def test_system_counters_set_labels_and_step_name(self):
mock_client, mock_job_result = self.setup_mock_client_result(
self.SYSTEM_COUNTERS_LIST)
test_object = dataflow_metrics.DataflowMetrics(mock_client, mock_job_result)
all_metrics = test_object.all_metrics()
matchers = [
MetricResultMatcher(
name='ElementCount',
labels={
'original_name' : 'ToIsmRecordForMultimap-out0-ElementCount',
'output_user_name' : 'ToIsmRecordForMultimap-out0'
},
attempted=42,
committed=42
),
MetricResultMatcher(
name='MeanByteCount',
labels={
'original_name' : 'Read-out0-MeanByteCount',
'output_user_name' : 'GroupByKey/Read-out0'
},
attempted=31,
committed=31
),
MetricResultMatcher(
name='ExecutionTime_ProcessElement',
step='write/Write/Write',
attempted=1000,
committed=1000
)
]
errors = metric_result_matchers.verify_all(all_metrics, matchers)
self.assertFalse(errors, errors)
def test_system_counters_set_labels_and_step_name(self):
mock_client, mock_job_result = self.setup_mock_client_result(
self.SYSTEM_COUNTERS_LIST)
test_object = dataflow_metrics.DataflowMetrics(mock_client,
mock_job_result)
all_metrics = test_object.all_metrics()
matchers = [
MetricResultMatcher(name='ElementCount',
labels={
'original_name':
'ToIsmRecordForMultimap-out0-ElementCount',
'output_user_name':
'ToIsmRecordForMultimap-out0'
},
attempted=42,
committed=42),
MetricResultMatcher(name='MeanByteCount',
labels={
'original_name': 'Read-out0-MeanByteCount',
'output_user_name': 'GroupByKey/Read-out0'
},
attempted=31,
committed=31),
MetricResultMatcher(name='ExecutionTime_ProcessElement',
step='write/Write/Write',
attempted=1000,
committed=1000)
]
errors = metric_result_matchers.verify_all(all_metrics, matchers)
self.assertFalse(errors, errors)
def test_streaming_pipeline_returns_expected_user_metrics_fnapi_it(self):
"""
Runs streaming Dataflow job and verifies that user metrics are reported
correctly.
"""
self._inject_words(self.input_topic, MESSAGES_TO_PUBLISH)
result = self.run_pipeline()
METRIC_NAMESPACE = \
('apache_beam.runners.dataflow.'
'dataflow_exercise_streaming_metrics_pipeline.StreamingUserMetricsDoFn')
matchers = [
# System metrics
MetricResultMatcher(
name='ElementCount',
labels={
"output_user_name": "generate_metrics-out0",
"original_name": "generate_metrics-out0-ElementCount"
},
attempted=len(MESSAGES_TO_PUBLISH),
committed=len(MESSAGES_TO_PUBLISH),
),
MetricResultMatcher(
name='ElementCount',
labels={
"output_user_name": "ReadFromPubSub/Read-out0",
"original_name": "ReadFromPubSub/Read-out0-ElementCount"
},
attempted=len(MESSAGES_TO_PUBLISH),
committed=len(MESSAGES_TO_PUBLISH),
),
# User Counter Metrics.
MetricResultMatcher(name='double_msg_counter_name',
namespace=METRIC_NAMESPACE,
step='generate_metrics',
attempted=len(MESSAGES_TO_PUBLISH) * 2,
committed=len(MESSAGES_TO_PUBLISH) * 2),
MetricResultMatcher(
name='msg_len_dist_metric_name',
namespace=METRIC_NAMESPACE,
step='generate_metrics',
attempted=DistributionMatcher(
sum_value=len(''.join(MESSAGES_TO_PUBLISH)),
count_value=len(MESSAGES_TO_PUBLISH),
min_value=len(MESSAGES_TO_PUBLISH[0]),
max_value=len(MESSAGES_TO_PUBLISH[1])),
committed=DistributionMatcher(
sum_value=len(''.join(MESSAGES_TO_PUBLISH)),
count_value=len(MESSAGES_TO_PUBLISH),
min_value=len(MESSAGES_TO_PUBLISH[0]),
max_value=len(MESSAGES_TO_PUBLISH[1]))),
]
metrics = result.metrics().all_metrics()
errors = metric_result_matchers.verify_all(metrics, matchers)
self.assertFalse(errors, str(errors))
def test_metrics_fnapi_it(self):
result = self.run_pipeline(experiment='beam_fn_api')
errors = metric_result_matchers.verify_all(
result.metrics().all_metrics(),
dataflow_exercise_metrics_pipeline.fn_api_metric_matchers())
self.assertFalse(errors, str(errors))
def test_metrics_it(self):
result = self.run_pipeline()
errors = metric_result_matchers.verify_all(
result.metrics().all_metrics(),
dataflow_exercise_metrics_pipeline.legacy_metric_matchers())
self.assertFalse(errors, str(errors))
def test_metrics_fnapi_it(self):
result = self.run_pipeline(experiment='beam_fn_api')
errors = metric_result_matchers.verify_all(
result.metrics().all_metrics(),
dataflow_exercise_metrics_pipeline.fn_api_metric_matchers())
self.assertFalse(errors, str(errors))
def test_metrics_it(self):
result = self.run_pipeline()
errors = metric_result_matchers.verify_all(
result.metrics().all_metrics(),
dataflow_exercise_metrics_pipeline.legacy_metric_matchers())
self.assertFalse(errors, str(errors))
