Source code for

# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.
"""This module contains a Dataproc Job sensor."""
# pylint: disable=C0302
import time
from typing import TYPE_CHECKING, Optional, Sequence

from google.api_core.exceptions import ServerError
from import JobStatus

from airflow.exceptions import AirflowException
from import DataprocHook
from airflow.sensors.base import BaseSensorOperator

    from airflow.utils.context import Context

[docs]class DataprocJobSensor(BaseSensorOperator): """ Check for the state of a previously submitted Dataproc job. :param dataproc_job_id: The Dataproc job ID to poll. (templated) :param region: Required. The Cloud Dataproc region in which to handle the request. (templated) :param project_id: The ID of the google cloud project in which to create the cluster. (templated) :param gcp_conn_id: The connection ID to use connecting to Google Cloud Platform. :param wait_timeout: How many seconds wait for job to be ready. """
[docs] template_fields: Sequence[str] = ('project_id', 'region', 'dataproc_job_id')
[docs] ui_color = '#f0eee4'
def __init__( self, *, dataproc_job_id: str, region: str, project_id: Optional[str] = None, gcp_conn_id: str = 'google_cloud_default', wait_timeout: Optional[int] = None, **kwargs, ) -> None: super().__init__(**kwargs) self.project_id = project_id self.gcp_conn_id = gcp_conn_id self.dataproc_job_id = dataproc_job_id self.region = region self.wait_timeout = wait_timeout self.start_sensor_time: Optional[float] = None
[docs] def execute(self, context: "Context") -> None: self.start_sensor_time = time.monotonic() super().execute(context)
def _duration(self): return time.monotonic() - self.start_sensor_time
[docs] def poke(self, context: "Context") -> bool: hook = DataprocHook(gcp_conn_id=self.gcp_conn_id) if self.wait_timeout: try: job = hook.get_job( job_id=self.dataproc_job_id, region=self.region, project_id=self.project_id ) except ServerError as err: duration = self._duration()"DURATION RUN: %f", duration) if duration > self.wait_timeout: raise AirflowException( f"Timeout: dataproc job {self.dataproc_job_id} " f"is not ready after {self.wait_timeout}s" )"Retrying. Dataproc API returned server error when waiting for job: %s", err) return False else: job = hook.get_job(job_id=self.dataproc_job_id, region=self.region, project_id=self.project_id) state = job.status.state if state == JobStatus.State.ERROR: raise AirflowException(f'Job failed:\n{job}') elif state in { JobStatus.State.CANCELLED, JobStatus.State.CANCEL_PENDING, JobStatus.State.CANCEL_STARTED, }: raise AirflowException(f'Job was cancelled:\n{job}') elif JobStatus.State.DONE == state: self.log.debug("Job %s completed successfully.", self.dataproc_job_id) return True elif JobStatus.State.ATTEMPT_FAILURE == state: self.log.debug("Job %s attempt has failed.", self.dataproc_job_id)"Waiting for job %s to complete.", self.dataproc_job_id) return False

Was this entry helpful?