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https://git.anonymousland.org/anonymousland/synapse-product.git
synced 2024-10-01 08:25:44 -04:00
remove old metrics libs
This commit is contained in:
Amber Brown
parent
ab5e888927
commit
f258deffcb
@ -1,328 +0,0 @@
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# -*- 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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import logging
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import re
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logger = logging.getLogger(__name__)
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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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return '"%s"' % (_escape_label_value(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[object]): values for each of the labels,
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(which get stringified).
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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 tuple).
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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 GaugeMetric(BaseMetric):
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"""A metric that can go up or down
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"""
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def __init__(self, *args, **kwargs):
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super(GaugeMetric, 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 tuple).
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self.guages = {}
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def set(self, v, *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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self.guages[values] = v
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def render(self):
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return flatten(
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self._render_for_labels(k, self.guages[k])
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for k in sorted(self.guages.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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try:
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value = self.callback()
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except Exception:
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logger.exception("Failed to render %s", self.name)
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return ["# FAILED to render " + self.name]
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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__ = (
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"name", "cache_name", "hits", "misses", "evicted_size", "size_callback",
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)
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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.evicted_size = 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 inc_evictions(self, size=1):
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self.evicted_size += size
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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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"""%s:evicted_size{name="%s"} %d""" % (
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self.name, self.cache_name, self.evicted_size
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),
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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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def _escape_character(m):
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"""Replaces a single character with its escape sequence.
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Args:
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m (re.MatchObject): A match object whose first group is the single
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character to replace
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Returns:
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str
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"""
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c = m.group(1)
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if c == "\\":
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return "\\\\"
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elif c == "\"":
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return "\\\""
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elif c == "\n":
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return "\\n"
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return c
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def _escape_label_value(value):
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"""Takes a label value and escapes quotes, newlines and backslashes
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"""
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return re.sub(r"([\n\"\\])", _escape_character, str(value))
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@ -1,122 +0,0 @@
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# -*- 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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import os
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TICKS_PER_SEC = 100
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BYTES_PER_PAGE = 4096
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HAVE_PROC_STAT = os.path.exists("/proc/stat")
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HAVE_PROC_SELF_STAT = os.path.exists("/proc/self/stat")
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HAVE_PROC_SELF_LIMITS = os.path.exists("/proc/self/limits")
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HAVE_PROC_SELF_FD = os.path.exists("/proc/self/fd")
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# Field indexes from /proc/self/stat, taken from the proc(5) manpage
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STAT_FIELDS = {
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"utime": 14,
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"stime": 15,
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"starttime": 22,
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"vsize": 23,
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"rss": 24,
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}
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stats = {}
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# In order to report process_start_time_seconds we need to know the
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# machine's boot time, because the value in /proc/self/stat is relative to
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# this
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boot_time = None
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if HAVE_PROC_STAT:
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with open("/proc/stat") as _procstat:
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for line in _procstat:
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if line.startswith("btime "):
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boot_time = int(line.split()[1])
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def update_resource_metrics():
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if HAVE_PROC_SELF_STAT:
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global stats
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with open("/proc/self/stat") as s:
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line = s.read()
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# line is PID (command) more stats go here ...
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raw_stats = line.split(") ", 1)[1].split(" ")
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for (name, index) in STAT_FIELDS.iteritems():
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# subtract 3 from the index, because proc(5) is 1-based, and
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# we've lost the first two fields in PID and COMMAND above
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stats[name] = int(raw_stats[index - 3])
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def _count_fds():
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# Not every OS will have a /proc/self/fd directory
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if not HAVE_PROC_SELF_FD:
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return 0
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return len(os.listdir("/proc/self/fd"))
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def register_process_collector(process_metrics):
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process_metrics.register_collector(update_resource_metrics)
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if HAVE_PROC_SELF_STAT:
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process_metrics.register_callback(
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"cpu_user_seconds_total",
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lambda: float(stats["utime"]) / TICKS_PER_SEC
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)
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process_metrics.register_callback(
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"cpu_system_seconds_total",
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lambda: float(stats["stime"]) / TICKS_PER_SEC
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)
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process_metrics.register_callback(
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"cpu_seconds_total",
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lambda: (float(stats["utime"] + stats["stime"])) / TICKS_PER_SEC
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)
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process_metrics.register_callback(
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"virtual_memory_bytes",
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lambda: int(stats["vsize"])
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)
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process_metrics.register_callback(
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"resident_memory_bytes",
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lambda: int(stats["rss"]) * BYTES_PER_PAGE
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)
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process_metrics.register_callback(
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"start_time_seconds",
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lambda: boot_time + int(stats["starttime"]) / TICKS_PER_SEC
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)
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if HAVE_PROC_SELF_FD:
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process_metrics.register_callback(
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"open_fds",
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lambda: _count_fds()
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)
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if HAVE_PROC_SELF_LIMITS:
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def _get_max_fds():
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with open("/proc/self/limits") as limits:
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for line in limits:
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if not line.startswith("Max open files "):
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continue
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# Line is Max open files $SOFT $HARD
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return int(line.split()[3])
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return None
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process_metrics.register_callback(
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"max_fds",
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lambda: _get_max_fds()
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)
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