def test_get_queries_geomean_performance_samples_passing(self):
b_p = agg.EdwBenchmarkPerformance(total_iterations=2,
expected_queries=[Q1_NAME, Q2_NAME])
i1_p = agg.EdwPowerIterationPerformance('1', 2)
i1_p.add_query_performance(Q1_NAME, 1.0, {'job_id': 'q1_i1_job_id'})
i1_p.add_query_performance(Q2_NAME, 2.0, {'job_id': 'q2_i1_job_id'})
b_p.add_power_iteration_performance(i1_p)
i2_p = agg.EdwPowerIterationPerformance('2', 2)
i2_p.add_query_performance(Q1_NAME, 3.0, {'job_id': 'q1_i2_job_id'})
i2_p.add_query_performance(Q2_NAME, 4.0, {})
b_p.add_power_iteration_performance(i2_p)
actual_sample_list = b_p.get_queries_geomean_performance_samples(
{'benchmark_name': 'b_name'})
self.assertEqual(len(actual_sample_list), 3)
self.assertSameElements([x.metric for x in actual_sample_list], [
'edw_iteration_geomean_time', 'edw_iteration_geomean_time',
'edw_aggregated_geomean'
])
raw_samples = list(
filter(lambda x: x.metric == 'edw_iteration_geomean_time',
actual_sample_list))
actual_raw_samples_values = [x.value for x in raw_samples]
expected_raw_samples_values = [
agg.geometric_mean([1.0, 2.0]),
agg.geometric_mean([3.0, 4.0])
]
self.assertSameElements(actual_raw_samples_values,
expected_raw_samples_values)
aggregated_sample = list(
filter(lambda x: x.metric == 'edw_aggregated_geomean',
actual_sample_list))[0]
self.assertEqual(
aggregated_sample.value,
agg.geometric_mean([(1.0 + 3.0) / 2, (2.0 + 4.0) / 2]))
def test_get_aggregated_geomean_performance_sample_passing(self):
b_p = agg.EdwBenchmarkPerformance(
total_iterations=2, expected_suite_queries=[Q1_NAME, Q2_NAME])
s1_p = agg.EdwSuitePerformance('suite_name', 'suite_seq_1', 2)
q11_p = agg.EdwQueryPerformance(Q1_NAME, 1.0, {'job_id': 'q1_s1_job_id'})
s1_p.add_query_performance(q11_p)
q12_p = agg.EdwQueryPerformance(Q2_NAME, 2.0, {'job_id': 'q2_s1_job_id'})
s1_p.add_query_performance(q12_p)
b_p.add_suite_performance('suite_seq_1', s1_p)
s2_p = agg.EdwSuitePerformance('suite_name', 'suite_seq_2', 2)
q21_p = agg.EdwQueryPerformance(Q1_NAME, 3.0, {'job_id': 'q1_s2_job_id'})
s2_p.add_query_performance(q21_p)
q22_p = agg.EdwQueryPerformance(Q2_NAME, 4.0, {})
s2_p.add_query_performance(q22_p)
b_p.add_suite_performance('suite_seq_2', s2_p)
actual_sample = b_p.get_aggregated_geomean_performance_sample(
{'benchmark_name': 'b_name'})
self.assertEqual(actual_sample.metric, 'edw_aggregated_geomean')
self.assertEqual(actual_sample.value,
agg.geometric_mean([(1.0 + 3.0) / 2, (2.0 + 4.0) / 2]))
self.assertEqual(actual_sample.unit, 'seconds')
expected_metadata = {
'benchmark_name': 'b_name',
'intra_query_aggregation_method': 'mean',
'inter_query_aggregation_method': 'geomean'
}
self.assertDictEqual(actual_sample.metadata, expected_metadata)
def test_get_queries_geomean_performance_samples_passing(self):
b_p = agg.EdwBenchmarkPerformance(total_iterations=2)
s1_p = agg.EdwSuitePerformance('suite_name', 'suite_seq_1', 2)
q11_p = agg.EdwQueryPerformance(Q1_NAME, 1.0,
{'job_id': 'q1_s1_job_id'})
s1_p.add_query_performance(q11_p)
q12_p = agg.EdwQueryPerformance(Q2_NAME, 2.0,
{'job_id': 'q2_s1_job_id'})
s1_p.add_query_performance(q12_p)
b_p.add_suite_performance('suite_seq_1', s1_p)
s2_p = agg.EdwSuitePerformance('suite_name', 'suite_seq_2', 2)
q21_p = agg.EdwQueryPerformance(Q1_NAME, 3.0,
{'job_id': 'q1_s2_job_id'})
s2_p.add_query_performance(q21_p)
q22_p = agg.EdwQueryPerformance(Q2_NAME, 4.0, {})
s2_p.add_query_performance(q22_p)
b_p.add_suite_performance('suite_seq_2', s2_p)
actual_sample_list = b_p.get_queries_geomean_performance_samples(
{'benchmark_name': 'b_name'}, [Q1_NAME, Q2_NAME])
self.assertEqual(len(actual_sample_list), 3)
self.assertSameElements([x.metric for x in actual_sample_list], [
'edw_raw_geomean_time', 'edw_raw_geomean_time',
'edw_aggregated_geomean'
])
raw_samples = list(
filter(lambda x: x.metric == 'edw_raw_geomean_time',
actual_sample_list))
actual_raw_samples_values = [x.value for x in raw_samples]
expected_raw_samples_values = [
agg.geometric_mean([1.0, 2.0]),
agg.geometric_mean([3.0, 4.0])
]
self.assertSameElements(actual_raw_samples_values,
expected_raw_samples_values)
aggregated_sample = list(
filter(lambda x: x.metric == 'edw_aggregated_geomean',
actual_sample_list))[0]
self.assertEqual(
aggregated_sample.value,
agg.geometric_mean([(1.0 + 3.0) / 2, (2.0 + 4.0) / 2]))
def test_get_queries_geomean_performance(self):
i_p = agg.EdwPowerIterationPerformance('1', 2)
i_p.add_query_performance(Q1_NAME, Q1_PERFORMANCE, METADATA_EMPTY)
i_p.add_query_performance(Q2_NAME, Q2_PERFORMANCE, METADATA_EMPTY)
iteration_geomean_performance_sample = i_p.get_queries_geomean_performance_sample(
expected_queries=[Q1_NAME, Q2_NAME], metadata=METADATA_EMPTY)
self.assertEqual(iteration_geomean_performance_sample.metric,
'edw_iteration_geomean_time')
self.assertEqual(iteration_geomean_performance_sample.value,
agg.geometric_mean([Q1_PERFORMANCE, Q2_PERFORMANCE]))
self.assertEqual(iteration_geomean_performance_sample.unit, SECS)
self.assertDictEqual(iteration_geomean_performance_sample.metadata,
METADATA_EMPTY)
def test_get_queries_geomean_performance(self):
s_p = agg.EdwSuitePerformance('suite_name', 'suite_seq', 2)
q1_p = agg.EdwQueryPerformance(Q1_NAME, Q1_PERFORMANCE, METADATA_EMPTY)
s_p.add_query_performance(q1_p)
q2_p = agg.EdwQueryPerformance(Q2_NAME, Q2_PERFORMANCE, METADATA_EMPTY)
s_p.add_query_performance(q2_p)
suite_wall_time_performance_sample = s_p.get_queries_geomean_performance_sample(
METADATA_EMPTY)
self.assertEqual(suite_wall_time_performance_sample.metric,
'edw_raw_geomean_time')
self.assertEqual(suite_wall_time_performance_sample.value,
agg.geometric_mean([Q1_PERFORMANCE, Q2_PERFORMANCE]))
self.assertEqual(suite_wall_time_performance_sample.unit, SECS)
self.assertDictEqual(suite_wall_time_performance_sample.metadata,
METADATA_EMPTY)
def test_get_aggregated_geomean_performance_sample_passing(self):
b_p = agg.EdwBenchmarkPerformance(total_iterations=2,
expected_queries=[Q1_NAME, Q2_NAME])
i1_p = agg.EdwPowerIterationPerformance('1', 2)
i1_p.add_query_performance(Q1_NAME, 1.0, {'job_id': 'q1_i1_job_id'})
i1_p.add_query_performance(Q2_NAME, 2.0, {'job_id': 'q2_i1_job_id'})
b_p.add_power_iteration_performance(i1_p)
i2_p = agg.EdwPowerIterationPerformance('2', 2)
i2_p.add_query_performance(Q1_NAME, 3.0, {'job_id': 'q1_i2_job_id'})
i2_p.add_query_performance(Q2_NAME, 4.0, {})
b_p.add_power_iteration_performance(i2_p)
actual_sample = b_p.get_aggregated_geomean_performance_sample(
{'benchmark_name': 'b_name'})
self.assertEqual(actual_sample.metric, 'edw_aggregated_geomean')
self.assertEqual(
actual_sample.value,
agg.geometric_mean([(1.0 + 3.0) / 2, (2.0 + 4.0) / 2]))
self.assertEqual(actual_sample.unit, 'seconds')
expected_metadata = {
'benchmark_name': 'b_name',
'intra_query_aggregation_method': 'mean',
'inter_query_aggregation_method': 'geomean'
}
self.assertDictEqual(actual_sample.metadata, expected_metadata)
def test_geometric_mean_no_values(self):
performance_iterable = []
with self.assertRaises(agg.EdwPerformanceAggregationError):
agg.geometric_mean(performance_iterable)
def test_geometric_mean_include_negative_value(self):
performance_iterable = [1.0, -2.0, 3.0]
with self.assertRaises(agg.EdwPerformanceAggregationError):
agg.geometric_mean(performance_iterable)
def test_geometric_mean_valid_values(self):
performance_iterable = [1.0, 2.0, 3.0]
expected_geometric_mean = agg.geometric_mean(performance_iterable)
self.assertEqual('%.2f' % expected_geometric_mean, '1.82')
