> ## Documentation Index
> Fetch the complete documentation index at: https://astronomer.io/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Run the Kubernetes executor on Astronomer Software

The [Kubernetes Executor](https://airflow.apache.org/docs/apache-airflow/stable/executor/kubernetes.html) creates individual Pods that dynamically delegate work and resources to individual tasks. For each task that needs to run, the executor works with the Kubernetes API and dynamically launches Pods which terminate when the task is completed.

You can customize your Kubernetes Pods to scale depending on how many Airflow tasks you're running at a given time. It also means you can configure the following for each individual Airflow task:

* Memory allocation
* Service accounts
* Airflow image

To configure these resources for a given task's Pod, you specify a `pod_override` in your DAG code. To specify a Pod template for many or all of your tasks, you can write a helper function to construct a `pod_override` in your DAGs or configure a global setting. For more information on configuring Pod template values, reference the [Kubernetes documentation](https://kubernetes.io/docs/concepts/workloads/pods/#pod-templates).

## Prerequisites

You must have an Airflow Deployment on Astronomer running with the Kubernetes executor. For more information on configuring an executor, see [Configure a Deployment](/docs/astro-private-cloud/v-0-34/configure-deployment). To learn more about different executor types, see [Airflow executors explained](/docs/learn/airflow-executors-explained).

## Configure the default worker Pod for all Deployments

By default, the Kubernetes executor launches workers based on a `podTemplate` configuration in the [Astronomer Airflow Helm chart](https://github.com/astronomer/airflow-chart/blob/master/values.yaml).

You can modify the default `podTemplate` to configure the default worker Pods for all Deployments using the Kubernetes executor on your Astronomer Software installation. You can then override this default at the task level using a `pod_override` file. See [Configure the worker Pod for a specific task](#configure-the-worker-pod-for-a-specific-task).

1. In your `values.yaml` file, copy the complete `podTemplate` configuration from your version of the [Astronomer Airflow Helm chart](https://github.com/astronomer/airflow-chart/blob/master/values.yaml). Your file should look like the following:

   ```yaml expandable wrap theme={null}
   astronomer:
     houston:
       config:
         deployments:
           helm:
             airflow:
               podTemplate: |
                   # Licensed to the Apache Software Foundation (ASF) under one
                   # or more contributor license agreements.  See the NOTICE file
                   # distributed with this work for additional information
                   # regarding copyright ownership.  The ASF licenses this file
                   # to you under the Apache License, Version 2.0 (the
                   # "License"); you may not use this file except in compliance
                   # with the License.  You may obtain a copy of the License at
                   #
                   #   http://www.apache.org/licenses/LICENSE-2.0
                   #
                   # Unless required by applicable law or agreed to in writing,
                   # software distributed under the License is distributed on an
                   # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
                   # KIND, either express or implied.  See the License for the
                   # specific language governing permissions and limitations
                   # under the License.
                   ---
                   {{- $nodeSelector := or .Values.nodeSelector .Values.workers.nodeSelector }}
                   {{- $affinity := or .Values.affinity .Values.workers.affinity }}
                   {{- $tolerations := or .Values.tolerations .Values.workers.tolerations }}
                   apiVersion: v1
                   kind: Pod
                   metadata:
                     name: astronomer-pod-template-file
                     labels:
                       tier: airflow
                       component: worker
                       release: {{ .Release.Name }}
                   {{- with .Values.labels }}
                   {{ toYaml . | indent 4 }}
                   {{- end }}
                     {{- if .Values.airflowPodAnnotations }}
                     annotations:
                     {{- toYaml .Values.airflowPodAnnotations | nindent 4 }}
                     {{- end }}
                   spec:
                     {{- if or (and .Values.dags.gitSync.enabled (not .Values.dags.persistence.enabled)) .Values.workers.extraInitContainers }}
                     initContainers:
                       {{- if and .Values.dags.gitSync.enabled (not .Values.dags.persistence.enabled) }}
                       {{- include "git_sync_container" (dict "Values" .Values "is_init" "true") | nindent 4 }}
                       {{- end }}
                       {{- if .Values.workers.extraInitContainers }}
                       {{- toYaml .Values.workers.extraInitContainers | nindent 4 }}
                       {{- end }}
                     {{- end }}
                     containers:
                       - args: []
                         command: []
                         envFrom:
                         {{- include "custom_airflow_environment_from" . | default "\n  []" | indent 6 }}
                         env:
                           - name: AIRFLOW__CORE__EXECUTOR
                             value: LocalExecutor
                   {{- include "standard_airflow_environment" . | indent 6}}
                   {{- include "custom_airflow_environment" . | indent 6 }}
                         image: {{ template "pod_template_image" . }}
                         imagePullPolicy: {{ .Values.images.airflow.pullPolicy }}
                         name: base
                         ports: []
                         volumeMounts:
                           - mountPath: {{ template "airflow_logs" . }}
                             name: logs
                           - name: config
                             mountPath: {{ template "airflow_config_path" . }}
                             subPath: airflow.cfg
                             readOnly: true
                   {{- if .Values.airflowLocalSettings }}
                           - name: config
                             mountPath: {{ template "airflow_local_setting_path" . }}
                             subPath: airflow_local_settings.py
                             readOnly: true
                   {{- end }}
                   {{- if or .Values.dags.gitSync.enabled .Values.dags.persistence.enabled }}
                           {{- include "airflow_dags_mount" . | nindent 8 }}
                   {{- end }}
                   {{- if .Values.workers.extraVolumeMounts }}
                   {{ toYaml .Values.workers.extraVolumeMounts | indent 8 }}
                   {{- end }}
                   {{- if .Values.workers.extraContainers }}
                   {{- toYaml .Values.workers.extraContainers | nindent 4 }}
                   {{- end }}
                     hostNetwork: false
                     {{- if or .Values.registry.secretName .Values.registry.connection }}
                     imagePullSecrets:
                       - name: {{ template "registry_secret" . }}
                     {{- end }}
                     restartPolicy: Never
                     securityContext:
                       runAsUser: {{ .Values.uid }}
                       fsGroup: {{ .Values.gid }}
                     nodeSelector: {{ toYaml $nodeSelector | nindent 4 }}
                     affinity: {{ toYaml $affinity | nindent 4 }}
                     tolerations: {{ toYaml $tolerations | nindent 4 }}
                     serviceAccountName: {{ include "worker.serviceAccountName" . }}
                     volumes:
                     {{- if .Values.dags.persistence.enabled }}
                     - name: dags
                       persistentVolumeClaim:
                         claimName: {{ template "airflow_dags_volume_claim" . }}
                     {{- else if .Values.dags.gitSync.enabled }}
                     - name: dags
                       emptyDir: {}
                     {{- end }}
                     {{- if .Values.logs.persistence.enabled }}
                     - name: logs
                       persistentVolumeClaim:
                         claimName: {{ template "airflow_logs_volume_claim" . }}
                     {{- else }}
                     - emptyDir: {}
                       name: logs
                     {{- end }}
                     {{- if and .Values.dags.gitSync.enabled .Values.dags.gitSync.sshKeySecret }}
                     {{- include "git_sync_ssh_key_volume" . | nindent 2 }}
                     {{- end }}
                     - configMap:
                         name: {{ include "airflow_config" . }}
                       name: config
                     {{- if .Values.workers.extraVolumes }}
                     {{ toYaml .Values.workers.extraVolumes | nindent 2 }}
                     {{- end }}
   ```

2. Customize the pod template configuration based on your use case, such as by requesting default limits on CPU and memory usage. To configure these resources for each Pod, you configure a Pod template. For more information on configuring Pod template values, see the [Kubernetes documentation](https://kubernetes.io/docs/concepts/workloads/pods/#pod-templates).

3. Push the configuration change to your platform. See [Apply a config change](/docs/astro-private-cloud/v-0-34/apply-platform-config).

## Configure the worker Pod for a specific task

For each task with the Kubernetes executor, you can customize its individual worker Pod and override the defaults used in Astronomer Software by configuring a `pod_override` file.

1. Add the following import to your DAG file:

   ```sh wrap theme={null}
   from kubernetes.client import models as k8s
   ```

2. Add a `pod_override` configuration to the DAG file containing the task. See the [`kubernetes-client`](https://github.com/kubernetes-client/python/blob/master/kubernetes/docs/V1Container.md) GitHub for a list of all possible settings you can include in the configuration.

3. Specify the `pod_override` in the task's parameters.

### Example: Set CPU or memory limits and requests

One of the most common use cases for customizing a Kubernetes worker Pod is to request a specific amount of resources for a task.

The following example shows how you can use a `pod_override` configuration in your DAG code to request custom resources for a task:

```python expandable wrap theme={null}
import pendulum
import time
from airflow import DAG
from airflow.decorators import task
from airflow.operators.bash import BashOperator
from airflow.operators.python import PythonOperator
from airflow.example_dags.libs.helper import print_stuff
from kubernetes.client import models as k8s
k8s_exec_config_resource_requirements = {
    "pod_override": k8s.V1Pod(
        spec=k8s.V1PodSpec(
            containers=[
                k8s.V1Container(
                    name="base",
                    resources=k8s.V1ResourceRequirements(
                        requests={"cpu": 0.5, "memory": "1024Mi"},
                        limits={"cpu": 0.5, "memory": "1024Mi"}
                    )
                )
            ]
        )
    )
}
with DAG(
    dag_id="example_kubernetes_executor_pod_override_sources",
    schedule=None,
    start_date=pendulum.datetime(2023, 1, 1, tz="UTC"),
    catchup=False
):
    BashOperator(
      task_id="bash_resource_requirements_override_example",
      bash_command="echo hi",
      executor_config=k8s_exec_config_resource_requirements
    )

    @task(executor_config=k8s_exec_config_resource_requirements)
    def resource_requirements_override_example():
        print_stuff()
        time.sleep(60)

    resource_requirements_override_example()
```

When this DAG runs, it launches a Kubernetes Pod with exactly 0.5m of CPU and 1024Mi of memory, as long as that infrastructure is available in your cluster. Once the task finishes, the Pod terminates gracefully.

## Mount secret environment variables to worker Pods

[Deployment environment variables](/docs/astro-private-cloud/v-0-34/environment-variables) marked as secrets are stored in a Kubernetes secret called `<release-name>-env` on your Deployment namespace. To use a secret value in a task running on the KubernetesExecutor, mount the secret to the Pod running the task.

1. Run the following command to find the namespace (release name) of your Airflow Deployment:

   ```sh wrap theme={null}
   kubectl get ns
   ```

2. Add the following import to your DAG file:

   ```python wrap theme={null}
   from airflow.kubernetes.secret import Secret
   ```

3. Define a Kubernetes `Secret` in your DAG instantiation using the following format:

   ```python wrap theme={null}
   secret_env = Secret(deploy_type="env", deploy_target="<SECRET_KEY>", secret="<release-name>-env", key="<SECRET_KEY>")
   namespace = conf.get("kubernetes", "<release-name>")
   ```

4. Specify the `Secret` in the `secret_key_ref` section of your `pod_override` configuration.

5. In the task where you want to use the secret value, add the following task-level argument:

   ```python wrap theme={null}
   op_kwargs={
           "env_name": secret_env.deploy_target
   },
   ```

6. In the executable for the task, call the secret value using `os.environ[env_name]`.

In the following example, a secret named `MY_SECRET` is pulled from `infrared-photon-7780-env` and printed to logs.

```python expandable wrap theme={null}
import pendulum
from kubernetes.client import models as k8s

from airflow.configuration import conf
from airflow.kubernetes.secret import Secret
from airflow.models import DAG
from airflow.providers.cncf.kubernetes.operators.kubernetes_pod import KubernetesPodOperator
from airflow.operators.python import PythonOperator

def print_env(env_name):
    import os
    print(os.environ[env_name])

with DAG(
        dag_id='test-secret',
        start_date=pendulum.datetime(2022, 1, 1, tz="UTC"),
        end_date=pendulum.datetime(2022, 1, 5, tz="UTC"),
        schedule_interval="@once",
) as dag:
    secret_env = Secret(deploy_type="env", deploy_target="MY_SECRET", secret="infrared-photon-7780-env", key="MY_SECRET")
    namespace = conf.get("kubernetes", "infrared-photon-7780")

    p = PythonOperator(
        python_callable=print_env,
        op_kwargs={
            "env_name": secret_env.deploy_target
        },
        task_id='test-py-env',
        executor_config={
            "pod_override": k8s.V1Pod(
                spec=k8s.V1PodSpec(
                    containers=[
                        k8s.V1Container(
                            name="base",
                            env=[
                                k8s.V1EnvVar(
                                    name=secret_env.deploy_target,
                                    value_from=k8s.V1EnvVarSource(
                                        secret_key_ref=k8s.V1SecretKeySelector(name=secret_env.secret,
                                                                               key=secret_env.key)
                                    ),
                                )
                            ],
                        )
                    ]
                )
            ),
        }
    )
```