def test_should_run_beam_job(
self,
state_mock,
):
# given
state_mock.return_value = "DONE"
driver = CountWordsDriver()
job = BeamJob(
id='count_words',
entry_point=driver.run,
entry_point_arguments={
'words_to_filter': ['valid', 'word'],
'words_to_count': ['trash', 'valid', 'word', 'valid']
},
test_pipeline=self._test_pipeline_with_label('count_words'))
# when
job.execute(JobContext.make())
# then executes the job with the arguments
self.assertEqual(driver.counter, {'valid': 2, 'word': 1})
# and passes the context
self.assertIsNotNone(driver.context)
self.assertTrue(isinstance(driver.context, JobContext))
# and labels the job
self.assertEqual(driver.pipeline._options.get_all_options()['labels'],
['workflow_id=count_words'])
# and sets default value for execution_timeout_sec
self.assertEqual(job.execution_timeout_sec,
DEFAULT_EXECUTION_TIMEOUT_IN_SECONDS)
def test_add_docker_to_pipelineoptions(
self,
get_project_spec_mock: mock.Mock,
_create_pipeline_mock: mock.Mock,
):
# given
_create_pipeline_mock.return_value.run.return_value = RunnerResult(
'DONE', None)
get_project_spec_mock.return_value.version = "1.2.3"
get_project_spec_mock.return_value.docker_repository = "my_repo"
driver = CountWordsDriver()
job = BeamJob(
id='count_words',
entry_point=driver.nope,
pipeline_options={},
use_docker_image=True,
)
# when
job.execute(JobContext.make())
# then
options2 = _create_pipeline_mock.call_args[1][
'options'].get_all_options(drop_default=True)
self.assertIn('job_name', options2)
self.assertEqual('my_repo:1.2.3',
options2['worker_harness_container_image'])
self.assertIn('use_runner_v2', options2['experiments'])
def test_add_setuppy_onle_for_dataflow_runner(
self,
_create_pipeline_mock: mock.Mock,
):
# given
_create_pipeline_mock.return_value.run.return_value = RunnerResult(
'DONE', None)
driver = CountWordsDriver()
options = {
'job_name': "custom-my-job",
'runner': 'DirectRunner',
}
job = BeamJob(
id='count_words',
entry_point=driver.nope,
pipeline_options=options,
)
# when
job.execute(JobContext.make())
# then
options2 = _create_pipeline_mock.call_args[1][
'options'].get_all_options(drop_default=True)
self.assertNotIn('setup_file', options2)
def test_create_pipeline_dict_options(
self,
_create_pipeline_mock: mock.Mock,
):
# given
_create_pipeline_mock.return_value.run.return_value = RunnerResult(
'DONE', None)
driver = CountWordsDriver()
options = {
'job_name': "custom-my-job",
'labels': ["lable1", "label2"],
'streaming': True,
'project': "my-gcp-project",
'machine_type': "n2-standard-16",
}
job = BeamJob(
id='count_words',
entry_point=driver.nope,
pipeline_options=options,
execution_timeout_sec=10,
)
# when
job.execute(JobContext.make())
# then
options2 = _create_pipeline_mock.call_args[1][
'options'].get_all_options(drop_default=True)
self.assertTrue(options2.pop('setup_file'))
self.assertEqual(options2.pop('runner'), 'DataflowRunner')
self.assertDictEqual(options, options2)
def test_should_run_beam_job_with_timeout_without_cancel(
self,
wait_until_finish_mock,
cancel_mock,
state_mock,
):
# given
wait_until_finish_mock.return_value = 'DONE'
state_mock.return_value = 'DONE'
driver = CountWordsDriver()
job = BeamJob(
id='count_words',
entry_point=driver.nope,
test_pipeline=self._test_pipeline_with_label('count_words'),
execution_timeout_sec=600)
# when
job.execute(JobContext.make())
# then
self.assertEqual(cancel_mock.call_count, 0)
wait_until_finish_mock.assert_called_with(
(600 - DEFAULT_PIPELINE_LEVEL_EXECUTION_TIMEOUT_SHIFT_IN_SECONDS) *
1000)
def test_should_run_new_entry_point(
self,
state_mock,
):
# given
state_mock.return_value = "DONE"
driver = CountWordsDriver()
job = BeamJob(
id='count_words',
entry_point=driver.run_ex,
entry_point_args=(['trash', 'valid', 'word', 'valid'], ),
entry_point_kwargs={
'words_to_filter': ['valid', 'word'],
},
test_pipeline=self._test_pipeline_with_label('count_words'))
# when
job.execute(JobContext.make())
# then executes the job with the arguments
self.assertEqual(driver.counter, {'valid': 2, 'word': 1})
# and passes the context
self.assertIsNotNone(driver.context)
self.assertTrue(isinstance(driver.context, JobContext))
# and labels the job
self.assertEqual(driver.pipeline._options.get_all_options()['labels'],
['workflow_id=count_words'])
def test_should_run_old_entry_point_withoutargs(
self,
state_mock,
):
# given
state_mock.return_value = "DONE"
driver = CountWordsDriver()
job = BeamJob(
id='count_words',
entry_point=driver.nope_driverargs,
test_pipeline=self._test_pipeline_with_label('count_words'))
# when
job.execute(JobContext.make())
# then
self.assertTrue(driver.context, "Driver was called")
def test_should_create_pipeline_from_pipeline_options(
self,
_create_pipeline_mock: mock.Mock,
):
# given
_create_pipeline_mock.return_value.run.return_value = RunnerResult(
'DONE', None)
driver = CountWordsDriver()
options = PipelineOptions()
options.view_as(StandardOptions).runner = 'DataflowRunner'
options.view_as(GoogleCloudOptions).project = 'gcp_project_id'
options.view_as(GoogleCloudOptions).job_name = 'beam-wordcount-uuid'
options.view_as(
GoogleCloudOptions).staging_location = "gs://staging_location"
options.view_as(
GoogleCloudOptions).temp_location = "gs://temp_location"
options.view_as(GoogleCloudOptions).region = 'region'
options.view_as(
GoogleCloudOptions).service_account_email = 'service-account'
options.view_as(WorkerOptions).machine_type = 'n2-standard-8'
options.view_as(WorkerOptions).max_num_workers = 2
options.view_as(
WorkerOptions).autoscaling_algorithm = 'THROUGHPUT_BASED'
options.view_as(SetupOptions).setup_file = "/path/to/setup.py"
job = BeamJob(
id='count_words',
entry_point=driver.nope,
pipeline_options=options,
execution_timeout_sec=10,
)
# when
job.execute(JobContext.make())
# then
options.get_all_options()
self.assertDictEqual(
options.get_all_options(),
_create_pipeline_mock.call_args[1]['options'].get_all_options(),
)