.. 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. Kubernetes ---------- Kubernetes Executor ^^^^^^^^^^^^^^^^^^^ The kubernetes executor is introduced in Apache Airflow 1.10.0. The Kubernetes executor will create a new pod for every task instance. Example helm charts are available at ``scripts/ci/kubernetes/kube/{airflow,volumes,postgres}.yaml`` in the source distribution. The volumes are optional and depend on your configuration. There are two volumes available: - **Dags**: - By storing dags onto persistent disk, it will be made available to all workers - Another option is to use ``git-sync``. Before starting the container, a git pull of the dags repository will be performed and used throughout the lifecycle of the pod - **Logs**: - By storing logs onto a persistent disk, the files are accessible by workers and the webserver. If you don't configure this, the logs will be lost after the worker pods shuts down - Another option is to use S3/GCS/etc to store logs Kubernetes Operator ^^^^^^^^^^^^^^^^^^^ .. code:: python from airflow.contrib.operators import KubernetesOperator from airflow.contrib.operators.kubernetes_pod_operator import KubernetesPodOperator from airflow.contrib.kubernetes.secret import Secret from airflow.contrib.kubernetes.volume import Volume from airflow.contrib.kubernetes.volume_mount import VolumeMount from airflow.contrib.kubernetes.pod import Port secret_file = Secret('volume', '/etc/sql_conn', 'airflow-secrets', 'sql_alchemy_conn') secret_env = Secret('env', 'SQL_CONN', 'airflow-secrets', 'sql_alchemy_conn') secret_all_keys = Secret('env', None, 'airflow-secrets-2') volume_mount = VolumeMount('test-volume', mount_path='/root/mount_file', sub_path=None, read_only=True) port = Port('http', 80) configmaps = ['test-configmap-1', 'test-configmap-2'] volume_config= { 'persistentVolumeClaim': { 'claimName': 'test-volume' } } volume = Volume(name='test-volume', configs=volume_config) affinity = { 'nodeAffinity': { 'preferredDuringSchedulingIgnoredDuringExecution': [ { "weight": 1, "preference": { "matchExpressions": { "key": "disktype", "operator": "In", "values": ["ssd"] } } } ] }, "podAffinity": { "requiredDuringSchedulingIgnoredDuringExecution": [ { "labelSelector": { "matchExpressions": [ { "key": "security", "operator": "In", "values": ["S1"] } ] }, "topologyKey": "failure-domain.beta.kubernetes.io/zone" } ] }, "podAntiAffinity": { "requiredDuringSchedulingIgnoredDuringExecution": [ { "labelSelector": { "matchExpressions": [ { "key": "security", "operator": "In", "values": ["S2"] } ] }, "topologyKey": "kubernetes.io/hostname" } ] } } tolerations = [ { 'key': "key", 'operator': 'Equal', 'value': 'value' } ] k = KubernetesPodOperator(namespace='default', image="ubuntu:16.04", cmds=["bash", "-cx"], arguments=["echo", "10"], labels={"foo": "bar"}, secrets=[secret_file, secret_env, secret_all_keys], ports=[port] volumes=[volume], volume_mounts=[volume_mount], name="test", task_id="task", affinity=affinity, is_delete_operator_pod=True, hostnetwork=False, tolerations=tolerations, configmaps=configmaps ) See :class:`airflow.contrib.operators.kubernetes_pod_operator.KubernetesPodOperator` Pod Mutation Hook ^^^^^^^^^^^^^^^^^ Your local Airflow settings file 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 is 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. .. code:: python def pod_mutation_hook(pod: Pod): pod.annotations['airflow.apache.org/launched-by'] = 'Tests'