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new cc9c4c6 Add reference link for KubernetesPodOperator in
kubernetes.rst (#11782)
cc9c4c6 is described below
commit cc9c4c682e4b5fb23949f22745639ed62c8e712d
Author: Kaxil Naik <[email protected]>
AuthorDate: Fri Oct 23 16:36:11 2020 +0100
Add reference link for KubernetesPodOperator in kubernetes.rst (#11782)
This makes it easy to go to the class definition and find the
arguments/params that can be passed to the Operator
---
docs/howto/operator/kubernetes.rst | 15 ++++++++++-----
1 file changed, 10 insertions(+), 5 deletions(-)
diff --git a/docs/howto/operator/kubernetes.rst
b/docs/howto/operator/kubernetes.rst
index 85043a7..a556919 100644
--- a/docs/howto/operator/kubernetes.rst
+++ b/docs/howto/operator/kubernetes.rst
@@ -40,13 +40,15 @@ you to create and run Pods on a Kubernetes cluster.
How does this operator work?
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-The ``KubernetesPodOperator`` uses the Kubernetes API to launch a pod in a
Kubernetes cluster. By supplying an
+The
:class:`~airflow.providers.cncf.kubernetes.operators.kubernetes_pod.KubernetesPodOperator`
uses the
+Kubernetes API to launch a pod in a Kubernetes cluster. By supplying an
image URL and a command with optional arguments, the operator uses the Kube
Python Client to generate a Kubernetes API
request that dynamically launches those individual pods.
Users can specify a kubeconfig file using the ``config_file`` parameter,
otherwise the operator will default
to ``~/.kube/config``.
-The ``KubernetesPodOperator`` enables task-level resource configuration and is
optimal for custom Python
+The
:class:`~airflow.providers.cncf.kubernetes.operators.kubernetes_pod.KubernetesPodOperator`
enables task-level
+resource configuration and is optimal for custom Python
dependencies that are not available through the public PyPI repository. It
also allows users to supply a template
YAML file using the ``pod_template_file`` parameter.
Ultimately, it allows Airflow to act a job orchestrator - no matter the
language those jobs are written in.
@@ -73,7 +75,8 @@ and type safety. While we have removed almost all Kubernetes
convenience classes
Difference between ``KubernetesPodOperator`` and Kubernetes object spec
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-The ``KubernetesPodOperator`` can be considered a substitute for a Kubernetes
object spec definition that is able
+The
:class:`~airflow.providers.cncf.kubernetes.operators.kubernetes_pod.KubernetesPodOperator`
can be considered
+a substitute for a Kubernetes object spec definition that is able
to be run in the Airflow scheduler in the DAG context. If using the operator,
there is no need to create the
equivalent YAML/JSON object spec for the Pod you would like to run.
The YAML file can still be provided with the ``pod_template_file`` or even the
Pod Spec constructed in Python via
@@ -81,7 +84,8 @@ the ``full_pod_spec`` parameter which requires a Kubernetes
``V1Pod``.
How to use private images (container registry)?
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-By default, the ``KubernetesPodOperator`` will look for images hosted publicly
on Dockerhub.
+By default, the
:class:`~airflow.providers.cncf.kubernetes.operators.kubernetes_pod.KubernetesPodOperator`
will
+look for images hosted publicly on Dockerhub.
To pull images from a private registry (such as ECR, GCR, Quay, or others),
you must create a
Kubernetes Secret that represents the credentials for accessing images from
the private registry that is ultimately
specified in the ``image_pull_secrets`` parameter.
@@ -104,7 +108,8 @@ Then use it in your pod like so:
How does XCom work?
^^^^^^^^^^^^^^^^^^^
-The ``KubernetesPodOperator`` handles XCom values differently than other
operators. In order to pass a XCom value
+The
:class:`~airflow.providers.cncf.kubernetes.operators.kubernetes_pod.KubernetesPodOperator`
handles
+XCom values differently than other operators. In order to pass a XCom value
from your Pod you must specify the ``do_xcom_push`` as ``True``. This will
create a sidecar container that runs
alongside the Pod. The Pod must write the XCom value into this location at the
``/airflow/xcom/return.json`` path.
