def _DistributionFromHistogram(metric, values_by_suffix):
"""Instantiate a rdf_stats.Distribution from a Prometheus Histogram.
Prometheus Histogram uses cumulative "buckets" lower or equal to an upper
bound. At instantiation, +Inf is implicitly appended to the upper bounds.
The delimiters [0.0, 0.1, 0.2 (, +Inf)] produce the following buckets:
Bucket "0.0" : -Inf <= values <= 0.0
Bucket "0.1" : -Inf <= values <= 0.1
Bucket "0.2" : -Inf <= values <= 0.2
Bucket "+Inf": -Inf <= values <= +Inf
Distribution uses exclusive bins greater or equal to a lower bound and
strictly lower than the next lower bound. At instantiation, -Inf is implicitly
prepended. The delimiters [(-Inf,) 0.0, 0.1, 0.2] produce the following bins:
Bin "-Inf": -Inf <= values < 0.0
Bin "0.0" : 0.0 <= values < 0.1
Bin "0.1" : 0.1 <= values < 0.2
Bin "0.2" : 0.2 <= values <= +Inf
Thus, Histogram buckets can be transformed to Distribution bins, by reading
in the same order and subtracting the value of the previous bin to remove the
cumulative sum. There is a slight incompatibility for values equal to bin
boundaries, because boundaries describe the upper bound for Prometheus and
the lower bound for our internal implementation.
Args:
metric: prometheus_stats_collector.Metric
values_by_suffix: dict of metric name suffixes and sample values lists
Returns:
rdf_stats.Distribution
Raises:
ValueError: The Histogram and metadata bin count do not match.
"""
dist = rdf_stats.Distribution(bins=list(metric.metadata.bins))
if metric.metadata.bins and len(dist.heights) != len(
values_by_suffix["_bucket"]):
raise ValueError(
"Trying to create Distribution with {} bins, but underlying"
"Histogram has {} buckets".format(len(
dist.heights), len(values_by_suffix["_bucket"])))
dist.heights = values_by_suffix["_bucket"]
# Remove cumulative sum by subtracting the value of the previous bin
for i in reversed(range(1, len(dist.heights))):
dist.heights[i] -= dist.heights[i - 1]
dist.count = values_by_suffix["_count"][0]
dist.sum = values_by_suffix["_sum"][0]
return dist
def testStatsEntryId_IgnoreMetricValues(self):
"""Ensures metric values have no influence id generation."""
int_entry = stats_values.StatsStoreEntry(
process_id="test_process",
metric_name="test_metric",
metric_value=stats_values.StatsStoreValue(
value_type=rdf_stats.MetricMetadata.ValueType.INT,
int_value=42),
timestamp=_one_second_timestamp)
float_entry = stats_values.StatsStoreEntry(
process_id="test_process",
metric_name="test_metric",
metric_value=stats_values.StatsStoreValue(
value_type=rdf_stats.MetricMetadata.ValueType.FLOAT,
float_value=4.2),
timestamp=_one_second_timestamp)
# TODO: String gauges are deprecated.
str_entry = stats_values.StatsStoreEntry(
process_id="test_process",
metric_name="test_metric",
metric_value=stats_values.StatsStoreValue(
value_type=rdf_stats.MetricMetadata.ValueType.DEPRECATED_STR,
str_value="foo"),
timestamp=_one_second_timestamp)
distribution_entry = stats_values.StatsStoreEntry(
process_id="test_process",
metric_name="test_metric",
metric_value=stats_values.StatsStoreValue(
value_type=rdf_stats.MetricMetadata.ValueType.DISTRIBUTION,
distribution_value=rdf_stats.Distribution()),
timestamp=_one_second_timestamp)
expected_id = (
b"\x8e\xf4\xe7\xdb\x03\x01}sB\x97\x98\x957\x18\x02U\xb0\xe6x\x9f"
b"\x97Xfs/C\xedT\xd3\x89N\xe5")
self.assertEqual(db_utils.GenerateStatsEntryId(int_entry), expected_id)
self.assertEqual(db_utils.GenerateStatsEntryId(float_entry),
expected_id)
self.assertEqual(db_utils.GenerateStatsEntryId(str_entry), expected_id)
self.assertEqual(db_utils.GenerateStatsEntryId(distribution_entry),
expected_id)
def _DefaultValue(self):
return rdf_stats.Distribution(bins=self._bins)
