airflow-core/docs/administration-and-deployment/kubernetes.rst
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Apache Airflow aims to be a very Kubernetes-friendly project, and many users run Airflow from within a Kubernetes cluster in order to take advantage of the increased stability and autoscaling options that Kubernetes provides.
We maintain an :doc:official Helm chart <helm-chart:index> for Airflow that helps you define, install, and upgrade deployment. The Helm Chart uses :doc:official Docker image and Dockerfile <docker-stack:index> that is also maintained and released by the community.
Kubernetes Executor ^^^^^^^^^^^^^^^^^^^
The :doc:Kubernetes Executor <apache-airflow-providers-cncf-kubernetes:kubernetes_executor> allows you to run all the Airflow tasks on
Kubernetes as separate Pods.
KubernetesPodOperator ^^^^^^^^^^^^^^^^^^^^^
The :ref:KubernetesPodOperator <howto/operator:kubernetespodoperator> allows you to create
Pods on Kubernetes.
.. _kubernetes:pod_mutation_hook:
Pod Mutation Hook ^^^^^^^^^^^^^^^^^
The Airflow local settings file (airflow_local_settings.py) can define a pod_mutation_hook function
that has the ability to mutate pod objects before sending them to the Kubernetes client
for scheduling. It receives a single argument as a reference to pod objects, and
are expected to alter its attributes.
This could be used, for instance, to add sidecar or init containers to every worker pod launched by KubernetesExecutor or KubernetesPodOperator.
See :ref:Configuring local settings <set-config:configuring-local-settings> for details on how to
configure local settings.
.. code-block:: python
from kubernetes.client.models import V1Pod
def pod_mutation_hook(pod: V1Pod):
pod.metadata.annotations["airflow.apache.org/launched-by"] = "Tests"