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19d274085f
Prometheus handles all metrics as floats, and sometimes we store non-integer values in them (notably, durations in seconds), so let's render them as floats too. (Note that the standard client libraries also treat Counters as floats.)
203 lines
6.2 KiB
Python
203 lines
6.2 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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# TODO(paul): I can't believe Python doesn't have one of these
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def map_concat(func, items):
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# flatten a list-of-lists
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return list(chain.from_iterable(map(func, items)))
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class BaseMetric(object):
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def __init__(self, name, labels=[]):
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self.name = name
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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: some kind of value escape
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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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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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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_item(self, k):
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return ["%s%s %.12g" % (self.name, self._render_key(k), self.counts[k])]
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def render(self):
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return map_concat(self.render_item, sorted(self.counts.keys()))
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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 ["%s %.12g" % (self.name, value)]
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return ["%s%s %.12g" % (self.name, self._render_key(k), value[k])
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for k in sorted(value.keys())]
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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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