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 all the dags onto the persistent disks, all the workers can read the dags from there. Another option is using 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 the logs onto a persistent disk, all the logs will be available for all the workers and the webserver itself. 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 the logs.

Kubernetes Operator

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


secret_file = Secret('volume', '/etc/sql_conn', 'airflow-secrets', 'sql_alchemy_conn')
secret_env  = Secret('env', 'SQL_CONN', 'airflow-secrets', 'sql_alchemy_conn')
volume_mount = VolumeMount('test-volume',
                            mount_path='/root/mount_file',
                            sub_path=None,
                            read_only=True)

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]
                          volumes=[volume],
                          volume_mounts=[volume_mount]
                          name="test",
                          task_id="task",
                          affinity=affinity,
                          is_delete_operator_pod=True,
                          hostnetwork=False,
                          tolerations=tolerations
                          )
class airflow.contrib.kubernetes.secret.Secret(deploy_type, deploy_target, secret, key)[source]

Defines Kubernetes Secret Volume