Source code for airflow.providers.apache.flink.sensors.flink_kubernetes
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from __future__ import annotations
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 FlinkKubernetesSensor(BaseSensorOperator):
"""
Checks flinkDeployment object in kubernetes cluster.
.. seealso::
For more detail about Flink Deployment Object have a look at the reference:
https://nightlies.apache.org/flink/flink-kubernetes-operator-docs-main/docs/custom-resource/reference/#flinkdeployment
:param application_name: flink Application resource name
:param namespace: the kubernetes namespace where the flinkDeployment 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 flinkDeployment
:param api_version: kubernetes api version of flinkDeployment
:param plural: kubernetes api custom object plural
"""
[docs] template_fields: Sequence[str] = ("application_name", "namespace")
[docs] FAILURE_STATES = ("MISSING", "ERROR")
[docs] SUCCESS_STATES = ("READY",)
def __init__(
self,
*,
application_name: str,
attach_log: bool = False,
namespace: str | None = None,
kubernetes_conn_id: str = "kubernetes_default",
api_group: str = "flink.apache.org",
api_version: str = "v1beta1",
plural: str = "flinkdeployments",
**kwargs,
) -> None:
super().__init__(**kwargs)
self.application_name = application_name
self.attach_log = attach_log
self.namespace = namespace
self.kubernetes_conn_id = kubernetes_conn_id
self.hook = KubernetesHook(conn_id=self.kubernetes_conn_id)
self.api_group = api_group
self.api_version = api_version
self.plural = plural
def _log_driver(self, application_state: str, response: dict) -> None:
log_method = self.log.error if application_state in self.FAILURE_STATES else self.log.info
if not self.attach_log:
return
status_info = response["status"]
if "jobStatus" in status_info:
job_status = status_info["jobStatus"]
job_state = job_status["state"] if "state" in job_status else "StateFetchError"
self.log.info("Flink Job status is %s", job_state)
else:
return
task_manager_labels = status_info["taskManager"]["labelSelector"]
all_pods = self.hook.get_namespaced_pod_list(
namespace="default", watch=False, label_selector=task_manager_labels
)
namespace = response["metadata"]["namespace"]
for task_manager in all_pods.items:
task_manager_pod_name = task_manager.metadata.name
self.log.info("Starting logging of task manager pod %s ", task_manager_pod_name)
try:
log = ""
for line in self.hook.get_pod_logs(task_manager_pod_name, namespace=namespace):
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 flinkDeployment spec.\n"
"underlying exception: %s",
task_manager_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=self.plural,
name=self.application_name,
namespace=self.namespace,
)
try:
application_state = response["status"]["jobManagerDeploymentStatus"]
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"Flink application failed with state: {application_state}"
raise AirflowException(message)
elif application_state in self.SUCCESS_STATES:
self.log.info("Flink application ended successfully")
return True
else:
self.log.info("Flink application is still in state: %s", application_state)
return False