airflow dataproc 源码
airflow dataproc 代码
文件路径:/airflow/providers/google/cloud/sensors/dataproc.py
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# 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."""
from __future__ import annotations
# pylint: disable=C0302
import time
from typing import TYPE_CHECKING, Sequence
from google.api_core.exceptions import ServerError
from google.cloud.dataproc_v1.types import JobStatus
from airflow.exceptions import AirflowException
from airflow.providers.google.cloud.hooks.dataproc import DataprocHook
from airflow.sensors.base import BaseSensorOperator
if TYPE_CHECKING:
from airflow.utils.context import Context
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.
"""
template_fields: Sequence[str] = ('project_id', 'region', 'dataproc_job_id')
ui_color = '#f0eee4'
def __init__(
self,
*,
dataproc_job_id: str,
region: str,
project_id: str | None = None,
gcp_conn_id: str = 'google_cloud_default',
wait_timeout: int | None = 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: float | None = None
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
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()
self.log.info("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"
)
self.log.info("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)
self.log.info("Waiting for job %s to complete.", self.dataproc_job_id)
return False
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