Source code for airflow.providers.google.cloud.sensors.dataproc

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"""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 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


[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()
                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