def _histogram(self, prefix, channel, version, dates, metric, value,
expected_count):
if metric.endswith('CONTENT_DOCUMENTS_DESTROYED'
): # Ignore USE_COUNTER2_ support histograms
return
resp = self.as_json(
self.app.get(
'/aggregates_by/{}/channels/{}/?version={}&dates={}&metric={}'.
format(prefix, channel, version, ','.join(dates), metric)))
self.assertEqual(len(resp['data']), len(dates))
bucket_index = COUNT_HISTOGRAM_LABELS.index(COUNT_SCALAR_BUCKET)
for res in resp['data']:
# From pings before bug 1218576 (old), `count` is the number of processes.
# From pings after bug 1218576 (new), `count` is the number of process types.
old_pings_expected_count = expected_count * (
NUM_PINGS_PER_DIMENSIONS -
NUM_AGGREGATED_CHILD_PINGS) / NUM_PINGS_PER_DIMENSIONS
new_pings_expected_count = expected_count * NUM_AGGREGATED_CHILD_PINGS / NUM_PINGS_PER_DIMENSIONS
self.assertEqual(
res['count'], new_pings_expected_count * NUM_PROCESS_TYPES +
old_pings_expected_count * (NUM_CHILDREN_PER_PING + 1))
if value['histogram_type'] == 4: # Count histogram
current = pd.Series(res['histogram'],
index=map(int, resp['buckets']))
expected = pd.Series(index=COUNT_HISTOGRAM_LABELS, data=0)
expected[COUNT_SCALAR_BUCKET] = res['count']
self.assertEqual(res['histogram'][bucket_index], res['count'])
self.assertEqual(res['sum'], value['sum'] * res['count'])
self.assertTrue((current == expected).all())
elif metric.startswith('USE_COUNTER2_'):
if metric.endswith('_PAGE'):
destroyed = histograms_template[
'TOP_LEVEL_CONTENT_DOCUMENTS_DESTROYED']['sum']
else:
destroyed = histograms_template[
'CONTENT_DOCUMENTS_DESTROYED']['sum']
value['values']['0'] = destroyed - value['values']['1']
current = pd.Series(res['histogram'],
index=map(int, resp['buckets']))
expected = Histogram(metric, value).get_value() * res['count']
self.assertTrue((current == expected).all())
self.assertEqual(res['sum'], value['sum'] * res['count'])
else:
ind_type = int if value[
'histogram_type'] != 5 else str # Categorical histograms
current = pd.Series(res['histogram'],
index=map(ind_type, resp['buckets']))
expected = Histogram(metric, value).get_value() * res['count']
self.assertTrue((current == expected).all())
self.assertEqual(res['sum'], value['sum'] * res['count'])
def test_categorical_histogram_dict_value():
cat2 = Histogram('TELEMETRY_TEST_CATEGORICAL',
{'values': {
u'0': 2,
u'1': 1,
u'2': 0,
u'3': 0
}})
assert all(cat2.get_value() == series)
def get_dates_metrics(prefix, channel):
mapping = {"true": True, "false": False}
dimensions = {k: mapping.get(v, v) for k, v in request.args.iteritems()}
# Get dates
dates = dimensions.pop('dates', "").split(',')
version = dimensions.pop('version', None)
metric = dimensions.get('metric', None)
if not dates or not version or not metric:
abort(404)
if metric == "SEARCH_COUNTS":
abort(404)
# Get bucket labels
if metric.startswith("SIMPLE_MEASURES_"):
labels = simple_measures_labels
kind = "exponential"
description = ""
else:
revision = histogram_revision_map.get(channel, "nightly") # Use nightly revision if the channel is unknown
try:
definition = Histogram(metric, {"values": {}}, revision=revision)
except KeyError:
# Couldn't find the histogram definition
abort(404)
kind = definition.kind
description = definition.definition.description()
if kind == "count":
labels = count_histogram_labels
dimensions["metric"] = "[[COUNT]]_{}".format(metric)
elif kind == "flag":
labels = [0, 1]
else:
labels = definition.get_value().keys().tolist()
# Fetch metrics
result = execute_query("select * from batched_get_metric(%s, %s, %s, %s, %s)", (prefix, channel, version, dates, json.dumps(dimensions)))
if not result:
abort(404)
pretty_result = {"data": [], "buckets": labels, "kind": kind, "description": description}
for row in result:
date = row[0]
label = row[1]
histogram = row[2][:-2]
sum = row[2][-2]
count = row[2][-1]
pretty_result["data"].append({"date": date, "label": label, "histogram": histogram, "count": count, "sum": sum})
return json.dumps(pretty_result)
def _get_complete_histogram(channel, metric, values):
revision = histogram_revision_map[channel]
for prefix, labels in SCALAR_MEASURE_MAP.iteritems():
if metric.startswith(prefix):
histogram = pd.Series({int(k): v for k, v in values.iteritems()},
index=labels).fillna(0)
break
else:
histogram = Histogram(metric, {"values": values},
revision=revision).get_value(autocast=False)
return {str(k): long(v) for k, v in histogram.to_dict().iteritems()}
def _get_complete_histogram(channel, metric, values):
revision = histogram_revision_map[channel]
for prefix, labels in SCALAR_MEASURE_MAP.iteritems():
if metric.startswith(prefix):
histogram = pd.Series({int(k): v
for k, v in values.iteritems()},
index=labels).fillna(0)
break
else:
histogram = Histogram(metric, {
"values": values
}, revision=revision).get_value(autocast=False)
return {str(k): long(v) for k, v in histogram.to_dict().iteritems()}
def test_histogram(prefix, channel, version, dates, metric, value,
expected_count):
if metric.endswith("CONTENT_DOCUMENTS_DESTROYED"
): # Ignore USE_COUNTER2_ support histograms
return
reply = requests.get(
"{}/aggregates_by/{}/channels/{}?version={}&dates={}&metric={}".format(
SERVICE_URI, prefix, channel, version, ",".join(dates),
metric)).json()
assert (len(reply["data"]) == len(dates))
bucket_index = count_histogram_labels.index(COUNT_SCALAR_BUCKET)
for res in reply["data"]:
assert (res["count"] == expected_count * (NUM_CHILDREN_PER_PING + 1))
if value["histogram_type"] == 4: # Count histogram
current = pd.Series(res["histogram"],
index=map(int, reply["buckets"]))
expected = pd.Series(index=count_histogram_labels, data=0)
expected[COUNT_SCALAR_BUCKET] = res["count"]
assert (res["histogram"][bucket_index] == res["count"])
assert (res["sum"] == value["sum"] * res["count"])
assert ((current == expected).all())
elif metric.startswith("USE_COUNTER2_"):
if metric.endswith("_PAGE"):
destroyed = histograms_template[
"TOP_LEVEL_CONTENT_DOCUMENTS_DESTROYED"]["sum"]
else:
destroyed = histograms_template["CONTENT_DOCUMENTS_DESTROYED"][
"sum"]
value["values"]["0"] = destroyed - value["values"]["1"]
current = pd.Series(res["histogram"],
index=map(int, reply["buckets"]))
expected = Histogram(metric, value).get_value() * res["count"]
assert ((current == expected).all())
assert (res["sum"] == value["sum"] * res["count"])
else:
current = pd.Series(res["histogram"],
index=map(int, reply["buckets"]))
expected = Histogram(metric, value).get_value() * res["count"]
assert ((current == expected).all())
assert (res["sum"] == value["sum"] * res["count"])
def test_categorical_histogram_add():
cat2 = Histogram("TELEMETRY_TEST_CATEGORICAL", [1, 1, 0, 1])
added = categorical_hist + cat2
assert added.buckets['CommonLabel'] == 3
assert added.buckets['Label2'] == 2
assert added.buckets['Label3'] == 0
assert added.buckets[CATEGORICAL_HISTOGRAM_SPILL_BUCKET_NAME] == 1
def test_histogram_with_revision():
# Histogram with revision
Histogram("HTTPCONNMGR_USED_SPECULATIVE_CONN", [
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0.693147182464599, 0.480453014373779, -1, -1
], "http://hg.mozilla.org/mozilla-central/rev/37ddc5e2eb72")
def _keyed_histogram(self, prefix, channel, version, dates, metric,
histograms, expected_count):
resp = self.as_json(
self.app.get(
'/aggregates_by/{}/channels/{}/?version={}&dates={}&metric={}'.
format(prefix, channel, version, ','.join(dates), metric)))
self.assertEqual(len(resp['data']), len(histograms) * len(dates))
for label, value in histograms.iteritems():
resp = self.as_json(
self.app.get(
'/aggregates_by/{}/channels/{}/?version={}&dates={}&metric={}&label={}'
.format(prefix, channel, version, ','.join(dates), metric,
label)))
self.assertEqual(len(resp['data']), len(dates))
for res in resp['data']:
old_pings_expected_count = expected_count * (
NUM_PINGS_PER_DIMENSIONS -
NUM_AGGREGATED_CHILD_PINGS) / NUM_PINGS_PER_DIMENSIONS
new_pings_expected_count = expected_count * NUM_AGGREGATED_CHILD_PINGS / NUM_PINGS_PER_DIMENSIONS
self.assertEqual(
res['count'],
new_pings_expected_count * NUM_PROCESS_TYPES +
old_pings_expected_count * (NUM_CHILDREN_PER_PING + 1))
current = pd.Series(res['histogram'],
index=map(int, resp['buckets']))
expected = Histogram(metric, value).get_value() * res['count']
self.assertTrue((current == expected).all())
self.assertEqual(res['sum'], value['sum'] * res['count'])
def test_keyed_histogram(prefix, channel, version, dates, metric, histograms,
expected_count):
reply = requests.get(
"{}/aggregates_by/{}/channels/{}?version={}&dates={}&metric={}".format(
SERVICE_URI, prefix, channel, version, ",".join(dates),
metric)).json()
assert (len(reply["data"]) == len(histograms) * len(dates))
for label, value in histograms.iteritems():
reply = requests.get(
"{}/aggregates_by/{}/channels/{}?version={}&dates={}&metric={}&label={}"
.format(SERVICE_URI, prefix, channel, version, ",".join(dates),
metric, label)).json()
assert (len(reply["data"]) == len(dates))
for res in reply["data"]:
assert (res["count"] == expected_count *
(NUM_CHILDREN_PER_PING + 1))
current = pd.Series(res["histogram"],
index=map(int, reply["buckets"]))
expected = Histogram(metric, value).get_value() * res["count"]
assert ((current == expected).all())
assert (res["sum"] == value["sum"] * res["count"])
def test_startup_histogram():
# Startup histogram
Histogram("STARTUP_HTTPCONNMGR_USED_SPECULATIVE_CONN",
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0.693147182464599, 0.480453014373779,
-1, -1])
def setup_module():
global categorical_hist, series
categorical_hist = Histogram("TELEMETRY_TEST_CATEGORICAL", [2, 1, 0, 0])
series = pd.Series([2, 1, 0, 0],
index=[
'CommonLabel', 'Label2', 'Label3',
CATEGORICAL_HISTOGRAM_SPILL_BUCKET_NAME
],
dtype='int64')
def test_histogram_with_computed_value():
# Histogram with computed value
Histogram("GC_REASON_2",
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 1,
0, 0, 0, 11, 36, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2517, -1, -1,
116979, 0])
def test_histogram(prefix, channel, version, dates, metric, value, expected_count):
if metric.endswith("CONTENT_DOCUMENTS_DESTROYED"): # Ignore USE_COUNTER2_ support histograms
return
reply = requests.get("{}/aggregates_by/{}/channels/{}?version={}&dates={}&metric={}".format(SERVICE_URI, prefix, channel, version, ",".join(dates), metric)).json()
assert(len(reply["data"]) == len(dates))
bucket_index = COUNT_HISTOGRAM_LABELS.index(COUNT_SCALAR_BUCKET)
for res in reply["data"]:
# From pings before bug 1218576 (old), `count` is the number of processes.
# From pings after bug 1218576 (new), `count` is the number of process types.
old_pings_expected_count = expected_count * (NUM_PINGS_PER_DIMENSIONS - NUM_AGGREGATED_CHILD_PINGS) / NUM_PINGS_PER_DIMENSIONS
new_pings_expected_count = expected_count * NUM_AGGREGATED_CHILD_PINGS / NUM_PINGS_PER_DIMENSIONS
assert(res["count"] == new_pings_expected_count*NUM_PROCESS_TYPES + old_pings_expected_count*(NUM_CHILDREN_PER_PING + 1))
if value["histogram_type"] == 4: # Count histogram
current = pd.Series(res["histogram"], index=map(int, reply["buckets"]))
expected = pd.Series(index=COUNT_HISTOGRAM_LABELS, data=0)
expected[COUNT_SCALAR_BUCKET] = res["count"]
assert(res["histogram"][bucket_index] == res["count"])
assert(res["sum"] == value["sum"]*res["count"])
assert((current == expected).all())
elif metric.startswith("USE_COUNTER2_"):
if metric.endswith("_PAGE"):
destroyed = histograms_template["TOP_LEVEL_CONTENT_DOCUMENTS_DESTROYED"]["sum"]
else:
destroyed = histograms_template["CONTENT_DOCUMENTS_DESTROYED"]["sum"]
value["values"]["0"] = destroyed - value["values"]["1"]
current = pd.Series(res["histogram"], index=map(int, reply["buckets"]))
expected = Histogram(metric, value).get_value()*res["count"]
assert((current == expected).all())
assert(res["sum"] == value["sum"]*res["count"])
else:
ind_type = int if value["histogram_type"] != 5 else str #categorical histograms
current = pd.Series(res["histogram"], index=map(ind_type, reply["buckets"]))
expected = Histogram(metric, value).get_value()*res["count"]
assert((current == expected).all())
assert(res["sum"] == value["sum"]*res["count"])
def get_dates_metrics(prefix, channel):
mapping = {"true": True, "false": False}
dimensions = {k: mapping.get(v, v) for k, v in request.args.iteritems()}
# Get dates
dates = dimensions.pop('dates', "").split(',')
version = dimensions.pop('version', None)
metric = dimensions.get('metric', None)
if not dates or not version or not metric:
abort(404)
# Get bucket labels
if metric.startswith("SIMPLE_MEASURES_"):
labels = simple_measures_labels
kind = "exponential"
description = ""
else:
revision = histogram_revision_map.get(
channel,
"nightly") # Use nightly revision if the channel is unknown
try:
definition = Histogram(metric, {"values": {}}, revision=revision)
except KeyError:
# Couldn't find the histogram definition
abort(404)
kind = definition.kind
description = definition.definition.description()
if kind == "count":
labels = count_histogram_labels
dimensions["metric"] = "[[COUNT]]_{}".format(metric)
elif kind == "flag":
labels = [0, 1]
else:
labels = definition.get_value().keys().tolist()
# Fetch metrics
result = execute_query(
"select * from batched_get_metric(%s, %s, %s, %s, %s)",
(prefix, channel, version, dates, json.dumps(dimensions)))
if not result:
abort(404)
pretty_result = {
"data": [],
"buckets": labels,
"kind": kind,
"description": description
}
for row in result:
date = row[0]
label = row[1]
histogram = row[2][:-2]
sum = row[2][-2]
count = row[2][-1]
pretty_result["data"].append({
"date": date,
"label": label,
"histogram": histogram,
"count": count,
"sum": sum
})
return json.dumps(pretty_result)
def get_dates_metrics(prefix, channel):
mapping = {"true": True, "false": False}
dimensions = {k: mapping.get(v, v) for k, v in request.args.iteritems()}
extra_dimensions = dimensions.viewkeys() - ALLOWED_DIMENSIONS
if extra_dimensions:
# We received an unsupported query string to filter by, return 405.
valid_url = '{}?{}'.format(
request.path,
urlencode({k: v for k, v in dimensions.items() if k in ALLOWED_DIMENSIONS}))
raise MethodNotAllowed(valid_methods=[valid_url])
if 'child' in dimensions:
# Process types in the db are true/false, not content/process
new_process_map = {"content": True, "parent": False}
dimensions['child'] = new_process_map.get(dimensions['child'], dimensions['child'])
# Get dates
dates = dimensions.pop('dates', '').split(',')
version = dimensions.pop('version', None)
metric = dimensions.get('metric')
if not dates or not version or not metric:
abort(404, description="Missing date or version or metric. All three are required.")
if not _allow_metric(channel, metric):
abort(404, description="This metric is not allowed to be served.")
# Get bucket labels
for _prefix, _labels in SCALAR_MEASURE_MAP.iteritems():
if metric.startswith(_prefix) and _prefix != COUNT_HISTOGRAM_PREFIX:
labels = _labels
kind = "exponential"
try:
description = _get_description(channel, _prefix, metric)
except MissingScalarError:
abort(404, description="Cannot find this scalar definition.")
break
else:
revision = histogram_revision_map[channel]
try:
definition = Histogram(metric, {"values": {}}, revision=revision)
except KeyError:
# Couldn't find the histogram definition
abort(404, description="Cannot find this histogram definition.")
kind = definition.kind
description = definition.definition.description()
if kind == "count":
labels = COUNT_HISTOGRAM_LABELS
dimensions["metric"] = "{}_{}".format(COUNT_HISTOGRAM_PREFIX, metric)
elif kind == "flag":
labels = [0, 1]
else:
labels = definition.get_value().keys().tolist()
altered_dimensions = deepcopy(dimensions)
if 'child' in dimensions:
# Bug 1339139 - when adding gpu processes, child process went from True/False to "true"/"false"/"gpu"
reverse_map = {True: 'true', False: 'false'}
altered_dimensions['child'] = reverse_map.get(altered_dimensions['child'], altered_dimensions['child'])
# Fetch metrics
if metric.startswith("USE_COUNTER2_"):
# Bug 1412382 - Use Counters need to be composed from reported True
# values and False values supplied by *CONTENT_DOCUMENTS_DESTROYED.
denominator = "TOP_LEVEL_CONTENT_DOCUMENTS_DESTROYED"
if metric.endswith("_DOCUMENT"):
denominator = "CONTENT_DOCUMENTS_DESTROYED"
denominator = "{}_{}".format(COUNT_HISTOGRAM_PREFIX, denominator)
denominator_dimensions = deepcopy(dimensions)
denominator_dimensions["metric"] = denominator
denominator_new_dimensions = deepcopy(altered_dimensions)
denominator_new_dimensions["metric"] = denominator
result = execute_query(
"select * from batched_get_use_counter(%s, %s, %s, %s, %s, %s, %s, %s)", (
prefix, channel, version, dates, json.dumps(denominator_dimensions),
json.dumps(denominator_new_dimensions), json.dumps(dimensions), json.dumps(altered_dimensions)))
else:
result = execute_query(
"select * from batched_get_metric(%s, %s, %s, %s, %s, %s)", (
prefix, channel, version, dates, json.dumps(dimensions), json.dumps(altered_dimensions)))
if not result:
abort(404, description="No data found for this metric.")
pretty_result = {"data": [], "buckets": labels, "kind": kind, "description": description}
for row in result:
date = row[0]
label = row[1]
histogram = row[2][:-2]
sum = row[2][-2]
count = row[2][-1]
pretty_result["data"].append({"date": date, "label": label, "histogram": histogram, "count": count, "sum": sum})
return Response(json.dumps(pretty_result), mimetype="application/json")
def test_histogram_without_revision():
# Histogram without revision
Histogram("STARTUP_CRASH_DETECTED",
[1, 0, 0, 0, -1, -1, 0, 0],
"http://hg.mozilla.org/mozilla-central/rev/da2f28836843")
def get_dates_metrics(prefix, channel):
mapping = {"true": True, "false": False}
dimensions = {k: mapping.get(v, v) for k, v in request.args.iteritems()}
extra_dimensions = dimensions.viewkeys() - ALLOWED_DIMENSIONS
if extra_dimensions:
# We received an unsupported query string to filter by, return 405.
valid_url = '{}?{}'.format(
request.path,
urlencode({k: v for k, v in dimensions.items() if k in ALLOWED_DIMENSIONS}))
raise MethodNotAllowed(valid_methods=[valid_url])
if 'child' in dimensions:
# Process types in the db are true/false, not content/process
new_process_map = {"content": True, "parent": False}
dimensions['child'] = new_process_map.get(dimensions['child'], dimensions['child'])
# Get dates
dates = dimensions.pop('dates', '').split(',')
version = dimensions.pop('version', None)
metric = dimensions.get('metric')
if not dates or not version or not metric:
abort(404, description="Missing date or version or metric. All three are required.")
if not _allow_metric(channel, metric):
abort(404, description="This metric is not allowed to be served.")
# Get bucket labels
for _prefix, _labels in SCALAR_MEASURE_MAP.iteritems():
if metric.startswith(_prefix) and _prefix != COUNT_HISTOGRAM_PREFIX:
labels = _labels
kind = "exponential"
try:
description = _get_description(channel, _prefix, metric)
except MissingScalarError:
abort(404, description="Cannot find this scalar definition.")
break
else:
revision = histogram_revision_map[channel]
try:
definition = Histogram(metric, {"values": {}}, revision=revision)
except KeyError:
# Couldn't find the histogram definition
abort(404, description="Cannot find this histogram definition.")
kind = definition.kind
description = definition.definition.description()
if kind == "count":
labels = COUNT_HISTOGRAM_LABELS
dimensions["metric"] = "{}_{}".format(COUNT_HISTOGRAM_PREFIX, metric)
elif kind == "flag":
labels = [0, 1]
else:
labels = definition.get_value().keys().tolist()
altered_dimensions = deepcopy(dimensions)
if 'child' in dimensions:
# Bug 1339139 - when adding gpu processes, child process went from True/False to "true"/"false"/"gpu"
reverse_map = {True: 'true', False: 'false'}
altered_dimensions['child'] = reverse_map.get(altered_dimensions['child'], altered_dimensions['child'])
# Fetch metrics
if metric.startswith("USE_COUNTER2_"):
# Bug 1412382 - Use Counters need to be composed from reported True
# values and False values supplied by *CONTENT_DOCUMENTS_DESTROYED.
denominator = "TOP_LEVEL_CONTENT_DOCUMENTS_DESTROYED"
if metric.endswith("_DOCUMENT"):
denominator = "CONTENT_DOCUMENTS_DESTROYED"
denominator = "{}_{}".format(COUNT_HISTOGRAM_PREFIX, denominator)
denominator_dimensions = deepcopy(dimensions)
denominator_dimensions["metric"] = denominator
denominator_new_dimensions = deepcopy(altered_dimensions)
denominator_new_dimensions["metric"] = denominator
result = execute_query(
"select * from batched_get_use_counter(%s, %s, %s, %s, %s, %s, %s, %s)", (
prefix, channel, version, dates, json.dumps(denominator_dimensions),
json.dumps(denominator_new_dimensions), json.dumps(dimensions), json.dumps(altered_dimensions)))
else:
result = execute_query(
"select * from batched_get_metric(%s, %s, %s, %s, %s, %s)", (
prefix, channel, version, dates, json.dumps(dimensions), json.dumps(altered_dimensions)))
if not result:
abort(404, description="No data found for this metric.")
pretty_result = {"data": [], "buckets": labels, "kind": kind, "description": description}
for row in result:
date = row[0]
label = row[1]
histogram = row[2][:-2]
sum = row[2][-2]
count = row[2][-1]
pretty_result["data"].append({"date": date, "label": label, "histogram": histogram, "count": count, "sum": sum})
return Response(json.dumps(pretty_result), mimetype="application/json")
def test_categorical_histogram_dict_value():
cat2 = Histogram('TELEMETRY_TEST_CATEGORICAL', {'values': {u'0': 2, u'1': 1, u'2': 0, u'3': 0}})
assert all(cat2.get_value() == series)
def test_histogram_without_revision():
# Histogram without revision
Histogram("STARTUP_CRASH_DETECTED",
[1, 0, 0, 0, -1, -1, 0, 0],
"https://hg.mozilla.org/mozilla-central/rev/838652a84b76")
def test_malformed_categorical():
# See bug 1353196
cat2 = Histogram('TELEMETRY_TEST_CATEGORICAL', {})
assert all(cat2.get_value() == 0)
def test_malformed_non_categorical():
hist = Histogram('GC_REASON_2', {})
assert all(hist.get_value() == 0)
def test_large_categorical_values():
hist = Histogram('TELEMETRY_TEST_CATEGORICAL', {'values': {u'3.0683611344442837e+257': 1}})
assert all(hist.get_value() == 0)
def test_large_values():
hist = Histogram('GC_REASON_2', {'values': {u'3.0683611344442837e+257': 1}})
assert all(hist.get_value() == 0)
