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"""This module contains a Dataproc Job sensor."""
from __future__ import annotations
import time
from typing import TYPE_CHECKING, Sequence
from google.api_core.exceptions import ServerError
from google.cloud.dataproc_v1.types import Batch, JobStatus
from airflow.exceptions import AirflowException, AirflowSkipException
from airflow.providers.google.cloud.hooks.dataproc import DataprocHook
from airflow.sensors.base import BaseSensorOperator
if TYPE_CHECKING:
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")
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
[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()
self.log.info("DURATION RUN: %f", duration)
if duration > self.wait_timeout:
# TODO: remove this if check when min_airflow_version is set to higher than 2.7.1
message = (
f"Timeout: dataproc job {self.dataproc_job_id} "
f"is not ready after {self.wait_timeout}s"
)
if self.soft_fail:
raise AirflowSkipException(message)
raise AirflowException(message)
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:
# TODO: remove this if check when min_airflow_version is set to higher than 2.7.1
message = f"Job failed:\n{job}"
if self.soft_fail:
raise AirflowSkipException(message)
raise AirflowException(message)
elif state in {
JobStatus.State.CANCELLED,
JobStatus.State.CANCEL_PENDING,
JobStatus.State.CANCEL_STARTED,
}:
# TODO: remove this if check when min_airflow_version is set to higher than 2.7.1
message = f"Job was cancelled:\n{job}"
if self.soft_fail:
raise AirflowSkipException(message)
raise AirflowException(message)
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
[docs]class DataprocBatchSensor(BaseSensorOperator):
"""
Check for the state of batch.
:param batch_id: The Dataproc batch 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", "batch_id")
def __init__(
self,
*,
batch_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.batch_id = batch_id
self.project_id = project_id
self.gcp_conn_id = gcp_conn_id
self.region = region
self.wait_timeout = wait_timeout
self.start_sensor_time: float | None = 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:
batch = hook.get_batch(batch_id=self.batch_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 batch {self.batch_id} is not ready after {self.wait_timeout}s"
)
self.log.info("Retrying. Dataproc API returned server error when waiting for batch: %s", err)
return False
else:
batch = hook.get_batch(batch_id=self.batch_id, region=self.region, project_id=self.project_id)
state = batch.state
if state == Batch.State.FAILED:
# TODO: remove this if check when min_airflow_version is set to higher than 2.7.1
message = "Batch failed"
if self.soft_fail:
raise AirflowSkipException(message)
raise AirflowException(message)
elif state in {
Batch.State.CANCELLED,
Batch.State.CANCELLING,
}:
# TODO: remove this if check when min_airflow_version is set to higher than 2.7.1
message = "Batch was cancelled."
if self.soft_fail:
raise AirflowSkipException(message)
raise AirflowException(message)
elif state == Batch.State.SUCCEEDED:
self.log.debug("Batch %s completed successfully.", self.batch_id)
return True
self.log.info("Waiting for the batch %s to complete.", self.batch_id)
return False