def test_bad_growth_factors(self):
with self.assertRaises(AssertionError):
distribution.GeometricBucketer(growth_factor=-1)
with self.assertRaises(AssertionError):
distribution.GeometricBucketer(growth_factor=0)
with self.assertRaises(AssertionError):
distribution.GeometricBucketer(growth_factor=1)
def test_small_scale(self):
with self.assertRaises(ValueError):
distribution.GeometricBucketer(growth_factor=-1)
with self.assertRaises(ValueError):
distribution.GeometricBucketer(growth_factor=0)
with self.assertRaises(ValueError):
distribution.GeometricBucketer(growth_factor=1)
def test_generate_geometric_distribution_with_scale(self):
bucketer = distribution.GeometricBucketer(growth_factor=10.0,
num_finite_buckets=10,
scale=0.1)
dists = [(metrics.NonCumulativeDistributionMetric('test0',
'test',
None,
bucketer=bucketer),
metrics_pb2.GAUGE),
(metrics.CumulativeDistributionMetric('test1',
'test',
None,
bucketer=bucketer),
metrics_pb2.CUMULATIVE)]
for dist, stream_kind in dists:
data_set, data = self._test_distribution_proto(dist)
self.assertListEqual([1, 0, 4, 1, 0, 0, 1, 0, 0, 0, 0, 0],
list(data.distribution_value.bucket_count))
self.assertEqual(
10,
data.distribution_value.exponential_buckets.num_finite_buckets)
self.assertEqual(
10.0,
data.distribution_value.exponential_buckets.growth_factor)
self.assertEqual(0.1,
data.distribution_value.exponential_buckets.scale)
self.assertEqual(stream_kind, data_set.stream_kind)
self.assertEqual(7, data.distribution_value.count)
def modify_fn(dist, delta):
# This is the same as the modify_fn in _DistributionMetricBase's add().
if dist == 0:
dist = distribution.Distribution(
distribution.GeometricBucketer())
dist.add(delta)
return dist
def __init__(self, name, description, field_spec, is_cumulative=True,
bucketer=None, start_time=None, units=None, target_type=None):
self._start_time = start_time
if bucketer is None:
bucketer = distribution.GeometricBucketer()
self._is_cumulative = is_cumulative
self.bucketer = bucketer
super(_DistributionMetricBase, self).__init__(
name, description, field_spec, units=units, target_type=target_type)
def test_scale(self):
b = distribution.GeometricBucketer(growth_factor=4,
num_finite_buckets=4,
scale=.1)
# bucket lower bounds will be approximately [float('-Inf'), .1, .4, 1.6,
# 6.4, 25.6], but to avoid floating point errors affecting test assert on
# bucket_for_value instead of using assertLowerBounds.
self.assertEquals(0, b.bucket_for_value(float('-Inf')))
self.assertEquals(0, b.bucket_for_value(.05))
self.assertEquals(1, b.bucket_for_value(.2))
self.assertEquals(5, b.bucket_for_value(float('Inf')))
def __init__(self, name, is_cumulative=True, bucketer=None, fields=None,
start_time=None, description=None):
super(DistributionMetric, self).__init__(
name, fields=fields, description=description)
self._start_time = start_time
if bucketer is None:
bucketer = distribution.GeometricBucketer()
self._is_cumulative = is_cumulative
self.bucketer = bucketer
def test_bucket_for_value(self):
b = distribution.GeometricBucketer(growth_factor=2,
num_finite_buckets=5)
self.assertEquals(0, b.bucket_for_value(float('-Inf')))
self.assertEquals(0, b.bucket_for_value(-100))
self.assertEquals(0, b.bucket_for_value(-1))
self.assertEquals(0, b.bucket_for_value(0))
self.assertEquals(1, b.bucket_for_value(1))
self.assertEquals(5, b.bucket_for_value(31))
self.assertEquals(6, b.bucket_for_value(32))
self.assertEquals(6, b.bucket_for_value(100000))
self.assertEquals(6, b.bucket_for_value(float('Inf')))
def test_populate_canonical(self):
pb = metrics_pb2.MetricsData()
m = metrics.DistributionMetric('test')
m._populate_value(
pb, distribution.Distribution(distribution.GeometricBucketer()),
1234)
self.assertEquals(
pb.distribution.spec_type,
metrics_pb2.PrecomputedDistribution.CANONICAL_POWERS_OF_10_P_0_2)
m._populate_value(
pb, distribution.Distribution(distribution.GeometricBucketer(2)),
1234)
self.assertEquals(
pb.distribution.spec_type,
metrics_pb2.PrecomputedDistribution.CANONICAL_POWERS_OF_2)
m._populate_value(
pb, distribution.Distribution(distribution.GeometricBucketer(10)),
1234)
self.assertEquals(
pb.distribution.spec_type,
metrics_pb2.PrecomputedDistribution.CANONICAL_POWERS_OF_10)
def test_add(self):
d = distribution.Distribution(distribution.GeometricBucketer())
self.assertEqual(0, d.sum)
self.assertEqual(0, d.count)
self.assertEqual({}, d.buckets)
d.add(1)
d.add(10)
d.add(100)
self.assertEqual(111, d.sum)
self.assertEqual(3, d.count)
self.assertEqual({1: 1, 5: 1, 10: 1}, d.buckets)
d.add(50)
self.assertEqual(161, d.sum)
self.assertEqual(4, d.count)
self.assertEqual({1: 1, 5: 1, 9: 1, 10: 1}, d.buckets)
def test_populate_custom(self):
pb = metrics_pb2.MetricsData()
m = metrics.DistributionMetric('test')
m._populate_value(
pb, distribution.Distribution(distribution.GeometricBucketer(4)),
1234)
self.assertEquals(
pb.distribution.spec_type,
metrics_pb2.PrecomputedDistribution.CUSTOM_PARAMETERIZED)
self.assertEquals(0, pb.distribution.width)
self.assertEquals(4, pb.distribution.growth_factor)
self.assertEquals(100, pb.distribution.num_buckets)
m._populate_value(
pb, distribution.Distribution(distribution.FixedWidthBucketer(10)),
1234)
self.assertEquals(
pb.distribution.spec_type,
metrics_pb2.PrecomputedDistribution.CUSTOM_PARAMETERIZED)
self.assertEquals(10, pb.distribution.width)
self.assertEquals(0, pb.distribution.growth_factor)
self.assertEquals(100, pb.distribution.num_buckets)
# Copyright 2015 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
from infra_libs.ts_mon.common import distribution
from infra_libs.ts_mon.common import metrics
# Extending HTTP status codes to client-side errors and timeouts.
STATUS_OK = 200
STATUS_ERROR = 901
STATUS_TIMEOUT = 902
STATUS_EXCEPTION = 909
# 90% of durations are in the range 11-1873ms. Growth factor 10^0.06 puts that
# range into 37 buckets. Max finite bucket value is 12 minutes.
_duration_bucketer = distribution.GeometricBucketer(10**0.06)
# 90% of sizes are in the range 0.17-217014 bytes. Growth factor 10^0.1 puts
# that range into 54 buckets. Max finite bucket value is 6.3GB.
_size_bucketer = distribution.GeometricBucketer(10**0.1)
request_bytes = metrics.CumulativeDistributionMetric(
'http/request_bytes',
'Bytes sent per http request (body only).', [
metrics.StringField('name'),
metrics.StringField('client'),
],
bucketer=_size_bucketer)
response_bytes = metrics.CumulativeDistributionMetric(
'http/response_bytes',
'Bytes received per http request (content only).', [
def test_large_size(self):
b = distribution.GeometricBucketer(growth_factor=4,
num_finite_buckets=4)
self.assertLowerBounds(b, [float('-Inf'), 1, 4, 16, 64, 256])
def test_zero_size(self):
b = distribution.GeometricBucketer(num_finite_buckets=0)
self.assertLowerBounds(b, [float('-Inf'), 1])
def test_zero_size(self):
b = distribution.GeometricBucketer(num_finite_buckets=0)
self.assertBucketCounts(b, 2)
self.assertBoundaries(b, [])
def test_negative_size(self):
with self.assertRaises(ValueError):
distribution.GeometricBucketer(num_finite_buckets=-1)
def test_equality(self):
b = distribution.GeometricBucketer(growth_factor=4,
num_finite_buckets=4,
scale=.1)
self.assertEquals(b, b)
def test_large_size(self):
b = distribution.GeometricBucketer(growth_factor=4,
num_finite_buckets=4)
self.assertBucketCounts(b, 6)
self.assertBoundaries(b, [1, 4, 16, 64])
