mirror of
https://git.anonymousland.org/anonymousland/synapse.git
synced 2024-12-22 15:35:00 -05:00
156 lines
4.8 KiB
Python
156 lines
4.8 KiB
Python
# -*- coding: utf-8 -*-
|
|
# Copyright 2015 OpenMarket Ltd
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
|
|
from itertools import chain
|
|
|
|
|
|
# TODO(paul): I can't believe Python doesn't have one of these
|
|
def map_concat(func, items):
|
|
# flatten a list-of-lists
|
|
return list(chain.from_iterable(map(func, items)))
|
|
|
|
|
|
class BaseMetric(object):
|
|
|
|
def __init__(self, name, labels=[]):
|
|
self.name = name
|
|
self.labels = labels # OK not to clone as we never write it
|
|
|
|
def dimension(self):
|
|
return len(self.labels)
|
|
|
|
def is_scalar(self):
|
|
return not len(self.labels)
|
|
|
|
def _render_labelvalue(self, value):
|
|
# TODO: some kind of value escape
|
|
return '"%s"' % (value)
|
|
|
|
def _render_key(self, values):
|
|
if self.is_scalar():
|
|
return ""
|
|
return "{%s}" % (
|
|
",".join(["%s=%s" % (k, self._render_labelvalue(v))
|
|
for k, v in zip(self.labels, values)])
|
|
)
|
|
|
|
def render(self):
|
|
return map_concat(self.render_item, sorted(self.counts.keys()))
|
|
|
|
|
|
class CounterMetric(BaseMetric):
|
|
"""The simplest kind of metric; one that stores a monotonically-increasing
|
|
integer that counts events."""
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
super(CounterMetric, self).__init__(*args, **kwargs)
|
|
|
|
self.counts = {}
|
|
|
|
# Scalar metrics are never empty
|
|
if self.is_scalar():
|
|
self.counts[()] = 0
|
|
|
|
def inc_by(self, incr, *values):
|
|
if len(values) != self.dimension():
|
|
raise ValueError(
|
|
"Expected as many values to inc() as labels (%d)" % (self.dimension())
|
|
)
|
|
|
|
# TODO: should assert that the tag values are all strings
|
|
|
|
if values not in self.counts:
|
|
self.counts[values] = incr
|
|
else:
|
|
self.counts[values] += incr
|
|
|
|
def inc(self, *values):
|
|
self.inc_by(1, *values)
|
|
|
|
def render_item(self, k):
|
|
return ["%s%s %d" % (self.name, self._render_key(k), self.counts[k])]
|
|
|
|
|
|
class CallbackMetric(BaseMetric):
|
|
"""A metric that returns the numeric value returned by a callback whenever
|
|
it is rendered. Typically this is used to implement gauges that yield the
|
|
size or other state of some in-memory object by actively querying it."""
|
|
|
|
def __init__(self, name, callback, labels=[]):
|
|
super(CallbackMetric, self).__init__(name, labels=labels)
|
|
|
|
self.callback = callback
|
|
|
|
def render(self):
|
|
value = self.callback()
|
|
|
|
if self.is_scalar():
|
|
return ["%s %d" % (self.name, value)]
|
|
|
|
return ["%s%s %d" % (self.name, self._render_key(k), value[k])
|
|
for k in sorted(value.keys())]
|
|
|
|
|
|
class DistributionMetric(object):
|
|
"""A combination of an event counter and an accumulator, which counts
|
|
both the number of events and accumulates the total value. Typically this
|
|
could be used to keep track of method-running times, or other distributions
|
|
of values that occur in discrete occurances.
|
|
|
|
TODO(paul): Try to export some heatmap-style stats?
|
|
"""
|
|
|
|
def __init__(self, name, *args, **kwargs):
|
|
self.counts = CounterMetric(name + ":count", **kwargs)
|
|
self.totals = CounterMetric(name + ":total", **kwargs)
|
|
|
|
def inc_by(self, inc, *values):
|
|
self.counts.inc(*values)
|
|
self.totals.inc_by(inc, *values)
|
|
|
|
def render(self):
|
|
return self.counts.render() + self.totals.render()
|
|
|
|
|
|
class CacheMetric(object):
|
|
"""A combination of two CounterMetrics, one to count cache hits and one to
|
|
count a total, and a callback metric to yield the current size.
|
|
|
|
This metric generates standard metric name pairs, so that monitoring rules
|
|
can easily be applied to measure hit ratio."""
|
|
|
|
def __init__(self, name, size_callback, labels=[]):
|
|
self.name = name
|
|
|
|
self.hits = CounterMetric(name + ":hits", labels=labels)
|
|
self.total = CounterMetric(name + ":total", labels=labels)
|
|
|
|
self.size = CallbackMetric(
|
|
name + ":size",
|
|
callback=size_callback,
|
|
labels=labels,
|
|
)
|
|
|
|
def inc_hits(self, *values):
|
|
self.hits.inc(*values)
|
|
self.total.inc(*values)
|
|
|
|
def inc_misses(self, *values):
|
|
self.total.inc(*values)
|
|
|
|
def render(self):
|
|
return self.hits.render() + self.total.render() + self.size.render()
|