256 lines
7.8 KiB
Python
256 lines
7.8 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright 2015, 2016 OpenMarket Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from itertools import chain
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def flatten(items):
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"""Flatten a list of lists
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Args:
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items: iterable[iterable[X]]
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Returns:
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list[X]: flattened list
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"""
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return list(chain.from_iterable(items))
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class BaseMetric(object):
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"""Base class for metrics which report a single value per label set
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"""
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def __init__(self, name, labels=[], alternative_names=[]):
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"""
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Args:
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name (str): principal name for this metric
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labels (list(str)): names of the labels which will be reported
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for this metric
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alternative_names (iterable(str)): list of alternative names for
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this metric. This can be useful to provide a migration path
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when renaming metrics.
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"""
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self._names = [name] + list(alternative_names)
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self.labels = labels # OK not to clone as we never write it
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def dimension(self):
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return len(self.labels)
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def is_scalar(self):
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return not len(self.labels)
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def _render_labelvalue(self, value):
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# TODO: escape backslashes, quotes and newlines
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return '"%s"' % (value)
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def _render_key(self, values):
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if self.is_scalar():
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return ""
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return "{%s}" % (
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",".join(["%s=%s" % (k, self._render_labelvalue(v))
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for k, v in zip(self.labels, values)])
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)
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def _render_for_labels(self, label_values, value):
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"""Render this metric for a single set of labels
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Args:
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label_values (list[str]): values for each of the labels
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value: value of the metric at with these labels
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Returns:
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iterable[str]: rendered metric
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"""
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rendered_labels = self._render_key(label_values)
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return (
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"%s%s %.12g" % (name, rendered_labels, value)
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for name in self._names
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)
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def render(self):
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"""Render this metric
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Each metric is rendered as:
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name{label1="val1",label2="val2"} value
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https://prometheus.io/docs/instrumenting/exposition_formats/#text-format-details
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Returns:
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iterable[str]: rendered metrics
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"""
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raise NotImplementedError()
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class CounterMetric(BaseMetric):
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"""The simplest kind of metric; one that stores a monotonically-increasing
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value that counts events or running totals.
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Example use cases for Counters:
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- Number of requests processed
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- Number of items that were inserted into a queue
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- Total amount of data that a system has processed
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Counters can only go up (and be reset when the process restarts).
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"""
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def __init__(self, *args, **kwargs):
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super(CounterMetric, self).__init__(*args, **kwargs)
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# dict[list[str]]: value for each set of label values. the keys are the
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# label values, in the same order as the labels in self.labels.
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#
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# (if the metric is a scalar, the (single) key is the empty list).
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self.counts = {}
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# Scalar metrics are never empty
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if self.is_scalar():
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self.counts[()] = 0.
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def inc_by(self, incr, *values):
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if len(values) != self.dimension():
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raise ValueError(
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"Expected as many values to inc() as labels (%d)" % (self.dimension())
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)
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# TODO: should assert that the tag values are all strings
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if values not in self.counts:
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self.counts[values] = incr
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else:
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self.counts[values] += incr
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def inc(self, *values):
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self.inc_by(1, *values)
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def render(self):
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return flatten(
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self._render_for_labels(k, self.counts[k])
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for k in sorted(self.counts.keys())
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)
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class CallbackMetric(BaseMetric):
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"""A metric that returns the numeric value returned by a callback whenever
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it is rendered. Typically this is used to implement gauges that yield the
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size or other state of some in-memory object by actively querying it."""
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def __init__(self, name, callback, labels=[]):
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super(CallbackMetric, self).__init__(name, labels=labels)
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self.callback = callback
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def render(self):
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value = self.callback()
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if self.is_scalar():
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return list(self._render_for_labels([], value))
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return flatten(
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self._render_for_labels(k, value[k])
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for k in sorted(value.keys())
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)
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class DistributionMetric(object):
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"""A combination of an event counter and an accumulator, which counts
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both the number of events and accumulates the total value. Typically this
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could be used to keep track of method-running times, or other distributions
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of values that occur in discrete occurances.
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TODO(paul): Try to export some heatmap-style stats?
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"""
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def __init__(self, name, *args, **kwargs):
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self.counts = CounterMetric(name + ":count", **kwargs)
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self.totals = CounterMetric(name + ":total", **kwargs)
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def inc_by(self, inc, *values):
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self.counts.inc(*values)
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self.totals.inc_by(inc, *values)
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def render(self):
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return self.counts.render() + self.totals.render()
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class CacheMetric(object):
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__slots__ = ("name", "cache_name", "hits", "misses", "size_callback")
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def __init__(self, name, size_callback, cache_name):
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self.name = name
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self.cache_name = cache_name
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self.hits = 0
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self.misses = 0
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self.size_callback = size_callback
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def inc_hits(self):
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self.hits += 1
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def inc_misses(self):
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self.misses += 1
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def render(self):
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size = self.size_callback()
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hits = self.hits
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total = self.misses + self.hits
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return [
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"""%s:hits{name="%s"} %d""" % (self.name, self.cache_name, hits),
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"""%s:total{name="%s"} %d""" % (self.name, self.cache_name, total),
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"""%s:size{name="%s"} %d""" % (self.name, self.cache_name, size),
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]
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class MemoryUsageMetric(object):
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"""Keeps track of the current memory usage, using psutil.
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The class will keep the current min/max/sum/counts of rss over the last
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WINDOW_SIZE_SEC, by polling UPDATE_HZ times per second
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"""
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UPDATE_HZ = 2 # number of times to get memory per second
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WINDOW_SIZE_SEC = 30 # the size of the window in seconds
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def __init__(self, hs, psutil):
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clock = hs.get_clock()
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self.memory_snapshots = []
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self.process = psutil.Process()
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clock.looping_call(self._update_curr_values, 1000 / self.UPDATE_HZ)
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def _update_curr_values(self):
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max_size = self.UPDATE_HZ * self.WINDOW_SIZE_SEC
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self.memory_snapshots.append(self.process.memory_info().rss)
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self.memory_snapshots[:] = self.memory_snapshots[-max_size:]
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def render(self):
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if not self.memory_snapshots:
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return []
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max_rss = max(self.memory_snapshots)
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min_rss = min(self.memory_snapshots)
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sum_rss = sum(self.memory_snapshots)
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len_rss = len(self.memory_snapshots)
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return [
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"process_psutil_rss:max %d" % max_rss,
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"process_psutil_rss:min %d" % min_rss,
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"process_psutil_rss:total %d" % sum_rss,
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"process_psutil_rss:count %d" % len_rss,
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]
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