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instance of the template had one parameter, and that parameter was also used in the Job's name. However, [names](/docs/concepts/overview/working-with-objects/names/#names) are restricted to contain only certain characters. This slightly more complex example uses the [Jinja template language](https://palletsprojects.com/p/jinja/) 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`: ```liquid {% 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: ```shell alias render\_template='python -c "from jinja2 import Template; import sys; print(Template(sys.stdin.read()).render());"' ``` Use `render\_template` to convert the parameters and template into a single YAML file containing Kubernetes manifests: ```shell # This requires the alias you defined earlier cat job.yaml.jinja2 | render\_template > jobs.yaml ``` You can view `jobs.yaml` to verify that the `render\_template` script worked correctly. Once you are happy that `render\_template` is working how you intend, you can pipe its output into `kubectl`: ```shell cat job.yaml.jinja2 | render\_template | kubectl apply -f - ``` Kubernetes accepts and runs the Jobs you created. ### Clean up {#cleanup-2} ```shell # Remove the Jobs you created # Your cluster automatically cleans up their Pods kubectl 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 {{< glossary\_tooltip text="labels" term\_id="label" >}} 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.
| null |
./vendor/kubernetes/content/en/docs/tasks/job/parallel-processing-expansion.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/job/parallel-processing-expansion.md
|
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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](#create-jobs-based-on-a-template) 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](/docs/concepts/overview/working-with-objects/common-labels/#labels) that you can use if you wish. {{< /note >}} ## 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 {{< glossary\_tooltip text="resource quota" term\_id="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](/docs/concepts/workloads/controllers/job/#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](/docs/concepts/architecture/controller/) to manage Job objects automatically.
| null |
./vendor/kubernetes/content/en/docs/tasks/job/parallel-processing-expansion.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/job/parallel-processing-expansion.md
|
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] | 0.182931
|
This guide demonstrates how to install and write extensions for [kubectl](/docs/reference/kubectl/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`. ## {{% heading "prerequisites" %}} 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](https://krew.dev/). Krew is a plugin manager maintained by the Kubernetes SIG CLI community. {{< caution >}} Kubectl plugins available via the Krew [plugin index](https://krew.sigs.k8s.io/plugins/) 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. {{< /caution >}} ### 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](https://krew.dev/) to discover and install `kubectl` plugins from a community-curated [plugin index](https://krew.sigs.k8s.io/plugins/). #### Create plugins `kubectl` allows plugins to add custom create commands of the shape `kubectl create something` by providing a `kubectl-create-something` binary in the `PATH`. #### Limitations It is currently not possible to create plugins that overwrite existing `kubectl` commands or extend commands other than `create`. 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 attach vm` by naming your plugin `kubectl-attach-vm` 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 ```bash #!/bin/bash # optional argument handling if [[ "$1" == "version" ]] then echo "1.0.0" exit 0 fi # optional argument handling if [[ "$1" == "config" ]] then echo "$KUBECONFIG" exit 0 fi echo "I am a plugin named kubectl-foo" ``` ### Using a plugin To use a plugin, make the plugin executable: ```shell sudo chmod +x ./kubectl-foo ``` and place it anywhere in your `PATH`: ```shell sudo mv ./kubectl-foo /usr/local/bin ``` You may now invoke your plugin as a `kubectl` command: ```shell kubectl foo ``` ``` I am a plugin named kubectl-foo ``` All args and flags are passed as-is to the executable: ```shell kubectl foo version ``` ``` 1.0.0 ``` All environment variables are
| null |
./vendor/kubernetes/content/en/docs/tasks/extend-kubectl/kubectl-plugins.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/extend-kubectl/kubectl-plugins.md
|
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|
```shell sudo mv ./kubectl-foo /usr/local/bin ``` You may now invoke your plugin as a `kubectl` command: ```shell kubectl foo ``` ``` I am a plugin named kubectl-foo ``` All args and flags are passed as-is to the executable: ```shell kubectl foo version ``` ``` 1.0.0 ``` All environment variables are also passed as-is to the executable: ```bash export KUBECONFIG=~/.kube/config kubectl foo config ``` ``` /home//.kube/config ``` ```shell KUBECONFIG=/etc/kube/config kubectl foo config ``` ``` /etc/kube/config ``` 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. {{< /note >}} kubectl plugins must parse and validate all of the arguments passed to them. See [using the command line runtime package](#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: ```bash # create a plugin echo -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 $PATH sudo mv ./kubectl-foo-bar-baz /usr/local/bin # check that kubectl recognizes your plugin kubectl 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: ```bash # create a plugin containing an underscore in its filename echo -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 $PATH sudo mv ./kubectl-foo\_bar /usr/local/bin # You can now invoke your plugin via kubectl: kubectl foo-bar
| null |
./vendor/kubernetes/content/en/docs/tasks/extend-kubectl/kubectl-plugins.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/extend-kubectl/kubectl-plugins.md
|
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] | 0.028891
|
create a plugin containing an underscore in its filename echo -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 $PATH sudo 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 (`\_`): ```bash # You can invoke your custom command with a dash kubectl foo-bar ``` ``` I am a plugin with a dash in my name ``` ```bash # You can also invoke your custom command with an underscore kubectl 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: ```bash 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: ```bash # for a given kubectl command, the plugin with the longest possible filename will always be preferred kubectl foo bar baz ``` ``` Plugin kubectl-foo-bar-baz is executed ``` ```bash kubectl foo bar ``` ``` Plugin kubectl-foo-bar is executed ``` ```bash kubectl foo bar baz buz ``` ``` Plugin kubectl-foo-bar-baz is executed, with "buz" as its first argument ``` ```bash 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: ```bash ls ./plugin\_command\_tree ``` ``` kubectl-parent kubectl-parent-subcommand kubectl-parent-subcommand-subsubcommand ``` ### Checking for plugin warnings 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. ```bash 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
| null |
./vendor/kubernetes/content/en/docs/tasks/extend-kubectl/kubectl-plugins.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/extend-kubectl/kubectl-plugins.md
|
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] | 0.057034
|
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](https://github.com/kubernetes/cli-runtime) utility libraries. These libraries provide helpers for parsing or updating a user's [kubeconfig](/docs/concepts/configuration/organize-cluster-access-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](https://github.com/kubernetes/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 {#distributing-krew} [Krew](https://krew.dev/) 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](https://krew.sigs.k8s.io/plugins/) so that other people can discover your plugin and install it. ### Native / platform specific package management {#distributing-native} 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 {#distributing-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. ## {{% heading "whatsnext" %}} \* Check the Sample CLI Plugin repository for a [detailed example](https://github.com/kubernetes/sample-cli-plugin) of a plugin written in Go. In case of any questions, feel free to reach out to the [SIG CLI team](https://github.com/kubernetes/community/tree/master/sig-cli). \* Read about [Krew](https://krew.dev/), a package manager for kubectl plugins.
| null |
./vendor/kubernetes/content/en/docs/tasks/extend-kubectl/kubectl-plugins.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/extend-kubectl/kubectl-plugins.md
|
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|
`kubectl` supports using the [Kustomize object management tool](/docs/tasks/manage-kubernetes-objects/kustomization/) 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`. ## {{% heading "prerequisites" %}} {{< include "task-tutorial-prereqs.md" >}} ## Create a Secret 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`. {{< note >}} The `stringData` field for a Secret does not work well with server-side apply. {{< /note >}} ### Create the kustomization file {{< tabs name="Secret data" >}} {{< tab name="Literals" codelang="yaml" >}} secretGenerator: - name: database-creds literals: - username=admin - password=1f2d1e2e67df {{< /tab >}} {{% tab name="Files" %}} 1. Store the credentials in files. The filenames are the keys of the secret: ```shell echo -n 'admin' > ./username.txt echo -n '1f2d1e2e67df' > ./password.txt ``` The `-n` flag ensures that there's no newline character at the end of your files. 1. Create the `kustomization.yaml` file: ```yaml secretGenerator: - name: database-creds files: - username.txt - password.txt ``` {{% /tab %}} {{% tab name=".env files" %}} 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: ```yaml secretGenerator: - name: db-user-pass envs: - .env.secret ``` {{% /tab %}} {{< /tabs >}} In all cases, you don't need to encode the values in base64. 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: ```shell kubectl apply -k ``` 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, ```shell kubectl get -k -o jsonpath='{.data}' ``` The output is similar to: ``` { "password": "MWYyZDFlMmU2N2Rm", "username": "YWRtaW4=" } ``` ``` echo 'MWYyZDFlMmU2N2Rm' | base64 --decode ``` The output is similar to: ``` 1f2d1e2e67df ``` For more information, refer to [Managing Secrets using kubectl](/docs/tasks/configmap-secret/managing-secret-using-kubectl/#verify-the-secret) and [Declarative Management of Kubernetes Objects Using Kustomize](/docs/tasks/manage-kubernetes-objects/kustomization/). ## Edit a Secret {#edit-secret} 1. In your `kustomization.yaml` file, modify the data, such as the `password`. 1. Apply the directory that contains the kustomization file: ```shell kubectl apply -k ``` 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. ## Clean up To delete a Secret, use `kubectl`: ```shell kubectl delete secret db-user-pass ``` ## {{% heading "whatsnext" %}} - Read more about the [Secret concept](/docs/concepts/configuration/secret/) - Learn how to [manage Secrets using kubectl](/docs/tasks/configmap-secret/managing-secret-using-kubectl/) - Learn how to [manage Secrets using config file](/docs/tasks/configmap-secret/managing-secret-using-config-file/)
| null |
./vendor/kubernetes/content/en/docs/tasks/configmap-secret/managing-secret-using-kustomize.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/configmap-secret/managing-secret-using-kustomize.md
|
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|
## {{% heading "prerequisites" %}} {{< include "task-tutorial-prereqs.md" >}} ## Create the Secret {#create-the-config-file} You can define the `Secret` object in a manifest first, in JSON or YAML format, and then create that object. The [Secret](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#secret-v1-core) 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. 1. Convert the strings to base64: ```shell echo -n 'admin' | base64 echo -n '1f2d1e2e67df' | base64 ``` {{< 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. {{< /note >}} The output is similar to: ``` YWRtaW4= MWYyZDFlMmU2N2Rm ``` 1. Create the manifest: ```yaml apiVersion: v1 kind: Secret metadata: name: mysecret type: Opaque data: username: YWRtaW4= password: MWYyZDFlMmU2N2Rm ``` Note that the name of a Secret object must be a valid [DNS subdomain name](/docs/concepts/overview/working-with-objects/names#dns-subdomain-names). 1. Create the Secret using [`kubectl apply`](/docs/reference/generated/kubectl/kubectl-commands#apply): ```shell kubectl apply -f ./secret.yaml ``` The output is similar to: ``` secret/mysecret created ``` To verify that the Secret was created and to decode the Secret data, refer to [Managing Secrets using kubectl](/docs/tasks/configmap-secret/managing-secret-using-kubectl/#verify-the-secret). ### Specify unencoded data when creating a Secret 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: ```yaml apiUrl: "https://my.api.com/api/v1" username: "" password: "" ``` You could store this in a Secret using the following definition: ```yaml apiVersion: v1 kind: Secret metadata: name: mysecret type: Opaque stringData: config.yaml: | apiUrl: "https://my.api.com/api/v1" username: password: ``` {{< note >}} The `stringData` field for a Secret does not work well with server-side apply. {{< /note >}} When you retrieve the Secret data, the command returns the encoded values, and not the plaintext values you provided in `stringData`. For example, if you run the following command: ```shell kubectl get secret mysecret -o yaml ``` The output is similar to: ```yaml apiVersion: v1 data: config.yaml: YXBpVXJsOiAiaHR0cHM6Ly9teS5hcGkuY29tL2FwaS92MSIKdXNlcm5hbWU6IHt7dXNlcm5hbWV9fQpwYXNzd29yZDoge3twYXNzd29yZH19 kind: Secret metadata: creationTimestamp: 2018-11-15T20:40:59Z name: mysecret namespace: default resourceVersion: "7225" uid: c280ad2e-e916-11e8-98f2-025000000001 type: Opaque ``` ### Specify both `data` and `stringData` If you specify a field in both `data` and `stringData`, the value from `stringData` is used. For example, if you define the following Secret: ```yaml apiVersion: v1 kind: Secret metadata: name: mysecret type: Opaque data: username: YWRtaW4= stringData: username: administrator ``` {{< note >}} The `stringData` field for a Secret does not work well with server-side apply. {{< /note >}} The `Secret` object is created as follows: ```yaml apiVersion: v1 data: username: YWRtaW5pc3RyYXRvcg== kind: Secret metadata: creationTimestamp: 2018-11-15T20:46:46Z name: mysecret namespace: default resourceVersion: "7579" uid: 91460ecb-e917-11e8-98f2-025000000001 type: Opaque ``` `YWRtaW5pc3RyYXRvcg==` decodes to `administrator`. ## Edit
| null |
./vendor/kubernetes/content/en/docs/tasks/configmap-secret/managing-secret-using-config-file.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/configmap-secret/managing-secret-using-config-file.md
|
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] | 0.105959
|
`stringData` field for a Secret does not work well with server-side apply. {{< /note >}} The `Secret` object is created as follows: ```yaml apiVersion: v1 data: username: YWRtaW5pc3RyYXRvcg== kind: Secret metadata: creationTimestamp: 2018-11-15T20:46:46Z name: mysecret namespace: default resourceVersion: "7579" uid: 91460ecb-e917-11e8-98f2-025000000001 type: Opaque ``` `YWRtaW5pc3RyYXRvcg==` decodes to `administrator`. ## Edit a Secret {#edit-secret} 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](/docs/concepts/configuration/secret/#secret-immutable). For example, if you want to change the password from the previous example to `birdsarentreal`, do the following: 1. Encode the new password string: ```shell echo -n 'birdsarentreal' | base64 ``` The output is similar to: ``` YmlyZHNhcmVudHJlYWw= ``` 1. Update the `data` field with your new password string: ```yaml apiVersion: v1 kind: Secret metadata: name: mysecret type: Opaque data: username: YWRtaW4= password: YmlyZHNhcmVudHJlYWw= ``` 1. Apply the manifest to your cluster: ```shell kubectl apply -f ./secret.yaml ``` The output is similar to: ``` secret/mysecret configured ``` 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](/docs/reference/using-api/server-side-apply/) instead. ## Clean up To delete the Secret you have created: ```shell kubectl delete secret mysecret ``` ## {{% heading "whatsnext" %}} - Read more about the [Secret concept](/docs/concepts/configuration/secret/) - Learn how to [manage Secrets using kubectl](/docs/tasks/configmap-secret/managing-secret-using-kubectl/) - Learn how to [manage Secrets using kustomize](/docs/tasks/configmap-secret/managing-secret-using-kustomize/)
| null |
./vendor/kubernetes/content/en/docs/tasks/configmap-secret/managing-secret-using-config-file.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/configmap-secret/managing-secret-using-config-file.md
|
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|
This page shows you how to create, edit, manage, and delete Kubernetes {{}} using the `kubectl` command-line tool. ## {{% heading "prerequisites" %}} {{< include "task-tutorial-prereqs.md" >}} ## Create a Secret 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=`. ### Use raw data Run the following command: ```shell kubectl create secret generic db-user-pass \ --from-literal=username=admin \ --from-literal=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. {{< note >}} The `stringData` field for a Secret does not work well with server-side apply. {{< /note >}} ### Use source files 1. Store the credentials in files: ```shell echo -n 'admin' > ./username.txt echo -n 'S!B\\*d$zDsb=' > ./password.txt ``` The `-n` flag ensures that the generated files do not have an extra newline character at the end of the text. This is important because when `kubectl` reads a file and encodes the content into a base64 string, the extra newline character gets encoded too. You do not need to escape special characters in strings that you include in a file. 1. Pass the file paths in the `kubectl` command: ```shell kubectl create secret generic db-user-pass \ --from-file=./username.txt \ --from-file=./password.txt ``` The default key name is the file name. You can optionally set the key name using `--from-file=[key=]source`. For example: ```shell kubectl create secret generic db-user-pass \ --from-file=username=./username.txt \ --from-file=password=./password.txt ``` With either method, the output is similar to: ``` secret/db-user-pass created ``` ### Verify the Secret {#verify-the-secret} Check that the Secret was created: ```shell kubectl get secrets ``` The output is similar to: ``` NAME TYPE DATA AGE db-user-pass Opaque 2 51s ``` View the details of the Secret: ```shell kubectl describe secret db-user-pass ``` The output is similar to: ``` Name: db-user-pass Namespace: default Labels: Annotations: Type: Opaque Data ==== password: 12 bytes username: 5 bytes ``` The commands `kubectl get` and `kubectl describe` avoid showing the contents of a `Secret` by default. This is to protect the `Secret` from being exposed accidentally, or from being stored in a terminal log. ### Decode the Secret {#decoding-secret} 1. View the contents of the Secret you created: ```shell kubectl get secret db-user-pass -o jsonpath='{.data}' ``` The output is similar to: ```json { "password": "UyFCXCpkJHpEc2I9", "username": "YWRtaW4=" } ``` 1. Decode the `password` data: ```shell echo 'UyFCXCpkJHpEc2I9' | base64 --decode ``` The output is similar to: ``` S!B\\*d$zDsb= ``` {{< 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. {{< /caution >}} ```shell kubectl get secret db-user-pass -o jsonpath='{.data.password}' | base64 --decode ``` ## Edit a Secret {#edit-secret} You can edit an existing `Secret` object unless it is [immutable](/docs/concepts/configuration/secret/#secret-immutable). To edit a Secret, run the following command: ```shell kubectl edit secrets ``` 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: ```yaml # Please edit the
| null |
./vendor/kubernetes/content/en/docs/tasks/configmap-secret/managing-secret-using-kubectl.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/configmap-secret/managing-secret-using-kubectl.md
|
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] | 0.055965
|
existing `Secret` object unless it is [immutable](/docs/concepts/configuration/secret/#secret-immutable). To edit a Secret, run the following command: ```shell kubectl edit secrets ``` 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: ```yaml # 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: v1 data: password: UyFCXCpkJHpEc2I9 username: YWRtaW4= kind: Secret metadata: creationTimestamp: "2022-06-28T17:44:13Z" name: db-user-pass namespace: default resourceVersion: "12708504" uid: 91becd59-78fa-4c85-823f-6d44436242ac type: Opaque ``` ## Clean up To delete a Secret, run the following command: ```shell kubectl delete secret db-user-pass ``` ## {{% heading "whatsnext" %}} - Read more about the [Secret concept](/docs/concepts/configuration/secret/) - Learn how to [manage Secrets using config file](/docs/tasks/configmap-secret/managing-secret-using-config-file/) - Learn how to [manage Secrets using kustomize](/docs/tasks/configmap-secret/managing-secret-using-kustomize/)
| null |
./vendor/kubernetes/content/en/docs/tasks/configmap-secret/managing-secret-using-kubectl.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/configmap-secret/managing-secret-using-kubectl.md
|
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|
This page shows how to define commands and arguments when you run a container in a {{< glossary\_tooltip term\_id="pod" >}}. ## {{% heading "prerequisites" %}} {{< include "task-tutorial-prereqs.md" >}} {{< version-check >}} ## 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`, and the `args` field corresponds to `CMD` in some container runtimes. {{< /note >}} In this exercise, you create a Pod that runs one container. The configuration file for the Pod defines a command and two arguments: {{% code\_sample file="pods/commands.yaml" %}} 1. Create a Pod based on the YAML configuration file: ```shell kubectl apply -f https://k8s.io/examples/pods/commands.yaml ``` 1. List the running Pods: ```shell kubectl get pods ``` The output shows that the container that ran in the command-demo Pod has completed. 1. To see the output of the command that ran in the container, view the logs from the Pod: ```shell 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: ```yaml env: - name: MESSAGE value: "hello world" command: ["/bin/echo"] args: ["$(MESSAGE)"] ``` This means you can define an argument for a Pod using any of the techniques available for defining environment variables, including [ConfigMaps](/docs/tasks/configure-pod-container/configure-pod-configmap/) and [Secrets](/docs/concepts/configuration/secret/). {{< note >}} The environment variable appears in parentheses, `"$(VAR)"`. This is required for the variable to be expanded in the `command` or `args` field. {{< /note >}} ## 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: ```shell command: ["/bin/sh"] args: ["-c", "while true; do echo hello; sleep 10;done"] ``` ## {{% heading "whatsnext" %}} \* Learn more about [configuring pods and containers](/docs/tasks/). \* Learn more about [running commands in a container](/docs/tasks/debug/debug-application/get-shell-running-container/). \* See [Container](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#container-v1-core).
| null |
./vendor/kubernetes/content/en/docs/tasks/inject-data-application/define-command-argument-container.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/inject-data-application/define-command-argument-container.md
|
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] | 0.076913
|
This page shows how to securely inject sensitive data, such as passwords and encryption keys, into Pods. ## {{% heading "prerequisites" %}} {{< include "task-tutorial-prereqs.md" >}} ### 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$vdg7Jb`. First, use a base64 encoding tool to convert your username and password to a base64 representation. Here's an example using the commonly available base64 program: ```shell echo -n 'my-app' | base64 echo -n '39528$vdg7Jb' | base64 ``` The output shows that the base-64 representation of your username is `bXktYXBw`, and the base-64 representation of your password is `Mzk1MjgkdmRnN0pi`. {{< caution >}} Use a local tool trusted by your OS to decrease the security risks of external tools. {{< /caution >}} ## Create a Secret Here is a configuration file you can use to create a Secret that holds your username and password: {{% code\_sample file="pods/inject/secret.yaml" %}} 1. Create the Secret ```shell kubectl apply -f https://k8s.io/examples/pods/inject/secret.yaml ``` 1. View information about the Secret: ```shell kubectl get secret test-secret ``` Output: ``` NAME TYPE DATA AGE test-secret Opaque 2 1m ``` 1. View more detailed information about the Secret: ```shell kubectl describe secret test-secret ``` Output: ``` Name: test-secret Namespace: default Labels: Annotations: Type: Opaque Data ==== password: 13 bytes username: 7 bytes ``` ### Create a Secret directly with kubectl If you want to skip the Base64 encoding step, you can create the same Secret using the `kubectl create secret` command. For example: ```shell kubectl create secret generic test-secret --from-literal='username=my-app' --from-literal='password=39528$vdg7Jb' ``` This is more convenient. The detailed approach shown earlier runs through each step explicitly to demonstrate what is happening. ## Create a Pod that has access to the secret data through a Volume Here is a configuration file you can use to create a Pod: {{% code\_sample file="pods/inject/secret-pod.yaml" %}} 1. Create the Pod: ```shell kubectl apply -f https://k8s.io/examples/pods/inject/secret-pod.yaml ``` 1. Verify that your Pod is running: ```shell kubectl get pod secret-test-pod ``` Output: ``` NAME READY STATUS RESTARTS AGE secret-test-pod 1/1 Running 0 42m ``` 1. Get a shell into the Container that is running in your Pod: ```shell kubectl exec -i -t secret-test-pod -- /bin/bash ``` 1. The secret data is exposed to the Container through a Volume mounted under `/etc/secret-volume`. In your shell, list the files in the `/etc/secret-volume` directory: ```shell # Run this in the shell inside the container ls /etc/secret-volume ``` The output shows two files, one for each piece of secret data: ``` password username ``` 1. In your shell, display the contents of the `username` and `password` files: ```shell # Run this in the shell inside the container echo "$( 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: ```yaml apiVersion: v1 kind: Pod metadata: name: mypod spec: containers: - name: mypod image: redis volumeMounts: - name: foo mountPath: "/etc/foo" readOnly: true volumes: - name: foo secret: secretName: mysecret items: - key: username path: my-group/my-username ``` When you deploy this Pod, the following happens: - 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
| null |
./vendor/kubernetes/content/en/docs/tasks/inject-data-application/distribute-credentials-secure.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/inject-data-application/distribute-credentials-secure.md
|
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] | 0.1005
|
- name: foo mountPath: "/etc/foo" readOnly: true volumes: - name: foo secret: secretName: mysecret items: - key: username path: my-group/my-username ``` When you deploy this Pod, the following happens: - 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: ```yaml apiVersion: v1 kind: Pod metadata: name: mypod spec: containers: - name: mypod image: redis volumeMounts: - name: foo mountPath: "/etc/foo" volumes: - name: foo secret: secretName: mysecret defaultMode: 0400 ``` 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. {{< /note >}} ## 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: ```shell kubectl create secret generic backend-user --from-literal=backend-username='backend-admin' ``` - Assign the `backend-username` value defined in the Secret to the `SECRET\_USERNAME` environment variable in the Pod specification. {{% code\_sample file="pods/inject/pod-single-secret-env-variable.yaml" %}} - Create the Pod: ```shell kubectl create -f https://k8s.io/examples/pods/inject/pod-single-secret-env-variable.yaml ``` - In your shell, display the content of `SECRET\_USERNAME` container environment variable. ```shell kubectl exec -i -t env-single-secret -- /bin/sh -c 'echo $SECRET\_USERNAME' ``` The output is similar to: ``` backend-admin ``` ### Define container environment variables with data from multiple Secrets - As with the previous example, create the Secrets first. ```shell kubectl create secret generic backend-user --from-literal=backend-username='backend-admin' kubectl create secret generic db-user --from-literal=db-username='db-admin' ``` - Define the environment variables in the Pod specification. {{% code\_sample file="pods/inject/pod-multiple-secret-env-variable.yaml" %}} - Create the Pod: ```shell kubectl create -f https://k8s.io/examples/pods/inject/pod-multiple-secret-env-variable.yaml ``` - In your shell, display the container environment variables. ```shell kubectl exec -i -t envvars-multiple-secrets -- /bin/sh -c 'env | grep \_USERNAME' ``` The output is similar to: ``` DB\_USERNAME=db-admin BACKEND\_USERNAME=backend-admin ``` ## Configure all key-value pairs in a Secret as container environment variables {{< note >}} This functionality is available in Kubernetes v1.6 and later. {{< /note >}} - Create a Secret containing multiple key-value pairs ```shell kubectl create secret generic test-secret --from-literal=username='my-app' --from-literal=password='39528$vdg7Jb' ``` - 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. {{% code\_sample file="pods/inject/pod-secret-envFrom.yaml" %}} -
| null |
./vendor/kubernetes/content/en/docs/tasks/inject-data-application/distribute-credentials-secure.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/inject-data-application/distribute-credentials-secure.md
|
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|
>}} - Create a Secret containing multiple key-value pairs ```shell kubectl create secret generic test-secret --from-literal=username='my-app' --from-literal=password='39528$vdg7Jb' ``` - 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. {{% code\_sample file="pods/inject/pod-secret-envFrom.yaml" %}} - Create the Pod: ```shell kubectl create -f https://k8s.io/examples/pods/inject/pod-secret-envFrom.yaml ``` - In your shell, display `username` and `password` container environment variables. ```shell kubectl exec -i -t envfrom-secret -- /bin/sh -c 'echo "username: $username\npassword: $password\n"' ``` The output is similar to: ``` username: my-app password: 39528$vdg7Jb ``` ## Example: Provide prod/test credentials to Pods using Secrets {#provide-prod-test-creds} This example illustrates a Pod which consumes a secret containing production credentials and another Pod which consumes a secret with test environment credentials. 1. Create a secret for prod environment credentials: ```shell kubectl create secret generic prod-db-secret --from-literal=username=produser --from-literal=password=Y4nys7f11 ``` The output is similar to: ``` secret "prod-db-secret" created ``` 1. Create a secret for test environment credentials. ```shell kubectl create secret generic test-db-secret --from-literal=username=testuser --from-literal=password=iluvtests ``` The output is similar to: ``` secret "test-db-secret" created ``` {{< note >}} Special characters such as `$`, `\`, `\*`, `=`, and `!` will be interpreted by your [shell](https://en.wikipedia.org/wiki/Shell\_(computing)) and require escaping. In most shells, the easiest way to escape the password is to surround it with single quotes (`'`). For example, if your actual password is `S!B\\*d$zDsb=`, you should execute the command as follows: ```shell kubectl create secret generic dev-db-secret --from-literal=username=devuser --from-literal=password='S!B\\*d$zDsb=' ``` You do not need to escape special characters in passwords from files (`--from-file`). {{< /note >}} 1. Create the Pod manifests: ```shell cat < pod.yaml apiVersion: v1 kind: List items: - kind: Pod apiVersion: v1 metadata: name: prod-db-client-pod labels: name: prod-db-client spec: volumes: - name: secret-volume secret: secretName: prod-db-secret containers: - name: db-client-container image: myClientImage volumeMounts: - name: secret-volume readOnly: true mountPath: "/etc/secret-volume" - kind: Pod apiVersion: v1 metadata: name: test-db-client-pod labels: name: test-db-client spec: volumes: - name: secret-volume secret: secretName: test-db-secret containers: - name: db-client-container image: myClientImage volumeMounts: - name: secret-volume readOnly: true mountPath: "/etc/secret-volume" EOF ``` {{< note >}} How the specs for the two Pods differ only in one field; this facilitates creating Pods with different capabilities from a common Pod template. {{< /note >}} 1. Apply all those objects on the API server by running: ```shell kubectl create -f pod.yaml ``` Both containers will have the following files present on their filesystems with the values for each container's environment: ``` /etc/secret-volume/username /etc/secret-volume/password ``` You could further simplify the base Pod specification by using two service accounts: 1. `prod-user` with the `prod-db-secret` 1. `test-user` with the `test-db-secret` The Pod specification is shortened to: ```yaml apiVersion: v1 kind: Pod metadata: name: prod-db-client-pod labels: name: prod-db-client spec: serviceAccount: prod-db-client containers: - name: db-client-container image: myClientImage ``` ### References - [Secret](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#secret-v1-core) - [Volume](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#volume-v1-core) - [Pod](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#pod-v1-core) ## {{% heading "whatsnext" %}} - Learn more about [Secrets](/docs/concepts/configuration/secret/). - Learn about [Volumes](/docs/concepts/storage/volumes/).
| null |
./vendor/kubernetes/content/en/docs/tasks/inject-data-application/distribute-credentials-secure.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/inject-data-application/distribute-credentials-secure.md
|
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|
This page shows how to define environment variables for a container in a Kubernetes Pod. ## {{% heading "prerequisites" %}} {{< include "task-tutorial-prereqs.md" >}} ## Define an environment variable for a container 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. You can read more about [ConfigMap](/docs/tasks/configure-pod-container/configure-pod-configmap/#configure-all-key-value-pairs-in-a-configmap-as-container-environment-variables) and [Secret](/docs/tasks/inject-data-application/distribute-credentials-secure/#configure-all-key-value-pairs-in-a-secret-as-container-environment-variables). This page explains how to use `env`. 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: {{% code\_sample file="pods/inject/envars.yaml" %}} 1. Create a Pod based on that manifest: ```shell kubectl apply -f https://k8s.io/examples/pods/inject/envars.yaml ``` 1. List the running Pods: ```shell kubectl get pods -l purpose=demonstrate-envars ``` The output is similar to: ``` NAME READY STATUS RESTARTS AGE envar-demo 1/1 Running 0 9s ``` 1. List the Pod's container environment variables: ```shell 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 >}} {{< 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. {{< /note >}} ## 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. Environment variable names may consist of any [printable ASCII characters](https://www.ascii-code.com/characters/printable-characters) except '='. ```yaml apiVersion: v1 kind: Pod metadata: name: print-greeting spec: containers: - name: env-print-demo image: bash env: - name: GREETING value: "Warm greetings to" - name: HONORIFIC value: "The Most Honorable" - name: NAME value: "Kubernetes" - name: MESSAGE value: "$(GREETING) $(HONORIFIC) $(NAME)" command: ["echo"] args: ["$(MESSAGE)"] ``` Upon creation, the command `echo Warm greetings to The Most Honorable Kubernetes` is run on the container. ## {{% heading "whatsnext" %}} \* Learn more about [environment variables](/docs/tasks/inject-data-application/environment-variable-expose-pod-information/). \* Learn about [using secrets as environment variables](/docs/concepts/configuration/secret/#using-secrets-as-environment-variables). \* See [EnvVarSource](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#envvarsource-v1-core).
| null |
./vendor/kubernetes/content/en/docs/tasks/inject-data-application/define-environment-variable-container.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/inject-data-application/define-environment-variable-container.md
|
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|
This page shows how a Pod can use a [`downwardAPI` volume](/docs/concepts/storage/volumes/#downwardapi), 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: \* [Environment variables](/docs/tasks/inject-data-application/environment-variable-expose-pod-information/) \* Volume files, as explained in this task Together, these two ways of exposing Pod and container fields are called the \_downward API\_. ## {{% heading "prerequisites" %}} {{< include "task-tutorial-prereqs.md" >}} ## Store Pod fields 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: {{% code\_sample file="pods/inject/dapi-volume.yaml" %}} 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. {{< /note >}} Create the Pod: ```shell kubectl apply -f https://k8s.io/examples/pods/inject/dapi-volume.yaml ``` Verify that the container in the Pod is running: ```shell kubectl get pods ``` View the container's logs: ```shell kubectl logs kubernetes-downwardapi-volume-example ``` The output shows the contents of the `labels` file and the `annotations` file: ``` cluster="test-cluster1" rack="rack-22" zone="us-est-coast" build="two" builder="john-doe" ``` Get a shell into the container that is running in your Pod: ```shell kubectl exec -it kubernetes-downwardapi-volume-example -- sh ``` In your shell, view the `labels` file: ```shell /# cat /etc/podinfo/labels ``` The output shows that all of the Pod's labels have been written to the `labels` file: ```shell cluster="test-cluster1" rack="rack-22" zone="us-est-coast" ``` Similarly, view the `annotations` file: ```shell /# cat /etc/podinfo/annotations ``` View the files in the `/etc/podinfo` directory: ```shell /# ls -laR /etc/podinfo ``` 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)](http://man7.org/linux/man-pages/man2/rename.2.html). {{< note >}} A container using Downward API as a [subPath](/docs/concepts/storage/volumes/#using-subpath) volume mount will not receive Downward API updates. {{< /note >}} Exit the shell: ```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](/docs/reference/kubernetes-api/workload-resources/pod-v1/#Container) rather than from the Pod overall. Here is a manifest for a Pod that again has just one container: {{% code\_sample file="pods/inject/dapi-volume-resources.yaml" %}} In the manifest, you can see that the Pod has a [`downwardAPI` volume](/docs/concepts/storage/volumes/#downwardapi), 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
| null |
./vendor/kubernetes/content/en/docs/tasks/inject-data-application/downward-api-volume-expose-pod-information.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/inject-data-application/downward-api-volume-expose-pod-information.md
|
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|
Pod that again has just one container: {{% code\_sample file="pods/inject/dapi-volume-resources.yaml" %}} In the manifest, you can see that the Pod has a [`downwardAPI` volume](/docs/concepts/storage/volumes/#downwardapi), 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. Create the Pod: ```shell kubectl apply -f https://k8s.io/examples/pods/inject/dapi-volume-resources.yaml ``` Get a shell into the container that is running in your Pod: ```shell kubectl exec -it kubernetes-downwardapi-volume-example-2 -- sh ``` In your shell, view the `cpu\_limit` file: ```shell # Run this in a shell inside the container cat /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](/docs/concepts/configuration/secret/). ## {{% heading "whatsnext" %}} \* Read the [`spec`](/docs/reference/kubernetes-api/workload-resources/pod-v1/#PodSpec) API definition for Pod. This includes the definition of Container (part of Pod). \* Read the list of [available fields](/docs/concepts/workloads/pods/downward-api/#available-fields) that you can expose using the downward API. Read about volumes in the legacy API reference: \* Check the [`Volume`](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#volume-v1-core) API definition which defines a generic volume in a Pod for containers to access. \* Check the [`DownwardAPIVolumeSource`](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#downwardapivolumesource-v1-core) API definition which defines a volume that contains Downward API information. \* Check the [`DownwardAPIVolumeFile`](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#downwardapivolumefile-v1-core) API definition which contains references to object or resource fields for populating a file in the Downward API volume. \* Check the [`ResourceFieldSelector`](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#resourcefieldselector-v1-core) API definition which specifies the container resources and their output format.
| null |
./vendor/kubernetes/content/en/docs/tasks/inject-data-application/downward-api-volume-expose-pod-information.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/inject-data-application/downward-api-volume-expose-pod-information.md
|
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] | 0.192131
|
This page shows how to define dependent environment variables for a container in a Kubernetes Pod. ## {{% heading "prerequisites" %}} {{< include "task-tutorial-prereqs.md" >}} ## 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 $(VAR\_NAME) in the `value` of `env` in the configuration file. In this exercise, you create a Pod that runs one container. The configuration file for the Pod defines a dependent environment variable with common usage defined. Here is the configuration manifest for the Pod: {{% code\_sample file="pods/inject/dependent-envars.yaml" %}} 1. Create a Pod based on that manifest: ```shell kubectl apply -f https://k8s.io/examples/pods/inject/dependent-envars.yaml ``` ``` pod/dependent-envars-demo created ``` 2. List the running Pods: ```shell kubectl get pods dependent-envars-demo ``` ``` NAME READY STATUS RESTARTS AGE dependent-envars-demo 1/1 Running 0 9s ``` 3. Check the logs for the container running in your Pod: ```shell kubectl logs pod/dependent-envars-demo ``` ``` UNCHANGED\_REFERENCE=$(PROTOCOL)://172.17.0.1:80 SERVICE\_ADDRESS=https://172.17.0.1:80 ESCAPED\_REFERENCE=$(PROTOCOL)://172.17.0.1:80 ``` As shown above, you have defined the correct dependency reference of `SERVICE\_ADDRESS`, bad dependency reference of `UNCHANGED\_REFERENCE` and skip dependent references of `ESCAPED\_REFERENCE`. When an environment variable is already defined when being referenced, the reference can be correctly resolved, such as in the `SERVICE\_ADDRESS` case. Note that order matters in the `env` list. An environment variable is not considered "defined" if it is specified further down the list. That is why `UNCHANGED\_REFERENCE` fails to resolve `$(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. ## {{% heading "whatsnext" %}} \* Learn more about [environment variables](/docs/tasks/inject-data-application/environment-variable-expose-pod-information/). \* See [EnvVarSource](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#envvarsource-v1-core).
| null |
./vendor/kubernetes/content/en/docs/tasks/inject-data-application/define-interdependent-environment-variables.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/inject-data-application/define-interdependent-environment-variables.md
|
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] | 0.08897
|
{{< feature-state feature\_gate\_name="EnvFiles" >}} This page show how to configure environment variables for containers in a Pod via file. ## {{% heading "prerequisites" %}} {{< include "task-tutorial-prereqs.md" >}} {{% version-check %}} ## How the design works In this exercise, you will create a Pod that sources environment variables from files, projecting these values into the running container. {{% code\_sample file="pods/inject/envars-file-container.yaml" %}} In this manifest, you can see the `initContainer` mounts an `emptyDir` volume and writes environment variables to a file within it, and the regular containers reference both the file and the environment variable key through the `fileKeyRef` field without needing to mount the volume. When `optional` field is set to false, the specified `key` in `fileKeyRef` must exist in the environment variables file. The volume will only be mounted to the container that writes to the file (`initContainer`), while the consumer container that consumes the environment variable will not have the volume mounted. The env file format adheres to the [kubernetes env file standard](/docs/tasks/inject-data-application/define-environment-variable-via-file/#env-file-syntax). During container initialization, the kubelet retrieves environment variables from specified files in the `emptyDir` volume and exposes them to the container. {{< note >}} All container types (initContainers, regular containers, sidecars containers, and ephemeral containers) support environment variable loading from files. While these environment variables can store sensitive information, `emptyDir` volumes don't provide the same protection mechanisms as dedicated Secret objects. Therefore, exposing confidential environment variables to containers through this feature is not considered a security best practice. {{< /note >}} Create the Pod: ```shell kubectl apply -f https://k8s.io/examples/pods/inject/envars-file-container.yaml ``` Verify that the container in the Pod is running: ```shell # If the new Pod isn't yet healthy, rerun this command a few times. kubectl get pods ``` Check container logs for environment variables: ```shell kubectl logs dapi-test-pod -c use-envfile | grep DB\_ADDRESS ``` The output shows the values of selected environment variables: ``` DB\_ADDRESS=address ``` ## Env file syntax {#env-file-syntax} The env file format used by Kubernetes is a well-defined subset of the environment variable semantics for POSIX-compliant bash. Any env file supported by Kubernetes will produce the same environment variables as when interpreted by a POSIX-compliant bash. However, POSIX-compliant bash supports some additional formats that Kubernetes does not accept. Example: ``` MY\_VAR='my-literal-value' ``` ### Rules \* Variable declaration: Use the form `VAR='value'`. Spaces surrounding `=` are ignored; leading spaces on a line are ignored; blank lines are ignored. \* Quoted values: Values must be enclosed in single quotes (`'`). \* The content inside single quotes is preserved literally. No escape-sequence processing, whitespace folding, or character interpretation is applied. \* Newlines inside single quotes are preserved (multi-line values are supported). \* Comments: Lines that begin with `#` are treated as comments and ignored. A `#` character inside a single-quoted value is not a comment. Examples: ``` # comment DB\_ADDRESS='address' MULTI='line1 line2' ``` ### Unsupported forms \* Unquoted values are \*\*prohibited\*\*: \* `VAR=value` — not supported. \* Double-quoted values are \*\*prohibited\*\*: \* `VAR="value"` — not supported. \* Multiple adjacent quoted strings are \*\*not\*\* supported: \* `VAR='val1''val2'` — not supported. \* Any form of interpolation, expansion, or concatenation is \*\*not\*\* supported: \* `VAR='a'$OTHER` or `VAR=${OTHER}` — not supported. The strict single-quote requirement ensures the value is taken literally by the kubelet when loading environment variables from files. ## {{% heading "whatsnext" %}} \* Learn more about [environment variables](/docs/tasks/inject-data-application/environment-variable-expose-pod-information/). \* Read [Defining Environment Variables for a Container](/docs/tasks/inject-data-application/define-environment-variable-container/) \* Read [Expose Pod Information to Containers Through Environment Variables](/docs/tasks/inject-data-application/environment-variable-expose-pod-information)
| null |
./vendor/kubernetes/content/en/docs/tasks/inject-data-application/define-environment-variable-via-file.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/inject-data-application/define-environment-variable-via-file.md
|
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] | 0.083121
|
[environment variables](/docs/tasks/inject-data-application/environment-variable-expose-pod-information/). \* Read [Defining Environment Variables for a Container](/docs/tasks/inject-data-application/define-environment-variable-container/) \* Read [Expose Pod Information to Containers Through Environment Variables](/docs/tasks/inject-data-application/environment-variable-expose-pod-information)
| null |
./vendor/kubernetes/content/en/docs/tasks/inject-data-application/define-environment-variable-via-file.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/inject-data-application/define-environment-variable-via-file.md
|
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] | 0.152211
|
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: \* \_Environment variables\_, as explained in this task \* [Volume files](/docs/tasks/inject-data-application/downward-api-volume-expose-pod-information/) 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. Read more about accessing Services [here](/docs/tutorials/services/connect-applications-service/#accessing-the-service). ## {{% heading "prerequisites" %}} {{< include "task-tutorial-prereqs.md" >}} ## 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. {{% code\_sample file="pods/inject/dapi-envars-pod.yaml" %}} 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. {{< /note >}} Create the Pod: ```shell kubectl apply -f https://k8s.io/examples/pods/inject/dapi-envars-pod.yaml ``` Verify that the container in the Pod is running: ```shell # If the new Pod isn't yet healthy, rerun this command a few times. kubectl get pods ``` View the container's logs: ```shell kubectl logs dapi-envars-fieldref ``` The output shows the values of selected environment variables: ``` minikube dapi-envars-fieldref default 172.17.0.4 default ``` 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: ```shell kubectl exec -it dapi-envars-fieldref -- sh ``` In your shell, view the environment variables: ```shell # Run this in a shell inside the container printenv ``` The output shows that certain environment variables have been assigned the values of Pod fields: ``` MY\_POD\_SERVICE\_ACCOUNT=default ... MY\_POD\_NAMESPACE=default MY\_POD\_IP=172.17.0.4 ... MY\_NODE\_NAME=minikube ... MY\_POD\_NAME=dapi-envars-fieldref ``` ## 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](/docs/reference/kubernetes-api/workload-resources/pod-v1/#Container) rather than from the Pod overall. Here is a manifest for another Pod that again has just one container: {{% code\_sample file="pods/inject/dapi-envars-container.yaml" %}} 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. Create the Pod: ```shell kubectl apply -f https://k8s.io/examples/pods/inject/dapi-envars-container.yaml ``` Verify that the container in the Pod is running: ```shell # If the new Pod isn't yet healthy, rerun this command a few times. kubectl get pods ``` View the container's logs: ```shell kubectl logs dapi-envars-resourcefieldref ``` The output shows the values of selected environment variables: ``` 1 1 33554432 67108864 ``` ## {{% heading "whatsnext" %}} \* Read [Defining Environment Variables
| null |
./vendor/kubernetes/content/en/docs/tasks/inject-data-application/environment-variable-expose-pod-information.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/inject-data-application/environment-variable-expose-pod-information.md
|
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] | 0.168041
|
the new Pod isn't yet healthy, rerun this command a few times. kubectl get pods ``` View the container's logs: ```shell kubectl logs dapi-envars-resourcefieldref ``` The output shows the values of selected environment variables: ``` 1 1 33554432 67108864 ``` ## {{% heading "whatsnext" %}} \* Read [Defining Environment Variables for a Container](/docs/tasks/inject-data-application/define-environment-variable-container/) \* Read the [`spec`](/docs/reference/kubernetes-api/workload-resources/pod-v1/#PodSpec) API definition for Pod. This includes the definition of Container (part of Pod). \* Read the list of [available fields](/docs/concepts/workloads/pods/downward-api/#available-fields) that you can expose using the downward API. Read about Pods, containers and environment variables in the legacy API reference: \* [PodSpec](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#podspec-v1-core) \* [Container](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#container-v1-core) \* [EnvVar](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#envvar-v1-core) \* [EnvVarSource](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#envvarsource-v1-core) \* [ObjectFieldSelector](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#objectfieldselector-v1-core) \* [ResourceFieldSelector](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#resourcefieldselector-v1-core)
| null |
./vendor/kubernetes/content/en/docs/tasks/inject-data-application/environment-variable-expose-pod-information.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/inject-data-application/environment-variable-expose-pod-information.md
|
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] | 0.145142
|
This page demonstrates how to build a basic {{< glossary\_tooltip text="DaemonSet" term\_id="daemonset" >}} that runs a Pod on every node in a Kubernetes cluster. It covers a simple use case of mounting a file from the host, logging its contents using an [init container](/docs/concepts/workloads/pods/init-containers/), and utilizing a pause container. ## {{% heading "prerequisites" %}} {{< include "task-tutorial-prereqs.md" >}} A Kubernetes cluster with at least two nodes (one control plane node and one worker node) to demonstrate the behavior of DaemonSets. ## Define the DaemonSet In this task, a basic DaemonSet is created which ensures that the copy of a Pod is scheduled on every node. The Pod will use an init container to read and log the contents of `/etc/machine-id` from the host, while the main container will be a `pause` container, which keeps the Pod running. {{% code\_sample file="application/basic-daemonset.yaml" %}} 1. Create a DaemonSet based on the (YAML) manifest: ```shell kubectl apply -f https://k8s.io/examples/application/basic-daemonset.yaml ``` 1. Once applied, you can verify that the DaemonSet is running a Pod on every node in the cluster: ```shell kubectl get pods -o wide ``` The output will list one Pod per node, similar to: ``` NAME READY STATUS RESTARTS AGE IP NODE example-daemonset-xxxxx 1/1 Running 0 5m x.x.x.x node-1 example-daemonset-yyyyy 1/1 Running 0 5m x.x.x.x node-2 ``` 1. You can inspect the contents of the logged `/etc/machine-id` file by checking the log directory mounted from the host: ```shell kubectl exec -- cat /var/log/machine-id.log ``` Where `` is the name of one of your Pods. ## {{% heading "cleanup" %}} To delete the DaemonSet, run this command: ```shell kubectl delete --cascade=foreground --ignore-not-found --now daemonsets/example-daemonset ``` This simple DaemonSet example introduces key components like init containers and host path volumes, which can be expanded upon for more advanced use cases. For more details refer to [DaemonSet](/docs/concepts/workloads/controllers/daemonset/). ## {{% heading "whatsnext" %}} \* See [Performing a rolling update on a DaemonSet](/docs/tasks/manage-daemon/update-daemon-set/) \* See [Creating a DaemonSet to adopt existing DaemonSet pods](/docs/concepts/workloads/controllers/daemonset/)
| null |
./vendor/kubernetes/content/en/docs/tasks/manage-daemon/create-daemon-set.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/manage-daemon/create-daemon-set.md
|
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] | 0.234004
|
This page demonstrates how can you run {{}} on only some {{}} as part of a {{}} ## {{% heading "prerequisites" %}} {{< include "task-tutorial-prereqs.md" >}} ## Running Pods on only some Nodes Imagine that you want to run a {{}}, 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. ```shell kubectl label nodes example-node-1 example-node-2 ssd=true ``` ### Step 2: Create the manifest Let's create a {{}} which will provision the daemon pods on the SSD labeled {{}} only. Next, use a `nodeSelector` to ensure that the DaemonSet only runs Pods on nodes with the `ssd` label set to `"true"`. {{% code\_sample file="controllers/daemonset-label-selector.yaml" %}} ### Step 3: Create the DaemonSet Create the DaemonSet from the manifest by using `kubectl create` or `kubectl apply` Let's label another node as `ssd=true`. ```shell 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. ```shell kubectl get pods -o wide ``` The output is similar to: ``` NAME READY STATUS RESTARTS AGE IP NODE 1/1 Running 0 13s ..... example-node-1 1/1 Running 0 13s ..... example-node-2 1/1 Running 0 5s ..... example-node-3 ```
| null |
./vendor/kubernetes/content/en/docs/tasks/manage-daemon/pods-some-nodes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/manage-daemon/pods-some-nodes.md
|
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|
This page shows how to perform a rolling update on a DaemonSet. ## {{% heading "prerequisites" %}} {{< include "task-tutorial-prereqs.md" >}} ## DaemonSet Update Strategy DaemonSet has two update strategy types: \* `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`. You may want to set [`.spec.updateStrategy.rollingUpdate.maxUnavailable`](/docs/reference/kubernetes-api/workload-resources/daemon-set-v1/#DaemonSetSpec) (default to 1), [`.spec.minReadySeconds`](/docs/reference/kubernetes-api/workload-resources/daemon-set-v1/#DaemonSetSpec) (default to 0) and [`.spec.updateStrategy.rollingUpdate.maxSurge`](/docs/reference/kubernetes-api/workload-resources/daemon-set-v1/#DaemonSetSpec) (defaults to 0) as well. ### Creating a DaemonSet with `RollingUpdate` update strategy This YAML file specifies a DaemonSet with an update strategy as 'RollingUpdate' {{% code\_sample file="controllers/fluentd-daemonset.yaml" %}} After verifying the update strategy of the DaemonSet manifest, create the DaemonSet: ```shell kubectl create -f https://k8s.io/examples/controllers/fluentd-daemonset.yaml ``` Alternatively, use `kubectl apply` to create the same DaemonSet if you plan to update the DaemonSet with `kubectl apply`. ```shell kubectl apply -f https://k8s.io/examples/controllers/fluentd-daemonset.yaml ``` ### Checking DaemonSet `RollingUpdate` update strategy Check the update strategy of your DaemonSet, and make sure it's set to `RollingUpdate`: ```shell kubectl get ds/fluentd-elasticsearch -o go-template='{{.spec.updateStrategy.type}}{{"\n"}}' -n kube-system ``` If you haven't created the DaemonSet in the system, check your DaemonSet manifest with the following command instead: ```shell kubectl apply -f https://k8s.io/examples/controllers/fluentd-daemonset.yaml --dry-run=client -o go-template='{{.spec.updateStrategy.type}}{{"\n"}}' ``` The output from both commands should be: ``` RollingUpdate ``` 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. {{% code\_sample file="controllers/fluentd-daemonset-update.yaml" %}} #### Declarative commands If you update DaemonSets using [configuration files](/docs/tasks/manage-kubernetes-objects/declarative-config/), use `kubectl apply`: ```shell kubectl apply -f https://k8s.io/examples/controllers/fluentd-daemonset-update.yaml ``` #### Imperative commands If you update DaemonSets using [imperative commands](/docs/tasks/manage-kubernetes-objects/imperative-command/), use `kubectl edit` : ```shell kubectl edit ds/fluentd-elasticsearch -n kube-system ``` ##### Updating only the container image If you only need to update the container image in the DaemonSet template, i.e. `.spec.template.spec.containers[\*].image`, use `kubectl set image`: ```shell 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: ```shell kubectl rollout status ds/fluentd-elasticsearch -n kube-system ``` When the rollout is complete, the output is similar to this: ```shell 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](/docs/concepts/scheduling-eviction/node-pressure-eviction/). 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: ```shell 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
| null |
./vendor/kubernetes/content/en/docs/tasks/manage-daemon/update-daemon-set.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/manage-daemon/update-daemon-set.md
|
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|
of: ```shell 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](/docs/tasks/run-application/configure-pdb/) either. {{< /note >}} #### 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. ## Clean up Delete DaemonSet from a namespace : ```shell kubectl delete ds fluentd-elasticsearch -n kube-system ``` ## {{% heading "whatsnext" %}} \* See [Performing a rollback on a DaemonSet](/docs/tasks/manage-daemon/rollback-daemon-set/) \* See [Creating a DaemonSet to adopt existing DaemonSet pods](/docs/concepts/workloads/controllers/daemonset/)
| null |
./vendor/kubernetes/content/en/docs/tasks/manage-daemon/update-daemon-set.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/manage-daemon/update-daemon-set.md
|
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] | 0.125186
|
This page shows how to perform a rollback on a {{< glossary\_tooltip term\_id="daemonset" >}}. ## {{% heading "prerequisites" %}} {{< include "task-tutorial-prereqs.md" >}} {{< version-check >}} You should already know how to [perform a rolling update on a DaemonSet](/docs/tasks/manage-daemon/update-daemon-set/). ## Performing a rollback on a DaemonSet ### Step 1: Find the DaemonSet revision you want to roll back to You can skip this step if you only want to roll back to the last revision. List all revisions of a DaemonSet: ```shell kubectl rollout history daemonset ``` This returns a list of DaemonSet revisions: ``` daemonsets "" REVISION CHANGE-CAUSE 1 ... 2 ... ... ``` \* 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: ```shell kubectl rollout history daemonset --revision=1 ``` This returns the details of that revision: ``` daemonsets "" with revision #1 Pod Template: Labels: foo=bar Containers: app: Image: ... Port: ... Environment: ... Mounts: ... Volumes: ... ``` ### Step 2: Roll back to a specific revision ```shell # Specify the revision number you get from Step 1 in --to-revision kubectl rollout undo daemonset --to-revision= ``` If it succeeds, the command returns: ``` daemonset "" rolled back ``` {{< note >}} If `--to-revision` flag is not specified, kubectl picks the most recent revision. {{< /note >}} ### 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 {{< glossary\_tooltip term\_id="control-plane" text="control plane" >}}. To watch the progress of the rollback: ```shell kubectl rollout status ds/ ``` When the rollback is complete, the output is similar to: ``` daemonset "" 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: ```shell kubectl get controllerrevision -l = ``` This returns a list of ControllerRevisions: ``` NAME CONTROLLER REVISION AGE - DaemonSet/ 1 1h - DaemonSet/ 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`. {{< /note >}} ## Troubleshooting \* See [troubleshooting DaemonSet rolling update](/docs/tasks/manage-daemon/update-daemon-set/#troubleshooting).
| null |
./vendor/kubernetes/content/en/docs/tasks/manage-daemon/rollback-daemon-set.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/manage-daemon/rollback-daemon-set.md
|
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|
Sometimes things go wrong. This guide helps you gather the relevant information and resolve issues. It has four sections: \* [Debugging your application](/docs/tasks/debug/debug-application/) - Useful for users who are deploying code into Kubernetes and wondering why it is not working. \* [Debugging your cluster](/docs/tasks/debug/debug-cluster/) - Useful for cluster administrators and operators troubleshooting issues with the Kubernetes cluster itself. \* [Logging in Kubernetes](/docs/tasks/debug/logging/) - Useful for cluster administrators who want to set up and manage logging in Kubernetes. \* [Monitoring in Kubernetes](/docs/tasks/debug/monitoring/) - Useful for cluster administrators who want to enable monitoring in a Kubernetes cluster. You should also check the known issues for the [release](https://github.com/kubernetes/kubernetes/releases) 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](/docs/concepts/) explain the Kubernetes architecture and how each component works, while [Setup](/docs/setup/) provides practical instructions for getting started. [Tasks](/docs/tasks/) show how to accomplish commonly used tasks, and [Tutorials](/docs/tutorials/) are more comprehensive walkthroughs of real-world, industry-specific, or end-to-end development scenarios. The [Reference](/docs/reference/) section provides detailed documentation on the [Kubernetes API](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/) and command-line interfaces (CLIs), such as [`kubectl`](/docs/reference/kubectl/). ## Help! My question isn't covered! I need help now! ### Stack Exchange, Stack Overflow, or Server Fault {#stack-exchange} If you have questions related to \*software development\* for your containerized app, you can ask those on [Stack Overflow](https://stackoverflow.com/questions/tagged/kubernetes). If you have Kubernetes questions related to \*cluster management\* or \*configuration\*, you can ask those on [Server Fault](https://serverfault.com/questions/tagged/kubernetes). 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](https://devops.stackexchange.com/questions/tagged/kubernetes), [Software Engineering](https://softwareengineering.stackexchange.com/questions/tagged/kubernetes), or [InfoSec](https://security.stackexchange.com/questions/tagged/kubernetes). Someone else from the community may have already asked a similar question or may be able to help with your problem. The Kubernetes team will also monitor [posts tagged Kubernetes](https://stackoverflow.com/questions/tagged/kubernetes). If there aren't any existing questions that help, \*\*please ensure that your question is [on-topic on Stack Overflow](https://stackoverflow.com/help/on-topic), [Server Fault](https://serverfault.com/help/on-topic), or the Stack Exchange Network site you're asking on\*\*, and read through the guidance on [how to ask a new question](https://stackoverflow.com/help/how-to-ask), before asking a new one! ### Slack Many people from the Kubernetes community hang out on Kubernetes Slack in the `#kubernetes-users` channel. Slack requires registration; you can [request an invitation](https://slack.kubernetes.io), and registration is open to everyone). Feel free to come and ask any and all questions. Once registered, access the [Kubernetes organisation in Slack](https://kubernetes.slack.com) 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`](https://kubernetes.slack.com/messages/kubernetes-novice) channel. As another example, developers should join the [`#kubernetes-contributors`](https://kubernetes.slack.com/messages/kubernetes-contributors) channel. There are also many country specific / local language channels. Feel free to join these channels for localized support and info: {{< table caption="Country / language specific Slack channels" >}} Country | Channels :---------|:------------ China | [`#cn-users`](https://kubernetes.slack.com/messages/cn-users), [`#cn-events`](https://kubernetes.slack.com/messages/cn-events) Finland | [`#fi-users`](https://kubernetes.slack.com/messages/fi-users) France | [`#fr-users`](https://kubernetes.slack.com/messages/fr-users), [`#fr-events`](https://kubernetes.slack.com/messages/fr-events) Germany | [`#de-users`](https://kubernetes.slack.com/messages/de-users), [`#de-events`](https://kubernetes.slack.com/messages/de-events) India | [`#in-users`](https://kubernetes.slack.com/messages/in-users), [`#in-events`](https://kubernetes.slack.com/messages/in-events) Italy | [`#it-users`](https://kubernetes.slack.com/messages/it-users), [`#it-events`](https://kubernetes.slack.com/messages/it-events) Japan | [`#jp-users`](https://kubernetes.slack.com/messages/jp-users), [`#jp-events`](https://kubernetes.slack.com/messages/jp-events) Korea | [`#kr-users`](https://kubernetes.slack.com/messages/kr-users) Netherlands | [`#nl-users`](https://kubernetes.slack.com/messages/nl-users) Norway | [`#norw-users`](https://kubernetes.slack.com/messages/norw-users) Poland | [`#pl-users`](https://kubernetes.slack.com/messages/pl-users) Russia | [`#ru-users`](https://kubernetes.slack.com/messages/ru-users) Spain | [`#es-users`](https://kubernetes.slack.com/messages/es-users) Sweden | [`#se-users`](https://kubernetes.slack.com/messages/se-users) Turkey | [`#tr-users`](https://kubernetes.slack.com/messages/tr-users), [`#tr-events`](https://kubernetes.slack.com/messages/tr-events) {{< /table >}} ### Forum You're welcome to join the official Kubernetes Forum: [discuss.kubernetes.io](https://discuss.kubernetes.io). ### Bugs and feature requests If you have what looks like a bug, or you would like to make a feature request, please use the [GitHub issue tracking system](https://github.com/kubernetes/kubernetes/issues).
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/_index.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/_index.md
|
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] | 0.099614
|
Sweden | [`#se-users`](https://kubernetes.slack.com/messages/se-users) Turkey | [`#tr-users`](https://kubernetes.slack.com/messages/tr-users), [`#tr-events`](https://kubernetes.slack.com/messages/tr-events) {{< /table >}} ### Forum You're welcome to join the official Kubernetes Forum: [discuss.kubernetes.io](https://discuss.kubernetes.io). ### Bugs and feature requests If you have what looks like a bug, or you would like to make a feature request, please use the [GitHub issue tracking system](https://github.com/kubernetes/kubernetes/issues). 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
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/_index.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/_index.md
|
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] | 0.1359
|
This page explains how to debug Pods running (or crashing) on a Node. ## {{% heading "prerequisites" %}} \* Your {{< glossary\_tooltip text="Pod" term\_id="pod" >}} should already be scheduled and running. If your Pod is not yet running, start with [Debugging Pods](/docs/tasks/debug/debug-application/). \* 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. {{% code\_sample file="application/nginx-with-request.yaml" %}} Create deployment by running following command: ```shell kubectl apply -f https://k8s.io/examples/application/nginx-with-request.yaml ``` ```none deployment.apps/nginx-deployment created ``` Check pod status by following command: ```shell kubectl get pods ``` ```none 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: ```shell kubectl describe pod nginx-deployment-67d4bdd6f5-w6kd7 ``` ```none 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: 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: 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: DownwardAPI: true QoS Class: Guaranteed Node-Selectors: 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
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-application/debug-running-pod.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-application/debug-running-pod.md
|
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] | 0.053892
|
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.) ```shell kubectl get pods ``` ```none 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: ```shell kubectl describe pod nginx-deployment-1370807587-fz9sd ``` ```none 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 ```shell 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: ```shell 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. ```shell kubectl get pod nginx-deployment-1006230814-6winp -o yaml ``` ```yaml apiVersion: v1 kind: Pod metadata: creationTimestamp: "2022-02-17T21:51:01Z" generateName: nginx-deployment-67d4bdd6f5-
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-application/debug-running-pod.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-application/debug-running-pod.md
|
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|
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. ```shell kubectl get pod nginx-deployment-1006230814-6winp -o yaml ``` ```yaml apiVersion: v1 kind: Pod metadata: creationTimestamp: "2022-02-17T21:51:01Z" generateName: nginx-deployment-67d4bdd6f5- labels: app: nginx pod-template-hash: 67d4bdd6f5 name: nginx-deployment-67d4bdd6f5-w6kd7 namespace: default ownerReferences: - apiVersion: apps/v1 blockOwnerDeletion: true controller: true kind: ReplicaSet name: nginx-deployment-67d4bdd6f5 uid: 7d41dfd4-84c0-4be4-88ab-cedbe626ad82 resourceVersion: "1364" uid: a6501da1-0447-4262-98eb-c03d4002222e spec: containers: - image: nginx imagePullPolicy: Always name: nginx ports: - containerPort: 80 protocol: TCP resources: limits: cpu: 500m memory: 128Mi requests: cpu: 500m memory: 128Mi terminationMessagePath: /dev/termination-log terminationMessagePolicy: File volumeMounts: - mountPath: /var/run/secrets/kubernetes.io/serviceaccount name: kube-api-access-bgsgp readOnly: true dnsPolicy: ClusterFirst enableServiceLinks: true nodeName: kube-worker-1 preemptionPolicy: PreemptLowerPriority priority: 0 restartPolicy: Always schedulerName: default-scheduler securityContext: {} serviceAccount: default serviceAccountName: default terminationGracePeriodSeconds: 30 tolerations: - effect: NoExecute key: node.kubernetes.io/not-ready operator: Exists tolerationSeconds: 300 - effect: NoExecute key: node.kubernetes.io/unreachable operator: Exists tolerationSeconds: 300 volumes: - name: kube-api-access-bgsgp projected: defaultMode: 420 sources: - serviceAccountToken: expirationSeconds: 3607 path: token - configMap: items: - key: ca.crt path: ca.crt name: kube-root-ca.crt - downwardAPI: items: - fieldRef: apiVersion: v1 fieldPath: metadata.namespace path: namespace status: conditions: - lastProbeTime: null lastTransitionTime: "2022-02-17T21:51:01Z" status: "True" type: Initialized - lastProbeTime: null lastTransitionTime: "2022-02-17T21:51:06Z" status: "True" type: Ready - lastProbeTime: null lastTransitionTime: "2022-02-17T21:51:06Z" status: "True" type: ContainersReady - lastProbeTime: null lastTransitionTime: "2022-02-17T21:51:01Z" status: "True" type: PodScheduled containerStatuses: - containerID: containerd://5403af59a2b46ee5a23fb0ae4b1e077f7ca5c5fb7af16e1ab21c00e0e616462a image: docker.io/library/nginx:latest imageID: docker.io/library/nginx@sha256:2834dc507516af02784808c5f48b7cbe38b8ed5d0f4837f16e78d00deb7e7767 lastState: {} name: nginx ready: true restartCount: 0 started: true state: running: startedAt: "2022-02-17T21:51:05Z" hostIP: 192.168.0.113 phase: Running podIP: 10.88.0.3 podIPs: - ip: 10.88.0.3 - ip: 2001:db8::1 qosClass: Guaranteed startTime: "2022-02-17T21:51:01Z" ``` ## Examining pod logs {#examine-pod-logs} First, look at the logs of the affected container: ```shell kubectl logs ${POD\_NAME} -c ${CONTAINER\_NAME} ``` If your container has previously crashed, you can access the previous container's crash log with: ```shell kubectl logs ${POD\_NAME} -c ${CONTAINER\_NAME} --previous ``` ## Debugging with container exec {#container-exec} If the {{< glossary\_tooltip text="container image" term\_id="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`: ```shell kubectl exec ${POD\_NAME} -c ${CONTAINER\_NAME} -- ${CMD} ${ARG1} ${ARG2} ... ${ARGN} ``` {{< note >}} `-c ${CONTAINER\_NAME}` is optional. You can omit it for Pods that only contain a single container. {{< /note >}} As an example, to look at the logs from a running Cassandra pod, you might run ```shell kubectl exec cassandra -- cat /var/log/cassandra/system.log ``` You can run a shell that's connected to your terminal using the `-i` and `-t` arguments to `kubectl exec`, for example: ```shell kubectl exec -it cassandra -- sh ``` For more details, see [Get a Shell to a Running Container]( /docs/tasks/debug/debug-application/get-shell-running-container/). ## Debugging with an ephemeral debug container {#ephemeral-container} {{< feature-state state="stable" for\_k8s\_version="v1.25" >}} {{< glossary\_tooltip text="Ephemeral containers" term\_id="ephemeral-container" >}} 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]( https://github.com/GoogleContainerTools/distroless). ### Example debugging using ephemeral containers {#ephemeral-container-example} You can use the `kubectl debug` command to add ephemeral containers to a running Pod. First, create a pod for the example: ```shell 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. ```shell kubectl exec -it ephemeral-demo -- sh ``` ``` OCI runtime
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-application/debug-running-pod.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-application/debug-running-pod.md
|
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] | 0.141066
|
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. ```shell 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. ```shell kubectl debug -it ephemeral-demo --image=busybox:1.28 --target=ephemeral-demo ``` ``` 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]( /docs/tasks/configure-pod-container/share-process-namespace/) in the pod it creates. {{< note >}} The `--target` parameter must be supported by the {{< glossary\_tooltip text="Container Runtime" term\_id="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. {{< /note >}} You can view the state of the newly created ephemeral container using `kubectl describe`: ```shell kubectl describe pod ephemeral-demo ``` ``` ... Ephemeral Containers: debugger-8xzrl: Container ID: docker://b888f9adfd15bd5739fefaa39e1df4dd3c617b9902082b1cfdc29c4028ffb2eb Image: busybox Image ID: docker-pullable://busybox@sha256:1828edd60c5efd34b2bf5dd3282ec0cc04d47b2ff9caa0b6d4f07a21d1c08084 Port: Host Port: State: Running Started: Wed, 12 Feb 2020 14:25:42 +0100 Ready: False Restart Count: 0 Environment: Mounts: ... ``` Use `kubectl delete` to remove the Pod when you're finished: ```shell 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`: ```shell 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: ```shell kubectl debug myapp -it --image=ubuntu --share-processes --copy-to=myapp-debug ``` ``` 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]( /docs/tasks/configure-pod-container/share-process-namespace/). {{< /note >}} Don't forget to clean up the debugging Pod when you're finished with it: ```shell kubectl delete pod myapp myapp-debug ``` ### Copying a Pod while changing its command Sometimes it's useful to change
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-application/debug-running-pod.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-application/debug-running-pod.md
|
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] | 0.100535
|
about how this works, see [Share Process Namespace between Containers in a Pod]( /docs/tasks/configure-pod-container/share-process-namespace/). {{< /note >}} Don't forget to clean up the debugging Pod when you're finished with it: ```shell 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: ``` Containers: myapp: Image: busybox ... Args: false State: Waiting Reason: CrashLoopBackOff Last State: Terminated Reason: Error Exit Code: 1 ``` 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`. {{< /note >}} Don't forget to clean up the debugging Pod when you're finished with it: ```shell 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`: ``` kubectl debug myapp --copy-to=myapp-debug --set-image=\*=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: ```shell kubectl delete pod myapp myapp-debug ``` ## Debugging via a shell on the node {#node-shell-session} 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: ```shell 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 or use the `--profile=sysadmin` flag. Don't forget to clean up the debugging Pod when you're finished with it: ```shell kubectl delete pod node-debugger-mynode-pdx84 ``` ## Debugging a Pod or Node while applying a profile {#debugging-profiles} When using `kubectl debug` to debug a node
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-application/debug-running-pod.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-application/debug-running-pod.md
|
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|
need a privileged pod, create it manually or use the `--profile=sysadmin` flag. Don't forget to clean up the debugging Pod when you're finished with it: ```shell kubectl delete pod node-debugger-mynode-pdx84 ``` ## Debugging a Pod or Node while applying a profile {#debugging-profiles} When using `kubectl debug` to debug a node via a debugging Pod, a Pod via an ephemeral container, or a copied Pod, you can apply a profile to them. By applying a profile, specific properties such as [securityContext](/docs/tasks/configure-pod-container/security-context/) are set, allowing for adaptation to various scenarios. There are two types of profiles, static profile and custom profile. ### Applying a Static Profile {#static-profile} A static profile is a set of predefined properties, and you can apply them using the `--profile` flag. The available profiles are as follows: | Profile | Description | | ------------ | --------------------------------------------------------------- | | legacy | A set of properties backwards compatibility with 1.22 behavior | | general | A reasonable set of generic properties for each debugging journey | | baseline | A set of properties compatible with [PodSecurityStandard baseline policy](/docs/concepts/security/pod-security-standards/#baseline) | | restricted | A set of properties compatible with [PodSecurityStandard restricted policy](/docs/concepts/security/pod-security-standards/#restricted) | | netadmin | A set of properties including Network Administrator privileges | | sysadmin | A set of properties including System Administrator (root) privileges | {{< note >}} If you don't specify `--profile`, the `legacy` profile is used by default, but it is planned to be deprecated in the near future. So it is recommended to use other profiles such as `general`. {{< /note >}} Assume that you create a Pod and debug it. First, create a Pod named `myapp` as an example: ```shell kubectl run myapp --image=busybox:1.28 --restart=Never -- sleep 1d ``` Then, debug the Pod using an ephemeral container. If the ephemeral container needs to have privilege, you can use the `sysadmin` profile: ```shell kubectl debug -it myapp --image=busybox:1.28 --target=myapp --profile=sysadmin ``` ``` Targeting container "myapp". If you don't see processes from this container it may be because the container runtime doesn't support this feature. Defaulting debug container name to debugger-6kg4x. If you don't see a command prompt, try pressing enter. / # ``` Check the capabilities of the ephemeral container process by running the following command inside the container: ```shell / # grep Cap /proc/$$/status ``` ``` ... CapPrm: 000001ffffffffff CapEff: 000001ffffffffff ... ``` This means the container process is granted full capabilities as a privileged container by applying `sysadmin` profile. See more details about [capabilities](/docs/tasks/configure-pod-container/security-context/#set-capabilities-for-a-container). You can also check that the ephemeral container was created as a privileged container: ```shell kubectl get pod myapp -o jsonpath='{.spec.ephemeralContainers[0].securityContext}' ``` ``` {"privileged":true} ``` Clean up the Pod when you're finished with it: ```shell kubectl delete pod myapp ``` ### Applying Custom Profile {#custom-profile} {{< feature-state for\_k8s\_version="v1.32" state="stable" >}} You can define a partial container spec for debugging as a custom profile in either YAML or JSON format, and apply it using the `--custom` flag. {{< note >}} Custom profile only supports the modification of the container spec, but modifications to `name`, `image`, `command`, `lifecycle` and `volumeDevices` fields of the container spec are not allowed. It does not support the modification of the Pod spec. {{< /note >}} Create a Pod named myapp as an example: ```shell kubectl run myapp --image=busybox:1.28 --restart=Never -- sleep 1d ``` Create a custom profile in YAML or JSON format. Here, create a YAML format file named `custom-profile.yaml`: ```yaml env: - name: ENV\_VAR\_1 value: value\_1 - name: ENV\_VAR\_2 value: value\_2 securityContext: capabilities: add: - NET\_ADMIN - SYS\_TIME ``` Run this command to debug the Pod using an ephemeral container with the custom profile:
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-application/debug-running-pod.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-application/debug-running-pod.md
|
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] | 0.123029
|
custom profile in YAML or JSON format. Here, create a YAML format file named `custom-profile.yaml`: ```yaml env: - name: ENV\_VAR\_1 value: value\_1 - name: ENV\_VAR\_2 value: value\_2 securityContext: capabilities: add: - NET\_ADMIN - SYS\_TIME ``` Run this command to debug the Pod using an ephemeral container with the custom profile: ```shell kubectl debug -it myapp --image=busybox:1.28 --target=myapp --profile=general --custom=custom-profile.yaml ``` You can check that the ephemeral container has been added to the target Pod with the custom profile applied: ```shell kubectl get pod myapp -o jsonpath='{.spec.ephemeralContainers[0].env}' ``` ``` [{"name":"ENV\_VAR\_1","value":"value\_1"},{"name":"ENV\_VAR\_2","value":"value\_2"}] ``` ```shell kubectl get pod myapp -o jsonpath='{.spec.ephemeralContainers[0].securityContext}' ``` ``` {"capabilities":{"add":["NET\_ADMIN","SYS\_TIME"]}} ``` Clean up the Pod when you're finished with it: ```shell kubectl delete pod myapp ```
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-application/debug-running-pod.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-application/debug-running-pod.md
|
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|
This page shows how to use `kubectl exec` to get a shell to a running container. ## {{% heading "prerequisites" %}} {{< include "task-tutorial-prereqs.md" >}} ## Getting a shell to a container In this exercise, you create a Pod that has one container. The container runs the nginx image. Here is the configuration file for the Pod: {{% code\_sample file="application/shell-demo.yaml" %}} Create the Pod: ```shell kubectl apply -f https://k8s.io/examples/application/shell-demo.yaml ``` Verify that the container is running: ```shell kubectl get pod shell-demo ``` Get a shell to the running container: ```shell kubectl exec --stdin --tty shell-demo -- /bin/bash ``` {{< note >}} The double dash (`--`) separates the arguments you want to pass to the command from the kubectl arguments. {{< /note >}} In your shell, list the root directory: ```shell # Run this inside the container ls / ``` In your shell, experiment with other commands. Here are some examples: ```shell # You can run these example commands inside the container ls / 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: ```shell # Run this inside the container echo 'Hello shell demo' > /usr/share/nginx/html/index.html ``` In your shell, send a GET request to the nginx server: ```shell # Run this in the shell inside your container apt-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`. ```shell 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: ```shell kubectl exec shell-demo -- env ``` Experiment with running other commands. Here are some examples: ```shell 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. ```shell kubectl exec -i -t my-pod --container main-app -- /bin/bash ``` {{< note >}} The short options `-i` and `-t` are the same as the long options `--stdin` and `--tty` {{< /note >}} ## {{% heading "whatsnext" %}} \* Read about [kubectl exec](/docs/reference/generated/kubectl/kubectl-commands/#exec)
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-application/get-shell-running-container.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-application/get-shell-running-container.md
|
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] | 0.091828
|
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](/docs/tasks/debug/debug-cluster). ## Diagnosing the problem The first step in troubleshooting is triage. What is the problem? Is it your Pods, your Replication Controller or your Service? \* [Debugging Pods](#debugging-pods) \* [Debugging Replication Controllers](#debugging-replication-controllers) \* [Debugging Services](#debugging-services) ### Debugging Pods 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: ```shell 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](/docs/concepts/configuration/manage-resources-containers/) 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 `. #### 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](/docs/concepts/overview/working-with-objects/finalizers/) and there is an [admission webhook](/docs/reference/access-authn-authz/extensible-admission-controllers/) 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
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-application/debug-pods.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-application/debug-pods.md
|
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] | 0.12188
|
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](/docs/tasks/debug/debug-application/debug-running-pod/) 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: ```shell 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](#debugging-pods) to debug your pods. You can also use `kubectl describe rc ${CONTROLLER\_NAME}` to introspect events related to the replication controller. ### Debugging Services Services provide load balancing across a set of pods. There are several common problems that can make Services not work properly. The following instructions should help debug Service problems. First, verify that there are endpoints for the service. For every Service object, the apiserver makes one or more `EndpointSlice` resources available. You can view these resources with: ```shell kubectl get endpointslices -l kubernetes.io/service-name=${SERVICE\_NAME} ``` Make sure that the endpoints in the EndpointSlices 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 endpoint slices. #### 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: ```yaml ... spec: - selector: name: nginx type: frontend ``` You can use: ```shell 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` ####
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-application/debug-pods.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-application/debug-pods.md
|
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] | 0.140867
|
spec: - selector: name: nginx type: frontend ``` You can use: ```shell 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` #### Network traffic is not forwarded Please see [debugging service](/docs/tasks/debug/debug-application/debug-service/) for more information. ## {{% heading "whatsnext" %}} If none of the above solves your problem, follow the instructions in [Debugging Service document](/docs/tasks/debug/debug-application/debug-service/) 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. You may also visit [troubleshooting document](/docs/tasks/debug/) for more information.
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-application/debug-pods.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-application/debug-pods.md
|
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] | 0.089317
|
This task shows you how to debug a StatefulSet. ## {{% heading "prerequisites" %}} \* 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: ```shell 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](/docs/tasks/run-application/delete-stateful-set/) task for instructions on how to deal with them. You can debug individual Pods in a StatefulSet using the [Debugging Pods](/docs/tasks/debug/debug-application/debug-pods/) guide. ## {{% heading "whatsnext" %}} Learn more about [debugging an init-container](/docs/tasks/debug/debug-application/debug-init-containers/).
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-application/debug-statefulset.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-application/debug-statefulset.md
|
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] | 0.152913
|
This page shows how to investigate problems related to the execution of Init Containers. The example command lines below refer to the Pod as `` and the Init Containers as `` and ``. ## {{% heading "prerequisites" %}} {{< include "task-tutorial-prereqs.md" >}} {{< version-check >}} \* You should be familiar with the basics of [Init Containers](/docs/concepts/workloads/pods/init-containers/). \* You should have [Configured an Init Container](/docs/tasks/configure-pod-container/configure-pod-initialization/#create-a-pod-that-has-an-init-container). ## Checking the status of Init Containers Display the status of your pod: ```shell kubectl get pod ``` For example, a status of `Init:1/2` indicates that one of two Init Containers has completed successfully: ``` NAME READY STATUS RESTARTS AGE 0/1 Init:1/2 0 7s ``` See [Understanding Pod status](#understanding-pod-status) for more examples of status values and their meanings. ## Getting details about Init Containers View more detailed information about Init Container execution: ```shell kubectl describe pod ``` For example, a Pod with two Init Containers might show the following: ``` Init Containers: : Container ID: ... ... State: Terminated Reason: Completed Exit Code: 0 Started: ... Finished: ... Ready: True Restart Count: 0 ... : Container ID: ... ... State: Waiting Reason: CrashLoopBackOff Last State: Terminated Reason: Error Exit Code: 1 Started: ... Finished: ... Ready: False Restart Count: 3 ... ``` You can also access the Init Container statuses programmatically by reading the `status.initContainerStatuses` field on the Pod Spec: ```shell kubectl get pod --template '{{.status.initContainerStatuses}}' ``` This command will return the same information as above, formatted using a [Go template](https://pkg.go.dev/text/template). ## Accessing logs from Init Containers Pass the Init Container name along with the Pod name to access its logs. ```shell kubectl logs -c ``` 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.
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-application/debug-init-containers.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-application/debug-init-containers.md
|
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] | 0.094503
|
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](/docs/concepts/cluster-administration/logging/). ## {{% heading "prerequisites" %}} {{< include "task-tutorial-prereqs.md" >}} ## Writing and reading a termination message In this exercise, you create a Pod that runs one container. The manifest for that Pod specifies a command that runs when the container starts: {{% code\_sample file="debug/termination.yaml" %}} 1. Create a Pod based on the YAML configuration file: ```shell kubectl apply -f https://k8s.io/examples/debug/termination.yaml ``` 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. 1. Display information about the Pod: ```shell kubectl get pod termination-demo ``` Repeat the preceding command until the Pod is no longer running. 1. Display detailed information about the Pod: ```shell kubectl get pod termination-demo --output=yaml ``` The output includes the "Sleep expired" message: ```yaml apiVersion: v1 kind: Pod ... lastState: terminated: containerID: ... exitCode: 0 finishedAt: ... message: | Sleep expired ... ``` 1. Use a Go template to filter the output so that it includes only the termination message: ```shell 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: ```shell 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: ```yaml apiVersion: v1 kind: Pod metadata: name: msg-path-demo spec: containers: - name: msg-path-demo-container image: debian terminationMessagePath: "/tmp/my-log" ``` 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. ## {{% heading "whatsnext" %}} \* See the `terminationMessagePath` field in [Container](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#container-v1-core). \* See [ImagePullBackOff](/docs/concepts/containers/images/#imagepullbackoff) in [Images](/docs/concepts/containers/images/). \* Learn about [retrieving logs](/docs/concepts/cluster-administration/logging/). \* Learn about [Go templates](https://pkg.go.dev/text/template). \* Learn about [Pod status](/docs/tasks/debug/debug-application/debug-init-containers/#understanding-pod-status) and [Pod phase](/docs/concepts/workloads/pods/pod-lifecycle/#pod-phase). \* Learn about [container states](/docs/concepts/workloads/pods/pod-lifecycle/#container-states).
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-application/determine-reason-pod-failure.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-application/determine-reason-pod-failure.md
|
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] | 0.157677
|
"whatsnext" %}} \* See the `terminationMessagePath` field in [Container](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#container-v1-core). \* See [ImagePullBackOff](/docs/concepts/containers/images/#imagepullbackoff) in [Images](/docs/concepts/containers/images/). \* Learn about [retrieving logs](/docs/concepts/cluster-administration/logging/). \* Learn about [Go templates](https://pkg.go.dev/text/template). \* Learn about [Pod status](/docs/tasks/debug/debug-application/debug-init-containers/#understanding-pod-status) and [Pod phase](/docs/concepts/workloads/pods/pod-lifecycle/#pod-phase). \* Learn about [container states](/docs/concepts/workloads/pods/pod-lifecycle/#container-states).
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-application/determine-reason-pod-failure.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-application/determine-reason-pod-failure.md
|
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] | 0.212704
|
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: ```none 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. {{< /note >}} If you already have a running Pod that you prefer to use, you can run a command in it using: ```shell kubectl exec -c -- ``` ## Setup 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. ```shell kubectl create deployment hostnames --image=registry.k8s.io/serve\_hostname ``` ```none deployment.apps/hostnames created ``` `kubectl` commands will print the type and name of the resource created or mutated, which can then be used in subsequent commands. Let's scale the deployment to 3 replicas. ```shell kubectl scale deployment hostnames --replicas=3 ``` ```none deployment.apps/hostnames scaled ``` Note that this is the same as if you had started the Deployment with the following YAML: ```yaml apiVersion: apps/v1 kind: Deployment metadata: labels: app: hostnames name: hostnames spec: selector: matchLabels: app: hostnames replicas: 3 template: metadata: labels: app: hostnames spec: containers: - name: hostnames image: registry.k8s.io/serve\_hostname ``` The label "app" is automatically set by `kubectl create deployment` to the name of the Deployment. You can confirm your Pods are running: ```shell kubectl get pods -l app=hostnames ``` ```none 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. ```shell kubectl get pods -l app=hostnames \ -o go-template='{{range .items}}{{.status.podIP}}{{"\n"}}{{end}}' ``` ```none 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: ```shell for ep in 10.244.0.5:9376 10.244.0.6:9376 10.244.0.7:9376; do wget -qO- $ep done ``` This should produce something like: ``` hostnames-632524106-bbpiw hostnames-632524106-ly40y hostnames-632524106-tlaok ``` 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: ```shell wget -O- hostnames ``` ```none Resolving hostnames (hostnames)... failed: Name or service not known. wget: unable to resolve host address 'hostnames' ``` The
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-application/debug-service.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-application/debug-service.md
|
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|
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: ```shell wget -O- hostnames ``` ```none 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: ```shell kubectl get svc hostnames ``` ```none 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. ```shell kubectl expose deployment hostnames --port=80 --target-port=9376 ``` ```none service/hostnames exposed ``` And read it back: ```shell kubectl get svc hostnames ``` ```none NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE hostnames ClusterIP 10.0.1.175 80/TCP 5s ``` Now you know that the Service exists. As before, this is the same as if you had started the Service with YAML: ```yaml apiVersion: v1 kind: Service metadata: labels: app: hostnames name: hostnames spec: selector: app: hostnames ports: - name: default protocol: TCP port: 80 targetPort: 9376 ``` 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. Please refer to [Network Policies](/docs/concepts/services-networking/network-policies/) for more details. ## Does the Service work by DNS name? One of the most common ways that clients consume a Service is through a DNS name. From a Pod in the same Namespace: ```shell nslookup hostnames ``` ```none Address 1: 10.0.0.10 kube-dns.kube-system.svc.cluster.local Name: hostnames Address 1: 10.0.1.175 hostnames.default.svc.cluster.local ``` If this fails, perhaps your Pod and Service are in different Namespaces, try a namespace-qualified name (again, from within a Pod): ```shell nslookup hostnames.default ``` ```none Address 1: 10.0.0.10 kube-dns.kube-system.svc.cluster.local Name: hostnames.default Address 1: 10.0.1.175 hostnames.default.svc.cluster.local ``` 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: ```shell nslookup hostnames.default.svc.cluster.local ``` ```none Address 1: 10.0.0.10 kube-dns.kube-system.svc.cluster.local Name: hostnames.default.svc.cluster.local Address 1: 10.0.1.175 hostnames.default.svc.cluster.local ``` 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. {{< /note >}} ```shell nslookup hostnames.default.svc.cluster.local 10.0.0.10 ``` ```none Server: 10.0.0.10 Address: 10.0.0.10#53 Name: hostnames.default.svc.cluster.local Address: 10.0.1.175 ``` 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: ```shell cat /etc/resolv.conf ``` You should see something like: ``` nameserver 10.0.0.10 search default.svc.cluster.local svc.cluster.local cluster.local example.com options ndots:5 ``` 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
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-application/debug-service.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-application/debug-service.md
|
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] | 0.132002
|
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? {#does-any-service-exist-in-dns} 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: ```shell nslookup kubernetes.default ``` ```none Server: 10.0.0.10 Address 1: 10.0.0.10 kube-dns.kube-system.svc.cluster.local Name: kubernetes.default Address 1: 10.0.0.1 kubernetes.default.svc.cluster.local ``` If this fails, please see the [kube-proxy](#is-the-kube-proxy-working) 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). ```shell for i in $(seq 1 3); do wget -qO- 10.0.1.175:80 done ``` This should produce something like: ``` hostnames-632524106-bbpiw hostnames-632524106-ly40y hostnames-632524106-tlaok ``` 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: ```shell kubectl get service hostnames -o json ``` ```json { "kind": "Service", "apiVersion": "v1", "metadata": { "name": "hostnames", "namespace": "default", "uid": "428c8b6c-24bc-11e5-936d-42010af0a9bc", "resourceVersion": "347189", "creationTimestamp": "2015-07-07T15:24:29Z", "labels": { "app": "hostnames" } }, "spec": { "ports": [ { "name": "default", "protocol": "TCP", "port": 80, "targetPort": 9376, "nodePort": 0 } ], "selector": { "app": "hostnames" }, "clusterIP": "10.0.1.175", "type": "ClusterIP", "sessionAffinity": "None" }, "status": { "loadBalancer": {} } } ``` \* 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 EndpointSlices? 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: ```shell kubectl get pods -l app=hostnames ``` ```none 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
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-application/debug-service.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-application/debug-service.md
|
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] | 0.097847
|
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](/docs/tasks/debug/debug-application/debug-pods). Inside the Kubernetes system is a control loop which evaluates the selector of every Service and saves the results into one or more EndpointSlice objects. ```shell kubectl get endpointslices -l k8s.io/service-name=hostnames NAME ADDRESSTYPE PORTS ENDPOINTS hostnames-ytpni IPv4 9376 10.244.0.5,10.244.0.6,10.244.0.7 ``` This confirms that the EndpointSlice controller has found the correct Pods for your Service. If the `ENDPOINTS` column is ``, 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). {{< /note >}} From within a Pod: ```shell for ep in 10.244.0.5:9376 10.244.0.6:9376 10.244.0.7:9376; do wget -qO- $ep done ``` This should produce something like: ``` hostnames-632524106-bbpiw hostnames-632524106-ly40y hostnames-632524106-tlaok ``` 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 EndpointSlices, 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: ```shell ps auxw | grep kube-proxy ``` ```none root 4194 0.4 0.1 101864 17696 ? Sl Jul04 25:43 /usr/local/bin/kube-proxy --master=https://kubernetes-master --kubeconfig=/var/lib/kube-proxy/kubeconfig --v=2 ``` 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: ```none 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
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-application/debug-service.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-application/debug-service.md
|
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] | 0.123008
|
like: ```none 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. 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: ```shell iptables-save | grep hostnames ``` ```none -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-` chain. For each Pod endpoint, there should be a small number of rules in that `KUBE-SVC-` and one `KUBE-SEP-` 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: ```shell ipvsadm -ln ``` ```none Prot LocalAddress:Port Scheduler Flags -> RemoteAddress:Port Forward Weight ActiveConn InActConn ... TCP 10.0.1.175:80 rr -> 10.244.0.5:9376 Masq 1 0 0 -> 10.244.0.6:9376 Masq 1 0 0 -> 10.244.0.7:9376 Masq 1 0 0 ... ``` 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: ```shell curl 10.0.1.175:80 ``` ```none hostnames-632524106-bbpiw ``` If this still fails, look at the `kube-proxy` logs for specific lines like: ```none 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 {#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-mode` [flag](/docs/reference/command-line-tools-reference/kubelet/) that
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-application/debug-service.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-application/debug-service.md
|
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] | 0.048243
|
{#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-mode` [flag](/docs/reference/command-line-tools-reference/kubelet/) 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. ```shell ps auxw | grep kubelet ``` ```none root 3392 1.1 0.8 186804 65208 ? Sl 00:51 11:11 /usr/local/bin/kubelet --enable-debugging-handlers=true --config=/etc/kubernetes/manifests --allow-privileged=True --v=4 --cluster-dns=10.0.0.10 --cluster-domain=cluster.local --configure-cbr0=true --cgroup-root=/ --system-cgroups=/system --hairpin-mode=promiscuous-bridge --runtime-cgroups=/docker-daemon --kubelet-cgroups=/kubelet --babysit-daemons=true --max-pods=110 --serialize-image-pulls=false --outofdisk-transition-frequency=0 ``` \* 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. ```none 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: ```shell for intf in /sys/devices/virtual/net/cbr0/brif/\*; do cat $intf/hairpin\_mode; done ``` ```none 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: ```shell ifconfig cbr0 |grep PROMISC ``` ```none 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 EndpointSlices, 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! Contact us on [Slack](https://slack.k8s.io/) or [Forum](https://discuss.kubernetes.io) or [GitHub](https://github.com/kubernetes/kubernetes). ## {{% heading "whatsnext" %}} Visit the [troubleshooting overview document](/docs/tasks/debug/) for more information.
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-application/debug-service.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-application/debug-service.md
|
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|
{{< feature-state for\_k8s\_version="v1.11" state="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](https://github.com/kubernetes-sigs/cri-tools) repository. ## {{% heading "prerequisites" %}} `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](https://github.com/kubernetes-sigs/cri-tools/releases), 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 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`. {{}} 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: ``` runtime-endpoint: unix:///var/run/containerd/containerd.sock image-endpoint: unix:///var/run/containerd/containerd.sock timeout: 10 debug: true ``` To learn more about `crictl`, refer to the [`crictl` documentation](https://github.com/kubernetes-sigs/cri-tools/blob/master/docs/crictl.md). ## Example crictl commands The following examples show some `crictl` commands and example output. ### List pods List all pods: ```shell 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: ```shell 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: ```shell 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 ``` ### List images List all images: ```shell crictl images ``` The output is similar to this: ``` IMAGE TAG IMAGE ID SIZE busybox latest 8c811b4aec35f 1.15MB k8s-gcrio.azureedge.net/hyperkube-amd64 v1.10.3 e179bbfe5d238 665MB k8s-gcrio.azureedge.net/pause-amd64 3.1 da86e6ba6ca19 742kB nginx latest cd5239a0906a6 109MB ``` List images by repository: ```shell crictl images nginx ``` The output is similar to this: ``` IMAGE TAG IMAGE ID SIZE nginx latest cd5239a0906a6 109MB ``` Only list image IDs: ```shell crictl images -q ``` The output is similar to this: ``` sha256:8c811b4aec35f259572d0f79207bc0678df4c736eeec50bc9fec37ed936a472a sha256:e179bbfe5d238de6069f3b03fccbecc3fb4f2019af741bfff1233c4d7b2970c5 sha256:da86e6ba6ca197bf6bc5e9d900febd906b133eaa4750e6bed647b0fbe50ed43e sha256:cd5239a0906a6ccf0562354852fae04bc5b52d72a2aff9a871ddb6bd57553569 ``` ### List containers List all containers: ```shell crictl ps -a ``` The output is similar to this: ``` 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: ```shell 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
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-cluster/crictl.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-cluster/crictl.md
|
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] | 0.098708
|
Running nginx 0 1941fb4da154f k8s-gcrio.azureedge.net/hyperkube-amd64@sha256:00d814b1f7763f4ab5be80c58e98140dfc69df107f253d7fdd714b30a714260a 18 hours ago Running kube-proxy 0 ``` List running containers: ```shell 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 ``` ### Execute a command in a running container ```shell crictl exec -i -t 1f73f2d81bf98 ls ``` The output is similar to this: ``` bin dev etc home proc root sys tmp usr var ``` ### Get a container's logs Get all container logs: ```shell crictl logs 87d3992f84f74 ``` The output is similar to this: ``` 10.240.0.96 - - [06/Jun/2018:02:45:49 +0000] "GET / HTTP/1.1" 200 612 "-" "curl/7.47.0" "-" 10.240.0.96 - - [06/Jun/2018:02:45:50 +0000] "GET / HTTP/1.1" 200 612 "-" "curl/7.47.0" "-" 10.240.0.96 - - [06/Jun/2018:02:45:51 +0000] "GET / HTTP/1.1" 200 612 "-" "curl/7.47.0" "-" ``` Get only the latest `N` lines of logs: ```shell crictl logs --tail=1 87d3992f84f74 ``` The output is similar to this: ``` 10.240.0.96 - - [06/Jun/2018:02:45:51 +0000] "GET / HTTP/1.1" 200 612 "-" "curl/7.47.0" "-" ``` ## {{% heading "whatsnext" %}} \* [Learn more about `crictl`](https://github.com/kubernetes-sigs/cri-tools).
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-cluster/crictl.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-cluster/crictl.md
|
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] | 0.064102
|
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 can manage topology within nodes. This means helping the kubelet to configure the host operating system so that Pods and containers are placed on the correct side of inner boundaries, such as \_NUMA domains\_. (NUMA is an abbreviation of \_non-uniform memory access\_, and refers to an idea that CPUs might be topologically closer to specific regions of memory, due to the physical layout of the hardware components and the way that these are connected). ## Sources of troubleshooting information You can use the following means to troubleshoot the reason why a pod could not be deployed or became rejected at a node, in the context of topology management: - \_Pod status\_ - indicates topology affinity errors - \_system logs\_ - include valuable information for debugging; for example, about generated hints - \_kubelet state file\_ - the dump of internal state of the Memory Manager (including the \_node map\_ and \_memory maps\_) - You can use the [device plugin resource API](#device-plugin-resource-api) to retrieve information about the memory reserved for containers ## Troubleshoot `TopologyAffinityError` {#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: ```shell kubectl get pods ``` ```none NAME READY STATUS RESTARTS AGE guaranteed 0/1 TopologyAffinityError 0 113s ``` Use `kubectl describe pod ` or `kubectl events` to obtain a detailed error message: ```none Warning TopologyAffinityError 10m kubelet, dell8 Resources cannot be allocated with Topology locality ``` ## Examine system logs Search system logs with respect to a particular pod. The set of hints generated by CPU Manager should be present in the logs. Also, the set of hints that Memory Manager generated for the pod can be found in the logs. Topology Manager merges these hints to calculate a single best hint. The best hint should also be 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; for example to find out information about `cgroups` and `cpuset.mems` updates. ## Examples ### Examine the memory manager state on a node Let us first deploy a sample `Guaranteed` pod whose specification is as follows: ```yaml apiVersion: v1 kind: Pod metadata: name: guaranteed spec: containers: - name: guaranteed image: consumer imagePullPolicy: Never resources: limits: cpu: "2" memory: 150Gi requests: cpu: "2" memory: 150Gi command: ["sleep","infinity"] ``` Next, log into the node where it was deployed and examine the state file in `/var/lib/kubelet/memory\_manager\_state`: ```json { "policyName":"Static", "machineState":{ "0":{ "numberOfAssignments":1, "memoryMap":{ "hugepages-1Gi":{ "total":0, "systemReserved":0, "allocatable":0, "reserved":0, "free":0 }, "memory":{ "total":134987354112, "systemReserved":3221225472, "allocatable":131766128640, "reserved":131766128640, "free":0 } }, "nodes":[ 0, 1 ] }, "1":{ "numberOfAssignments":1, "memoryMap":{ "hugepages-1Gi":{ "total":0, "systemReserved":0, "allocatable":0, "reserved":0, "free":0 }, "memory":{ "total":135286722560, "systemReserved":2252341248, "allocatable":133034381312, "reserved":29295144960, "free":103739236352 } }, "nodes":[ 0, 1 ] } }, "entries":{ "fa9bdd38-6df9-4cf9-aa67-8c4814da37a8":{ "guaranteed":[ { "numaAffinity":[ 0, 1 ], "type":"memory", "size":161061273600 } ] } }, "checksum":4142013182 } ``` It can be deduced from the state file that the pod was pinned to both NUMA nodes, i.e.: ```json "numaAffinity":[ 0,
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-cluster/topology.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-cluster/topology.md
|
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] | 0.271574
|
"systemReserved":2252341248, "allocatable":133034381312, "reserved":29295144960, "free":103739236352 } }, "nodes":[ 0, 1 ] } }, "entries":{ "fa9bdd38-6df9-4cf9-aa67-8c4814da37a8":{ "guaranteed":[ { "numaAffinity":[ 0, 1 ], "type":"memory", "size":161061273600 } ] } }, "checksum":4142013182 } ``` It can be deduced from the state file that the pod was pinned to both NUMA nodes, i.e.: ```json "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. 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. ## Check the device plugin resource API {#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](/docs/concepts/extend-kubernetes/compute-storage-net/device-plugins/#grpc-endpoint-list), 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.
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-cluster/topology.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-cluster/topology.md
|
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] | 0.253446
|
## Node-level troubleshooting {#troubleshooting-node} 1. 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](/docs/concepts/windows/intro/#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 configuration file. {{< /note >}} 1. My pods show status as `ErrImgPull` or `ImagePullBackOff` Ensure that your Pod is getting scheduled to a [compatible](https://docs.microsoft.com/virtualization/windowscontainers/deploy-containers/version-compatibility) Windows Node. More information on how to specify a compatible node for your Pod can be found in [this guide](/docs/concepts/windows/user-guide/#ensuring-os-specific-workloads-land-on-the-appropriate-container-host). ## Network troubleshooting {#troubleshooting-network} 1. 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). 1. 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 ` with corresponding `curl ` commands. If you are still facing problems, most likely your network configuration in [cni.conf](https://github.com/Microsoft/SDN/blob/master/Kubernetes/flannel/l2bridge/cni/config/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](/docs/concepts/cluster-administration/networking/)) is for cluster communication to occur without NAT internally. To honor this requirement, there is an [ExceptionList](https://github.com/Microsoft/SDN/blob/master/Kubernetes/flannel/l2bridge/cni/config/cni.conf#L20) 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: ```conf "ExceptionList": [ "10.244.0.0/16", # Cluster subnet "10.96.0.0/12", # Service subnet "10.127.130.0/24" # Management (host) subnet ] ``` 1. 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. 1. 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](/docs/reference/command-line-tools-reference/kube-proxy/). To resolve it, users need to pass the hostname to kube-proxy as follows: ```powershell C:\k\kube-proxy.exe --hostname-override=$(hostname) ``` 1. 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. 1. 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: ```powershell Get-HnsNetwork | ? Name -ieq "cbr0" Get-NetAdapter | ? Name -Like "vEthernet (Ethernet\*" ``` Often it is worthwhile to modify the [InterfaceName](https://github.com/microsoft/SDN/blob/master/Kubernetes/flannel/start.ps1#L7) 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. 1. DNS resolution is not properly working Check the DNS limitations for Windows in this [section](/docs/concepts/services-networking/dns-pod-service/#dns-windows). 1. `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
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-cluster/windows.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-cluster/windows.md
|
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] | 0.097076
|
[section](/docs/concepts/services-networking/dns-pod-service/#dns-windows). 1. `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](https://github.com/kubernetes/kubernetes/tree/master/build/pause/windows/wincat). 1. 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: ```PowerShell [Environment]::SetEnvironmentVariable("HTTP\_PROXY", "http://proxy.example.com:80/", [EnvironmentVariableTarget]::Machine) [Environment]::SetEnvironmentVariable("HTTPS\_PROXY", "http://proxy.example.com:443/", [EnvironmentVariableTarget]::Machine) ``` ### Flannel troubleshooting 1. 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: ```powershell Remove-Item C:\k\SourceVip.json Remove-Item C:\k\SourceVipRequest.json ``` 1. Flanneld is stuck in "Waiting for the Network to be created" There are numerous reports of this [issue](https://github.com/coreos/flannel/issues/1066); 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: ```powershell [Environment]::SetEnvironmentVariable("NODE\_NAME", "") C:\flannel\flanneld.exe --kubeconfig-file=c:\k\config --iface= --ip-masq=1 --kube-subnet-mgr=1 ``` 1. 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: ```env FLANNEL\_NETWORK=10.244.0.0/16 FLANNEL\_SUBNET=10.244.4.1/24 FLANNEL\_MTU=1500 FLANNEL\_IPMASQ=true ``` ### Further investigation If these steps don't resolve your problem, you can get help running Windows containers on Windows nodes in Kubernetes through: \* StackOverflow [Windows Server Container](https://stackoverflow.com/questions/tagged/windows-server-container) topic \* Kubernetes Official Forum [discuss.kubernetes.io](https://discuss.kubernetes.io/) \* Kubernetes Slack [#SIG-Windows Channel](https://kubernetes.slack.com/messages/sig-windows)
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-cluster/windows.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-cluster/windows.md
|
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|
This documentation is about investigating and diagnosing {{}} 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. ## {{% heading "prerequisites" %}} \* You need to have a Kubernetes cluster. \* You also need to have `kubectl` installed - see [install tools](/docs/tasks/tools/#kubectl) ## 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. Check kubectl version: ```shell kubectl version ``` You'll see a similar output: ```console Client Version: version.Info{Major:"1", Minor:"27", GitVersion:"v1.27.4",GitCommit:"fa3d7990104d7c1f16943a67f11b154b71f6a132", GitTreeState:"clean",BuildDate:"2023-07-19T12:20:54Z", GoVersion:"go1.20.6", Compiler:"gc", Platform:"linux/amd64"} Kustomize Version: v5.0.1 Server Version: version.Info{Major:"1", Minor:"27", GitVersion:"v1.27.3",GitCommit:"25b4e43193bcda6c7328a6d147b1fb73a33f1598", GitTreeState:"clean",BuildDate:"2023-06-14T09:47:40Z", GoVersion:"go1.20.5", Compiler:"gc", Platform:"linux/amd64"} ``` If you see `Unable to connect to the server: dial tcp :8443: i/o timeout`, instead of `Server Version`, you need to troubleshoot kubectl connectivity with your cluster. Make sure you have installed the kubectl by following the [official documentation for installing kubectl](/docs/tasks/tools/#kubectl), and you have properly configured the `$PATH` environment variable. ## Check kubeconfig The `kubectl` requires a `kubeconfig` file to connect to a Kubernetes cluster. The `kubeconfig` file is usually located under the `~/.kube/config` directory. Make sure that you have a valid `kubeconfig` file. If you don't have a `kubeconfig` file, you can obtain it from your Kubernetes administrator, or you can copy it from your Kubernetes control plane's `/etc/kubernetes/admin.conf` directory. If you have deployed your Kubernetes cluster on a cloud platform and lost your `kubeconfig` file, you can re-generate it using your cloud provider's tools. Refer the cloud provider's documentation for re-generating a `kubeconfig` file. Check if the `$KUBECONFIG` environment variable is configured correctly. You can set `$KUBECONFIG`environment 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](/docs/tasks/access-application-cluster/configure-access-multiple-clusters/). Ensure that you are using the correct context to interact with your cluster. List available contexts: ```shell kubectl config get-contexts ``` Switch to the appropriate context: ```shell kubectl config use-context ``` ## API server and load balancer The {{}} 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 \* Additional tools required - `base64` and `openssl` version 3.0 or above. The Kubernetes API server only serves HTTPS
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-cluster/troubleshoot-kubectl.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-cluster/troubleshoot-kubectl.md
|
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] | 0.039936
|
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 \* Additional tools required - `base64` and `openssl` version 3.0 or above. 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. Verify the expiry of these certificates: ```shell kubectl config view --flatten --output 'jsonpath={.clusters[0].cluster.certificate-authority-data}' | base64 -d | openssl x509 -noout -dates ``` output: ```console notBefore=Feb 13 05:57:47 2024 GMT notAfter=Feb 10 06:02:47 2034 GMT ``` ```shell kubectl config view --flatten --output 'jsonpath={.users[0].user.client-certificate-data}'| base64 -d | openssl x509 -noout -dates ``` output: ```console notBefore=Feb 13 05:57:47 2024 GMT notAfter=Feb 12 06:02:50 2025 GMT ``` ## Verify kubectl helpers 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: ```shell kubectl config view ``` If you previously used a helper tool (for example, `kubectl-oidc-login`), ensure that it is still installed and configured correctly.
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-cluster/troubleshoot-kubectl.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-cluster/troubleshoot-kubectl.md
|
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0.04625057056546211,
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0.008843811228871346,
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] | 0.033562
|
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](/docs/reference/command-line-tools-reference/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. - `Panic` - Events generated when a panic occurred. {{< note >}} The configuration of an [Audit Event configuration](/docs/reference/config-api/apiserver-audit.v1/#audit-k8s-io-v1-Event) is different from the [Event](/docs/reference/generated/kubernetes-api/{{< param "version" >}}/#event-v1-core) API object. {{< /note >}} 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](/docs/reference/config-api/apiserver-audit.v1/#audit-k8s-io-v1-Policy). 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 events with metadata (requesting user, timestamp, resource, verb, etc.) but not request or response body. - `Request` - log events with request metadata and body but not response body. This does not apply for non-resource requests. - `RequestResponse` - log events with request metadata, request body and response body. 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. Below is an example audit policy file: {{% code\_sample file="audit/audit-policy.yaml" %}} You can use a minimal audit policy file to log all requests at the `Metadata` level: ```yaml # Log all requests at the Metadata level. apiVersion: audit.k8s.io/v1 kind: Policy rules: - 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](https://github.com/kubernetes/kubernetes/blob/master/cluster/gce/gci/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. You can also refer to the [`Policy` configuration reference](/docs/reference/config-api/apiserver-audit.v1/#audit-k8s-io-v1-Policy) for details about the fields defined. ## Audit backends 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
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-cluster/audit.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-cluster/audit.md
|
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|
script. You can also refer to the [`Policy` configuration reference](/docs/reference/config-api/apiserver-audit.v1/#audit-k8s-io-v1-Policy) for details about the fields defined. ## Audit backends 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](/docs/reference/config-api/apiserver-audit.v1/#audit-k8s-io-v1-Event). {{< 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`: ```json [ { "op": "replace", "path": "/spec/parallelism", "value": 0 }, { "op": "remove", "path": "/spec/template/spec/containers/0/terminationMessagePolicy" } ] ``` {{< /note >}} ### Log backend The log backend writes audit events to a file in [JSONlines](https://jsonlines.org/) 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: ```yaml - --audit-policy-file=/etc/kubernetes/audit-policy.yaml - --audit-log-path=/var/log/kubernetes/audit/audit.log ``` then mount the volumes: ```yaml ... volumeMounts: - mountPath: /etc/kubernetes/audit-policy.yaml name: audit readOnly: true - mountPath: /var/log/kubernetes/audit/ name: audit-log readOnly: false ``` and finally configure the `hostPath`: ```yaml ... volumes: - name: audit hostPath: path: /etc/kubernetes/audit-policy.yaml type: File - name: audit-log hostPath: path: /var/log/kubernetes/audit/ type: DirectoryOrCreate ``` ### Webhook backend 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](/docs/tasks/access-application-cluster/configure-access-multiple-clusters). - `--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 {#batching} Both `log` and `webhook` backends support batching. Below is a list of available flags specific to each backend. By default, batching and throttling are \*\*enabled\*\* for the `webhook` backend and \*\*disabled\*\* for the `log` backend. {{< tabs name="tab\_with\_md" >}} {{% tab name="webhook" %}} - `--audit-webhook-mode` defines the buffering strategy. One of the following: - `batch` - buffer events and asynchronously process them in batches. This is the default mode for the `webhook` backend. - `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. The default value is 10000. - `--audit-webhook-batch-max-size` defines the maximum number of events in one batch. The default value is 400. - `--audit-webhook-batch-max-wait` defines the maximum
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-cluster/audit.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-cluster/audit.md
|
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] | 0.162394
|
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. The default value is 10000. - `--audit-webhook-batch-max-size` defines the maximum number of events in one batch. The default value is 400. - `--audit-webhook-batch-max-wait` defines the maximum amount of time to wait before unconditionally batching events in the queue. The default value is 30 seconds. - `--audit-webhook-batch-throttle-enable` defines whether batching throttling is enabled. Throttling is enabled by default. - `--audit-webhook-batch-throttle-qps` defines the maximum average number of batches generated per second. The default value is 10. - `--audit-webhook-batch-throttle-burst` defines the maximum number of batches generated at the same moment if the allowed QPS was underutilized previously. The default value is 15. {{% /tab %}} {{% tab name="log" %}} - `--audit-log-mode` defines the buffering strategy. One of the following: - `batch` - buffer events and asynchronously process them in batches. Batching is not recommended for the `log` backend. - `blocking` - block API server responses on processing each individual event. This is the default mode for the `log` backend. - `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 (batching is \*\*disabled\*\* by default for the `log` backend, and when batching is disabled, all batching-related flags are ignored): - `--audit-log-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-log-batch-max-size` defines the maximum number of events in one batch. - `--audit-log-batch-max-wait` defines the maximum amount of time to wait before unconditionally batching events in the queue. - `--audit-log-batch-throttle-enable` defines whether batching throttling is enabled. - `--audit-log-batch-throttle-qps` defines the maximum average number of batches generated per second. - `--audit-log-batch-throttle-burst` defines the maximum number of batches generated at the same moment if the allowed QPS was underutilized previously. {{% /tab %}} {{< /tabs >}} ## 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 {#truncate} 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. ## {{%
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-cluster/audit.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-cluster/audit.md
|
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] | 0.103868
|
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. ## {{% heading "whatsnext" %}} \* Learn about [Mutating webhook auditing annotations](/docs/reference/access-authn-authz/extensible-admission-controllers/#mutating-webhook-auditing-annotations). \* Learn more about [`Event`](/docs/reference/config-api/apiserver-audit.v1/#audit-k8s-io-v1-Event) and the [`Policy`](/docs/reference/config-api/apiserver-audit.v1/#audit-k8s-io-v1-Policy) resource types by reading the Audit configuration reference.
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-cluster/audit.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-cluster/audit.md
|
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] | 0.107785
|
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](/docs/concepts/workloads/pods/), [services](/docs/concepts/services-networking/service/), 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](#resource-metrics-pipeline) or [full metrics](#full-metrics-pipeline) 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](/docs/tasks/run-application/horizontal-pod-autoscale/) controller, as well as the `kubectl top` utility. These metrics are collected by the lightweight, short-term, in-memory [metrics-server](https://github.com/kubernetes-sigs/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](/docs/reference/command-line-tools-reference/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](https://github.com/google/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. Kubernetes is designed to work with [OpenMetrics](https://openmetrics.io/), which is one of the [CNCF Observability and Analysis - Monitoring Projects](https://landscape.cncf.io/?group=projects-and-products&view-mode=card#observability-and-analysis--monitoring), built upon and carefully extending [Prometheus exposition format](https://prometheus.io/docs/instrumenting/exposition\_formats/) in almost 100% backwards-compatible ways. If you glance over at the [CNCF Landscape](https://landscape.cncf.io/?group=projects-and-products&view-mode=card#observability-and-analysis--monitoring), 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](https://landscape.cncf.io/?group=projects-and-products&view-mode=card#observability-and-analysis--monitoring) are available. Your monitoring system should be capable of handling the [OpenMetrics](https://openmetrics.io/) metrics transmission standard and needs to be chosen to best fit into your overall design and deployment of your infrastructure platform. ## {{% heading "whatsnext" %}} Learn about additional debugging tools, including: \* [Logging](/docs/concepts/cluster-administration/logging/) \* [Getting into containers via `exec`](/docs/tasks/debug/debug-application/get-shell-running-container/) \* [Connecting to
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-cluster/resource-usage-monitoring.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-cluster/resource-usage-monitoring.md
|
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] | 0.179168
|
should be capable of handling the [OpenMetrics](https://openmetrics.io/) metrics transmission standard and needs to be chosen to best fit into your overall design and deployment of your infrastructure platform. ## {{% heading "whatsnext" %}} Learn about additional debugging tools, including: \* [Logging](/docs/concepts/cluster-administration/logging/) \* [Getting into containers via `exec`](/docs/tasks/debug/debug-application/get-shell-running-container/) \* [Connecting to containers via proxies](/docs/tasks/extend-kubernetes/http-proxy-access-api/) \* [Connecting to containers via port forwarding](/docs/tasks/access-application-cluster/port-forward-access-application-cluster/) \* [Inspect Kubernetes node with crictl](/docs/tasks/debug/debug-cluster/crictl/)
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-cluster/resource-usage-monitoring.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-cluster/resource-usage-monitoring.md
|
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] | 0.146858
|
\*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 [Condition](/docs/concepts/architecture/nodes/#condition)s or as [Event](/docs/reference/kubernetes-api/cluster-resources/event-v1)s. To learn how to install and use Node Problem Detector, see [Node Problem Detector project documentation](https://github.com/kubernetes/node-problem-detector). ## {{% heading "prerequisites" %}} {{< include "task-tutorial-prereqs.md" >}} ## Limitations \* 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](#support-other-log-format). ## Enabling Node Problem Detector Some cloud providers enable Node Problem Detector as an {{< glossary\_tooltip text="Addon" term\_id="addons" >}}. You can also enable Node Problem Detector with `kubectl` or by creating an Addon DaemonSet. ### Using kubectl to enable Node Problem Detector {#using-kubectl} `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: 1. Create a Node Problem Detector configuration similar to `node-problem-detector.yaml`: {{% code\_sample file="debug/node-problem-detector.yaml" %}} {{< note >}} You should verify that the system log directory is right for your operating system distribution. {{< /note >}} 1. Start node problem detector with `kubectl`: ```shell kubectl apply -f https://k8s.io/examples/debug/node-problem-detector.yaml ``` ### Using an Addon pod to enable Node Problem Detector {#using-addon-pod} 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](https://github.com/kubernetes/node-problem-detector/tree/v0.8.12/config) is embedded when building the Docker image of Node Problem Detector. However, you can use a [`ConfigMap`](/docs/tasks/configure-pod-container/configure-pod-configmap/) to overwrite the configuration: 1. Change the configuration files in `config/` 1. Create the `ConfigMap` `node-problem-detector-config`: ```shell kubectl create configmap node-problem-detector-config --from-file=config/ ``` 1. Change the `node-problem-detector.yaml` to use the `ConfigMap`: {{% code\_sample file="debug/node-problem-detector-configmap.yaml" %}} 1. Recreate the Node Problem Detector with the new configuration file: ```shell # If you have a node-problem-detector running, delete before recreating kubectl 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`. {{< /note >}} 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](https://github.com/kubernetes/node-problem-detector/blob/v0.8.12/config/kernel-monitor-filelog.json), [kmsg](https://github.com/kubernetes/node-problem-detector/blob/v0.8.12/config/kernel-monitor.json), [kernel](https://github.com/kubernetes/node-problem-detector/blob/v0.8.12/config/kernel-monitor-counter.json), [abrt](https://github.com/kubernetes/node-problem-detector/blob/v0.8.12/config/abrt-adaptor.json), and [systemd](https://github.com/kubernetes/node-problem-detector/blob/v0.8.12/config/systemd-monitor-counter.json). - A `SystemStatsMonitor` type of daemon collects various health-related system stats as metrics. You can customize its behavior by updating its [configuration file](https://github.com/kubernetes/node-problem-detector/blob/v0.8.12/config/system-stats-monitor.json). - 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](https://github.com/kubernetes/node-problem-detector/blob/v0.8.12/config/custom-plugin-monitor.json). - A `HealthChecker` type of daemon checks the health of the kubelet and container runtime on a node. ### Adding support for other log format {#support-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](https://github.com/kubernetes/node-problem-detector/blob/v0.8.12/pkg/systemlogmonitor/logwatchers/types/log\_watcher.go). ### Adding
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-cluster/monitor-node-health.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-cluster/monitor-node-health.md
|
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] | 0.21722
|
- A `HealthChecker` type of daemon checks the health of the kubelet and container runtime on a node. ### Adding support for other log format {#support-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](https://github.com/kubernetes/node-problem-detector/blob/v0.8.12/pkg/systemlogmonitor/logwatchers/types/log\_watcher.go). ### 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](https://docs.google.com/document/d/1jK\_5YloSYtboj-DtfjmYKxfNnUxCAvohLnsH5aGCAYQ/edit#). ## 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](https://github.com/kubernetes/node-problem-detector/blob/v0.8.12/config/exporter/stackdriver-exporter.json). ## 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](https://github.com/kubernetes/node-problem-detector/issues/2#issuecomment-220255629).
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-cluster/monitor-node-health.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-cluster/monitor-node-health.md
|
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] | 0.217944
|
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. The [HorizontalPodAutoscaler](/docs/concepts/workloads/autoscaling/horizontal-pod-autoscale/) (HPA) and [VerticalPodAutoscaler](/docs/concepts/workloads/autoscaling/vertical-pod-autoscale/) (VPA) use data from the metrics API to adjust workload replicas and resources to meet customer demand. You can also view the resource metrics using the [`kubectl top`](/docs/reference/generated/kubectl/kubectl-commands#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](/docs/tasks/debug/debug-cluster/resource-usage-monitoring/#full-metrics-pipeline) that uses the \_Custom Metrics API\_. {{< /note >}} Figure 1 illustrates the architecture of the resource metrics pipeline. {{< mermaid >}} flowchart RL subgraph cluster[Cluster] direction RL S[ ] A[Metrics- Server] subgraph B[Nodes] direction TB D[cAdvisor] --> C[kubelet] E[Container runtime] --> D E1[Container runtime] --> D P[pod data] -.- C end L[API server] W[HPA] C ---->|node level resource metrics| A -->|metrics API| L --> W end L ---> K[kubectl top] classDef box fill:#fff,stroke:#000,stroke-width:1px,color:#000; class W,B,P,K,cluster,D,E,E1 box classDef spacewhite fill:#ffffff,stroke:#fff,stroke-width:0px,color:#000 class S spacewhite classDef k8s fill:#326ce5,stroke:#fff,stroke-width:1px,color:#fff; class A,L,C k8s {{< /mermaid >}} Figure 1. Resource Metrics Pipeline The architecture components, from right to left in the figure, consist of the following: \* [cAdvisor](https://github.com/google/cadvisor): Daemon for collecting, aggregating and exposing container metrics included in Kubelet. \* [kubelet](/docs/concepts/architecture/#kubelet): Node agent for managing container resources. Resource metrics are accessible using the `/metrics/resource` and `/stats` kubelet API endpoints. \* [node level resource metrics](/docs/reference/instrumentation/node-metrics): API provided by the kubelet for discovering and retrieving per-node summarized stats available through the `/metrics/resource` endpoint. \* [metrics-server](#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](#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](https://github.com/kubernetes/community/blob/master/contributors/devel/sig-node/cri-container-stats.md) in order for metrics to be available to the kubelet. {{< /note >}} ## Metrics API {{< feature-state for\_k8s\_version="1.8" state="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: ```shell kubectl get --raw "/apis/metrics.k8s.io/v1beta1/nodes/minikube" | jq '.' ``` Here is the same API call using `curl`: ```shell curl http://localhost:8080/apis/metrics.k8s.io/v1beta1/nodes/minikube ``` Sample response: ```json { "kind": "NodeMetrics", "apiVersion": "metrics.k8s.io/v1beta1", "metadata": { "name": "minikube", "selfLink": "/apis/metrics.k8s.io/v1beta1/nodes/minikube", "creationTimestamp": "2022-01-27T18:48:43Z" }, "timestamp": "2022-01-27T18:48:33Z", "window": "30s", "usage": { "cpu": "487558164n", "memory": "732212Ki" } } ``` 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: ```shell kubectl get --raw
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-cluster/resource-metrics-pipeline.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-cluster/resource-metrics-pipeline.md
|
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|
{ "name": "minikube", "selfLink": "/apis/metrics.k8s.io/v1beta1/nodes/minikube", "creationTimestamp": "2022-01-27T18:48:43Z" }, "timestamp": "2022-01-27T18:48:33Z", "window": "30s", "usage": { "cpu": "487558164n", "memory": "732212Ki" } } ``` 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: ```shell kubectl get --raw "/apis/metrics.k8s.io/v1beta1/namespaces/kube-system/pods/kube-scheduler-minikube" | jq '.' ``` Here is the same API call using `curl`: ```shell curl http://localhost:8080/apis/metrics.k8s.io/v1beta1/namespaces/kube-system/pods/kube-scheduler-minikube ``` Sample response: ```json { "kind": "PodMetrics", "apiVersion": "metrics.k8s.io/v1beta1", "metadata": { "name": "kube-scheduler-minikube", "namespace": "kube-system", "selfLink": "/apis/metrics.k8s.io/v1beta1/namespaces/kube-system/pods/kube-scheduler-minikube", "creationTimestamp": "2022-01-27T19:25:00Z" }, "timestamp": "2022-01-27T19:24:31Z", "window": "30s", "containers": [ { "name": "kube-scheduler", "usage": { "cpu": "9559630n", "memory": "22244Ki" } } ] } ``` The Metrics API is defined in the [k8s.io/metrics](https://github.com/kubernetes/metrics) repository. You must enable the [API aggregation layer](/docs/tasks/extend-kubernetes/configure-aggregation-layer/) and register an [APIService](/docs/reference/kubernetes-api/cluster-resources/api-service-v1/) for the `metrics.k8s.io` API. To learn more about the Metrics API, see [resource metrics API design](https://git.k8s.io/design-proposals-archive/instrumentation/resource-metrics-api.md), the [metrics-server repository](https://github.com/kubernetes-sigs/metrics-server) and the [resource metrics API](https://github.com/kubernetes/metrics#resource-metrics-api). {{< note >}} You must deploy the metrics-server or alternative adapter that serves the Metrics API to be able to access it. {{< /note >}} ## 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](/docs/concepts/configuration/manage-resources-containers/#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](/docs/concepts/configuration/manage-resources-containers/#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](/docs/reference/command-line-tools-reference/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 ## {{% heading "whatsnext" %}} To learn more about the metrics-server, see the [metrics-server repository](https://github.com/kubernetes-sigs/metrics-server). You can also check out the following: \* [metrics-server design](https://git.k8s.io/design-proposals-archive/instrumentation/metrics-server.md) \* [metrics-server FAQ](https://github.com/kubernetes-sigs/metrics-server/blob/master/FAQ.md) \* [metrics-server known issues](https://github.com/kubernetes-sigs/metrics-server/blob/master/KNOWN\_ISSUES.md) \* [metrics-server releases](https://github.com/kubernetes-sigs/metrics-server/releases) \* [Horizontal Pod Autoscaling](/docs/tasks/run-application/horizontal-pod-autoscale/) To learn about how the kubelet serves node metrics, and how you can access those via the Kubernetes API, read [Node Metrics Data](/docs/reference/instrumentation/node-metrics).
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-cluster/resource-metrics-pipeline.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-cluster/resource-metrics-pipeline.md
|
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] | 0.147097
|
You can also check out the following: \* [metrics-server design](https://git.k8s.io/design-proposals-archive/instrumentation/metrics-server.md) \* [metrics-server FAQ](https://github.com/kubernetes-sigs/metrics-server/blob/master/FAQ.md) \* [metrics-server known issues](https://github.com/kubernetes-sigs/metrics-server/blob/master/KNOWN\_ISSUES.md) \* [metrics-server releases](https://github.com/kubernetes-sigs/metrics-server/releases) \* [Horizontal Pod Autoscaling](/docs/tasks/run-application/horizontal-pod-autoscale/) To learn about how the kubelet serves node metrics, and how you can access those via the Kubernetes API, read [Node Metrics Data](/docs/reference/instrumentation/node-metrics).
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-cluster/resource-metrics-pipeline.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-cluster/resource-metrics-pipeline.md
|
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{{% thirdparty-content %}} 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](/docs/tasks/debug/debug-application/get-shell-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. ## {{% heading "prerequisites" %}} \* Kubernetes cluster is installed \* `kubectl` is configured to communicate with the cluster \* [Telepresence](https://www.telepresence.io/docs/latest/quick-start/) is installed ## 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://) ``` 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](https://www.getambassador.io/docs/telepresence/latest/concepts/intercepts/#global-intercept) or a subset of the traffic (when you create a [personal intercept](https://www.getambassador.io/docs/telepresence/latest/concepts/intercepts/#personal-intercept)) to your local development environment. ## {{% heading "whatsnext" %}} If you're interested in a hands-on tutorial, check out [this tutorial](https://cloud.google.com/community/tutorials/developing-services-with-k8s) that walks through locally developing the Guestbook application on Google Kubernetes Engine. For further reading, visit the [Telepresence website](https://www.telepresence.io).
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-cluster/local-debugging.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-cluster/local-debugging.md
|
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] | 0.174717
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This page shows how to debug a [node](/docs/concepts/architecture/nodes/) running on the Kubernetes cluster using `kubectl debug` command. ## {{% heading "prerequisites" %}} {{< include "task-tutorial-prereqs.md" >}} {{< version-check >}} 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: ```shell kubectl debug node/mynode -it --image=ubuntu ``` ```console 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. {{< /note >}} 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 or use the `--profile=sysadmin` flag. \* By applying [Debugging Profiles](/docs/tasks/debug/debug-application/debug-running-pod/#debugging-profiles), you can set specific properties such as [securityContext](/docs/tasks/configure-pod-container/security-context/) to a debugging Pod. ## {{% heading "cleanup" %}} When you finish using the debugging Pod, delete it: ```shell kubectl get pods ``` ```none NAME READY STATUS RESTARTS AGE node-debugger-mynode-pdx84 0/1 Completed 0 8m1s ``` ```shell # Change the pod name accordingly kubectl delete pod node-debugger-mynode-pdx84 --now ``` ```none 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 ](/docs/tasks/debug/debug-cluster/#example-debugging-a-down-unreachable-node) in that case. {{< /note >}}
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-cluster/kubectl-node-debug.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-cluster/kubectl-node-debug.md
|
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] | 0.131317
|
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](/docs/tasks/debug/debug-application/) for tips on application debugging. You may also visit the [troubleshooting overview document](/docs/tasks/debug/) for more information. For troubleshooting {{}}, refer to [Troubleshooting kubectl](/docs/tasks/debug/debug-cluster/troubleshoot-kubectl/). ## Listing your cluster The first thing to debug in your cluster is if your nodes are all registered correctly. Run the following command: ```shell 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: ```shell 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). ```shell kubectl get nodes ``` ```none NAME STATUS ROLES AGE VERSION kube-worker-1 NotReady 1h v1.23.3 kubernetes-node-bols Ready 1h v1.23.3 kubernetes-node-st6x Ready 1h v1.23.3 kubernetes-node-unaj Ready 1h v1.23.3 ``` ```shell kubectl describe node kube-worker-1 ``` ```none Name: kube-worker-1 Roles: 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 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: 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: ... ``` ```shell kubectl get node kube-worker-1 -o
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-cluster/_index.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-cluster/_index.md
|
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] | 0.129312
|
(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: ... ``` ```shell kubectl get node kube-worker-1 -o yaml ``` ```yaml apiVersion: v1 kind: Node metadata: annotations: node.alpha.kubernetes.io/ttl: "0" volumes.kubernetes.io/controller-managed-attach-detach: "true" creationTimestamp: "2022-02-17T21:46:30Z" 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 name: kube-worker-1 resourceVersion: "4026" uid: 98efe7cb-2978-4a0b-842a-1a7bf12c05f8 spec: {} status: addresses: - address: 192.168.0.113 type: InternalIP - address: kube-worker-1 type: Hostname allocatable: cpu: "2" ephemeral-storage: "14167048988" hugepages-2Mi: "0" memory: 1922788Ki pods: "110" capacity: cpu: "2" ephemeral-storage: 15372232Ki hugepages-2Mi: "0" memory: 2025188Ki pods: "110" conditions: - lastHeartbeatTime: "2022-02-17T22:20:32Z" lastTransitionTime: "2022-02-17T22:20:32Z" message: Weave pod has set this reason: WeaveIsUp status: "False" type: NetworkUnavailable - lastHeartbeatTime: "2022-02-17T22:20:15Z" lastTransitionTime: "2022-02-17T22:13:25Z" message: kubelet has sufficient memory available reason: KubeletHasSufficientMemory status: "False" type: MemoryPressure - lastHeartbeatTime: "2022-02-17T22:20:15Z" lastTransitionTime: "2022-02-17T22:13:25Z" message: kubelet has no disk pressure reason: KubeletHasNoDiskPressure status: "False" type: DiskPressure - lastHeartbeatTime: "2022-02-17T22:20:15Z" lastTransitionTime: "2022-02-17T22:13:25Z" message: kubelet has sufficient PID available reason: KubeletHasSufficientPID status: "False" type: PIDPressure - lastHeartbeatTime: "2022-02-17T22:20:15Z" lastTransitionTime: "2022-02-17T22:15:15Z" message: kubelet is posting ready status reason: KubeletReady status: "True" type: Ready daemonEndpoints: kubeletEndpoint: Port: 10250 nodeInfo: architecture: amd64 bootID: 22333234-7a6b-44d4-9ce1-67e31dc7e369 containerRuntimeVersion: containerd://1.5.9 kernelVersion: 5.13.0-28-generic kubeProxyVersion: v1.23.3 kubeletVersion: v1.23.3 machineID: 9384e2927f544209b5d7b67474bbf92b operatingSystem: linux osImage: Ubuntu 21.10 systemUUID: 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 {{}}, 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
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-cluster/_index.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-cluster/_index.md
|
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] | 0.170884
|
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 the 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 - Mitigates: Apiserver backing storage lost - Action: Use [high-availability](/docs/setup/production-environment/tools/kubeadm/high-availability/) configuration - Mitigates: Control plane node shutdown or control plane components (scheduler, API server, controller-manager) crashing - Will tolerate one or more simultaneous node or component failures - Mitigates: API server backing storage (i.e., etcd's data directory) lost - Assumes HA (highly-available) etcd configuration - Action: Snapshot apiserver PDs/EBS-volumes periodically - Mitigates: Apiserver backing storage lost - Mitigates: Some cases of operator error - Mitigates: Some cases of Kubernetes software fault - Action: use replication controller and services in front of pods - Mitigates: Node shutdown - Mitigates: Kubelet software fault - Action: applications (containers) designed to tolerate unexpected restarts - Mitigates: Node shutdown - Mitigates: Kubelet software fault ## {{% heading "whatsnext" %}} \* Learn about the metrics available in the [Resource Metrics Pipeline](/docs/tasks/debug/debug-cluster/resource-metrics-pipeline/) \* Discover additional tools for [monitoring resource usage](/docs/tasks/debug/debug-cluster/resource-usage-monitoring/) \* Use Node Problem Detector to [monitor node health](/docs/tasks/debug/debug-cluster/monitor-node-health/) \* Use `kubectl debug node` to [debug Kubernetes nodes](/docs/tasks/debug/debug-cluster/kubectl-node-debug) \* Use `crictl` to [debug Kubernetes nodes](/docs/tasks/debug/debug-cluster/crictl/) \* Get more information about [Kubernetes auditing](/docs/tasks/debug/debug-cluster/audit/) \* Use `telepresence` to [develop and debug services locally](/docs/tasks/debug/debug-cluster/local-debugging/)
| null |
./vendor/kubernetes/content/en/docs/tasks/debug/debug-cluster/_index.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/tasks/debug/debug-cluster/_index.md
|
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|
This section lists the different ways to set up and run Kubernetes. When you install Kubernetes, choose an installation type based on: ease of maintenance, security, control, available resources, and expertise required to operate and manage a cluster. You can [download Kubernetes](/releases/download/) to deploy a Kubernetes cluster on a local machine, into the cloud, or for your own datacenter. Several [Kubernetes components](/docs/concepts/overview/components/) such as {{< glossary\_tooltip text="kube-apiserver" term\_id="kube-apiserver" >}} or {{< glossary\_tooltip text="kube-proxy" term\_id="kube-proxy" >}} can also be deployed as [container images](/releases/download/#container-images) within the cluster. It is \*\*recommended\*\* to run Kubernetes components as container images wherever that is possible, and to have Kubernetes manage those components. Components that run containers - notably, the kubelet - can't be included in this category. If you don't want to manage a Kubernetes cluster yourself, you could pick a managed service, including [certified platforms](/docs/setup/production-environment/turnkey-solutions/). There are also other standardized and custom solutions across a wide range of cloud and bare metal environments. ## Learning environment If you're learning Kubernetes, use the tools supported by the Kubernetes community, or tools in the ecosystem to set up a Kubernetes cluster on a local machine. See [Learning environment](/docs/setup/learning-environment/) ## Production environment When evaluating a solution for a [production environment](/docs/setup/production-environment/), consider which aspects of operating a Kubernetes cluster (or \_abstractions\_) you want to manage yourself and which you prefer to hand off to a provider. For a cluster you're managing yourself, the officially supported tool for deploying Kubernetes is [kubeadm](/docs/setup/production-environment/tools/kubeadm/). ## {{% heading "whatsnext" %}} - [Download Kubernetes](/releases/download/) - Download and [install tools](/docs/tasks/tools/) including `kubectl` - Select a [container runtime](/docs/setup/production-environment/container-runtimes/) for your new cluster - Learn about [best practices](/docs/setup/best-practices/) for cluster setup Kubernetes is designed for its {{< glossary\_tooltip term\_id="control-plane" text="control plane" >}} to run on Linux. Within your cluster you can run applications on Linux or other operating systems, including Windows. - Learn to [set up clusters with Windows nodes](/docs/concepts/windows/)
| null |
./vendor/kubernetes/content/en/docs/setup/_index.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/setup/_index.md
|
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|
{{% dockershim-removal %}} You need to install a {{< glossary\_tooltip text="container runtime" term\_id="container-runtime" >}} into each node in the cluster so that Pods can run there. This page outlines what is involved and describes related tasks for setting up nodes. Kubernetes {{< skew currentVersion >}} requires that you use a runtime that conforms with the {{< glossary\_tooltip term\_id="cri" text="Container Runtime Interface">}} (CRI). See [CRI version support](#cri-versions) for more information. This page provides an outline of how to use several common container runtimes with Kubernetes. - [containerd](#containerd) - [CRI-O](#cri-o) - [Docker Engine](#docker) - [Mirantis Container Runtime](#mcr) {{< note >}} Kubernetes releases before v1.24 included a direct integration with Docker Engine, using a component named \_dockershim\_. That special direct integration is no longer part of Kubernetes (this removal was [announced](/blog/2020/12/08/kubernetes-1-20-release-announcement/#dockershim-deprecation) as part of the v1.20 release). You can read [Check whether Dockershim removal affects you](/docs/tasks/administer-cluster/migrating-from-dockershim/check-if-dockershim-removal-affects-you/) to understand how this removal might affect you. To learn about migrating from using dockershim, see [Migrating from dockershim](/docs/tasks/administer-cluster/migrating-from-dockershim/). If you are running a version of Kubernetes other than v{{< skew currentVersion >}}, check the documentation for that version. {{< /note >}} ## Install and configure prerequisites ### Network configuration By default, the Linux kernel does not allow IPv4 packets to be routed between interfaces. Most Kubernetes cluster networking implementations will change this setting (if needed), but some might expect the administrator to do it for them. (Some might also expect other sysctl parameters to be set, kernel modules to be loaded, etc; consult the documentation for your specific network implementation.) ### Enable IPv4 packet forwarding {#prerequisite-ipv4-forwarding-optional} To manually enable IPv4 packet forwarding: ```bash # sysctl params required by setup, params persist across reboots cat <}} are used to constrain resources that are allocated to processes. Both the {{< glossary\_tooltip text="kubelet" term\_id="kubelet" >}} and the underlying container runtime need to interface with control groups to enforce [resource management for pods and containers](/docs/concepts/configuration/manage-resources-containers/) and set resources such as cpu/memory requests and limits. To interface with control groups, the kubelet and the container runtime need to use a \*cgroup driver\*. It's critical that the kubelet and the container runtime use the same cgroup driver and are configured the same. There are two cgroup drivers available: \* [`cgroupfs`](#cgroupfs-cgroup-driver) \* [`systemd`](#systemd-cgroup-driver) ### cgroupfs driver {#cgroupfs-cgroup-driver} The `cgroupfs` driver is the [default cgroup driver in the kubelet](/docs/reference/config-api/kubelet-config.v1beta1). When the `cgroupfs` driver is used, the kubelet and the container runtime directly interface with the cgroup filesystem to configure cgroups. The `cgroupfs` driver is \*\*not\*\* recommended when [systemd](https://www.freedesktop.org/wiki/Software/systemd/) is the init system because systemd expects a single cgroup manager on the system. Additionally, if you use [cgroup v2](/docs/concepts/architecture/cgroups), use the `systemd` cgroup driver instead of `cgroupfs`. ### systemd cgroup driver {#systemd-cgroup-driver} When [systemd](https://www.freedesktop.org/wiki/Software/systemd/) is chosen as the init system for a Linux distribution, the init process generates and consumes a root control group (`cgroup`) and acts as a cgroup manager. systemd has a tight integration with cgroups and allocates a cgroup per systemd unit. As a result, if you use `systemd` as the init system with the `cgroupfs` driver, the system gets two different cgroup managers. Two cgroup managers result in two views of the available and in-use resources in the system. In some cases, nodes that are configured to use `cgroupfs` for the kubelet and container runtime, but use `systemd` for the rest of the processes become unstable under resource pressure. The approach to mitigate this instability is to use `systemd` as the cgroup driver for the kubelet and the container runtime when systemd is the selected init system. To set `systemd` as the cgroup driver, edit the [`KubeletConfiguration`](/docs/tasks/administer-cluster/kubelet-config-file/) option of `cgroupDriver` and
| null |
./vendor/kubernetes/content/en/docs/setup/production-environment/container-runtimes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/setup/production-environment/container-runtimes.md
|
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|
of the processes become unstable under resource pressure. The approach to mitigate this instability is to use `systemd` as the cgroup driver for the kubelet and the container runtime when systemd is the selected init system. To set `systemd` as the cgroup driver, edit the [`KubeletConfiguration`](/docs/tasks/administer-cluster/kubelet-config-file/) option of `cgroupDriver` and set it to `systemd`. For example: ```yaml apiVersion: kubelet.config.k8s.io/v1beta1 kind: KubeletConfiguration ... cgroupDriver: systemd ``` {{< note >}} Starting with v1.22 and later, when creating a cluster with kubeadm, if the user does not set the `cgroupDriver` field under `KubeletConfiguration`, kubeadm defaults it to `systemd`. {{< /note >}} If you configure `systemd` as the cgroup driver for the kubelet, you must also configure `systemd` as the cgroup driver for the container runtime. Refer to the documentation for your container runtime for instructions. For example: \* [containerd](#containerd-systemd) \* [CRI-O](#cri-o) In Kubernetes {{< skew currentVersion >}}, with the `KubeletCgroupDriverFromCRI` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/) enabled and a container runtime that supports the `RuntimeConfig` CRI RPC, the kubelet automatically detects the appropriate cgroup driver from the runtime, and ignores the `cgroupDriver` setting within the kubelet configuration. However, older versions of container runtimes (specifically, containerd 1.y and below) do not support the `RuntimeConfig` CRI RPC, and may not respond correctly to this query, and thus the Kubelet falls back to using the value in its own `--cgroup-driver` flag. In Kubernetes 1.36, this fallback behavior will be dropped, and older versions of containerd will fail with newer kubelets. {{< caution >}} Changing the cgroup driver of a Node that has joined a cluster is a sensitive operation. If the kubelet has created Pods using the semantics of one cgroup driver, changing the container runtime to another cgroup driver can cause errors when trying to re-create the Pod sandbox for such existing Pods. Restarting the kubelet may not solve such errors. If you have automation that makes it feasible, replace the node with another using the updated configuration, or reinstall it using automation. {{< /caution >}} ### Migrating to the `systemd` driver in kubeadm managed clusters If you wish to migrate to the `systemd` cgroup driver in existing kubeadm managed clusters, follow [configuring a cgroup driver](/docs/tasks/administer-cluster/kubeadm/configure-cgroup-driver/). ## CRI version support {#cri-versions} Your container runtime must support at least v1alpha2 of the container runtime interface. Kubernetes [starting v1.26](/blog/2022/11/18/upcoming-changes-in-kubernetes-1-26/#cri-api-removal) \_only works\_ with v1 of the CRI API. Earlier versions default to v1 version, however if a container runtime does not support the v1 API, the kubelet falls back to using the (deprecated) v1alpha2 API instead. ## Container runtimes {{% thirdparty-content %}} ### containerd This section outlines the necessary steps to use containerd as CRI runtime. To install containerd on your system, follow the instructions on [getting started with containerd](https://github.com/containerd/containerd/blob/main/docs/getting-started.md). Return to this step once you've created a valid `config.toml` configuration file. {{< tabs name="Finding your config.toml file" >}} {{% tab name="Linux" %}} You can find this file under the path `/etc/containerd/config.toml`. {{% /tab %}} {{% tab name="Windows" %}} You can find this file under the path `C:\Program Files\containerd\config.toml`. {{% /tab %}} {{< /tabs >}} On Linux the default CRI socket for containerd is `/run/containerd/containerd.sock`. On Windows the default CRI endpoint is `npipe://./pipe/containerd-containerd`. #### Configuring the `systemd` cgroup driver {#containerd-systemd} To use the `systemd` cgroup driver in `/etc/containerd/config.toml` with `runc`, set the following config based on your Containerd version Containerd versions 1.x: ``` [plugins."io.containerd.grpc.v1.cri".containerd.runtimes.runc] ... [plugins."io.containerd.grpc.v1.cri".containerd.runtimes.runc.options] SystemdCgroup = true ``` Containerd versions 2.x: ``` [plugins.'io.containerd.cri.v1.runtime'.containerd.runtimes.runc] ... [plugins.'io.containerd.cri.v1.runtime'.containerd.runtimes.runc.options] SystemdCgroup = true ``` The `systemd` cgroup driver is recommended if you use [cgroup v2](/docs/concepts/architecture/cgroups). {{< note >}} If you installed containerd from a package (for example, RPM or `.deb`), you may find that the
| null |
./vendor/kubernetes/content/en/docs/setup/production-environment/container-runtimes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/setup/production-environment/container-runtimes.md
|
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|
``` [plugins."io.containerd.grpc.v1.cri".containerd.runtimes.runc] ... [plugins."io.containerd.grpc.v1.cri".containerd.runtimes.runc.options] SystemdCgroup = true ``` Containerd versions 2.x: ``` [plugins.'io.containerd.cri.v1.runtime'.containerd.runtimes.runc] ... [plugins.'io.containerd.cri.v1.runtime'.containerd.runtimes.runc.options] SystemdCgroup = true ``` The `systemd` cgroup driver is recommended if you use [cgroup v2](/docs/concepts/architecture/cgroups). {{< note >}} If you installed containerd from a package (for example, RPM or `.deb`), you may find that the CRI integration plugin is disabled by default. You need CRI support enabled to use containerd with Kubernetes. Make sure that `cri` is not included in the`disabled\_plugins` list within `/etc/containerd/config.toml`; if you made changes to that file, also restart `containerd`. If you experience container crash loops after the initial cluster installation or after installing a CNI, the containerd configuration provided with the package might contain incompatible configuration parameters. Consider resetting the containerd configuration with `containerd config default > /etc/containerd/config.toml` as specified in [getting-started.md](https://github.com/containerd/containerd/blob/main/docs/getting-started.md#advanced-topics) and then set the configuration parameters specified above accordingly. {{< /note >}} If you apply this change, make sure to restart containerd: ```shell sudo systemctl restart containerd ``` When using kubeadm, manually configure the [cgroup driver for kubelet](/docs/tasks/administer-cluster/kubeadm/configure-cgroup-driver/#configuring-the-kubelet-cgroup-driver). In Kubernetes v1.28, you can enable automatic detection of the cgroup driver as an alpha feature. See [systemd cgroup driver](#systemd-cgroup-driver) for more details. #### Overriding the sandbox (pause) image {#override-pause-image-containerd} In your [containerd config](https://github.com/containerd/containerd/blob/main/docs/cri/config.md) you can overwrite the sandbox image by setting the following config: ```toml [plugins."io.containerd.grpc.v1.cri"] sandbox\_image = "registry.k8s.io/pause:3.10" ``` You might need to restart `containerd` as well once you've updated the config file: `systemctl restart containerd`. ### CRI-O This section contains the necessary steps to install CRI-O as a container runtime. To install CRI-O, follow [CRI-O Install Instructions](https://github.com/cri-o/packaging/blob/main/README.md#usage). #### cgroup driver CRI-O uses the systemd cgroup driver per default, which is likely to work fine for you. To switch to the `cgroupfs` cgroup driver, either edit `/etc/crio/crio.conf` or place a drop-in configuration in `/etc/crio/crio.conf.d/02-cgroup-manager.conf`, for example: ```toml [crio.runtime] conmon\_cgroup = "pod" cgroup\_manager = "cgroupfs" ``` You should also note the changed `conmon\_cgroup`, which has to be set to the value `pod` when using CRI-O with `cgroupfs`. It is generally necessary to keep the cgroup driver configuration of the kubelet (usually done via kubeadm) and CRI-O in sync. In Kubernetes v1.28, you can enable automatic detection of the cgroup driver as an alpha feature. See [systemd cgroup driver](#systemd-cgroup-driver) for more details. For CRI-O, the CRI socket is `/var/run/crio/crio.sock` by default. #### Overriding the sandbox (pause) image {#override-pause-image-cri-o} In your [CRI-O config](https://github.com/cri-o/cri-o/blob/main/docs/crio.conf.5.md) you can set the following config value: ```toml [crio.image] pause\_image="registry.k8s.io/pause:3.10" ``` This config option supports live configuration reload to apply this change: `systemctl reload crio` or by sending `SIGHUP` to the `crio` process. ### Docker Engine {#docker} {{< note >}} These instructions assume that you are using the [`cri-dockerd`](https://mirantis.github.io/cri-dockerd/) adapter to integrate Docker Engine with Kubernetes. {{< /note >}} 1. On each of your nodes, install Docker for your Linux distribution as per [Install Docker Engine](https://docs.docker.com/engine/install/#server). 2. Install [`cri-dockerd`](https://mirantis.github.io/cri-dockerd/usage/install), following the directions in the install section of the documentation. For `cri-dockerd`, the CRI socket is `/run/cri-dockerd.sock` by default. ### Mirantis Container Runtime {#mcr} [Mirantis Container Runtime](https://docs.mirantis.com/mcr/25.0/overview.html) (MCR) is a commercially available container runtime that was formerly known as Docker Enterprise Edition. You can use Mirantis Container Runtime with Kubernetes using the open source [`cri-dockerd`](https://mirantis.github.io/cri-dockerd/) component, included with MCR. To learn more about how to install Mirantis Container Runtime, visit [MCR Deployment Guide](https://docs.mirantis.com/mcr/25.0/install.html). Check the systemd unit named `cri-docker.socket` to find out the path to the CRI socket. #### Overriding the sandbox (pause) image {#override-pause-image-cri-dockerd-mcr} The `cri-dockerd` adapter accepts a command line argument for specifying which container image to use as the Pod infrastructure container (“pause image”). The command line argument to use is `--pod-infra-container-image`. ## {{% heading "whatsnext"
| null |
./vendor/kubernetes/content/en/docs/setup/production-environment/container-runtimes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/setup/production-environment/container-runtimes.md
|
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] | 0.075687
|
to find out the path to the CRI socket. #### Overriding the sandbox (pause) image {#override-pause-image-cri-dockerd-mcr} The `cri-dockerd` adapter accepts a command line argument for specifying which container image to use as the Pod infrastructure container (“pause image”). The command line argument to use is `--pod-infra-container-image`. ## {{% heading "whatsnext" %}} As well as a container runtime, your cluster will need a working [network plugin](/docs/concepts/cluster-administration/networking/#how-to-implement-the-kubernetes-network-model).
| null |
./vendor/kubernetes/content/en/docs/setup/production-environment/container-runtimes.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/setup/production-environment/container-runtimes.md
|
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] | 0.135426
|
A production-quality Kubernetes cluster requires planning and preparation. If your Kubernetes cluster is to run critical workloads, it must be configured to be resilient. This page explains steps you can take to set up a production-ready cluster, or to promote an existing cluster for production use. If you're already familiar with production setup and want the links, skip to [What's next](#what-s-next). ## Production considerations Typically, a production Kubernetes cluster environment has more requirements than a personal learning, development, or test environment Kubernetes. A production environment may require secure access by many users, consistent availability, and the resources to adapt to changing demands. As you decide where you want your production Kubernetes environment to live (on premises or in a cloud) and the amount of management you want to take on or hand to others, consider how your requirements for a Kubernetes cluster are influenced by the following issues: - \*Availability\*: A single-machine Kubernetes [learning environment](/docs/setup/#learning-environment) has a single point of failure. Creating a highly available cluster means considering: - Separating the control plane from the worker nodes. - Replicating the control plane components on multiple nodes. - Load balancing traffic to the cluster’s {{< glossary\_tooltip term\_id="kube-apiserver" text="API server" >}}. - Having enough worker nodes available, or able to quickly become available, as changing workloads warrant it. - \*Scale\*: If you expect your production Kubernetes environment to receive a stable amount of demand, you might be able to set up for the capacity you need and be done. However, if you expect demand to grow over time or change dramatically based on things like season or special events, you need to plan how to scale to relieve increased pressure from more requests to the control plane and worker nodes or scale down to reduce unused resources. - \*Security and access management\*: You have full admin privileges on your own Kubernetes learning cluster. But shared clusters with important workloads, and more than one or two users, require a more refined approach to who and what can access cluster resources. You can use role-based access control ([RBAC](/docs/reference/access-authn-authz/rbac/)) and other security mechanisms to make sure that users and workloads can get access to the resources they need, while keeping workloads, and the cluster itself, secure. You can set limits on the resources that users and workloads can access by managing [policies](/docs/concepts/policy/) and [container resources](/docs/concepts/configuration/manage-resources-containers/). Before building a Kubernetes production environment on your own, consider handing off some or all of this job to [Turnkey Cloud Solutions](/docs/setup/production-environment/turnkey-solutions/) providers or other [Kubernetes Partners](/partners/). Options include: - \*Serverless\*: Just run workloads on third-party equipment without managing a cluster at all. You will be charged for things like CPU usage, memory, and disk requests. - \*Managed control plane\*: Let the provider manage the scale and availability of the cluster's control plane, as well as handle patches and upgrades. - \*Managed worker nodes\*: Configure pools of nodes to meet your needs, then the provider makes sure those nodes are available and ready to implement upgrades when needed. - \*Integration\*: There are providers that integrate Kubernetes with other services you may need, such as storage, container registries, authentication methods, and development tools. Whether you build a production Kubernetes cluster yourself or work with partners, review the following sections to evaluate your needs as they relate to your cluster’s \*control plane\*, \*worker nodes\*, \*user access\*, and \*workload resources\*. ## Production cluster setup In a production-quality Kubernetes cluster, the control plane manages the cluster from services that can be spread across multiple computers in different ways. Each worker node, however, represents a single entity that is configured to run Kubernetes pods. ###
| null |
./vendor/kubernetes/content/en/docs/setup/production-environment/_index.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/setup/production-environment/_index.md
|
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|
\*worker nodes\*, \*user access\*, and \*workload resources\*. ## Production cluster setup In a production-quality Kubernetes cluster, the control plane manages the cluster from services that can be spread across multiple computers in different ways. Each worker node, however, represents a single entity that is configured to run Kubernetes pods. ### Production control plane The simplest Kubernetes cluster has the entire control plane and worker node services running on the same machine. You can grow that environment by adding worker nodes, as reflected in the diagram illustrated in [Kubernetes Components](/docs/concepts/overview/components/). If the cluster is meant to be available for a short period of time, or can be discarded if something goes seriously wrong, this might meet your needs. If you need a more permanent, highly available cluster, however, you should consider ways of extending the control plane. By design, one-machine control plane services running on a single machine are not highly available. If keeping the cluster up and running and ensuring that it can be repaired if something goes wrong is important, consider these steps: - \*Choose deployment tools\*: You can deploy a control plane using tools such as kubeadm, kops, and kubespray. See [Installing Kubernetes with deployment tools](/docs/setup/production-environment/tools/) to learn tips for production-quality deployments using each of those deployment methods. Different [Container Runtimes](/docs/setup/production-environment/container-runtimes/) are available to use with your deployments. - \*Manage certificates\*: Secure communications between control plane services are implemented using certificates. Certificates are automatically generated during deployment or you can generate them using your own certificate authority. See [PKI certificates and requirements](/docs/setup/best-practices/certificates/) for details. - \*Configure load balancer for apiserver\*: Configure a load balancer to distribute external API requests to the apiserver service instances running on different nodes. See [Create an External Load Balancer](/docs/tasks/access-application-cluster/create-external-load-balancer/) for details. - \*Separate and backup etcd service\*: The etcd services can either run on the same machines as other control plane services or run on separate machines, for extra security and availability. Because etcd stores cluster configuration data, backing up the etcd database should be done regularly to ensure that you can repair that database if needed. See the [etcd FAQ](https://etcd.io/docs/v3.5/faq/) for details on configuring and using etcd. See [Operating etcd clusters for Kubernetes](/docs/tasks/administer-cluster/configure-upgrade-etcd/) and [Set up a High Availability etcd cluster with kubeadm](/docs/setup/production-environment/tools/kubeadm/setup-ha-etcd-with-kubeadm/) for details. - \*Create multiple control plane systems\*: For high availability, the control plane should not be limited to a single machine. If the control plane services are run by an init service (such as systemd), each service should run on at least three machines. However, running control plane services as pods in Kubernetes ensures that the replicated number of services that you request will always be available. The scheduler should be fault tolerant, but not highly available. Some deployment tools set up [Raft](https://raft.github.io/) consensus algorithm to do leader election of Kubernetes services. If the primary goes away, another service elects itself and take over. - \*Span multiple zones\*: If keeping your cluster available at all times is critical, consider creating a cluster that runs across multiple data centers, referred to as zones in cloud environments. Groups of zones are referred to as regions. By spreading a cluster across multiple zones in the same region, it can improve the chances that your cluster will continue to function even if one zone becomes unavailable. See [Running in multiple zones](/docs/setup/best-practices/multiple-zones/) for details. - \*Manage on-going features\*: If you plan to keep your cluster over time, there are tasks you need to do to maintain its health and security. For example, if you installed with kubeadm, there are instructions to help you with [Certificate Management](/docs/tasks/administer-cluster/kubeadm/kubeadm-certs/) and [Upgrading kubeadm clusters](/docs/tasks/administer-cluster/kubeadm/kubeadm-upgrade/). See [Administer a
| null |
./vendor/kubernetes/content/en/docs/setup/production-environment/_index.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/setup/production-environment/_index.md
|
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details. - \*Manage on-going features\*: If you plan to keep your cluster over time, there are tasks you need to do to maintain its health and security. For example, if you installed with kubeadm, there are instructions to help you with [Certificate Management](/docs/tasks/administer-cluster/kubeadm/kubeadm-certs/) and [Upgrading kubeadm clusters](/docs/tasks/administer-cluster/kubeadm/kubeadm-upgrade/). See [Administer a Cluster](/docs/tasks/administer-cluster/) for a longer list of Kubernetes administrative tasks. To learn about available options when you run control plane services, see [kube-apiserver](/docs/reference/command-line-tools-reference/kube-apiserver/), [kube-controller-manager](/docs/reference/command-line-tools-reference/kube-controller-manager/), and [kube-scheduler](/docs/reference/command-line-tools-reference/kube-scheduler/) component pages. For highly available control plane examples, see [Options for Highly Available topology](/docs/setup/production-environment/tools/kubeadm/ha-topology/), [Creating Highly Available clusters with kubeadm](/docs/setup/production-environment/tools/kubeadm/high-availability/), and [Operating etcd clusters for Kubernetes](/docs/tasks/administer-cluster/configure-upgrade-etcd/). See [Backing up an etcd cluster](/docs/tasks/administer-cluster/configure-upgrade-etcd/#backing-up-an-etcd-cluster) for information on making an etcd backup plan. ### Production worker nodes Production-quality workloads need to be resilient and anything they rely on needs to be resilient (such as CoreDNS). Whether you manage your own control plane or have a cloud provider do it for you, you still need to consider how you want to manage your worker nodes (also referred to simply as \*nodes\*). - \*Configure nodes\*: Nodes can be physical or virtual machines. If you want to create and manage your own nodes, you can install a supported operating system, then add and run the appropriate [Node services](/docs/concepts/architecture/#node-components). Consider: - The demands of your workloads when you set up nodes by having appropriate memory, CPU, and disk speed and storage capacity available. - Whether generic computer systems will do or you have workloads that need GPU processors, Windows nodes, or VM isolation. - \*Validate nodes\*: See [Valid node setup](/docs/setup/best-practices/node-conformance/) for information on how to ensure that a node meets the requirements to join a Kubernetes cluster. - \*Add nodes to the cluster\*: If you are managing your own cluster you can add nodes by setting up your own machines and either adding them manually or having them register themselves to the cluster’s apiserver. See the [Nodes](/docs/concepts/architecture/nodes/) section for information on how to set up Kubernetes to add nodes in these ways. - \*Scale nodes\*: Have a plan for expanding the capacity your cluster will eventually need. See [Considerations for large clusters](/docs/setup/best-practices/cluster-large/) to help determine how many nodes you need, based on the number of pods and containers you need to run. If you are managing nodes yourself, this can mean purchasing and installing your own physical equipment. - \*Autoscale nodes\*: Read [Node Autoscaling](/docs/concepts/cluster-administration/node-autoscaling) to learn about the tools available to automatically manage your nodes and the capacity they provide. - \*Set up node health checks\*: For important workloads, you want to make sure that the nodes and pods running on those nodes are healthy. Using the [Node Problem Detector](/docs/tasks/debug/debug-cluster/monitor-node-health/) daemon, you can ensure your nodes are healthy. ## Production user management In production, you may be moving from a model where you or a small group of people are accessing the cluster to where there may potentially be dozens or hundreds of people. In a learning environment or platform prototype, you might have a single administrative account for everything you do. In production, you will want more accounts with different levels of access to different namespaces. Taking on a production-quality cluster means deciding how you want to selectively allow access by other users. In particular, you need to select strategies for validating the identities of those who try to access your cluster (authentication) and deciding if they have permissions to do what they are asking (authorization): - \*Authentication\*: The apiserver can authenticate users using client certificates, bearer tokens, an authenticating proxy, or HTTP basic auth. You can choose which authentication methods you want to use. Using plugins, the apiserver can leverage
| null |
./vendor/kubernetes/content/en/docs/setup/production-environment/_index.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/setup/production-environment/_index.md
|
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|
cluster (authentication) and deciding if they have permissions to do what they are asking (authorization): - \*Authentication\*: The apiserver can authenticate users using client certificates, bearer tokens, an authenticating proxy, or HTTP basic auth. You can choose which authentication methods you want to use. Using plugins, the apiserver can leverage your organization’s existing authentication methods, such as LDAP or Kerberos. See [Authentication](/docs/reference/access-authn-authz/authentication/) for a description of these different methods of authenticating Kubernetes users. - \*Authorization\*: When you set out to authorize your regular users, you will probably choose between RBAC and ABAC authorization. See [Authorization Overview](/docs/reference/access-authn-authz/authorization/) to review different modes for authorizing user accounts (as well as service account access to your cluster): - \*Role-based access control\* ([RBAC](/docs/reference/access-authn-authz/rbac/)): Lets you assign access to your cluster by allowing specific sets of permissions to authenticated users. Permissions can be assigned for a specific namespace (Role) or across the entire cluster (ClusterRole). Then using RoleBindings and ClusterRoleBindings, those permissions can be attached to particular users. - \*Attribute-based access control\* ([ABAC](/docs/reference/access-authn-authz/abac/)): Lets you create policies based on resource attributes in the cluster and will allow or deny access based on those attributes. Each line of a policy file identifies versioning properties (apiVersion and kind) and a map of spec properties to match the subject (user or group), resource property, non-resource property (/version or /apis), and readonly. See [Examples](/docs/reference/access-authn-authz/abac/#examples) for details. As someone setting up authentication and authorization on your production Kubernetes cluster, here are some things to consider: - \*Set the authorization mode\*: When the Kubernetes API server ([kube-apiserver](/docs/reference/command-line-tools-reference/kube-apiserver/)) starts, supported authorization modes must be set using an \*--authorization-config\* file or the \*--authorization-mode\* flag. For example, that flag in the \*kube-adminserver.yaml\* file (in \*/etc/kubernetes/manifests\*) could be set to Node,RBAC. This would allow Node and RBAC authorization for authenticated requests. - \*Create user certificates and role bindings (RBAC)\*: If you are using RBAC authorization, users can create a CertificateSigningRequest (CSR) that can be signed by the cluster CA. Then you can bind Roles and ClusterRoles to each user. See [Certificate Signing Requests](/docs/reference/access-authn-authz/certificate-signing-requests/) for details. - \*Create policies that combine attributes (ABAC)\*: If you are using ABAC authorization, you can assign combinations of attributes to form policies to authorize selected users or groups to access particular resources (such as a pod), namespace, or apiGroup. For more information, see [Examples](/docs/reference/access-authn-authz/abac/#examples). - \*Consider Admission Controllers\*: Additional forms of authorization for requests that can come in through the API server include [Webhook Token Authentication](/docs/reference/access-authn-authz/authentication/#webhook-token-authentication). Webhooks and other special authorization types need to be enabled by adding [Admission Controllers](/docs/reference/access-authn-authz/admission-controllers/) to the API server. ## Set limits on workload resources Demands from production workloads can cause pressure both inside and outside of the Kubernetes control plane. Consider these items when setting up for the needs of your cluster's workloads: - \*Set namespace limits\*: Set per-namespace quotas on things like memory and CPU. See [Manage Memory, CPU, and API Resources](/docs/tasks/administer-cluster/manage-resources/) for details. - \*Prepare for DNS demand\*: If you expect workloads to massively scale up, your DNS service must be ready to scale up as well. See [Autoscale the DNS service in a Cluster](/docs/tasks/administer-cluster/dns-horizontal-autoscaling/). - \*Create additional service accounts\*: User accounts determine what users can do on a cluster, while a service account defines pod access within a particular namespace. By default, a pod takes on the default service account from its namespace. See [Managing Service Accounts](/docs/reference/access-authn-authz/service-accounts-admin/) for information on creating a new service account. For example, you might want to: - Add secrets that a pod could use to pull images from a particular container registry. See [Configure Service Accounts for Pods](/docs/tasks/configure-pod-container/configure-service-account/) for an example. - Assign RBAC permissions to a
| null |
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https://github.com/kubernetes/website/blob/main//content/en/docs/setup/production-environment/_index.md
|
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] | 0.141009
|
namespace. See [Managing Service Accounts](/docs/reference/access-authn-authz/service-accounts-admin/) for information on creating a new service account. For example, you might want to: - Add secrets that a pod could use to pull images from a particular container registry. See [Configure Service Accounts for Pods](/docs/tasks/configure-pod-container/configure-service-account/) for an example. - Assign RBAC permissions to a service account. See [ServiceAccount permissions](/docs/reference/access-authn-authz/rbac/#service-account-permissions) for details. ## {{% heading "whatsnext" %}} - Decide if you want to build your own production Kubernetes or obtain one from available [Turnkey Cloud Solutions](/docs/setup/production-environment/turnkey-solutions/) or [Kubernetes Partners](/partners/). - If you choose to build your own cluster, plan how you want to handle [certificates](/docs/setup/best-practices/certificates/) and set up high availability for features such as [etcd](/docs/setup/production-environment/tools/kubeadm/setup-ha-etcd-with-kubeadm/) and the [API server](/docs/setup/production-environment/tools/kubeadm/ha-topology/). - Choose from [kubeadm](/docs/setup/production-environment/tools/kubeadm/), [kops](https://kops.sigs.k8s.io/) or [Kubespray](https://kubespray.io/) deployment methods. - Configure user management by determining your [Authentication](/docs/reference/access-authn-authz/authentication/) and [Authorization](/docs/reference/access-authn-authz/authorization/) methods. - Prepare for application workloads by setting up [resource limits](/docs/tasks/administer-cluster/manage-resources/), [DNS autoscaling](/docs/tasks/administer-cluster/dns-horizontal-autoscaling/) and [service accounts](/docs/reference/access-authn-authz/service-accounts-admin/).
| null |
./vendor/kubernetes/content/en/docs/setup/production-environment/_index.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/setup/production-environment/_index.md
|
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] | 0.119695
|
There are many methods and tools for setting up your own production Kubernetes cluster. For example: - [kubeadm](/docs/setup/production-environment/tools/kubeadm/) - [Cluster API](https://cluster-api.sigs.k8s.io/): A Kubernetes sub-project focused on providing declarative APIs and tooling to simplify provisioning, upgrading, and operating multiple Kubernetes clusters. - [kops](https://kops.sigs.k8s.io/): An automated cluster provisioning tool. For tutorials, best practices, configuration options and information on reaching out to the community, please check the [`kOps` website](https://kops.sigs.k8s.io/) for details. - [kubespray](https://kubespray.io/): A composition of [Ansible](https://docs.ansible.com/) playbooks, [inventory](https://github.com/kubernetes-sigs/kubespray/blob/master/docs/ansible/inventory.md), provisioning tools, and domain knowledge for generic OS/Kubernetes clusters configuration management tasks. You can reach out to the community on Slack channel [#kubespray](https://kubernetes.slack.com/messages/kubespray/).
| null |
./vendor/kubernetes/content/en/docs/setup/production-environment/tools/_index.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/setup/production-environment/tools/_index.md
|
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] | 0.109376
|
As with any program, you might run into an error installing or running kubeadm. This page lists some common failure scenarios and have provided steps that can help you understand and fix the problem. If your problem is not listed below, please follow the following steps: - If you think your problem is a bug with kubeadm: - Go to [github.com/kubernetes/kubeadm](https://github.com/kubernetes/kubeadm/issues) and search for existing issues. - If no issue exists, please [open one](https://github.com/kubernetes/kubeadm/issues/new) and follow the issue template. - If you are unsure about how kubeadm works, you can ask on [Slack](https://slack.k8s.io/) in `#kubeadm`, or open a question on [StackOverflow](https://stackoverflow.com/questions/tagged/kubernetes). Please include relevant tags like `#kubernetes` and `#kubeadm` so folks can help you. ## Not possible to join a v1.18 Node to a v1.17 cluster due to missing RBAC In v1.18 kubeadm added prevention for joining a Node in the cluster if a Node with the same name already exists. This required adding RBAC for the bootstrap-token user to be able to GET a Node object. However this causes an issue where `kubeadm join` from v1.18 cannot join a cluster created by kubeadm v1.17. To workaround the issue you have two options: Execute `kubeadm init phase bootstrap-token` on a control-plane node using kubeadm v1.18. Note that this enables the rest of the bootstrap-token permissions as well. or Apply the following RBAC manually using `kubectl apply -f ...`: ```yaml apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRole metadata: name: kubeadm:get-nodes rules: - apiGroups: - "" resources: - nodes verbs: - get --- apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRoleBinding metadata: name: kubeadm:get-nodes roleRef: apiGroup: rbac.authorization.k8s.io kind: ClusterRole name: kubeadm:get-nodes subjects: - apiGroup: rbac.authorization.k8s.io kind: Group name: system:bootstrappers:kubeadm:default-node-token ``` ## `ebtables` or some similar executable not found during installation If you see the following warnings while running `kubeadm init` ```console [preflight] WARNING: ebtables not found in system path [preflight] WARNING: ethtool not found in system path ``` Then you may be missing `ebtables`, `ethtool` or a similar executable on your node. You can install them with the following commands: - For Ubuntu/Debian users, run `apt install ebtables ethtool`. - For CentOS/Fedora users, run `yum install ebtables ethtool`. ## kubeadm blocks waiting for control plane during installation If you notice that `kubeadm init` hangs after printing out the following line: ```console [apiclient] Created API client, waiting for the control plane to become ready ``` This may be caused by a number of problems. The most common are: - network connection problems. Check that your machine has full network connectivity before continuing. - the cgroup driver of the container runtime differs from that of the kubelet. To understand how to configure it properly, see [Configuring a cgroup driver](/docs/tasks/administer-cluster/kubeadm/configure-cgroup-driver/). - control plane containers are crashlooping or hanging. You can check this by running `docker ps` and investigating each container by running `docker logs`. For other container runtime, see [Debugging Kubernetes nodes with crictl](/docs/tasks/debug/debug-cluster/crictl/). ## kubeadm blocks when removing managed containers The following could happen if the container runtime halts and does not remove any Kubernetes-managed containers: ```shell sudo kubeadm reset ``` ```console [preflight] Running pre-flight checks [reset] Stopping the kubelet service [reset] Unmounting mounted directories in "/var/lib/kubelet" [reset] Removing kubernetes-managed containers (block) ``` A possible solution is to restart the container runtime and then re-run `kubeadm reset`. You can also use `crictl` to debug the state of the container runtime. See [Debugging Kubernetes nodes with crictl](/docs/tasks/debug/debug-cluster/crictl/). ## Pods in `RunContainerError`, `CrashLoopBackOff` or `Error` state Right after `kubeadm init` there should not be any pods in these states. - If there are pods in one of these states \_right after\_ `kubeadm init`, please open an issue in the kubeadm repo. `coredns` (or
| null |
./vendor/kubernetes/content/en/docs/setup/production-environment/tools/kubeadm/troubleshooting-kubeadm.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/setup/production-environment/tools/kubeadm/troubleshooting-kubeadm.md
|
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|
[Debugging Kubernetes nodes with crictl](/docs/tasks/debug/debug-cluster/crictl/). ## Pods in `RunContainerError`, `CrashLoopBackOff` or `Error` state Right after `kubeadm init` there should not be any pods in these states. - If there are pods in one of these states \_right after\_ `kubeadm init`, please open an issue in the kubeadm repo. `coredns` (or `kube-dns`) should be in the `Pending` state until you have deployed the network add-on. - If you see Pods in the `RunContainerError`, `CrashLoopBackOff` or `Error` state after deploying the network add-on and nothing happens to `coredns` (or `kube-dns`), it's very likely that the Pod Network add-on that you installed is somehow broken. You might have to grant it more RBAC privileges or use a newer version. Please file an issue in the Pod Network providers' issue tracker and get the issue triaged there. ## `coredns` is stuck in the `Pending` state This is \*\*expected\*\* and part of the design. kubeadm is network provider-agnostic, so the admin should [install the pod network add-on](/docs/concepts/cluster-administration/addons/) of choice. You have to install a Pod Network before CoreDNS may be deployed fully. Hence the `Pending` state before the network is set up. ## `HostPort` services do not work The `HostPort` and `HostIP` functionality is available depending on your Pod Network provider. Please contact the author of the Pod Network add-on to find out whether `HostPort` and `HostIP` functionality are available. Calico, Canal, and Flannel CNI providers are verified to support HostPort. For more information, see the [CNI portmap documentation](https://github.com/containernetworking/plugins/blob/master/plugins/meta/portmap/README.md). If your network provider does not support the portmap CNI plugin, you may need to use the [NodePort feature of services](/docs/concepts/services-networking/service/#type-nodeport) or use `HostNetwork=true`. ## Pods are not accessible via their Service IP - Many network add-ons do not yet enable [hairpin mode](/docs/tasks/debug/debug-application/debug-service/#a-pod-fails-to-reach-itself-via-the-service-ip) which allows pods to access themselves via their Service IP. This is an issue related to [CNI](https://github.com/containernetworking/cni/issues/476). Please contact the network add-on provider to get the latest status of their support for hairpin mode. - If you are using VirtualBox (directly or via Vagrant), you will need to ensure that `hostname -i` returns a routable IP address. By default, the first interface is connected to a non-routable host-only network. A work around is to modify `/etc/hosts`, see this [Vagrantfile](https://github.com/errordeveloper/k8s-playground/blob/22dd39dfc06111235620e6c4404a96ae146f26fd/Vagrantfile#L11) for an example. ## TLS certificate errors The following error indicates a possible certificate mismatch. ```none # kubectl get pods Unable to connect to the server: x509: certificate signed by unknown authority (possibly because of "crypto/rsa: verification error" while trying to verify candidate authority certificate "kubernetes") ``` - Verify that the `$HOME/.kube/config` file contains a valid certificate, and regenerate a certificate if necessary. The certificates in a kubeconfig file are base64 encoded. The `base64 --decode` command can be used to decode the certificate and `openssl x509 -text -noout` can be used for viewing the certificate information. - Unset the `KUBECONFIG` environment variable using: ```sh unset KUBECONFIG ``` Or set it to the default `KUBECONFIG` location: ```sh export KUBECONFIG=/etc/kubernetes/admin.conf ``` - Another workaround is to overwrite the existing `kubeconfig` for the "admin" user: ```sh mv $HOME/.kube $HOME/.kube.bak mkdir $HOME/.kube sudo cp -i /etc/kubernetes/admin.conf $HOME/.kube/config sudo chown $(id -u):$(id -g) $HOME/.kube/config ``` ## Kubelet client certificate rotation fails {#kubelet-client-cert} By default, kubeadm configures a kubelet with automatic rotation of client certificates by using the `/var/lib/kubelet/pki/kubelet-client-current.pem` symlink specified in `/etc/kubernetes/kubelet.conf`. If this rotation process fails you might see errors such as `x509: certificate has expired or is not yet valid` in kube-apiserver logs. To fix the issue you must follow these steps: 1. Backup and delete `/etc/kubernetes/kubelet.conf` and `/var/lib/kubelet/pki/kubelet-client\*` from the failed node. 1. From a working control plane node in the cluster that has `/etc/kubernetes/pki/ca.key` execute
| null |
./vendor/kubernetes/content/en/docs/setup/production-environment/tools/kubeadm/troubleshooting-kubeadm.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/setup/production-environment/tools/kubeadm/troubleshooting-kubeadm.md
|
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] | 0.108819
|
see errors such as `x509: certificate has expired or is not yet valid` in kube-apiserver logs. To fix the issue you must follow these steps: 1. Backup and delete `/etc/kubernetes/kubelet.conf` and `/var/lib/kubelet/pki/kubelet-client\*` from the failed node. 1. From a working control plane node in the cluster that has `/etc/kubernetes/pki/ca.key` execute `kubeadm kubeconfig user --org system:nodes --client-name system:node:$NODE > kubelet.conf`. `$NODE` must be set to the name of the existing failed node in the cluster. Modify the resulted `kubelet.conf` manually to adjust the cluster name and server endpoint, or pass `kubeconfig user --config` (see [Generating kubeconfig files for additional users](/docs/tasks/administer-cluster/kubeadm/kubeadm-certs/#kubeconfig-additional-users)). If your cluster does not have the `ca.key` you must sign the embedded certificates in the `kubelet.conf` externally. 1. Copy this resulted `kubelet.conf` to `/etc/kubernetes/kubelet.conf` on the failed node. 1. Restart the kubelet (`systemctl restart kubelet`) on the failed node and wait for `/var/lib/kubelet/pki/kubelet-client-current.pem` to be recreated. 1. Manually edit the `kubelet.conf` to point to the rotated kubelet client certificates, by replacing `client-certificate-data` and `client-key-data` with: ```yaml client-certificate: /var/lib/kubelet/pki/kubelet-client-current.pem client-key: /var/lib/kubelet/pki/kubelet-client-current.pem ``` 1. Restart the kubelet. 1. Make sure the node becomes `Ready`. ## Default NIC When using flannel as the pod network in Vagrant The following error might indicate that something was wrong in the pod network: ```sh Error from server (NotFound): the server could not find the requested resource ``` - If you're using flannel as the pod network inside Vagrant, then you will have to specify the default interface name for flannel. Vagrant typically assigns two interfaces to all VMs. The first, for which all hosts are assigned the IP address `10.0.2.15`, is for external traffic that gets NATed. This may lead to problems with flannel, which defaults to the first interface on a host. This leads to all hosts thinking they have the same public IP address. To prevent this, pass the `--iface eth1` flag to flannel so that the second interface is chosen. ## Non-public IP used for containers In some situations `kubectl logs` and `kubectl run` commands may return with the following errors in an otherwise functional cluster: ```console Error from server: Get https://10.19.0.41:10250/containerLogs/default/mysql-ddc65b868-glc5m/mysql: dial tcp 10.19.0.41:10250: getsockopt: no route to host ``` - This may be due to Kubernetes using an IP that can not communicate with other IPs on the seemingly same subnet, possibly by policy of the machine provider. - DigitalOcean assigns a public IP to `eth0` as well as a private one to be used internally as anchor for their floating IP feature, yet `kubelet` will pick the latter as the node's `InternalIP` instead of the public one. Use `ip addr show` to check for this scenario instead of `ifconfig` because `ifconfig` will not display the offending alias IP address. Alternatively an API endpoint specific to DigitalOcean allows to query for the anchor IP from the droplet: ```sh curl http://169.254.169.254/metadata/v1/interfaces/public/0/anchor\_ipv4/address ``` The workaround is to tell `kubelet` which IP to use using `--node-ip`. When using DigitalOcean, it can be the public one (assigned to `eth0`) or the private one (assigned to `eth1`) should you want to use the optional private network. The `kubeletExtraArgs` section of the kubeadm [`NodeRegistrationOptions` structure](/docs/reference/config-api/kubeadm-config.v1beta4/#kubeadm-k8s-io-v1beta4-NodeRegistrationOptions) can be used for this. Then restart `kubelet`: ```sh systemctl daemon-reload systemctl restart kubelet ``` ## `coredns` pods have `CrashLoopBackOff` or `Error` state If you have nodes that are running SELinux with an older version of Docker, you might experience a scenario where the `coredns` pods are not starting. To solve that, you can try one of the following options: - Upgrade to a [newer version of Docker](/docs/setup/production-environment/container-runtimes/#docker). - [Disable SELinux](https://access.redhat.com/documentation/en-us/red\_hat\_enterprise\_linux/6/html/security-enhanced\_linux/sect-security-enhanced\_linux-enabling\_and\_disabling\_selinux-disabling\_selinux). - Modify the `coredns` deployment to set `allowPrivilegeEscalation` to `true`: ```bash
| null |
./vendor/kubernetes/content/en/docs/setup/production-environment/tools/kubeadm/troubleshooting-kubeadm.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/setup/production-environment/tools/kubeadm/troubleshooting-kubeadm.md
|
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] | 0.045686
|
an older version of Docker, you might experience a scenario where the `coredns` pods are not starting. To solve that, you can try one of the following options: - Upgrade to a [newer version of Docker](/docs/setup/production-environment/container-runtimes/#docker). - [Disable SELinux](https://access.redhat.com/documentation/en-us/red\_hat\_enterprise\_linux/6/html/security-enhanced\_linux/sect-security-enhanced\_linux-enabling\_and\_disabling\_selinux-disabling\_selinux). - Modify the `coredns` deployment to set `allowPrivilegeEscalation` to `true`: ```bash kubectl -n kube-system get deployment coredns -o yaml | \ sed 's/allowPrivilegeEscalation: false/allowPrivilegeEscalation: true/g' | \ kubectl apply -f - ``` Another cause for CoreDNS to have `CrashLoopBackOff` is when a CoreDNS Pod deployed in Kubernetes detects a loop. [A number of workarounds](https://github.com/coredns/coredns/tree/master/plugin/loop#troubleshooting-loops-in-kubernetes-clusters) are available to avoid Kubernetes trying to restart the CoreDNS Pod every time CoreDNS detects the loop and exits. {{< warning >}} Disabling SELinux or setting `allowPrivilegeEscalation` to `true` can compromise the security of your cluster. {{< /warning >}} ## etcd pods restart continually If you encounter the following error: ``` rpc error: code = 2 desc = oci runtime error: exec failed: container\_linux.go:247: starting container process caused "process\_linux.go:110: decoding init error from pipe caused \"read parent: connection reset by peer\"" ``` This issue appears if you run CentOS 7 with Docker 1.13.1.84. This version of Docker can prevent the kubelet from executing into the etcd container. To work around the issue, choose one of these options: - Roll back to an earlier version of Docker, such as 1.13.1-75 ``` yum downgrade docker-1.13.1-75.git8633870.el7.centos.x86\_64 docker-client-1.13.1-75.git8633870.el7.centos.x86\_64 docker-common-1.13.1-75.git8633870.el7.centos.x86\_64 ``` - Install one of the more recent recommended versions, such as 18.06: ```bash sudo yum-config-manager --add-repo https://download.docker.com/linux/centos/docker-ce.repo yum install docker-ce-18.06.1.ce-3.el7.x86\_64 ``` ## Not possible to pass a comma separated list of values to arguments inside a `--component-extra-args` flag `kubeadm init` flags such as `--component-extra-args` allow you to pass custom arguments to a control-plane component like the kube-apiserver. However, this mechanism is limited due to the underlying type used for parsing the values (`mapStringString`). If you decide to pass an argument that supports multiple, comma-separated values such as `--apiserver-extra-args "enable-admission-plugins=LimitRanger,NamespaceExists"` this flag will fail with `flag: malformed pair, expect string=string`. This happens because the list of arguments for `--apiserver-extra-args` expects `key=value` pairs and in this case `NamespacesExists` is considered as a key that is missing a value. Alternatively, you can try separating the `key=value` pairs like so: `--apiserver-extra-args "enable-admission-plugins=LimitRanger,enable-admission-plugins=NamespaceExists"` but this will result in the key `enable-admission-plugins` only having the value of `NamespaceExists`. A known workaround is to use the kubeadm [configuration file](/docs/reference/config-api/kubeadm-config.v1beta4/). ## kube-proxy scheduled before node is initialized by cloud-controller-manager In cloud provider scenarios, kube-proxy can end up being scheduled on new worker nodes before the cloud-controller-manager has initialized the node addresses. This causes kube-proxy to fail to pick up the node's IP address properly and has knock-on effects to the proxy function managing load balancers. The following error can be seen in kube-proxy Pods: ``` server.go:610] Failed to retrieve node IP: host IP unknown; known addresses: [] proxier.go:340] invalid nodeIP, initializing kube-proxy with 127.0.0.1 as nodeIP ``` A known solution is to patch the kube-proxy DaemonSet to allow scheduling it on control-plane nodes regardless of their conditions, keeping it off of other nodes until their initial guarding conditions abate: ``` kubectl -n kube-system patch ds kube-proxy -p='{ "spec": { "template": { "spec": { "tolerations": [ { "key": "CriticalAddonsOnly", "operator": "Exists" }, { "effect": "NoSchedule", "key": "node-role.kubernetes.io/control-plane" } ] } } } }' ``` The tracking issue for this problem is [here](https://github.com/kubernetes/kubeadm/issues/1027). ## `/usr` is mounted read-only on nodes {#usr-mounted-read-only} On Linux distributions such as Fedora CoreOS or Flatcar Container Linux, the directory `/usr` is mounted as a read-only filesystem. For [flex-volume support](https://github.com/kubernetes/community/blob/ab55d85/contributors/devel/sig-storage/flexvolume.md), Kubernetes components like the kubelet and kube-controller-manager use the default path of `/usr/libexec/kubernetes/kubelet-plugins/volume/exec/`, yet the flex-volume directory
| null |
./vendor/kubernetes/content/en/docs/setup/production-environment/tools/kubeadm/troubleshooting-kubeadm.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/setup/production-environment/tools/kubeadm/troubleshooting-kubeadm.md
|
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] | 0.069302
|
is [here](https://github.com/kubernetes/kubeadm/issues/1027). ## `/usr` is mounted read-only on nodes {#usr-mounted-read-only} On Linux distributions such as Fedora CoreOS or Flatcar Container Linux, the directory `/usr` is mounted as a read-only filesystem. For [flex-volume support](https://github.com/kubernetes/community/blob/ab55d85/contributors/devel/sig-storage/flexvolume.md), Kubernetes components like the kubelet and kube-controller-manager use the default path of `/usr/libexec/kubernetes/kubelet-plugins/volume/exec/`, yet the flex-volume directory \_must be writeable\_ for the feature to work. {{< note >}} FlexVolume was deprecated in the Kubernetes v1.23 release. {{< /note >}} To workaround this issue, you can configure the flex-volume directory using the kubeadm [configuration file](/docs/reference/config-api/kubeadm-config.v1beta4/). On the primary control-plane Node (created using `kubeadm init`), pass the following file using `--config`: ```yaml apiVersion: kubeadm.k8s.io/v1beta4 kind: InitConfiguration nodeRegistration: kubeletExtraArgs: - name: "volume-plugin-dir" value: "/opt/libexec/kubernetes/kubelet-plugins/volume/exec/" --- apiVersion: kubeadm.k8s.io/v1beta4 kind: ClusterConfiguration controllerManager: extraArgs: - name: "flex-volume-plugin-dir" value: "/opt/libexec/kubernetes/kubelet-plugins/volume/exec/" ``` On joining Nodes: ```yaml apiVersion: kubeadm.k8s.io/v1beta4 kind: JoinConfiguration nodeRegistration: kubeletExtraArgs: - name: "volume-plugin-dir" value: "/opt/libexec/kubernetes/kubelet-plugins/volume/exec/" ``` Alternatively, you can modify `/etc/fstab` to make the `/usr` mount writeable, but please be advised that this is modifying a design principle of the Linux distribution. ## `kubeadm upgrade plan` prints out `context deadline exceeded` error message This error message is shown when upgrading a Kubernetes cluster with `kubeadm` in the case of running an external etcd. This is not a critical bug and happens because older versions of kubeadm perform a version check on the external etcd cluster. You can proceed with `kubeadm upgrade apply ...`. This issue is fixed as of version 1.19. ## `kubeadm reset` unmounts `/var/lib/kubelet` If `/var/lib/kubelet` is being mounted, performing a `kubeadm reset` will effectively unmount it. To workaround the issue, re-mount the `/var/lib/kubelet` directory after performing the `kubeadm reset` operation. This is a regression introduced in kubeadm 1.15. The issue is fixed in 1.20. ## Cannot use the metrics-server securely in a kubeadm cluster In a kubeadm cluster, the [metrics-server](https://github.com/kubernetes-sigs/metrics-server) can be used insecurely by passing the `--kubelet-insecure-tls` to it. This is not recommended for production clusters. If you want to use TLS between the metrics-server and the kubelet there is a problem, since kubeadm deploys a self-signed serving certificate for the kubelet. This can cause the following errors on the side of the metrics-server: ``` x509: certificate signed by unknown authority x509: certificate is valid for IP-foo not IP-bar ``` See [Enabling signed kubelet serving certificates](/docs/tasks/administer-cluster/kubeadm/kubeadm-certs/#kubelet-serving-certs) to understand how to configure the kubelets in a kubeadm cluster to have properly signed serving certificates. Also see [How to run the metrics-server securely](https://github.com/kubernetes-sigs/metrics-server/blob/master/FAQ.md#how-to-run-metrics-server-securely). ## Upgrade fails due to etcd hash not changing Only applicable to upgrading a control plane node with a kubeadm binary v1.28.3 or later, where the node is currently managed by kubeadm versions v1.28.0, v1.28.1 or v1.28.2. Here is the error message you may encounter: ``` [upgrade/etcd] Failed to upgrade etcd: couldn't upgrade control plane. kubeadm has tried to recover everything into the earlier state. Errors faced: static Pod hash for component etcd on Node kinder-upgrade-control-plane-1 did not change after 5m0s: timed out waiting for the condition [upgrade/etcd] Waiting for previous etcd to become available I0907 10:10:09.109104 3704 etcd.go:588] [etcd] attempting to see if all cluster endpoints ([https://172.17.0.6:2379/ https://172.17.0.4:2379/ https://172.17.0.3:2379/]) are available 1/10 [upgrade/etcd] Etcd was rolled back and is now available static Pod hash for component etcd on Node kinder-upgrade-control-plane-1 did not change after 5m0s: timed out waiting for the condition couldn't upgrade control plane. kubeadm has tried to recover everything into the earlier state. Errors faced k8s.io/kubernetes/cmd/kubeadm/app/phases/upgrade.rollbackOldManifests cmd/kubeadm/app/phases/upgrade/staticpods.go:525 k8s.io/kubernetes/cmd/kubeadm/app/phases/upgrade.upgradeComponent cmd/kubeadm/app/phases/upgrade/staticpods.go:254 k8s.io/kubernetes/cmd/kubeadm/app/phases/upgrade.performEtcdStaticPodUpgrade cmd/kubeadm/app/phases/upgrade/staticpods.go:338 ... ``` The reason for this failure is that the affected versions generate an etcd manifest file with unwanted defaults in the PodSpec. This will result in a diff from the manifest comparison, and
| null |
./vendor/kubernetes/content/en/docs/setup/production-environment/tools/kubeadm/troubleshooting-kubeadm.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/setup/production-environment/tools/kubeadm/troubleshooting-kubeadm.md
|
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] | 0.066055
|
tried to recover everything into the earlier state. Errors faced k8s.io/kubernetes/cmd/kubeadm/app/phases/upgrade.rollbackOldManifests cmd/kubeadm/app/phases/upgrade/staticpods.go:525 k8s.io/kubernetes/cmd/kubeadm/app/phases/upgrade.upgradeComponent cmd/kubeadm/app/phases/upgrade/staticpods.go:254 k8s.io/kubernetes/cmd/kubeadm/app/phases/upgrade.performEtcdStaticPodUpgrade cmd/kubeadm/app/phases/upgrade/staticpods.go:338 ... ``` The reason for this failure is that the affected versions generate an etcd manifest file with unwanted defaults in the PodSpec. This will result in a diff from the manifest comparison, and kubeadm will expect a change in the Pod hash, but the kubelet will never update the hash. There are two way to workaround this issue if you see it in your cluster: - The etcd upgrade can be skipped between the affected versions and v1.28.3 (or later) by using: ```shell kubeadm upgrade {apply|node} [version] --etcd-upgrade=false ``` This is not recommended in case a new etcd version was introduced by a later v1.28 patch version. - Before upgrade, patch the manifest for the etcd static pod, to remove the problematic defaulted attributes: ```patch diff --git a/etc/kubernetes/manifests/etcd\_defaults.yaml b/etc/kubernetes/manifests/etcd\_origin.yaml index d807ccbe0aa..46b35f00e15 100644 --- a/etc/kubernetes/manifests/etcd\_defaults.yaml +++ b/etc/kubernetes/manifests/etcd\_origin.yaml @@ -43,7 +43,6 @@ spec: scheme: HTTP initialDelaySeconds: 10 periodSeconds: 10 - successThreshold: 1 timeoutSeconds: 15 name: etcd resources: @@ -59,26 +58,18 @@ spec: scheme: HTTP initialDelaySeconds: 10 periodSeconds: 10 - successThreshold: 1 timeoutSeconds: 15 - terminationMessagePath: /dev/termination-log - terminationMessagePolicy: File volumeMounts: - mountPath: /var/lib/etcd name: etcd-data - mountPath: /etc/kubernetes/pki/etcd name: etcd-certs - dnsPolicy: ClusterFirst - enableServiceLinks: true hostNetwork: true priority: 2000001000 priorityClassName: system-node-critical - restartPolicy: Always - schedulerName: default-scheduler securityContext: seccompProfile: type: RuntimeDefault - terminationGracePeriodSeconds: 30 volumes: - hostPath: path: /etc/kubernetes/pki/etcd ``` More information can be found in the [tracking issue](https://github.com/kubernetes/kubeadm/issues/2927) for this bug.
| null |
./vendor/kubernetes/content/en/docs/setup/production-environment/tools/kubeadm/troubleshooting-kubeadm.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/setup/production-environment/tools/kubeadm/troubleshooting-kubeadm.md
|
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] | 0.035385
|
This page shows how to install the `kubeadm` toolbox. For information on how to create a cluster with kubeadm once you have performed this installation process, see the [Creating a cluster with kubeadm](/docs/setup/production-environment/tools/kubeadm/create-cluster-kubeadm/) page. {{< doc-versions-list "installation guide" >}} ## {{% heading "prerequisites" %}} \* A compatible Linux host. The Kubernetes project provides generic instructions for Linux distributions based on Debian and Red Hat, and those distributions without a package manager. \* 2 GB or more of RAM per machine (any less will leave little room for your apps). \* 2 CPUs or more for control plane machines. \* Full network connectivity between all machines in the cluster (public or private network is fine). \* Unique hostname, MAC address, and product\_uuid for every node. See [here](#verify-mac-address) for more details. \* Certain ports are open on your machines. See [here](#check-required-ports) for more details. {{< note >}} The `kubeadm` installation is done via binaries that use dynamic linking and assumes that your target system provides `glibc`. This is a reasonable assumption on many Linux distributions (including Debian, Ubuntu, Fedora, CentOS, etc.) but it is not always the case with custom and lightweight distributions which don't include `glibc` by default, such as Alpine Linux. The expectation is that the distribution either includes `glibc` or a [compatibility layer](https://wiki.alpinelinux.org/wiki/Running\_glibc\_programs) that provides the expected symbols. {{< /note >}} ## Check your OS version {{% thirdparty-content %}} {{< tabs name="operating\_system\_version\_check" >}} {{% tab name="Linux" %}} \* The kubeadm project supports LTS kernels. See [List of LTS kernels](https://www.kernel.org/category/releases.html). \* You can get the kernel version using the command `uname -r` For more information, see [Linux Kernel Requirements](/docs/reference/node/kernel-version-requirements/). {{% /tab %}} {{% tab name="Windows" %}} \* The kubeadm project supports recent kernel versions. For a list of recent kernels, see [Windows Server Release Information](https://learn.microsoft.com/en-us/windows/release-health/windows-server-release-info). \* You can get the kernel version (also called the OS version) using the command `systeminfo` For more information, see [Windows OS version compatibility](/docs/concepts/windows/intro/#windows-os-version-support). {{% /tab %}} {{< /tabs >}} A Kubernetes cluster created by kubeadm depends on software that use kernel features. This software includes, but is not limited to the {{< glossary\_tooltip text="container runtime" term\_id="container-runtime" >}}, the {{< glossary\_tooltip term\_id="kubelet" text="kubelet">}}, and a {{< glossary\_tooltip text="Container Network Interface" term\_id="cni" >}} plugin. To help you avoid unexpected errors as a result of an unsupported kernel version, kubeadm runs the `SystemVerification` pre-flight check. This check fails if the kernel version is not supported. You may choose to skip the check, if you know that your kernel provides the required features, even though kubeadm does not support its version. ## Verify the MAC address and product\_uuid are unique for every node {#verify-mac-address} \* You can get the MAC address of the network interfaces using the command `ip link` or `ifconfig -a` \* The product\_uuid can be checked by using the command `sudo cat /sys/class/dmi/id/product\_uuid` It is very likely that hardware devices will have unique addresses, although some virtual machines may have identical values. Kubernetes uses these values to uniquely identify the nodes in the cluster. If these values are not unique to each node, the installation process may [fail](https://github.com/kubernetes/kubeadm/issues/31). ## Check network adapters If you have more than one network adapter, and your Kubernetes components are not reachable on the default route, we recommend you add IP route(s) so Kubernetes cluster addresses go via the appropriate adapter. ## Check required ports {#check-required-ports} These [required ports](/docs/reference/networking/ports-and-protocols/) need to be open in order for Kubernetes components to communicate with each other. You can use tools like [netcat](https://netcat.sourceforge.net) to check if a port is open. For example: ```shell nc 127.0.0.1 6443 -zv -w 2 ``` The pod network plugin
| null |
./vendor/kubernetes/content/en/docs/setup/production-environment/tools/kubeadm/install-kubeadm.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/setup/production-environment/tools/kubeadm/install-kubeadm.md
|
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|
adapter. ## Check required ports {#check-required-ports} These [required ports](/docs/reference/networking/ports-and-protocols/) need to be open in order for Kubernetes components to communicate with each other. You can use tools like [netcat](https://netcat.sourceforge.net) to check if a port is open. For example: ```shell nc 127.0.0.1 6443 -zv -w 2 ``` The pod network plugin you use may also require certain ports to be open. Since this differs with each pod network plugin, please see the documentation for the plugins about what port(s) those need. ## Swap configuration {#swap-configuration} The default behavior of a kubelet is to fail to start if swap memory is detected on a node. This means that swap should either be disabled or tolerated by kubelet. \* To tolerate swap, add `failSwapOn: false` to kubelet configuration or as a command line argument. Note: even if `failSwapOn: false` is provided, workloads wouldn't have swap access by default. This can be changed by setting a `swapBehavior`, again in the kubelet configuration file. To use swap, set a `swapBehavior` other than the default `NoSwap` setting. See [Swap memory management](/docs/concepts/cluster-administration/swap-memory-management) for more details. \* To disable swap, `sudo swapoff -a` can be used to disable swapping temporarily. To make this change persistent across reboots, make sure swap is disabled in config files like `/etc/fstab`, `systemd.swap`, depending how it was configured on your system. ## Installing a container runtime {#installing-runtime} To run containers in Pods, Kubernetes uses a {{< glossary\_tooltip term\_id="container-runtime" text="container runtime" >}}. By default, Kubernetes uses the {{< glossary\_tooltip term\_id="cri" text="Container Runtime Interface">}} (CRI) to interface with your chosen container runtime. If you don't specify a runtime, kubeadm automatically tries to detect an installed container runtime by scanning through a list of known endpoints. If multiple or no container runtimes are detected kubeadm will throw an error and will request that you specify which one you want to use. See [container runtimes](/docs/setup/production-environment/container-runtimes/) for more information. {{< note >}} Docker Engine does not implement the [CRI](/docs/concepts/architecture/cri/) which is a requirement for a container runtime to work with Kubernetes. For that reason, an additional service [cri-dockerd](https://mirantis.github.io/cri-dockerd/) has to be installed. cri-dockerd is a project based on the legacy built-in Docker Engine support that was [removed](/dockershim) from the kubelet in version 1.24. {{< /note >}} The tables below include the known endpoints for supported operating systems: {{< tabs name="container\_runtime" >}} {{% tab name="Linux" %}} {{< table caption="Linux container runtimes" >}} | Runtime | Path to Unix domain socket | |------------------------------------|----------------------------------------------| | containerd | `unix:///var/run/containerd/containerd.sock` | | CRI-O | `unix:///var/run/crio/crio.sock` | | Docker Engine (using cri-dockerd) | `unix:///var/run/cri-dockerd.sock` | {{< /table >}} {{% /tab %}} {{% tab name="Windows" %}} {{< table caption="Windows container runtimes" >}} | Runtime | Path to Windows named pipe | |------------------------------------|----------------------------------------------| | containerd | `npipe:////./pipe/containerd-containerd` | | Docker Engine (using cri-dockerd) | `npipe:////./pipe/cri-dockerd` | {{< /table >}} {{% /tab %}} {{< /tabs >}} ## Installing kubeadm, kubelet and kubectl You will install these packages on all of your machines: \* `kubeadm`: the command to bootstrap the cluster. \* `kubelet`: the component that runs on all of the machines in your cluster and does things like starting pods and containers. \* `kubectl`: the command line util to talk to your cluster. kubeadm \*\*will not\*\* install or manage `kubelet` or `kubectl` for you, so you will need to ensure they match the version of the Kubernetes control plane you want kubeadm to install for you. If you do not, there is a risk of a version skew occurring that can lead to unexpected, buggy behaviour. However, \_one\_ minor version skew between the kubelet and the control plane is supported, but the kubelet version may never exceed
| null |
./vendor/kubernetes/content/en/docs/setup/production-environment/tools/kubeadm/install-kubeadm.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/setup/production-environment/tools/kubeadm/install-kubeadm.md
|
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] | 0.1354
|
control plane you want kubeadm to install for you. If you do not, there is a risk of a version skew occurring that can lead to unexpected, buggy behaviour. However, \_one\_ minor version skew between the kubelet and the control plane is supported, but the kubelet version may never exceed the API server version. For example, the kubelet running 1.7.0 should be fully compatible with a 1.8.0 API server, but not vice versa. For information about installing `kubectl`, see [Install and set up kubectl](/docs/tasks/tools/). {{< warning >}} These instructions exclude all Kubernetes packages from any system upgrades. This is because kubeadm and Kubernetes require [special attention to upgrade](/docs/tasks/administer-cluster/kubeadm/kubeadm-upgrade/). {{}} For more information on version skews, see: \* Kubernetes [version and version-skew policy](/docs/setup/release/version-skew-policy/) \* Kubeadm-specific [version skew policy](/docs/setup/production-environment/tools/kubeadm/create-cluster-kubeadm/#version-skew-policy) {{% legacy-repos-deprecation %}} {{< note >}} There's a dedicated package repository for each Kubernetes minor version. If you want to install a minor version other than v{{< skew currentVersion >}}, please see the installation guide for your desired minor version. {{< /note >}} {{< tabs name="k8s\_install" >}} {{% tab name="Debian-based distributions" %}} These instructions are for Kubernetes v{{< skew currentVersion >}}. 1. Update the `apt` package index and install packages needed to use the Kubernetes `apt` repository: ```shell sudo apt-get update # apt-transport-https may be a dummy package; if so, you can skip that package sudo apt-get install -y apt-transport-https ca-certificates curl gpg ``` 2. 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: ```shell # If the directory `/etc/apt/keyrings` does not exist, it should be created before the curl command, read the note below. # sudo mkdir -p -m 755 /etc/apt/keyrings curl -fsSL https://pkgs.k8s.io/core:/stable:/{{< param "version" >}}/deb/Release.key | sudo gpg --dearmor -o /etc/apt/keyrings/kubernetes-apt-keyring.gpg ``` {{< note >}} In releases older than Debian 12 and Ubuntu 22.04, directory `/etc/apt/keyrings` does not exist by default, and it should be created before the curl command. {{< /note >}} 3. Add the appropriate Kubernetes `apt` repository. Please note that this repository have packages only for Kubernetes {{< skew currentVersion >}}; for other Kubernetes minor versions, you need to change the Kubernetes minor version in the URL to match your desired minor version (you should also check that you are reading the documentation for the version of Kubernetes that you plan to install). ```shell # This overwrites any existing configuration in /etc/apt/sources.list.d/kubernetes.list echo 'deb [signed-by=/etc/apt/keyrings/kubernetes-apt-keyring.gpg] https://pkgs.k8s.io/core:/stable:/{{< param "version" >}}/deb/ /' | sudo tee /etc/apt/sources.list.d/kubernetes.list ``` 4. Update the `apt` package index, install kubelet, kubeadm and kubectl, and pin their version: ```shell sudo apt-get update sudo apt-get install -y kubelet kubeadm kubectl sudo apt-mark hold kubelet kubeadm kubectl ``` 5. (Optional) Enable the kubelet service before running kubeadm: ```shell sudo systemctl enable --now kubelet ``` {{% /tab %}} {{% tab name="Red Hat-based distributions" %}} 1. Set SELinux to `permissive` mode: These instructions are for Kubernetes {{< skew currentVersion >}}. ```shell # Set SELinux in permissive mode (effectively disabling it) sudo setenforce 0 sudo sed -i 's/^SELINUX=enforcing$/SELINUX=permissive/' /etc/selinux/config ``` {{< caution >}} - Setting SELinux in permissive mode by running `setenforce 0` and `sed ...` effectively disables it. This is required to allow containers to access the host filesystem; for example, some cluster network plugins require that. You have to do this until SELinux support is improved in the kubelet. - You can leave SELinux enabled if you know how to configure it but it may require settings that are not supported by kubeadm. {{< /caution >}} 2. Add the Kubernetes `yum` repository. The `exclude` parameter in the repository definition
| null |
./vendor/kubernetes/content/en/docs/setup/production-environment/tools/kubeadm/install-kubeadm.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/setup/production-environment/tools/kubeadm/install-kubeadm.md
|
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] | 0.024185
|
do this until SELinux support is improved in the kubelet. - You can leave SELinux enabled if you know how to configure it but it may require settings that are not supported by kubeadm. {{< /caution >}} 2. Add the Kubernetes `yum` repository. The `exclude` parameter in the repository definition ensures that the packages related to Kubernetes are not upgraded upon running `yum update` as there's a special procedure that must be followed for upgrading Kubernetes. Please note that this repository have packages only for Kubernetes {{< skew currentVersion >}}; for other Kubernetes minor versions, you need to change the Kubernetes minor version in the URL to match your desired minor version (you should also check that you are reading the documentation for the version of Kubernetes that you plan to install). ```shell # This overwrites any existing configuration in /etc/yum.repos.d/kubernetes.repo cat <}}/rpm/ enabled=1 gpgcheck=1 gpgkey=https://pkgs.k8s.io/core:/stable:/{{< param "version" >}}/rpm/repodata/repomd.xml.key exclude=kubelet kubeadm kubectl cri-tools kubernetes-cni EOF ``` 3. Install kubelet, kubeadm and kubectl: For systems with DNF: ```shell sudo yum install -y kubelet kubeadm kubectl --disableexcludes=kubernetes ``` For systems with DNF5: ```shell sudo yum install -y kubelet kubeadm kubectl --setopt=disable\_excludes=kubernetes ``` 4. (Optional) Enable the kubelet service before running kubeadm: ```shell sudo systemctl enable --now kubelet ``` {{% /tab %}} {{% tab name="Without a package manager" %}} Install CNI plugins (required for most pod network): ```bash CNI\_PLUGINS\_VERSION="v1.3.0" ARCH="amd64" DEST="/opt/cni/bin" sudo mkdir -p "$DEST" curl -L "https://github.com/containernetworking/plugins/releases/download/${CNI\_PLUGINS\_VERSION}/cni-plugins-linux-${ARCH}-${CNI\_PLUGINS\_VERSION}.tgz" | sudo tar -C "$DEST" -xz ``` Define the directory to download command files: {{< note >}} The `DOWNLOAD\_DIR` variable must be set to a writable directory. If you are running Flatcar Container Linux, set `DOWNLOAD\_DIR="/opt/bin"`. {{< /note >}} ```bash DOWNLOAD\_DIR="/usr/local/bin" sudo mkdir -p "$DOWNLOAD\_DIR" ``` Optionally install crictl (required for interaction with the Container Runtime Interface (CRI), optional for kubeadm): ```bash CRICTL\_VERSION="v1.31.0" ARCH="amd64" curl -L "https://github.com/kubernetes-sigs/cri-tools/releases/download/${CRICTL\_VERSION}/crictl-${CRICTL\_VERSION}-linux-${ARCH}.tar.gz" | sudo tar -C $DOWNLOAD\_DIR -xz ``` Install `kubeadm`, `kubelet` and add a `kubelet` systemd service: ```bash RELEASE="$(curl -sSL https://dl.k8s.io/release/stable.txt)" ARCH="amd64" cd $DOWNLOAD\_DIR sudo curl -L --remote-name-all https://dl.k8s.io/release/${RELEASE}/bin/linux/${ARCH}/{kubeadm,kubelet} sudo chmod +x {kubeadm,kubelet} RELEASE\_VERSION="v0.16.2" curl -sSL "https://raw.githubusercontent.com/kubernetes/release/${RELEASE\_VERSION}/cmd/krel/templates/latest/kubelet/kubelet.service" | sed "s:/usr/bin:${DOWNLOAD\_DIR}:g" | sudo tee /usr/lib/systemd/system/kubelet.service sudo mkdir -p /usr/lib/systemd/system/kubelet.service.d curl -sSL "https://raw.githubusercontent.com/kubernetes/release/${RELEASE\_VERSION}/cmd/krel/templates/latest/kubeadm/10-kubeadm.conf" | sed "s:/usr/bin:${DOWNLOAD\_DIR}:g" | sudo tee /usr/lib/systemd/system/kubelet.service.d/10-kubeadm.conf ``` {{< note >}} Please refer to the note in the [Before you begin](#before-you-begin) section for Linux distributions that do not include `glibc` by default. {{< /note >}} Install `kubectl` by following the instructions on [Install Tools page](/docs/tasks/tools/#kubectl). Optionally, enable the kubelet service before running kubeadm: ```bash sudo systemctl enable --now kubelet ``` {{< note >}} The Flatcar Container Linux distribution mounts the `/usr` directory as a read-only filesystem. Before bootstrapping your cluster, you need to take additional steps to configure a writable directory. See the [Kubeadm Troubleshooting guide](/docs/setup/production-environment/tools/kubeadm/troubleshooting-kubeadm/#usr-mounted-read-only) to learn how to set up a writable directory. {{< /note >}} {{% /tab %}} {{< /tabs >}} The kubelet is now restarting every few seconds, as it waits in a crashloop for kubeadm to tell it what to do. ## Configuring a cgroup driver Both the container runtime and the kubelet have a property called ["cgroup driver"](/docs/setup/production-environment/container-runtimes/#cgroup-drivers), which is important for the management of cgroups on Linux machines. {{< warning >}} Matching the container runtime and kubelet cgroup drivers is required or otherwise the kubelet process will fail. See [Configuring a cgroup driver](/docs/tasks/administer-cluster/kubeadm/configure-cgroup-driver/) for more details. {{< /warning >}} ## Troubleshooting If you are running into difficulties with kubeadm, please consult our [troubleshooting docs](/docs/setup/production-environment/tools/kubeadm/troubleshooting-kubeadm/). ## {{% heading "whatsnext" %}} \* [Using kubeadm to Create a Cluster](/docs/setup/production-environment/tools/kubeadm/create-cluster-kubeadm/)
| null |
./vendor/kubernetes/content/en/docs/setup/production-environment/tools/kubeadm/install-kubeadm.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/setup/production-environment/tools/kubeadm/install-kubeadm.md
|
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] | 0.029281
|
If you are running into difficulties with kubeadm, please consult our [troubleshooting docs](/docs/setup/production-environment/tools/kubeadm/troubleshooting-kubeadm/). ## {{% heading "whatsnext" %}} \* [Using kubeadm to Create a Cluster](/docs/setup/production-environment/tools/kubeadm/create-cluster-kubeadm/)
| null |
./vendor/kubernetes/content/en/docs/setup/production-environment/tools/kubeadm/install-kubeadm.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/setup/production-environment/tools/kubeadm/install-kubeadm.md
|
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] | 0.110299
|
This page covers how to customize the components that kubeadm deploys. For control plane components you can use flags in the `ClusterConfiguration` structure or patches per-node. For the kubelet and kube-proxy you can use `KubeletConfiguration` and `KubeProxyConfiguration`, accordingly. All of these options are possible via the kubeadm configuration API. For more details on each field in the configuration you can navigate to our [API reference pages](/docs/reference/config-api/kubeadm-config.v1beta4/). {{< note >}} To reconfigure a cluster that has already been created see [Reconfiguring a kubeadm cluster](/docs/tasks/administer-cluster/kubeadm/kubeadm-reconfigure). {{< /note >}} ## Customizing the control plane with flags in `ClusterConfiguration` The kubeadm `ClusterConfiguration` object exposes a way for users to override the default flags passed to control plane components such as the APIServer, ControllerManager, Scheduler and Etcd. The components are defined using the following structures: - `apiServer` - `controllerManager` - `scheduler` - `etcd` These structures contain a common `extraArgs` field, that consists of `name` / `value` pairs. To override a flag for a control plane component: 1. Add the appropriate `extraArgs` to your configuration. 2. Add flags to the `extraArgs` field. 3. Run `kubeadm init` with `--config `. {{< note >}} You can generate a `ClusterConfiguration` object with default values by running `kubeadm config print init-defaults` and saving the output to a file of your choice. {{< /note >}} {{< note >}} The `ClusterConfiguration` object is currently global in kubeadm clusters. This means that any flags that you add, will apply to all instances of the same component on different nodes. To apply individual configuration per component on different nodes you can use [patches](#patches). {{< /note >}} {{< note >}} Duplicate flags (keys), or passing the same flag `--foo` multiple times, is currently not supported. To workaround that you must use [patches](#patches). {{< /note >}} ### APIServer flags For details, see the [reference documentation for kube-apiserver](/docs/reference/command-line-tools-reference/kube-apiserver/). Example usage: ```yaml apiVersion: kubeadm.k8s.io/v1beta4 kind: ClusterConfiguration kubernetesVersion: v1.16.0 apiServer: extraArgs: - name: "enable-admission-plugins" value: "AlwaysPullImages,DefaultStorageClass" - name: "audit-log-path" value: "/home/johndoe/audit.log" ``` ### ControllerManager flags For details, see the [reference documentation for kube-controller-manager](/docs/reference/command-line-tools-reference/kube-controller-manager/). Example usage: ```yaml apiVersion: kubeadm.k8s.io/v1beta4 kind: ClusterConfiguration kubernetesVersion: v1.16.0 controllerManager: extraArgs: - name: "cluster-signing-key-file" value: "/home/johndoe/keys/ca.key" - name: "deployment-controller-sync-period" value: "50" ``` ### Scheduler flags For details, see the [reference documentation for kube-scheduler](/docs/reference/command-line-tools-reference/kube-scheduler/). Example usage: ```yaml apiVersion: kubeadm.k8s.io/v1beta4 kind: ClusterConfiguration kubernetesVersion: v1.16.0 scheduler: extraArgs: - name: "config" value: "/etc/kubernetes/scheduler-config.yaml" extraVolumes: - name: schedulerconfig hostPath: /home/johndoe/schedconfig.yaml mountPath: /etc/kubernetes/scheduler-config.yaml readOnly: true pathType: "File" ``` ### Etcd flags For details, see the [etcd server documentation](https://etcd.io/docs/). Example usage: ```yaml apiVersion: kubeadm.k8s.io/v1beta4 kind: ClusterConfiguration etcd: local: extraArgs: - name: "election-timeout" value: 1000 ``` ## Customizing with patches {#patches} {{< feature-state for\_k8s\_version="v1.22" state="beta" >}} Kubeadm allows you to pass a directory with patch files to `InitConfiguration`, `JoinConfiguration` and `UpgradeConfiguration`. on individual nodes. These patches can be used as the last customization step before component configuration is written to disk. You can pass this file to `kubeadm init` with `--config `: ```yaml apiVersion: kubeadm.k8s.io/v1beta4 kind: InitConfiguration patches: directory: /home/user/somedir ``` {{< note >}} For `kubeadm init` you can pass a file containing both a `ClusterConfiguration` and `InitConfiguration` separated by `---`. {{< /note >}} You can pass this file to `kubeadm join` with `--config `: ```yaml apiVersion: kubeadm.k8s.io/v1beta4 kind: JoinConfiguration patches: directory: /home/user/somedir ``` If you are using `kubeadm upgrade apply` and `kubeadm upgrade node` to upgrade your kubeadm nodes, you must again provide the same patches, so that the customization is preserved after upgrade. ```yaml apiVersion: kubeadm.k8s.io/v1beta4 kind: UpgradeConfiguration apply: patches: directory: /home/user/somedir ``` ```yaml apiVersion: kubeadm.k8s.io/v1beta4 kind: UpgradeConfiguration node: patches: directory: /home/user/somedir ``` The directory must contain files named `target[suffix][+patchtype].extension`. For example, `kube-apiserver0+merge.yaml` or just `etcd.json`. - `target` can be one of
| null |
./vendor/kubernetes/content/en/docs/setup/production-environment/tools/kubeadm/control-plane-flags.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/setup/production-environment/tools/kubeadm/control-plane-flags.md
|
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|
the same patches, so that the customization is preserved after upgrade. ```yaml apiVersion: kubeadm.k8s.io/v1beta4 kind: UpgradeConfiguration apply: patches: directory: /home/user/somedir ``` ```yaml apiVersion: kubeadm.k8s.io/v1beta4 kind: UpgradeConfiguration node: patches: directory: /home/user/somedir ``` The directory must contain files named `target[suffix][+patchtype].extension`. For example, `kube-apiserver0+merge.yaml` or just `etcd.json`. - `target` can be one of `kube-apiserver`, `kube-controller-manager`, `kube-scheduler`, `etcd`, `kubeletconfiguration` and `corednsdeployment`. - `suffix` is an optional string that can be used to determine which patches are applied first alpha-numerically. - `patchtype` can be one of `strategic`, `merge` or `json` and these must match the patching formats [supported by kubectl](/docs/tasks/manage-kubernetes-objects/update-api-object-kubectl-patch). The default `patchtype` is `strategic`. - `extension` must be either `json` or `yaml`. ## Customizing the kubelet {#kubelet} To customize the kubelet you can add a [`KubeletConfiguration`](/docs/reference/config-api/kubelet-config.v1beta1/) next to the `ClusterConfiguration` or `InitConfiguration` separated by `---` within the same configuration file. This file can then be passed to `kubeadm init` and kubeadm will apply the same base `KubeletConfiguration` to all nodes in the cluster. For applying instance-specific configuration over the base `KubeletConfiguration` you can use the [`kubeletconfiguration` patch target](#patches). Alternatively, you can use kubelet flags as overrides by passing them in the `nodeRegistration.kubeletExtraArgs` field supported by both `InitConfiguration` and `JoinConfiguration`. Some kubelet flags are deprecated, so check their status in the [kubelet reference documentation](/docs/reference/command-line-tools-reference/kubelet) before using them. For additional details see [Configuring each kubelet in your cluster using kubeadm](/docs/setup/production-environment/tools/kubeadm/kubelet-integration) ## Customizing kube-proxy To customize kube-proxy you can pass a `KubeProxyConfiguration` next your `ClusterConfiguration` or `InitConfiguration` to `kubeadm init` separated by `---`. For more details you can navigate to our [API reference pages](/docs/reference/config-api/kubeadm-config.v1beta4/). {{< note >}} kubeadm deploys kube-proxy as a {{< glossary\_tooltip text="DaemonSet" term\_id="daemonset" >}}, which means that the `KubeProxyConfiguration` would apply to all instances of kube-proxy in the cluster. {{< /note >}} ## Customizing CoreDNS kubeadm allows you to customize the CoreDNS Deployment with patches against the [`corednsdeployment` patch target](#patches). Patches for other CoreDNS related API objects like the `kube-system/coredns` {{< glossary\_tooltip text="ConfigMap" term\_id="configmap" >}} are currently not supported. You must manually patch any of these objects using kubectl and recreate the CoreDNS {{< glossary\_tooltip text="Pods" term\_id="pod" >}} after that. Alternatively, you can disable the kubeadm CoreDNS deployment by including the following option in your `ClusterConfiguration`: ```yaml dns: disabled: true ``` Also, by executing the following command: ```shell kubeadm init phase addon coredns --print-manifest --config my-config.yaml` ``` you can obtain the manifest file kubeadm would create for CoreDNS on your setup.
| null |
./vendor/kubernetes/content/en/docs/setup/production-environment/tools/kubeadm/control-plane-flags.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/setup/production-environment/tools/kubeadm/control-plane-flags.md
|
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|
By default, kubeadm runs a local etcd instance on each control plane node. It is also possible to treat the etcd cluster as external and provision etcd instances on separate hosts. The differences between the two approaches are covered in the [Options for Highly Available topology](/docs/setup/production-environment/tools/kubeadm/ha-topology) page. This task walks through the process of creating a high availability external etcd cluster of three members that can be used by kubeadm during cluster creation. ## {{% heading "prerequisites" %}} - Three hosts that can talk to each other over TCP ports 2379 and 2380. This document assumes these default ports. However, they are configurable through the kubeadm config file. - Each host must have systemd and a bash compatible shell installed. - Each host must [have a container runtime, kubelet, and kubeadm installed](/docs/setup/production-environment/tools/kubeadm/install-kubeadm/). - Each host should have access to the Kubernetes container image registry (`registry.k8s.io`) or list/pull the required etcd image using `kubeadm config images list/pull`. This guide will set up etcd instances as [static pods](/docs/tasks/configure-pod-container/static-pod/) managed by a kubelet. - Some infrastructure to copy files between hosts. For example `ssh` and `scp` can satisfy this requirement. ## Setting up the cluster The general approach is to generate all certs on one node and only distribute the \_necessary\_ files to the other nodes. {{< note >}} kubeadm contains all the necessary cryptographic machinery to generate the certificates described below; no other cryptographic tooling is required for this example. {{< /note >}} {{< note >}} The examples below use IPv4 addresses but you can also configure kubeadm, the kubelet and etcd to use IPv6 addresses. Dual-stack is supported by some Kubernetes options, but not by etcd. For more details on Kubernetes dual-stack support see [Dual-stack support with kubeadm](/docs/setup/production-environment/tools/kubeadm/dual-stack-support/). {{< /note >}} 1. Configure the kubelet to be a service manager for etcd. {{< note >}}You must do this on every host where etcd should be running.{{< /note >}} Since etcd was created first, you must override the service priority by creating a new unit file that has higher precedence than the kubeadm-provided kubelet unit file. ```sh cat << EOF > /etc/systemd/system/kubelet.service.d/kubelet.conf # Replace "systemd" with the cgroup driver of your container runtime. The default value in the kubelet is "cgroupfs". # Replace the value of "containerRuntimeEndpoint" for a different container runtime if needed. # apiVersion: kubelet.config.k8s.io/v1beta1 kind: KubeletConfiguration authentication: anonymous: enabled: false webhook: enabled: false authorization: mode: AlwaysAllow cgroupDriver: systemd address: 127.0.0.1 containerRuntimeEndpoint: unix:///var/run/containerd/containerd.sock staticPodPath: /etc/kubernetes/manifests EOF cat << EOF > /etc/systemd/system/kubelet.service.d/20-etcd-service-manager.conf [Service] ExecStart= ExecStart=/usr/bin/kubelet --config=/etc/systemd/system/kubelet.service.d/kubelet.conf Restart=always EOF systemctl daemon-reload systemctl restart kubelet ``` Check the kubelet status to ensure it is running. ```sh systemctl status kubelet ``` 1. Create configuration files for kubeadm. Generate one kubeadm configuration file for each host that will have an etcd member running on it using the following script. ```sh # Update HOST0, HOST1 and HOST2 with the IPs of your hosts export HOST0=10.0.0.6 export HOST1=10.0.0.7 export HOST2=10.0.0.8 # Update NAME0, NAME1 and NAME2 with the hostnames of your hosts export NAME0="infra0" export NAME1="infra1" export NAME2="infra2" # Create temp directories to store files that will end up on other hosts mkdir -p /tmp/${HOST0}/ /tmp/${HOST1}/ /tmp/${HOST2}/ HOSTS=(${HOST0} ${HOST1} ${HOST2}) NAMES=(${NAME0} ${NAME1} ${NAME2}) for i in "${!HOSTS[@]}"; do HOST=${HOSTS[$i]} NAME=${NAMES[$i]} cat << EOF > /tmp/${HOST}/kubeadmcfg.yaml --- apiVersion: "kubeadm.k8s.io/v1beta4" kind: InitConfiguration nodeRegistration: name: ${NAME} localAPIEndpoint: advertiseAddress: ${HOST} --- apiVersion: "kubeadm.k8s.io/v1beta4" kind: ClusterConfiguration etcd: local: serverCertSANs: - "${HOST}" peerCertSANs: - "${HOST}" extraArgs: - name: initial-cluster value: ${NAMES[0]}=https://${HOSTS[0]}:2380,${NAMES[1]}=https://${HOSTS[1]}:2380,${NAMES[2]}=https://${HOSTS[2]}:2380 - name: initial-cluster-state value: new - name: name value: ${NAME} - name: listen-peer-urls value: https://${HOST}:2380 - name: listen-client-urls value: https://${HOST}:2379 - name: advertise-client-urls value: https://${HOST}:2379 - name: initial-advertise-peer-urls value: https://${HOST}:2380 EOF done
| null |
./vendor/kubernetes/content/en/docs/setup/production-environment/tools/kubeadm/setup-ha-etcd-with-kubeadm.md
|
https://github.com/kubernetes/website/blob/main//content/en/docs/setup/production-environment/tools/kubeadm/setup-ha-etcd-with-kubeadm.md
|
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] | 0.117651
|
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