https://<API_SERVER_IP_ADRESS>:6443 # the "Kubernetes API" server, in other words the IP address of kubernetes-remote-server.exampleproxy-url:socks5://localhost:1080 # the "SSH SOCKS5 proxy" in the diagram abovename:defaultcontexts:- context:cluster:defaultuser:defaultname:defaultcurrent-context:defaultkind:Configpreferences:{}users:- name:defaultuser:client-certificate-data:LS0tLS1CR==# shortened for readabilityclient-key-data:LS0tLS1CRUdJT= # shortened for readability
Once you have created the tunnel via the ssh command mentioned earlier, and defined either the environment variable or
the proxy-url attribute, you can interact with your cluster through that proxy. For example:
kubectl get pods
NAMESPACE NAME READY STATUS RESTARTS AGE
kube-system coredns-85cb69466-klwq8 1/1 Running 0 5m46s
Note:
Before kubectl 1.24, most kubectl commands worked when using a socks proxy, except kubectl exec.
kubectl supports both HTTPS_PROXY and https_proxy environment variables. These are used by other
programs that support SOCKS, such as curl. Therefore in some cases it
will be better to define the environment variable on the command line:
HTTPS_PROXY=socks5://localhost:1080 kubectl get pods
When using proxy-url, the proxy is used only for the relevant kubectl context,
whereas the environment variable will affect all contexts.
The k8s API server hostname can be further protected from DNS leakage by using the socks5h protocol name
instead of the more commonly known socks5 protocol shown above. In this case, kubectl will ask the proxy server
(such as an ssh bastion) to resolve the k8s API server domain name, instead of resolving it on the system running
kubectl. Note also that with socks5h, a k8s API server URL like https://localhost:6443/api does not refer
to your local client computer. Instead, it refers to localhost as known on the proxy server (eg the ssh bastion).
Clean up
Stop the ssh port-forwarding process by pressing CTRL+C on the terminal where it is running.
Type unset https_proxy in a terminal to stop forwarding http traffic through the proxy.
The Konnectivity service provides a TCP level proxy for the control plane to cluster
communication.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this
tutorial on a cluster with at least two nodes that are not acting as control
plane hosts. If you do not already have a cluster, you can create one by using
minikube.
Configure the Konnectivity service
The following steps require an egress configuration, for example:
apiVersion:apiserver.k8s.io/v1beta1kind:EgressSelectorConfigurationegressSelections:# Since we want to control the egress traffic to the cluster, we use the# "cluster" as the name. Other supported values are "etcd", and "controlplane".- name:clusterconnection:# This controls the protocol between the API Server and the Konnectivity# server. Supported values are "GRPC" and "HTTPConnect". There is no# end user visible difference between the two modes. You need to set the# Konnectivity server to work in the same mode.proxyProtocol:GRPCtransport:# This controls what transport the API Server uses to communicate with the# Konnectivity server. UDS is recommended if the Konnectivity server# locates on the same machine as the API Server. You need to configure the# Konnectivity server to listen on the same UDS socket.# The other supported transport is "tcp". You will need to set up TLS # config to secure the TCP transport.uds:udsName:/etc/kubernetes/konnectivity-server/konnectivity-server.socket
You need to configure the API Server to use the Konnectivity service
and direct the network traffic to the cluster nodes:
Generate or obtain a certificate and kubeconfig for konnectivity-server.
For example, you can use the OpenSSL command line tool to issue a X.509 certificate,
using the cluster CA certificate /etc/kubernetes/pki/ca.crt from a control-plane host.
Deploy the Konnectivity server on your control plane node. The provided
konnectivity-server.yaml manifest assumes
that the Kubernetes components are deployed as a static Pod in your cluster. If not, you can deploy the Konnectivity
server as a DaemonSet.
apiVersion:v1kind:Podmetadata:name:konnectivity-servernamespace:kube-systemspec:priorityClassName:system-cluster-criticalhostNetwork:truecontainers:- name:konnectivity-server-containerimage:registry.k8s.io/kas-network-proxy/proxy-server:v0.0.37command:["/proxy-server"]args:["--logtostderr=true",# This needs to be consistent with the value set in egressSelectorConfiguration."--uds-name=/etc/kubernetes/konnectivity-server/konnectivity-server.socket","--delete-existing-uds-file",# The following two lines assume the Konnectivity server is# deployed on the same machine as the apiserver, and the certs and# key of the API Server are at the specified location."--cluster-cert=/etc/kubernetes/pki/apiserver.crt","--cluster-key=/etc/kubernetes/pki/apiserver.key",# This needs to be consistent with the value set in egressSelectorConfiguration."--mode=grpc","--server-port=0","--agent-port=8132","--admin-port=8133","--health-port=8134","--agent-namespace=kube-system","--agent-service-account=konnectivity-agent","--kubeconfig=/etc/kubernetes/konnectivity-server.conf","--authentication-audience=system:konnectivity-server"]livenessProbe:httpGet:scheme:HTTPhost:127.0.0.1port:8134path:/healthzinitialDelaySeconds:30timeoutSeconds:60ports:- name:agentportcontainerPort:8132hostPort:8132- name:adminportcontainerPort:8133hostPort:8133- name:healthportcontainerPort:8134hostPort:8134volumeMounts:- name:k8s-certsmountPath:/etc/kubernetes/pkireadOnly:true- name:kubeconfigmountPath:/etc/kubernetes/konnectivity-server.confreadOnly:true- name:konnectivity-udsmountPath:/etc/kubernetes/konnectivity-serverreadOnly:falsevolumes:- name:k8s-certshostPath:path:/etc/kubernetes/pki- name:kubeconfighostPath:path:/etc/kubernetes/konnectivity-server.conftype:FileOrCreate- name:konnectivity-udshostPath:path:/etc/kubernetes/konnectivity-servertype:DirectoryOrCreate
Then deploy the Konnectivity agents in your cluster:
apiVersion:apps/v1# Alternatively, you can deploy the agents as Deployments. It is not necessary# to have an agent on each node.kind:DaemonSetmetadata:labels:addonmanager.kubernetes.io/mode:Reconcilek8s-app:konnectivity-agentnamespace:kube-systemname:konnectivity-agentspec:selector:matchLabels:k8s-app:konnectivity-agenttemplate:metadata:labels:k8s-app:konnectivity-agentspec:priorityClassName:system-cluster-criticaltolerations:- key:"CriticalAddonsOnly"operator:"Exists"containers:- image:us.gcr.io/k8s-artifacts-prod/kas-network-proxy/proxy-agent:v0.0.37name:konnectivity-agentcommand:["/proxy-agent"]args:["--logtostderr=true","--ca-cert=/var/run/secrets/kubernetes.io/serviceaccount/ca.crt",# Since the konnectivity server runs with hostNetwork=true,# this is the IP address of the master machine."--proxy-server-host=35.225.206.7","--proxy-server-port=8132","--admin-server-port=8133","--health-server-port=8134","--service-account-token-path=/var/run/secrets/tokens/konnectivity-agent-token"]volumeMounts:- mountPath:/var/run/secrets/tokensname:konnectivity-agent-tokenlivenessProbe:httpGet:port:8134path:/healthzinitialDelaySeconds:15timeoutSeconds:15serviceAccountName:konnectivity-agentvolumes:- name:konnectivity-agent-tokenprojected:sources:- serviceAccountToken:path:konnectivity-agent-tokenaudience:system:konnectivity-server
Last, if RBAC is enabled in your cluster, create the relevant RBAC rules:
Understand how to protect traffic within your cluster using Transport Layer Security (TLS).
12.1 - Configure Certificate Rotation for the Kubelet
This page shows how to enable and configure certificate rotation for the kubelet.
FEATURE STATE:Kubernetes v1.19 [stable]
Before you begin
Kubernetes version 1.8.0 or later is required
Overview
The kubelet uses certificates for authenticating to the Kubernetes API. By
default, these certificates are issued with one year expiration so that they do
not need to be renewed too frequently.
Kubernetes contains kubelet certificate
rotation,
that will automatically generate a new key and request a new certificate from
the Kubernetes API as the current certificate approaches expiration. Once the
new certificate is available, it will be used for authenticating connections to
the Kubernetes API.
Enabling client certificate rotation
The kubelet process accepts an argument --rotate-certificates that controls
if the kubelet will automatically request a new certificate as the expiration of
the certificate currently in use approaches.
The kube-controller-manager process accepts an argument
--cluster-signing-duration (--experimental-cluster-signing-duration prior to 1.19)
that controls how long certificates will be issued for.
Understanding the certificate rotation configuration
When a kubelet starts up, if it is configured to bootstrap (using the
--bootstrap-kubeconfig flag), it will use its initial certificate to connect
to the Kubernetes API and issue a certificate signing request. You can view the
status of certificate signing requests using:
kubectl get csr
Initially a certificate signing request from the kubelet on a node will have a
status of Pending. If the certificate signing requests meets specific
criteria, it will be auto approved by the controller manager, then it will have
a status of Approved. Next, the controller manager will sign a certificate,
issued for the duration specified by the
--cluster-signing-duration parameter, and the signed certificate
will be attached to the certificate signing request.
The kubelet will retrieve the signed certificate from the Kubernetes API and
write that to disk, in the location specified by --cert-dir. Then the kubelet
will use the new certificate to connect to the Kubernetes API.
As the expiration of the signed certificate approaches, the kubelet will
automatically issue a new certificate signing request, using the Kubernetes API.
This can happen at any point between 30% and 10% of the time remaining on the
certificate. Again, the controller manager will automatically approve the certificate
request and attach a signed certificate to the certificate signing request. The
kubelet will retrieve the new signed certificate from the Kubernetes API and
write that to disk. Then it will update the connections it has to the
Kubernetes API to reconnect using the new certificate.
12.2 - Manage TLS Certificates in a Cluster
Kubernetes provides a certificates.k8s.io API, which lets you provision TLS
certificates signed by a Certificate Authority (CA) that you control. These CA
and certificates can be used by your workloads to establish trust.
certificates.k8s.io API uses a protocol that is similar to the ACME
draft.
Note: Certificates created using the certificates.k8s.io API are signed by a
dedicated CA. It is possible to configure your cluster to use the cluster root
CA for this purpose, but you should never rely on this. Do not assume that
these certificates will validate against the cluster root CA.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Some steps in this page use the jq tool. If you don't have jq, you can
install it via your operating system's software sources, or fetch it from
https://jqlang.github.io/jq/.
Trusting TLS in a cluster
Trusting the custom CA from an application running as a pod usually requires
some extra application configuration. You will need to add the CA certificate
bundle to the list of CA certificates that the TLS client or server trusts. For
example, you would do this with a golang TLS config by parsing the certificate
chain and adding the parsed certificates to the RootCAs field in the
tls.Config struct.
Note:
Even though the custom CA certificate may be included in the filesystem (in the
ConfigMap kube-root-ca.crt),
you should not use that certificate authority for any purpose other than to verify internal
Kubernetes endpoints. An example of an internal Kubernetes endpoint is the
Service named kubernetes in the default namespace.
If you want to use a custom certificate authority for your workloads, you should generate
that CA separately, and distribute its CA certificate using a
ConfigMap that your pods
have access to read.
Requesting a certificate
The following section demonstrates how to create a TLS certificate for a
Kubernetes service accessed through DNS.
Note: This tutorial uses CFSSL: Cloudflare's PKI and TLS toolkit click here to know more.
Create a certificate signing request
Generate a private key and certificate signing request (or CSR) by running
the following command:
Where 192.0.2.24 is the service's cluster IP,
my-svc.my-namespace.svc.cluster.local is the service's DNS name,
10.0.34.2 is the pod's IP and my-pod.my-namespace.pod.cluster.local
is the pod's DNS name. You should see the output similar to:
This command generates two files; it generates server.csr containing the PEM
encoded PKCS#10 certification request,
and server-key.pem containing the PEM encoded key to the certificate that
is still to be created.
Create a CertificateSigningRequest object to send to the Kubernetes API
Generate a CSR manifest (in YAML), and send it to the API server. You can do that by
running the following command:
Notice that the server.csr file created in step 1 is base64 encoded
and stashed in the .spec.request field. You are also requesting a
certificate with the "digital signature", "key encipherment", and "server
auth" key usages, signed by an example example.com/serving signer.
A specific signerName must be requested.
View documentation for supported signer names
for more information.
The CSR should now be visible from the API in a Pending state. You can see
it by running:
kubectl describe csr my-svc.my-namespace
Name: my-svc.my-namespace
Labels: <none>
Annotations: <none>
CreationTimestamp: Tue, 01 Feb 2022 11:49:15 -0500
Requesting User: yourname@example.com
Signer: example.com/serving
Status: Pending
Subject:
Common Name: my-pod.my-namespace.pod.cluster.local
Serial Number:
Subject Alternative Names:
DNS Names: my-pod.my-namespace.pod.cluster.local
my-svc.my-namespace.svc.cluster.local
IP Addresses: 192.0.2.24
10.0.34.2
Events: <none>
Get the CertificateSigningRequest approved
Approving the certificate signing request
is either done by an automated approval process or on a one off basis by a cluster
administrator. If you're authorized to approve a certificate request, you can do that
manually using kubectl; for example:
NAME AGE SIGNERNAME REQUESTOR REQUESTEDDURATION CONDITION
my-svc.my-namespace 10m example.com/serving yourname@example.com <none> Approved
This means the certificate request has been approved and is waiting for the
requested signer to sign it.
Sign the CertificateSigningRequest
Next, you'll play the part of a certificate signer, issue the certificate, and upload it to the API.
A signer would typically watch the CertificateSigningRequest API for objects with its signerName,
check that they have been approved, sign certificates for those requests,
and update the API object status with the issued certificate.
Create a Certificate Authority
You need an authority to provide the digital signature on the new certificate.
First, create a signing certificate by running the following:
cat <<EOF | cfssl gencert -initca - | cfssljson -bare ca
{
"CN": "My Example Signer",
"key": {
"algo": "rsa",
"size": 2048
}
}
EOF
You should see output similar to:
2022/02/01 11:50:39 [INFO] generating a new CA key and certificate from CSR
2022/02/01 11:50:39 [INFO] generate received request
2022/02/01 11:50:39 [INFO] received CSR
2022/02/01 11:50:39 [INFO] generating key: rsa-2048
2022/02/01 11:50:39 [INFO] encoded CSR
2022/02/01 11:50:39 [INFO] signed certificate with serial number 263983151013686720899716354349605500797834580472
This produces a certificate authority key file (ca-key.pem) and certificate (ca.pem).
Note: This uses the command line tool jq to populate the base64-encoded
content in the .status.certificate field.
If you do not have jq, you can also save the JSON output to a file, populate this field manually, and
upload the resulting file.
Once the CSR is approved and the signed certificate is uploaded, run:
kubectl get csr
The output is similar to:
NAME AGE SIGNERNAME REQUESTOR REQUESTEDDURATION CONDITION
my-svc.my-namespace 20m example.com/serving yourname@example.com <none> Approved,Issued
Download the certificate and use it
Now, as the requesting user, you can download the issued certificate
and save it to a server.crt file by running the following:
Now you can populate server.crt and server-key.pem in a
Secret
that you could later mount into a Pod (for example, to use with a webserver
that serves HTTPS).
kubectl create secret tls server --cert server.crt --key server-key.pem
secret/server created
Finally, you can populate ca.pem into a ConfigMap
and use it as the trust root to verify the serving certificate:
A Kubernetes administrator (with appropriate permissions) can manually approve
(or deny) CertificateSigningRequests by using the kubectl certificate approve and kubectl certificate deny commands. However if you intend
to make heavy usage of this API, you might consider writing an automated
certificates controller.
Caution:
The ability to approve CSRs decides who trusts whom within your environment. The
ability to approve CSRs should not be granted broadly or lightly.
You should make sure that you confidently understand both the verification requirements
that fall on the approver and the repercussions of issuing a specific certificate
before you grant the approve permission.
Whether a machine or a human using kubectl as above, the role of the approver is
to verify that the CSR satisfies two requirements:
The subject of the CSR controls the private key used to sign the CSR. This
addresses the threat of a third party masquerading as an authorized subject.
In the above example, this step would be to verify that the pod controls the
private key used to generate the CSR.
The subject of the CSR is authorized to act in the requested context. This
addresses the threat of an undesired subject joining the cluster. In the
above example, this step would be to verify that the pod is allowed to
participate in the requested service.
If and only if these two requirements are met, the approver should approve
the CSR and otherwise should deny the CSR.
For more information on certificate approval and access control, read
the Certificate Signing Requests
reference page.
Configuring your cluster to provide signing
This page assumes that a signer is set up to serve the certificates API. The
Kubernetes controller manager provides a default implementation of a signer. To
enable it, pass the --cluster-signing-cert-file and
--cluster-signing-key-file parameters to the controller manager with paths to
your Certificate Authority's keypair.
12.3 - Manual Rotation of CA Certificates
This page shows how to manually rotate the certificate authority (CA) certificates.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
For more information about authentication in Kubernetes, see
Authenticating.
For more information about best practices for CA certificates, see
Single root CA.
Rotate the CA certificates manually
Caution:
Make sure to back up your certificate directory along with configuration files and any other necessary files.
This approach assumes operation of the Kubernetes control plane in a HA configuration with multiple API servers.
Graceful termination of the API server is also assumed so clients can cleanly disconnect from one API server and
reconnect to another.
Configurations with a single API server will experience unavailability while the API server is being restarted.
Distribute the new CA certificates and private keys (for example: ca.crt, ca.key, front-proxy-ca.crt,
and front-proxy-ca.key) to all your control plane nodes in the Kubernetes certificates directory.
Update the --root-ca-file flag for the kube-controller-manager to include
both old and new CA, then restart the kube-controller-manager.
Any ServiceAccount created after this point will get
Secrets that include both old and new CAs.
Note:
The files specified by the kube-controller-manager flags --client-ca-file and --cluster-signing-cert-file
cannot be CA bundles. If these flags and --root-ca-file point to the same ca.crt file which is now a
bundle (includes both old and new CA) you will face an error. To workaround this problem you can copy the new CA
to a separate file and make the flags --client-ca-file and --cluster-signing-cert-file point to the copy.
Once ca.crt is no longer a bundle you can restore the problem flags to point to ca.crt and delete the copy.
Issue 1350 for kubeadm tracks an bug with the
kube-controller-manager being unable to accept a CA bundle.
Wait for the controller manager to update ca.crt in the service account Secrets to include both old and new CA certificates.
If any Pods are started before new CA is used by API servers, the new Pods get this update and will trust both
old and new CAs.
Restart all pods using in-cluster configurations (for example: kube-proxy, CoreDNS, etc) so they can use the
updated certificate authority data from Secrets that link to ServiceAccounts.
Make sure CoreDNS, kube-proxy and other Pods using in-cluster configurations are working as expected.
Append the both old and new CA to the file against --client-ca-file and --kubelet-certificate-authority
flag in the kube-apiserver configuration.
Append the both old and new CA to the file against --client-ca-file flag in the kube-scheduler configuration.
Update certificates for user accounts by replacing the content of client-certificate-data and client-key-data
respectively.
Additionally, update the certificate-authority-data section in the kubeconfig files,
respectively with Base64-encoded old and new certificate authority data
Update the --root-ca-file flag for the Cloud Controller Manager to include
both old and new CA, then restart the cloud-controller-manager.
Note: If your cluster does not have a cloud-controller-manager, you can skip this step.
Follow the steps below in a rolling fashion.
Restart any other
aggregated API servers or
webhook handlers to trust the new CA certificates.
Restart the kubelet by update the file against clientCAFile in kubelet configuration and
certificate-authority-data in kubelet.conf to use both the old and new CA on all nodes.
If your kubelet is not using client certificate rotation, update client-certificate-data and
client-key-data in kubelet.conf on all nodes along with the kubelet client certificate file
usually found in /var/lib/kubelet/pki.
Restart API servers with the certificates (apiserver.crt, apiserver-kubelet-client.crt and
front-proxy-client.crt) signed by new CA.
You can use the existing private keys or new private keys.
If you changed the private keys then update these in the Kubernetes certificates directory as well.
Since the Pods in your cluster trust both old and new CAs, there will be a momentarily disconnection
after which pods' Kubernetes clients reconnect to the new API server.
The new API server uses a certificate signed by the new CA.
Restart the kube-scheduler to use and
trust the new CAs.
Make sure control plane components logs no TLS errors.
Note: To generate certificates and private keys for your cluster using the openssl command line tool,
see Certificates (openssl).
You can also use cfssl.
Annotate any DaemonSets and Deployments to trigger pod replacement in a safer rolling fashion.
for namespace in $(kubectl get namespace -o jsonpath='{.items[*].metadata.name}'); dofor name in $(kubectl get deployments -n $namespace -o jsonpath='{.items[*].metadata.name}'); do kubectl patch deployment -n ${namespace}${name} -p '{"spec":{"template":{"metadata":{"annotations":{"ca-rotation": "1"}}}}}';
donefor name in $(kubectl get daemonset -n $namespace -o jsonpath='{.items[*].metadata.name}'); do kubectl patch daemonset -n ${namespace}${name} -p '{"spec":{"template":{"metadata":{"annotations":{"ca-rotation": "1"}}}}}';
donedone
Check the logs from control plane components, along with the kubelet and the kube-proxy.
Ensure those components are not reporting any TLS errors; see
looking at the logs for more details.
Validate logs from any aggregated api servers and pods using in-cluster config.
Once the cluster functionality is successfully verified:
Update all service account tokens to include new CA certificate only.
All pods using an in-cluster kubeconfig will eventually need to be restarted to pick up the new Secret,
so that no Pods are relying on the old cluster CA.
Restart the control plane components by removing the old CA from the kubeconfig files and the files against
--client-ca-file, --root-ca-file flags resp.
On each node, restart the kubelet by removing the old CA from file against the clientCAFile flag
and from the kubelet kubeconfig file. You should carry this out as a rolling update.
If your cluster lets you make this change, you can also roll it out by replacing nodes rather than
reconfiguring them.
13 - Manage Cluster Daemons
Perform common tasks for managing a DaemonSet, such as performing a rolling update.
13.1 - Perform a Rolling Update on a DaemonSet
This page shows how to perform a rolling update on a DaemonSet.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
OnDelete: With OnDelete update strategy, after you update a DaemonSet template, new
DaemonSet pods will only be created when you manually delete old DaemonSet
pods. This is the same behavior of DaemonSet in Kubernetes version 1.5 or
before.
RollingUpdate: This is the default update strategy.
With RollingUpdate update strategy, after you update a
DaemonSet template, old DaemonSet pods will be killed, and new DaemonSet pods
will be created automatically, in a controlled fashion. At most one pod of
the DaemonSet will be running on each node during the whole update process.
Performing a Rolling Update
To enable the rolling update feature of a DaemonSet, you must set its
.spec.updateStrategy.type to RollingUpdate.
apiVersion:apps/v1kind:DaemonSetmetadata:name:fluentd-elasticsearchnamespace:kube-systemlabels:k8s-app:fluentd-loggingspec:selector:matchLabels:name:fluentd-elasticsearchupdateStrategy:type:RollingUpdaterollingUpdate:maxUnavailable:1template:metadata:labels:name:fluentd-elasticsearchspec:tolerations:# these tolerations are to have the daemonset runnable on control plane nodes# remove them if your control plane nodes should not run pods- key:node-role.kubernetes.io/control-planeoperator:Existseffect:NoSchedule- key:node-role.kubernetes.io/masteroperator:Existseffect:NoSchedulecontainers:- name:fluentd-elasticsearchimage:quay.io/fluentd_elasticsearch/fluentd:v2.5.2volumeMounts:- name:varlogmountPath:/var/log- name:varlibdockercontainersmountPath:/var/lib/docker/containersreadOnly:trueterminationGracePeriodSeconds:30volumes:- name:varloghostPath:path:/var/log- name:varlibdockercontainershostPath:path:/var/lib/docker/containers
After verifying the update strategy of the DaemonSet manifest, create the DaemonSet:
If the output isn't RollingUpdate, go back and modify the DaemonSet object or
manifest accordingly.
Updating a DaemonSet template
Any updates to a RollingUpdate DaemonSet .spec.template will trigger a rolling
update. Let's update the DaemonSet by applying a new YAML file. This can be done with several different kubectl commands.
apiVersion:apps/v1kind:DaemonSetmetadata:name:fluentd-elasticsearchnamespace:kube-systemlabels:k8s-app:fluentd-loggingspec:selector:matchLabels:name:fluentd-elasticsearchupdateStrategy:type:RollingUpdaterollingUpdate:maxUnavailable:1template:metadata:labels:name:fluentd-elasticsearchspec:tolerations:# these tolerations are to have the daemonset runnable on control plane nodes# remove them if your control plane nodes should not run pods- key:node-role.kubernetes.io/control-planeoperator:Existseffect:NoSchedule- key:node-role.kubernetes.io/masteroperator:Existseffect:NoSchedulecontainers:- name:fluentd-elasticsearchimage:quay.io/fluentd_elasticsearch/fluentd:v2.5.2resources:limits:memory:200Mirequests:cpu:100mmemory:200MivolumeMounts:- name:varlogmountPath:/var/log- name:varlibdockercontainersmountPath:/var/lib/docker/containersreadOnly:trueterminationGracePeriodSeconds:30volumes:- name:varloghostPath:path:/var/log- name:varlibdockercontainershostPath:path:/var/lib/docker/containers
If you only need to update the container image in the DaemonSet template, i.e.
.spec.template.spec.containers[*].image, use kubectl set image:
kubectl set image ds/fluentd-elasticsearch fluentd-elasticsearch=quay.io/fluentd_elasticsearch/fluentd:v2.6.0 -n kube-system
Watching the rolling update status
Finally, watch the rollout status of the latest DaemonSet rolling update:
kubectl rollout status ds/fluentd-elasticsearch -n kube-system
When the rollout is complete, the output is similar to this:
daemonset "fluentd-elasticsearch" successfully rolled out
Troubleshooting
DaemonSet rolling update is stuck
Sometimes, a DaemonSet rolling update may be stuck. Here are some possible
causes:
Some nodes run out of resources
The rollout is stuck because new DaemonSet pods can't be scheduled on at least one
node. This is possible when the node is
running out of resources.
When this happens, find the nodes that don't have the DaemonSet pods scheduled on
by comparing the output of kubectl get nodes and the output of:
kubectl get pods -l name=fluentd-elasticsearch -o wide -n kube-system
Once you've found those nodes, delete some non-DaemonSet pods from the node to
make room for new DaemonSet pods.
Note: This will cause service disruption when deleted pods are not controlled by any controllers or pods are not
replicated. This does not respect PodDisruptionBudget
either.
Broken rollout
If the recent DaemonSet template update is broken, for example, the container is
crash looping, or the container image doesn't exist (often due to a typo),
DaemonSet rollout won't progress.
To fix this, update the DaemonSet template again. New rollout won't be
blocked by previous unhealthy rollouts.
Clock skew
If .spec.minReadySeconds is specified in the DaemonSet, clock skew between
master and nodes will make DaemonSet unable to detect the right rollout
progress.
This page shows how to perform a rollback on a DaemonSet.
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Change cause is copied from DaemonSet annotation kubernetes.io/change-cause
to its revisions upon creation. You may specify --record=true in kubectl
to record the command executed in the change cause annotation.
To see the details of a specific revision:
kubectl rollout history daemonset <daemonset-name> --revision=1
# Specify the revision number you get from Step 1 in --to-revisionkubectl rollout undo daemonset <daemonset-name> --to-revision=<revision>
If it succeeds, the command returns:
daemonset "<daemonset-name>" rolled back
Note: If --to-revision flag is not specified, kubectl picks the most recent revision.
Step 3: Watch the progress of the DaemonSet rollback
kubectl rollout undo daemonset tells the server to start rolling back the
DaemonSet. The real rollback is done asynchronously inside the cluster
control plane.
To watch the progress of the rollback:
kubectl rollout status ds/<daemonset-name>
When the rollback is complete, the output is similar to:
daemonset "<daemonset-name>" successfully rolled out
Understanding DaemonSet revisions
In the previous kubectl rollout history step, you got a list of DaemonSet
revisions. Each revision is stored in a resource named ControllerRevision.
To see what is stored in each revision, find the DaemonSet revision raw
resources:
kubectl get controllerrevision -l <daemonset-selector-key>=<daemonset-selector-value>
This returns a list of ControllerRevisions:
NAME CONTROLLER REVISION AGE
<daemonset-name>-<revision-hash> DaemonSet/<daemonset-name> 1 1h
<daemonset-name>-<revision-hash> DaemonSet/<daemonset-name> 2 1h
Each ControllerRevision stores the annotations and template of a DaemonSet
revision.
kubectl rollout undo takes a specific ControllerRevision and replaces
DaemonSet template with the template stored in the ControllerRevision.
kubectl rollout undo is equivalent to updating DaemonSet template to a
previous revision through other commands, such as kubectl edit or kubectl apply.
Note: DaemonSet revisions only roll forward. That is to say, after a
rollback completes, the revision number (.revision field) of the
ControllerRevision being rolled back to will advance. For example, if you
have revision 1 and 2 in the system, and roll back from revision 2 to revision
1, the ControllerRevision with .revision: 1 will become .revision: 3.
This page demonstrates how can you run Pods on only some Nodes as part of a DaemonSet
Before you begin
You need to have a Kubernetes cluster, and the kubectl command-line tool must
be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a
cluster, you can create one by using
minikube
or you can use one of these Kubernetes playgrounds:
Imagine that you want to run a DaemonSet, but you only need to run those daemon pods
on nodes that have local solid state (SSD) storage. For example, the Pod might provide cache service to the
node, and the cache is only useful when low-latency local storage is available.
Step 1: Add labels to your nodes
Add the label ssd=true to the nodes which have SSDs.
Create the DaemonSet from the manifest by using kubectl create or kubectl apply
Let's label another node as ssd=true.
kubectl label nodes example-node-3 ssd=true
Labelling the node automatically triggers the control plane (specifically, the DaemonSet controller)
to run a new daemon pod on that node.
kubectl get pods -o wide
The output is similar to:
NAME READY STATUS RESTARTS AGE IP NODE
<daemonset-name><some-hash-01> 1/1 Running 0 13s ..... example-node-1
<daemonset-name><some-hash-02> 1/1 Running 0 13s ..... example-node-2
<daemonset-name><some-hash-03> 1/1 Running 0 5s ..... example-node-3
14 - Networking
Learn how to configure networking for your cluster.
14.1 - Adding entries to Pod /etc/hosts with HostAliases
Adding entries to a Pod's /etc/hosts file provides Pod-level override of hostname resolution when DNS and other options are not applicable. You can add these custom entries with the HostAliases field in PodSpec.
Modification not using HostAliases is not suggested because the file is managed by the kubelet and can be overwritten on during Pod creation/restart.
Default hosts file content
Start an Nginx Pod which is assigned a Pod IP:
kubectl run nginx --image nginx
pod/nginx created
Examine a Pod IP:
kubectl get pods --output=wide
NAME READY STATUS RESTARTS AGE IP NODE
nginx 1/1 Running 0 13s 10.200.0.4 worker0
By default, the hosts file only includes IPv4 and IPv6 boilerplates like
localhost and its own hostname.
Adding additional entries with hostAliases
In addition to the default boilerplate, you can add additional entries to the
hosts file.
For example: to resolve foo.local, bar.local to 127.0.0.1 and foo.remote,
bar.remote to 10.1.2.3, you can configure HostAliases for a Pod under
.spec.hostAliases:
with the additional entries specified at the bottom.
Why does the kubelet manage the hosts file?
The kubelet manages the
hosts file for each container of the Pod to prevent the container runtime from
modifying the file after the containers have already been started.
Historically, Kubernetes always used Docker Engine as its container runtime, and Docker Engine would
then modify the /etc/hosts file after each container had started.
Current Kubernetes can use a variety of container runtimes; even so, the kubelet manages the
hosts file within each container so that the outcome is as intended regardless of which
container runtime you use.
Caution:
Avoid making manual changes to the hosts file inside a container.
If you make manual changes to the hosts file,
those changes are lost when the container exits.
14.2 - Validate IPv4/IPv6 dual-stack
This document shares how to validate IPv4/IPv6 dual-stack enabled Kubernetes clusters.
Before you begin
Provider support for dual-stack networking (Cloud provider or otherwise must be able to provide Kubernetes nodes with routable IPv4/IPv6 network interfaces)
Your Kubernetes server must be at or later than version v1.23.
To check the version, enter kubectl version.
Note: While you can validate with an earlier version, the feature is only GA and officially supported since v1.23.
Validate addressing
Validate node addressing
Each dual-stack Node should have a single IPv4 block and a single IPv6 block allocated. Validate that IPv4/IPv6 Pod address ranges are configured by running the following command. Replace the sample node name with a valid dual-stack Node from your cluster. In this example, the Node's name is k8s-linuxpool1-34450317-0:
kubectl get nodes k8s-linuxpool1-34450317-0 -o go-template --template='{{range .spec.podCIDRs}}{{printf "%s\n" .}}{{end}}'
10.244.1.0/24
2001:db8::/64
There should be one IPv4 block and one IPv6 block allocated.
Validate that the node has an IPv4 and IPv6 interface detected. Replace node name with a valid node from the cluster. In this example the node name is k8s-linuxpool1-34450317-0:
Validate that a Pod has an IPv4 and IPv6 address assigned. Replace the Pod name with a valid Pod in your cluster. In this example the Pod name is pod01:
kubectl get pods pod01 -o go-template --template='{{range .status.podIPs}}{{printf "%s\n" .ip}}{{end}}'
10.244.1.4
2001:db8::4
You can also validate Pod IPs using the Downward API via the status.podIPs fieldPath. The following snippet demonstrates how you can expose the Pod IPs via an environment variable called MY_POD_IPS within a container.
The following command prints the value of the MY_POD_IPS environment variable from within a container. The value is a comma separated list that corresponds to the Pod's IPv4 and IPv6 addresses.
kubectl exec -it pod01 -- set | grep MY_POD_IPS
MY_POD_IPS=10.244.1.4,2001:db8::4
The Pod's IP addresses will also be written to /etc/hosts within a container. The following command executes a cat on /etc/hosts on a dual stack Pod. From the output you can verify both the IPv4 and IPv6 IP address for the Pod.
Create the following Service that does not explicitly define .spec.ipFamilyPolicy. Kubernetes will assign a cluster IP for the Service from the first configured service-cluster-ip-range and set the .spec.ipFamilyPolicy to SingleStack.
The Service has .spec.ipFamilyPolicy set to SingleStack and .spec.clusterIP set to an IPv4 address from the first configured range set via --service-cluster-ip-range flag on kube-controller-manager.
Create the following Service that explicitly defines IPv6 as the first array element in .spec.ipFamilies. Kubernetes will assign a cluster IP for the Service from the IPv6 range configured service-cluster-ip-range and set the .spec.ipFamilyPolicy to SingleStack.
The Service has .spec.ipFamilyPolicy set to SingleStack and .spec.clusterIP set to an IPv6 address from the IPv6 range set via --service-cluster-ip-range flag on kube-controller-manager.
Create the following Service that explicitly defines PreferDualStack in .spec.ipFamilyPolicy. Kubernetes will assign both IPv4 and IPv6 addresses (as this cluster has dual-stack enabled) and select the .spec.ClusterIP from the list of .spec.ClusterIPs based on the address family of the first element in the .spec.ipFamilies array.
The kubectl get svc command will only show the primary IP in the CLUSTER-IP field.
kubectl get svc -l app.kubernetes.io/name=MyApp
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
my-service ClusterIP 10.0.216.242 <none> 80/TCP 5s
Validate that the Service gets cluster IPs from the IPv4 and IPv6 address blocks using kubectl describe. You may then validate access to the service via the IPs and ports.
If the cloud provider supports the provisioning of IPv6 enabled external load balancers, create the following Service with PreferDualStack in .spec.ipFamilyPolicy, IPv6 as the first element of the .spec.ipFamilies array and the type field set to LoadBalancer.
Validate that the Service receives a CLUSTER-IP address from the IPv6 address block along with an EXTERNAL-IP. You may then validate access to the service via the IP and port.
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
my-service LoadBalancer 2001:db8:fd00::7ebc 2603:1030:805::5 80:30790/TCP 35s
15 - Extend kubectl with plugins
Extend kubectl by creating and installing kubectl plugins.
This guide demonstrates how to install and write extensions for kubectl. By thinking of core kubectl commands as essential building blocks for interacting with a Kubernetes cluster, a cluster administrator can think
of plugins as a means of utilizing these building blocks to create more complex behavior. Plugins extend kubectl with new sub-commands, allowing for new and custom features not included in the main distribution of kubectl.
Before you begin
You need to have a working kubectl binary installed.
Installing kubectl plugins
A plugin is a standalone executable file, whose name begins with kubectl-. To install a plugin, move its executable file to anywhere on your PATH.
You can also discover and install kubectl plugins available in the open source
using Krew. Krew is a plugin manager maintained by
the Kubernetes SIG CLI community.
Caution: Kubectl plugins available via the Krew plugin index
are not audited for security. You should install and run third-party plugins at your
own risk, since they are arbitrary programs running on your machine.
Discovering plugins
kubectl provides a command kubectl plugin list that searches your PATH for valid plugin executables.
Executing this command causes a traversal of all files in your PATH. Any files that are executable, and begin with kubectl- will show up in the order in which they are present in your PATH in this command's output.
A warning will be included for any files beginning with kubectl- that are not executable.
A warning will also be included for any valid plugin files that overlap each other's name.
You can use Krew to discover and install kubectl
plugins from a community-curated
plugin index.
Limitations
It is currently not possible to create plugins that overwrite existing kubectl commands. For example, creating a plugin kubectl-version will cause that plugin to never be executed, as the existing kubectl version command will always take precedence over it. Due to this limitation, it is also not possible to use plugins to add new subcommands to existing kubectl commands. For example, adding a subcommand kubectl create foo by naming your plugin kubectl-create-foo will cause that plugin to be ignored.
kubectl plugin list shows warnings for any valid plugins that attempt to do this.
Writing kubectl plugins
You can write a plugin in any programming language or script that allows you to write command-line commands.
There is no plugin installation or pre-loading required. Plugin executables receive
the inherited environment from the kubectl binary.
A plugin determines which command path it wishes to implement based on its name.
For example, a plugin named kubectl-foo provides a command kubectl foo. You must
install the plugin executable somewhere in your PATH.
Example plugin
#!/bin/bash
# optional argument handlingif[["$1"=="version"]]thenecho"1.0.0"exit0fi# optional argument handlingif[["$1"=="config"]]thenecho"$KUBECONFIG"exit0fiecho"I am a plugin named kubectl-foo"
Using a plugin
To use a plugin, make the plugin executable:
sudo chmod +x ./kubectl-foo
and place it anywhere in your PATH:
sudo mv ./kubectl-foo /usr/local/bin
You may now invoke your plugin as a kubectl command:
kubectl foo
I am a plugin named kubectl-foo
All args and flags are passed as-is to the executable:
kubectl foo version
1.0.0
All environment variables are also passed as-is to the executable:
Additionally, the first argument that is passed to a plugin will always be the full path to the location where it was invoked ($0 would equal /usr/local/bin/kubectl-foo in the example above).
Naming a plugin
As seen in the example above, a plugin determines the command path that it will implement based on its filename. Every sub-command in the command path that a plugin targets, is separated by a dash (-).
For example, a plugin that wishes to be invoked whenever the command kubectl foo bar baz is invoked by the user, would have the filename of kubectl-foo-bar-baz.
Flags and argument handling
Note:
The plugin mechanism does not create any custom, plugin-specific values or environment variables for a plugin process.
An older kubectl plugin mechanism provided environment variables such as KUBECTL_PLUGINS_CURRENT_NAMESPACE; that no longer happens.
kubectl plugins must parse and validate all of the arguments passed to them.
See using the command line runtime package for details of a Go library aimed at plugin authors.
Here are some additional cases where users invoke your plugin while providing additional flags and arguments. This builds upon the kubectl-foo-bar-baz plugin from the scenario above.
If you run kubectl foo bar baz arg1 --flag=value arg2, kubectl's plugin mechanism will first try to find the plugin with the longest possible name, which in this case
would be kubectl-foo-bar-baz-arg1. Upon not finding that plugin, kubectl then treats the last dash-separated value as an argument (arg1 in this case), and attempts to find the next longest possible name, kubectl-foo-bar-baz.
Upon having found a plugin with this name, kubectl then invokes that plugin, passing all args and flags after the plugin's name as arguments to the plugin process.
Example:
# create a pluginecho -e '#!/bin/bash\n\necho "My first command-line argument was $1"' > kubectl-foo-bar-baz
sudo chmod +x ./kubectl-foo-bar-baz
# "install" your plugin by moving it to a directory in your $PATHsudo mv ./kubectl-foo-bar-baz /usr/local/bin
# check that kubectl recognizes your pluginkubectl plugin list
The following kubectl-compatible plugins are available:
/usr/local/bin/kubectl-foo-bar-baz
# test that calling your plugin via a "kubectl" command works
# even when additional arguments and flags are passed to your
# plugin executable by the user.
kubectl foo bar baz arg1 --meaningless-flag=true
My first command-line argument was arg1
As you can see, your plugin was found based on the kubectl command specified by a user, and all extra arguments and flags were passed as-is to the plugin executable once it was found.
Names with dashes and underscores
Although the kubectl plugin mechanism uses the dash (-) in plugin filenames to separate the sequence of sub-commands processed by the plugin, it is still possible to create a plugin
command containing dashes in its commandline invocation by using underscores (_) in its filename.
Example:
# create a plugin containing an underscore in its filenameecho -e '#!/bin/bash\n\necho "I am a plugin with a dash in my name"' > ./kubectl-foo_bar
sudo chmod +x ./kubectl-foo_bar
# move the plugin into your $PATHsudo mv ./kubectl-foo_bar /usr/local/bin
# You can now invoke your plugin via kubectl:kubectl foo-bar
I am a plugin with a dash in my name
Note that the introduction of underscores to a plugin filename does not prevent you from having commands such as kubectl foo_bar.
The command from the above example, can be invoked using either a dash (-) or an underscore (_):
# You can invoke your custom command with a dashkubectl foo-bar
I am a plugin with a dash in my name
# You can also invoke your custom command with an underscorekubectl foo_bar
I am a plugin with a dash in my name
Name conflicts and overshadowing
It is possible to have multiple plugins with the same filename in different locations throughout your PATH.
For example, given a PATH with the following value: PATH=/usr/local/bin/plugins:/usr/local/bin/moreplugins, a copy of plugin kubectl-foo could exist in /usr/local/bin/plugins and /usr/local/bin/moreplugins,
such that the output of the kubectl plugin list command is:
PATH=/usr/local/bin/plugins:/usr/local/bin/moreplugins kubectl plugin list
The following kubectl-compatible plugins are available:
/usr/local/bin/plugins/kubectl-foo
/usr/local/bin/moreplugins/kubectl-foo
- warning: /usr/local/bin/moreplugins/kubectl-foo is overshadowed by a similarly named plugin: /usr/local/bin/plugins/kubectl-foo
error: one plugin warning was found
In the above scenario, the warning under /usr/local/bin/moreplugins/kubectl-foo tells you that this plugin will never be executed. Instead, the executable that appears first in your PATH, /usr/local/bin/plugins/kubectl-foo, will always be found and executed first by the kubectl plugin mechanism.
A way to resolve this issue is to ensure that the location of the plugin that you wish to use with kubectl always comes first in your PATH. For example, if you want to always use /usr/local/bin/moreplugins/kubectl-foo anytime that the kubectl command kubectl foo was invoked, change the value of your PATH to be /usr/local/bin/moreplugins:/usr/local/bin/plugins.
Invocation of the longest executable filename
There is another kind of overshadowing that can occur with plugin filenames. Given two plugins present in a user's PATH: kubectl-foo-bar and kubectl-foo-bar-baz, the kubectl plugin mechanism will always choose the longest possible plugin name for a given user command. Some examples below, clarify this further:
# for a given kubectl command, the plugin with the longest possible filename will always be preferredkubectl foo bar baz
Plugin kubectl-foo-bar-baz is executed
kubectl foo bar
Plugin kubectl-foo-bar is executed
kubectl foo bar baz buz
Plugin kubectl-foo-bar-baz is executed, with "buz" as its first argument
kubectl foo bar buz
Plugin kubectl-foo-bar is executed, with "buz" as its first argument
This design choice ensures that plugin sub-commands can be implemented across multiple files, if needed, and that these sub-commands can be nested under a "parent" plugin command:
You can use the aforementioned kubectl plugin list command to ensure that your plugin is visible by kubectl, and verify that there are no warnings preventing it from being called as a kubectl command.
kubectl plugin list
The following kubectl-compatible plugins are available:
test/fixtures/pkg/kubectl/plugins/kubectl-foo
/usr/local/bin/kubectl-foo
- warning: /usr/local/bin/kubectl-foo is overshadowed by a similarly named plugin: test/fixtures/pkg/kubectl/plugins/kubectl-foo
plugins/kubectl-invalid
- warning: plugins/kubectl-invalid identified as a kubectl plugin, but it is not executable
error: 2 plugin warnings were found
Using the command line runtime package
If you're writing a plugin for kubectl and you're using Go, you can make use
of the
cli-runtime utility libraries.
These libraries provide helpers for parsing or updating a user's
kubeconfig
file, for making REST-style requests to the API server, or to bind flags
associated with configuration and printing.
See the Sample CLI Plugin for
an example usage of the tools provided in the CLI Runtime repo.
Distributing kubectl plugins
If you have developed a plugin for others to use, you should consider how you
package it, distribute it and deliver updates to your users.
Krew
Krew offers a cross-platform way to package and
distribute your plugins. This way, you use a single packaging format for all
target platforms (Linux, Windows, macOS etc) and deliver updates to your users.
Krew also maintains a plugin
index so that other people can
discover your plugin and install it.
Native / platform specific package management
Alternatively, you can use traditional package managers such as, apt or yum
on Linux, Chocolatey on Windows, and Homebrew on macOS. Any package
manager will be suitable if it can place new executables placed somewhere
in the user's PATH.
As a plugin author, if you pick this option then you also have the burden
of updating your kubectl plugin's distribution package across multiple
platforms for each release.
Source code
You can publish the source code; for example, as a Git repository. If you
choose this option, someone who wants to use that plugin must fetch the code,
set up a build environment (if it needs compiling), and deploy the plugin.
If you also make compiled packages available, or use Krew, that will make
installs easier.
What's next
Check the Sample CLI Plugin repository for a
detailed example of a
plugin written in Go.
In case of any questions, feel free to reach out to the
SIG CLI team.
Read about Krew, a package manager for kubectl plugins.
16 - Manage HugePages
Configure and manage huge pages as a schedulable resource in a cluster.
FEATURE STATE:Kubernetes v1.28 [stable]
Kubernetes supports the allocation and consumption of pre-allocated huge pages
by applications in a Pod. This page describes how users can consume huge pages.
Before you begin
Kubernetes nodes must
pre-allocate huge pages
in order for the node to report its huge page capacity.
A node can pre-allocate huge pages for multiple sizes, for instance,
the following line in /etc/default/grub allocates 2*1GiB of 1 GiB
and 512*2 MiB of 2 MiB pages:
Note: For dynamically allocated pages (after boot), the Kubelet needs to be restarted
for the new allocations to be refrelected.
API
Huge pages can be consumed via container level resource requirements using the
resource name hugepages-<size>, where <size> is the most compact binary
notation using integer values supported on a particular node. For example, if a
node supports 2048KiB and 1048576KiB page sizes, it will expose a schedulable
resources hugepages-2Mi and hugepages-1Gi. Unlike CPU or memory, huge pages
do not support overcommit. Note that when requesting hugepage resources, either
memory or CPU resources must be requested as well.
A pod may consume multiple huge page sizes in a single pod spec. In this case it
must use medium: HugePages-<hugepagesize> notation for all volume mounts.
Huge page requests must equal the limits. This is the default if limits are
specified, but requests are not.
Huge pages are isolated at a container scope, so each container has own
limit on their cgroup sandbox as requested in a container spec.
EmptyDir volumes backed by huge pages may not consume more huge page memory
than the pod request.
Applications that consume huge pages via shmget() with SHM_HUGETLB must
run with a supplemental group that matches proc/sys/vm/hugetlb_shm_group.
Huge page usage in a namespace is controllable via ResourceQuota similar
to other compute resources like cpu or memory using the hugepages-<size>
token.
17 - Schedule GPUs
Configure and schedule GPUs for use as a resource by nodes in a cluster.
FEATURE STATE:Kubernetes v1.26 [stable]
Kubernetes includes stable support for managing AMD and NVIDIA GPUs
(graphical processing units) across different nodes in your cluster, using
device plugins.
This page describes how users can consume GPUs, and outlines
some of the limitations in the implementation.
Using device plugins
Kubernetes implements device plugins to let Pods access specialized hardware features such as GPUs.
Note:
This section links to third party projects that provide functionality required by Kubernetes. The Kubernetes project authors aren't responsible for these projects, which are listed alphabetically. To add a project to this list, read the content guide before submitting a change. More information.
As an administrator, you have to install GPU drivers from the corresponding
hardware vendor on the nodes and run the corresponding device plugin from the
GPU vendor. Here are some links to vendors' instructions:
Once you have installed the plugin, your cluster exposes a custom schedulable resource such as amd.com/gpu or nvidia.com/gpu.
You can consume these GPUs from your containers by requesting
the custom GPU resource, the same way you request cpu or memory.
However, there are some limitations in how you specify the resource
requirements for custom devices.
GPUs are only supposed to be specified in the limits section, which means:
You can specify GPU limits without specifying requests, because
Kubernetes will use the limit as the request value by default.
You can specify GPU in both limits and requests but these two values
must be equal.
You cannot specify GPU requests without specifying limits.
Here's an example manifest for a Pod that requests a GPU:
If different nodes in your cluster have different types of GPUs, then you
can use Node Labels and Node Selectors
to schedule pods to appropriate nodes.
For example:
# Label your nodes with the accelerator type they have.kubectl label nodes node1 accelerator=example-gpu-x100
kubectl label nodes node2 accelerator=other-gpu-k915
That label key accelerator is just an example; you can use
a different label key if you prefer.
Automatic node labelling
If you're using AMD GPU devices, you can deploy
Node Labeller.
Node Labeller is a controller that automatically
labels your nodes with GPU device properties.