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