def test_absent_use_counter(self):
# A use counter that isn't in the aggregator should still get a response from the service.
# This is a side-effect of bug 1412384
channel = ping_dimensions['channel'][0]
version = ping_dimensions['version'][0].split('.')[0]
template_build_id = [ping_dimensions['build_id'][0][:-6]]
metric = 'USE_COUNTER2_SIR_NOT_APPEARING_IN_THIS_DOCUMENT'
value = {
u'bucket_count': 3,
u'histogram_type': 2,
u'range': [1, 2],
u'values': {u'0': 640, u'1': 0, u'2': 0},
u'count': 0,
u'sum': 0,
}
expected_count = 1
for dimension, values in ping_dimensions.iteritems():
if dimension not in ['channel', 'version', 'build_id']:
expected_count *= len(values)
histogram_expected_count = NUM_PINGS_PER_DIMENSIONS * expected_count
self._histogram('build_id', channel, version, template_build_id,
metric, value, histogram_expected_count)
def test_absent_use_counter(self):
# A use counter that isn't in the aggregator should still get a response from the service.
# This is a side-effect of bug 1412384
channel = ping_dimensions['channel'][0]
version = ping_dimensions['version'][0].split('.')[0]
template_build_id = [ping_dimensions['build_id'][0][:-6]]
metric = 'USE_COUNTER2_SIR_NOT_APPEARING_IN_THIS_DOCUMENT'
value = {
u'bucket_count': 3,
u'histogram_type': 2,
u'range': [1, 2],
u'values': {
u'0': 640,
u'1': 0,
u'2': 0
},
u'count': 0,
u'sum': 0,
}
expected_count = 1
for dimension, values in ping_dimensions.iteritems():
if dimension not in ['channel', 'version', 'build_id']:
expected_count *= len(values)
histogram_expected_count = NUM_PINGS_PER_DIMENSIONS * expected_count
self._histogram('build_id', channel, version, template_build_id,
metric, value, histogram_expected_count)
def test_submission_dates_metrics(self):
template_channel = set(ping_dimensions['channel']) - {"release"}
template_version = [x.split('.')[0] for x in ping_dimensions['version']]
template_submission_date = ping_dimensions['submission_date']
expected_count = 1
for dimension, values in ping_dimensions.iteritems():
if dimension not in ['channel', 'version', 'submission_date']:
expected_count *= len(values)
for channel in template_channel:
for version in template_version:
histogram_expected_count = NUM_PINGS_PER_DIMENSIONS * expected_count
for metric, value in histograms_template.iteritems():
if value is None:
continue
self._histogram('submission_date', channel, version, template_submission_date,
metric, value, histogram_expected_count)
# Count = product(dimensions) * pings_per_dimensions
# 1 Count for parent, then 1 Count for each NUM_CHILDREN_PER_PING
simple_measure_expected_count = expected_count * NUM_PINGS_PER_DIMENSIONS * (NUM_CHILDREN_PER_PING + 1)
for simple_measure, value in simple_measurements_template.iteritems():
if not isinstance(value, int):
continue
metric = '{}_{}'.format(SIMPLE_MEASURES_PREFIX, simple_measure.upper())
self._simple_measure('submission_date', channel, version, template_submission_date,
metric, value, simple_measure_expected_count)
# for gpu and content process, NUM_AGGREGATED_CHILD_PINGS * expected_count gets the expected number of counts
# (we only add gpu and content scalars for aggregated child pings)
# for parent processes, NUM_PINGS_PER_DIMENSIONS * expected_count
numeric_scalar_expected_count = ((2 * NUM_AGGREGATED_CHILD_PINGS) + NUM_PINGS_PER_DIMENSIONS) * expected_count
for scalar, value in scalars_template.iteritems():
if not isinstance(value, int):
continue
metric = '{}_{}'.format(NUMERIC_SCALARS_PREFIX, scalar.upper())
self._numeric_scalar('submission_date', channel, version, template_submission_date,
metric, value, numeric_scalar_expected_count, NUMERIC_SCALAR_BUCKET,
NUMERIC_SCALARS_LABELS, True)
for metric, _dict in keyed_scalars_template.iteritems():
metric_name = '{}_{}'.format(NUMERIC_SCALARS_PREFIX, metric.upper())
self._keyed_numeric_scalar('submission_date', channel, version, template_submission_date,
metric_name, _dict, numeric_scalar_expected_count)
for metric, histograms in keyed_histograms_template.iteritems():
self._keyed_histogram('submission_date', channel, version, template_submission_date,
metric, histograms, histogram_expected_count)
def test_build_id_metrics(self):
template_channel = ping_dimensions['channel']
template_version = [
x.split('.')[0] for x in ping_dimensions['version']
]
template_build_id = [x[:-6] for x in ping_dimensions['build_id']]
expected_count = 1
for dimension, values in ping_dimensions.iteritems():
if dimension not in ['channel', 'version', 'build_id']:
expected_count *= len(values)
for channel in template_channel:
for version in template_version:
histogram_expected_count = NUM_PINGS_PER_DIMENSIONS * expected_count
for metric, value in histograms_template.iteritems():
if value is None:
continue
self._histogram('build_id', channel, version,
template_build_id, metric, value,
histogram_expected_count)
# Count = product(dimensions) * pings_per_dimensions
# 1 Count for parent, then 1 Count for each NUM_CHILDREN_PER_PING
simple_measure_expected_count = expected_count * NUM_PINGS_PER_DIMENSIONS * (
NUM_CHILDREN_PER_PING + 1)
for simple_measure, value in simple_measurements_template.iteritems(
):
if not isinstance(value, int):
continue
metric = '{}_{}'.format(SIMPLE_MEASURES_PREFIX,
simple_measure.upper())
self._simple_measure('build_id', channel, version,
template_build_id, metric, value,
simple_measure_expected_count)
# for gpu and content process, NUM_AGGREGATED_CHILD_PINGS * expected_count gets the expected number of counts
# (we only add gpu and content scalars for aggregated child pings)
# for parent processes, NUM_PINGS_PER_DIMENSIONS * expected_count
numeric_scalar_expected_count = (
(2 * NUM_AGGREGATED_CHILD_PINGS) +
NUM_PINGS_PER_DIMENSIONS) * expected_count
for scalar, value in scalars_template.iteritems():
if not isinstance(value, int):
continue
metric = '{}_{}'.format(NUMERIC_SCALARS_PREFIX,
scalar.upper())
self._numeric_scalar('build_id', channel, version,
template_build_id, metric, value,
numeric_scalar_expected_count,
NUMERIC_SCALAR_BUCKET,
NUMERIC_SCALARS_LABELS, True)
for metric, _dict in keyed_scalars_template.iteritems():
metric_name = '{}_{}'.format(NUMERIC_SCALARS_PREFIX,
metric.upper())
self._keyed_numeric_scalar('build_id', channel, version,
template_build_id, metric_name,
_dict,
numeric_scalar_expected_count)
for metric, histograms in keyed_histograms_template.iteritems(
):
self._keyed_histogram('build_id', channel, version,
template_build_id, metric,
histograms, histogram_expected_count)