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Kubernetes

Метки: airflow apache airflow

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

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 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.

def pod_mutation_hook(pod: Pod):
  pod.annotations['airflow.apache.org/launched-by'] = 'Tests'