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author | Erik Johnston <erik@matrix.org> | 2015-03-19 10:43:31 +0000 |
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committer | Erik Johnston <erik@matrix.org> | 2015-03-19 10:43:31 +0000 |
commit | d028207a6e421b97eae886a501e3e427577bab29 (patch) | |
tree | 4f452dfcb656771607eac291dfe34dd4ec49f812 /synapse/metrics/metric.py | |
parent | Merge branch 'release-v0.8.0' of github.com:matrix-org/synapse (diff) | |
parent | Update CHANGES (diff) | |
download | synapse-d028207a6e421b97eae886a501e3e427577bab29.tar.xz |
Merge branch 'release-v0.8.1' of github.com:matrix-org/synapse v0.8.1
Diffstat (limited to '')
-rw-r--r-- | synapse/metrics/metric.py | 155 |
1 files changed, 155 insertions, 0 deletions
diff --git a/synapse/metrics/metric.py b/synapse/metrics/metric.py new file mode 100644 index 0000000000..21b37748f6 --- /dev/null +++ b/synapse/metrics/metric.py @@ -0,0 +1,155 @@ +# -*- 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() |