First I found the relations between Kubernetespodoperator cmds and arguments properties to Dockers ENTRYPOINT and CMD. V1NodeAffinity ( preferred_during_scheduling_ignored_during_execution = ) ] ), ) ] ), pod_affinity = k8s. I found a technic for running multiple commands. V1Container ( name = "init-container", image = "ubuntu:16.04", env = init_environments, volume_mounts = init_container_volume_mounts, command =, args =, ) affinity = k8s. It helps to create a new context that is able to run any. V1ContainerPort ( name = "http", container_port = 80 ) init_container_volume_mounts = init_environments = init_container = k8s. This operator uses the Kubernetes Pod object to decouple the workflow logic from the Airflow context. In your specific use case, it could looks like the following: import datetime from airflow import models from import kubernetespodoperator from import sendemail from kubernetes.
#Airflow kubernetes pod operator example code
V1PersistentVolumeClaimVolumeSource ( claim_name = "test-volume" ), ) port = k8s. from airflow import DAG from import DummyOperator from import PythonOperator from datetime import datetime, timedelta def mycustomfunction ( ts,kwargs ): ''' This can be any python code you want and is called from the python operator.
V1Volume ( name = "test-volume", persistent_volume_claim = k8s. The intricacies of when I’ve found each to be useful (Airflow alone. TL DR: they both provide common interfaces that data teams can use to get on the same page. V1VolumeMount ( name = "test-volume", mount_path = "/root/mount_file", sub_path = None, read_only = True ) configmaps = volume = k8s. Let’s walk through a hypothetical scenario I’d run into as a consultant, to illustrate how Airflow + dbt operate on a parallel spiritual wavelength. This page walks you through an example Airflow DAG that includes the following KubernetesPodOperator configurations: Minimal configuration: Sets only the required parameters. 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 = k8s.
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