def test_wait(self, mock_hook):
job = self.create_job(JobStatus.RUNNING)
job_id = "job_id"
mock_hook.return_value.get_job.return_value = job
sensor = DataprocJobSensor(
task_id=TASK_ID,
location=GCP_LOCATION,
project_id=GCP_PROJECT,
dataproc_job_id=job_id,
gcp_conn_id=GCP_CONN_ID,
timeout=TIMEOUT,
)
ret = sensor.poke(context={})
mock_hook.return_value.get_job.assert_called_once_with(
job_id=job_id, location=GCP_LOCATION, project_id=GCP_PROJECT)
assert not ret
def test_cancelled(self, mock_hook):
job = self.create_job(JobStatus.CANCELLED)
job_id = "job_id"
mock_hook.return_value.get_job.return_value = job
sensor = DataprocJobSensor(
task_id=TASK_ID,
location=GCP_LOCATION,
project_id=GCP_PROJECT,
dataproc_job_id=job_id,
gcp_conn_id=GCP_CONN_ID,
timeout=TIMEOUT,
)
with pytest.raises(AirflowException, match="Job was cancelled"):
sensor.poke(context={})
mock_hook.return_value.get_job.assert_called_once_with(
job_id=job_id, location=GCP_LOCATION, project_id=GCP_PROJECT)
def test_error(self, mock_hook):
job = self.create_job(JobStatus.ERROR)
job_id = "job_id"
mock_hook.return_value.get_job.return_value = job
sensor = DataprocJobSensor(
task_id=TASK_ID,
location=GCP_LOCATION,
project_id=GCP_PROJECT,
dataproc_job_id=job_id,
gcp_conn_id=GCP_CONN_ID,
timeout=TIMEOUT,
)
with self.assertRaisesRegex(AirflowException, "Job failed"):
sensor.poke(context={})
mock_hook.return_value.get_job.assert_called_once_with(
job_id=job_id, location=GCP_LOCATION, project_id=GCP_PROJECT)
spark_task = DataprocSubmitJobOperator(task_id="spark_task",
job=SPARK_JOB,
region=REGION,
project_id=PROJECT_ID)
# [START cloud_dataproc_async_submit_sensor]
spark_task_async = DataprocSubmitJobOperator(task_id="spark_task_async",
job=SPARK_JOB,
region=REGION,
project_id=PROJECT_ID,
asynchronous=True)
spark_task_async_sensor = DataprocJobSensor(
task_id='spark_task_async_sensor_task',
region=REGION,
project_id=PROJECT_ID,
dataproc_job_id=spark_task_async.output,
poke_interval=10,
)
# [END cloud_dataproc_async_submit_sensor]
# [START how_to_cloud_dataproc_submit_job_to_cluster_operator]
pyspark_task = DataprocSubmitJobOperator(task_id="pyspark_task",
job=PYSPARK_JOB,
region=REGION,
project_id=PROJECT_ID)
# [END how_to_cloud_dataproc_submit_job_to_cluster_operator]
sparkr_task = DataprocSubmitJobOperator(task_id="sparkr_task",
job=SPARKR_JOB,
region=REGION,
spark_task = DataprocSubmitJobOperator(task_id="spark_task",
job=SPARK_JOB,
location=REGION,
project_id=PROJECT_ID)
# [START cloud_dataproc_async_submit_sensor]
spark_task_async = DataprocSubmitJobOperator(task_id="spark_task_async",
job=SPARK_JOB,
location=REGION,
project_id=PROJECT_ID,
asynchronous=True)
spark_task_async_sensor = DataprocJobSensor(
task_id='spark_task_async_sensor_task',
location=REGION,
project_id=PROJECT_ID,
dataproc_job_id=
"{{task_instance.xcom_pull(task_ids='spark_task_async')}}",
poke_interval=10,
)
# [END cloud_dataproc_async_submit_sensor]
# [START how_to_cloud_dataproc_submit_job_to_cluster_operator]
pyspark_task = DataprocSubmitJobOperator(task_id="pyspark_task",
job=PYSPARK_JOB,
location=REGION,
project_id=PROJECT_ID)
# [END how_to_cloud_dataproc_submit_job_to_cluster_operator]
sparkr_task = DataprocSubmitJobOperator(task_id="sparkr_task",
job=SPARKR_JOB,
location=REGION,
