Source code for airflow.providers.cncf.kubernetes.sensors.spark_kubernetes

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from __future__ import annotations

from functools import cached_property
from typing import TYPE_CHECKING, Sequence

from kubernetes import client

from airflow.exceptions import AirflowException
from airflow.providers.cncf.kubernetes.hooks.kubernetes import KubernetesHook
from airflow.sensors.base import BaseSensorOperator

if TYPE_CHECKING:
    from airflow.utils.context import Context


[docs]class SparkKubernetesSensor(BaseSensorOperator): """ Checks sparkApplication object in kubernetes cluster. .. seealso:: For more detail about Spark Application Object have a look at the reference: https://github.com/GoogleCloudPlatform/spark-on-k8s-operator/blob/v1beta2-1.1.0-2.4.5/docs/api-docs.md#sparkapplication :param application_name: spark Application resource name :param namespace: the kubernetes namespace where the sparkApplication reside in :param container_name: the kubernetes container name where the sparkApplication reside in :param kubernetes_conn_id: The :ref:`kubernetes connection<howto/connection:kubernetes>` to Kubernetes cluster. :param attach_log: determines whether logs for driver pod should be appended to the sensor log :param api_group: kubernetes api group of sparkApplication :param api_version: kubernetes api version of sparkApplication """
[docs] template_fields: Sequence[str] = ("application_name", "namespace")
[docs] FAILURE_STATES = ("FAILED", "UNKNOWN")
[docs] SUCCESS_STATES = ("COMPLETED",)
def __init__( self, *, application_name: str, attach_log: bool = False, namespace: str | None = None, container_name: str = "spark-kubernetes-driver", kubernetes_conn_id: str = "kubernetes_default", api_group: str = "sparkoperator.k8s.io", api_version: str = "v1beta2", **kwargs, ) -> None: super().__init__(**kwargs) self.application_name = application_name self.attach_log = attach_log self.namespace = namespace self.container_name = container_name self.kubernetes_conn_id = kubernetes_conn_id self.api_group = api_group self.api_version = api_version @cached_property
[docs] def hook(self) -> KubernetesHook: return KubernetesHook(conn_id=self.kubernetes_conn_id)
def _log_driver(self, application_state: str, response: dict) -> None: if not self.attach_log: return status_info = response["status"] if "driverInfo" not in status_info: return driver_info = status_info["driverInfo"] if "podName" not in driver_info: return driver_pod_name = driver_info["podName"] namespace = response["metadata"]["namespace"] log_method = self.log.error if application_state in self.FAILURE_STATES else self.log.info try: log = "" for line in self.hook.get_pod_logs( driver_pod_name, namespace=namespace, container=self.container_name ): log += line.decode() log_method(log) except client.rest.ApiException as e: self.log.warning( "Could not read logs for pod %s. It may have been disposed.\n" "Make sure timeToLiveSeconds is set on your SparkApplication spec.\n" "underlying exception: %s", driver_pod_name, e, )
[docs] def poke(self, context: Context) -> bool: self.log.info("Poking: %s", self.application_name) response = self.hook.get_custom_object( group=self.api_group, version=self.api_version, plural="sparkapplications", name=self.application_name, namespace=self.namespace, ) try: application_state = response["status"]["applicationState"]["state"] except KeyError: return False if self.attach_log and application_state in self.FAILURE_STATES + self.SUCCESS_STATES: self._log_driver(application_state, response) if application_state in self.FAILURE_STATES: message = f"Spark application failed with state: {application_state}" raise AirflowException(message) elif application_state in self.SUCCESS_STATES: self.log.info("Spark application ended successfully") return True else: self.log.info("Spark application is still in state: %s", application_state) return False

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