def test_query_structured_metrics(self): mock_client, mock_job_result = self.setup_mock_client_result( self.STRUCTURED_COUNTER_LIST) dm = dataflow_metrics.DataflowMetrics(mock_client, mock_job_result) dm._translate_step_name = types.MethodType(lambda self, x: 'split', dm) query_result = dm.query() expected_counters = [ MetricResult( MetricKey( 'split', MetricName('__main__.WordExtractingDoFn', 'word_lengths'), ), 109475, 109475), ] self.assertEqual(query_result['counters'], expected_counters) expected_distributions = [ MetricResult( MetricKey( 'split', MetricName('__main__.WordExtractingDoFn', 'word_length_dist'), ), DistributionResult(DistributionData(18, 2, 2, 16)), DistributionResult(DistributionData(18, 2, 2, 16))), ] self.assertEqual(query_result['distributions'], expected_distributions)
def test_direct_runner_metrics(self): class MyDoFn(beam.DoFn): def start_bundle(self): count = Metrics.counter(self.__class__, 'bundles') count.inc() def finish_bundle(self): count = Metrics.counter(self.__class__, 'finished_bundles') count.inc() def process(self, element): gauge = Metrics.gauge(self.__class__, 'latest_element') gauge.set(element) count = Metrics.counter(self.__class__, 'elements') count.inc() distro = Metrics.distribution(self.__class__, 'element_dist') distro.update(element) return [element] p = Pipeline(DirectRunner()) pcoll = (p | beam.Create([1, 2, 3, 4, 5]) | 'Do' >> beam.ParDo(MyDoFn())) assert_that(pcoll, equal_to([1, 2, 3, 4, 5])) result = p.run() result.wait_until_finish() metrics = result.metrics().query() namespace = '{}.{}'.format(MyDoFn.__module__, MyDoFn.__name__) hc.assert_that( metrics['counters'], hc.contains_inanyorder( MetricResult( MetricKey('Do', MetricName(namespace, 'elements')), 5, 5), MetricResult( MetricKey('Do', MetricName(namespace, 'bundles')), 1, 1), MetricResult( MetricKey('Do', MetricName(namespace, 'finished_bundles')), 1, 1))) hc.assert_that( metrics['distributions'], hc.contains_inanyorder( MetricResult( MetricKey('Do', MetricName(namespace, 'element_dist')), DistributionResult(DistributionData(15, 5, 1, 5)), DistributionResult(DistributionData(15, 5, 1, 5))))) gauge_result = metrics['gauges'][0] hc.assert_that( gauge_result.key, hc.equal_to(MetricKey('Do', MetricName(namespace, 'latest_element')))) hc.assert_that(gauge_result.committed.value, hc.equal_to(5)) hc.assert_that(gauge_result.attempted.value, hc.equal_to(5))
def test_direct_runner_metrics(self): from apache_beam.metrics.metric import Metrics class MyDoFn(beam.DoFn): def start_bundle(self): count = Metrics.counter(self.__class__, 'bundles') count.inc() def finish_bundle(self): count = Metrics.counter(self.__class__, 'finished_bundles') count.inc() def process(self, element): count = Metrics.counter(self.__class__, 'elements') count.inc() distro = Metrics.distribution(self.__class__, 'element_dist') distro.update(element) return [element] runner = DirectRunner() p = Pipeline(runner, options=PipelineOptions(self.default_properties)) # pylint: disable=expression-not-assigned (p | ptransform.Create([1, 2, 3, 4, 5]) | 'Do' >> beam.ParDo(MyDoFn())) result = p.run() result.wait_until_finish() metrics = result.metrics().query() namespace = '{}.{}'.format(MyDoFn.__module__, MyDoFn.__name__) hc.assert_that( metrics['counters'], hc.contains_inanyorder( MetricResult( MetricKey('Do', MetricName(namespace, 'elements')), 5, 5), MetricResult( MetricKey('Do', MetricName(namespace, 'bundles')), 1, 1), MetricResult( MetricKey('Do', MetricName(namespace, 'finished_bundles')), 1, 1))) hc.assert_that( metrics['distributions'], hc.contains_inanyorder( MetricResult( MetricKey('Do', MetricName(namespace, 'element_dist')), DistributionResult(DistributionData(15, 5, 1, 5)), DistributionResult(DistributionData(15, 5, 1, 5)))))
def _get_metric_value(self, metric): """Get a metric result object from a MetricUpdate from Dataflow API.""" if metric is None: return None if metric.scalar is not None: return metric.scalar.integer_value elif metric.distribution is not None: dist_count = _get_match( metric.distribution.object_value.properties, lambda x: x.key == 'count').value.integer_value dist_min = _get_match(metric.distribution.object_value.properties, lambda x: x.key == 'min').value.integer_value dist_max = _get_match(metric.distribution.object_value.properties, lambda x: x.key == 'max').value.integer_value dist_sum = _get_match(metric.distribution.object_value.properties, lambda x: x.key == 'sum').value.integer_value if not dist_sum: # distribution metric is not meant to use on large values, but in case # it is, the value can overflow and become double_value, the correctness # of the value may not be guaranteed. _LOGGER.info( "Distribution metric sum value seems to have " "overflowed integer_value range, the correctness of sum or mean " "value may not be guaranteed: %s" % metric.distribution) dist_sum = int( _get_match(metric.distribution.object_value.properties, lambda x: x.key == 'sum').value.double_value) return DistributionResult( DistributionData(dist_sum, dist_count, dist_min, dist_max)) else: return None
def _get_metric_value(self, metric): """Get a metric result object from a MetricUpdate from Dataflow API.""" if metric is None: return None if metric.scalar is not None: return metric.scalar.integer_value elif metric.distribution is not None: dist_count = _get_match( metric.distribution.object_value.properties, lambda x: x.key == 'count').value.integer_value dist_min = _get_match(metric.distribution.object_value.properties, lambda x: x.key == 'min').value.integer_value dist_max = _get_match(metric.distribution.object_value.properties, lambda x: x.key == 'max').value.integer_value dist_mean = _get_match( metric.distribution.object_value.properties, lambda x: x.key == 'mean').value.integer_value # Calculating dist_sum with a hack, as distribution sum is not yet # available in the Dataflow API. # TODO(pabloem) Switch to "sum" field once it's available in the API dist_sum = dist_count * dist_mean return DistributionResult( DistributionData(dist_sum, dist_count, dist_min, dist_max)) else: return None
def test_commit_logical_no_filter(self): metrics = DirectMetrics() metrics.commit_logical( self.bundle1, MetricUpdates( counters={ MetricKey('step1', self.name1): 5, MetricKey('step1', self.name2): 8 }, distributions={ MetricKey('step1', self.name1): DistributionData(8, 2, 3, 5) })) metrics.commit_logical( self.bundle1, MetricUpdates( counters={ MetricKey('step2', self.name1): 7, MetricKey('step1', self.name2): 4 }, distributions={ MetricKey('step1', self.name1): DistributionData(4, 1, 4, 4) })) results = metrics.query() hc.assert_that( results['counters'], hc.contains_inanyorder( *[ MetricResult(MetricKey('step1', self.name2), 12, 0), MetricResult(MetricKey('step2', self.name1), 7, 0), MetricResult(MetricKey('step1', self.name1), 5, 0) ])) hc.assert_that( results['distributions'], hc.contains_inanyorder( MetricResult( MetricKey('step1', self.name1), DistributionResult(DistributionData(12, 3, 3, 5)), DistributionResult(DistributionData(0, 0, None, None)))))
def test_combiner_functions(self): metrics = DirectMetrics() counter = metrics._counters['anykey'] counter.commit_logical(self.bundle1, 5) self.assertEqual(counter.extract_committed(), 5) with self.assertRaises(TypeError): counter.commit_logical(self.bundle1, None) distribution = metrics._distributions['anykey'] distribution.commit_logical(self.bundle1, DistributionData(4, 1, 4, 4)) self.assertEqual(distribution.extract_committed(), DistributionResult(DistributionData(4, 1, 4, 4))) with self.assertRaises(AttributeError): distribution.commit_logical(self.bundle1, None)
def extract_metric_result_map_value(monitoring_info_proto): """Returns the relevant GaugeResult, DistributionResult or int value. These are the proper format for use in the MetricResult.query() result. """ # Returns a metric result (AKA the legacy format). # from the MonitoringInfo if is_counter(monitoring_info_proto): return extract_counter_value(monitoring_info_proto) if is_distribution(monitoring_info_proto): (count, sum, min, max) = extract_distribution(monitoring_info_proto) return DistributionResult(DistributionData(sum, count, min, max)) if is_gauge(monitoring_info_proto): (timestamp, value) = extract_gauge_value(monitoring_info_proto) return GaugeResult(GaugeData(value, timestamp))
def extract_metric_result_map_value(monitoring_info_proto): """Returns the relevant GaugeResult, DistributionResult or int value. These are the proper format for use in the MetricResult.query() result. """ # Returns a metric result (AKA the legacy format). # from the MonitoringInfo if is_counter(monitoring_info_proto): return extract_counter_value(monitoring_info_proto) if is_distribution(monitoring_info_proto): distribution_data = extract_distribution(monitoring_info_proto) return DistributionResult( DistributionData(distribution_data.sum, distribution_data.count, distribution_data.min, distribution_data.max)) if is_gauge(monitoring_info_proto): timestamp_secs = to_timestamp_secs(monitoring_info_proto.timestamp) return GaugeResult(GaugeData( extract_counter_value(monitoring_info_proto), timestamp_secs))
def _get_metric_value(self, metric): """Get a metric result object from a MetricUpdate from Dataflow API.""" if metric is None: return None if metric.scalar is not None: return metric.scalar.integer_value elif metric.distribution is not None: dist_count = _get_match( metric.distribution.object_value.properties, lambda x: x.key == 'count').value.integer_value dist_min = _get_match(metric.distribution.object_value.properties, lambda x: x.key == 'min').value.integer_value dist_max = _get_match(metric.distribution.object_value.properties, lambda x: x.key == 'max').value.integer_value dist_sum = _get_match(metric.distribution.object_value.properties, lambda x: x.key == 'sum').value.integer_value return DistributionResult( DistributionData(dist_sum, dist_count, dist_min, dist_max)) else: return None
def _create_metric_result(data_dict): step = data_dict['step'] if 'step' in data_dict else '' labels = data_dict['labels'] if 'labels' in data_dict else dict() values = {} for key in ['attempted', 'committed']: if key in data_dict: if 'counter' in data_dict[key]: values[key] = data_dict[key]['counter'] elif 'distribution' in data_dict[key]: distribution = data_dict[key]['distribution'] values[key] = DistributionResult( DistributionData( distribution['sum'], distribution['count'], distribution['min'], distribution['max'], )) attempted = values['attempted'] if 'attempted' in values else None committed = values['committed'] if 'committed' in values else None metric_name = MetricName(data_dict['namespace'], data_dict['name']) metric_key = MetricKey(step, metric_name, labels) return MetricResult(metric_key, committed, attempted)
def test_apply_physical_logical(self): metrics = DirectMetrics() dist_zero = DistributionData(0, 0, None, None) metrics.update_physical( object(), MetricUpdates(counters={ MetricKey('step1', self.name1): 7, MetricKey('step1', self.name2): 5, MetricKey('step2', self.name1): 1 }, distributions={ MetricKey('step1', self.name1): DistributionData(3, 1, 3, 3), MetricKey('step2', self.name3): DistributionData(8, 2, 4, 4) })) results = metrics.query() hc.assert_that( results['counters'], hc.contains_inanyorder(*[ MetricResult(MetricKey('step1', self.name1), 0, 7), MetricResult(MetricKey('step1', self.name2), 0, 5), MetricResult(MetricKey('step2', self.name1), 0, 1) ])) hc.assert_that( results['distributions'], hc.contains_inanyorder(*[ MetricResult(MetricKey('step1', self.name1), DistributionResult(dist_zero), DistributionResult(DistributionData(3, 1, 3, 3))), MetricResult(MetricKey('step2', self.name3), DistributionResult(dist_zero), DistributionResult(DistributionData(8, 2, 4, 4))) ])) metrics.commit_physical( object(), MetricUpdates(counters={ MetricKey('step1', self.name1): -3, MetricKey('step2', self.name1): -5 }, distributions={ MetricKey('step1', self.name1): DistributionData(8, 4, 1, 5), MetricKey('step2', self.name2): DistributionData(8, 8, 1, 1) })) results = metrics.query() hc.assert_that( results['counters'], hc.contains_inanyorder(*[ MetricResult(MetricKey('step1', self.name1), 0, 4), MetricResult(MetricKey('step1', self.name2), 0, 5), MetricResult(MetricKey('step2', self.name1), 0, -4) ])) hc.assert_that( results['distributions'], hc.contains_inanyorder(*[ MetricResult(MetricKey('step1', self.name1), DistributionResult(dist_zero), DistributionResult(DistributionData(11, 5, 1, 5))), MetricResult(MetricKey('step2', self.name3), DistributionResult(dist_zero), DistributionResult(DistributionData(8, 2, 4, 4))), MetricResult(MetricKey('step2', self.name2), DistributionResult(dist_zero), DistributionResult(DistributionData(8, 8, 1, 1))) ])) metrics.commit_logical( object(), MetricUpdates(counters={ MetricKey('step1', self.name1): 3, MetricKey('step1', self.name2): 5, MetricKey('step2', self.name1): -3 }, distributions={ MetricKey('step1', self.name1): DistributionData(11, 5, 1, 5), MetricKey('step2', self.name2): DistributionData(8, 8, 1, 1), MetricKey('step2', self.name3): DistributionData(4, 1, 4, 4) })) results = metrics.query() hc.assert_that( results['counters'], hc.contains_inanyorder(*[ MetricResult(MetricKey('step1', self.name1), 3, 4), MetricResult(MetricKey('step1', self.name2), 5, 5), MetricResult(MetricKey('step2', self.name1), -3, -4) ])) hc.assert_that( results['distributions'], hc.contains_inanyorder(*[ MetricResult(MetricKey('step1', self.name1), DistributionResult(DistributionData(11, 5, 1, 5)), DistributionResult(DistributionData(11, 5, 1, 5))), MetricResult(MetricKey('step2', self.name3), DistributionResult(DistributionData(4, 1, 4, 4)), DistributionResult(DistributionData(8, 2, 4, 4))), MetricResult(MetricKey('step2', self.name2), DistributionResult(DistributionData(8, 8, 1, 1)), DistributionResult(DistributionData(8, 8, 1, 1))) ]))