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Diffstat (limited to 'synapse/metrics/metric.py')
| -rw-r--r-- | synapse/metrics/metric.py | 195 |
1 files changed, 0 insertions, 195 deletions
diff --git a/synapse/metrics/metric.py b/synapse/metrics/metric.py deleted file mode 100644 index e87b2b80a7..0000000000 --- a/synapse/metrics/metric.py +++ /dev/null @@ -1,195 +0,0 @@ -# -*- coding: utf-8 -*- -# Copyright 2015, 2016 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)]) - ) - - -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])] - - def render(self): - return map_concat(self.render_item, sorted(self.counts.keys())) - - -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 %.12g" % (self.name, value)] - - return ["%s%s %.12g" % (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): - __slots__ = ("name", "cache_name", "hits", "misses", "size_callback") - - def __init__(self, name, size_callback, cache_name): - self.name = name - self.cache_name = cache_name - - self.hits = 0 - self.misses = 0 - - self.size_callback = size_callback - - def inc_hits(self): - self.hits += 1 - - def inc_misses(self): - self.misses += 1 - - def render(self): - size = self.size_callback() - hits = self.hits - total = self.misses + self.hits - - return [ - """%s:hits{name="%s"} %d""" % (self.name, self.cache_name, hits), - """%s:total{name="%s"} %d""" % (self.name, self.cache_name, total), - """%s:size{name="%s"} %d""" % (self.name, self.cache_name, size), - ] - - -class MemoryUsageMetric(object): - """Keeps track of the current memory usage, using psutil. - - The class will keep the current min/max/sum/counts of rss over the last - WINDOW_SIZE_SEC, by polling UPDATE_HZ times per second - """ - - UPDATE_HZ = 2 # number of times to get memory per second - WINDOW_SIZE_SEC = 30 # the size of the window in seconds - - def __init__(self, hs, psutil): - clock = hs.get_clock() - self.memory_snapshots = [] - - self.process = psutil.Process() - - clock.looping_call(self._update_curr_values, 1000 / self.UPDATE_HZ) - - def _update_curr_values(self): - max_size = self.UPDATE_HZ * self.WINDOW_SIZE_SEC - self.memory_snapshots.append(self.process.memory_info().rss) - self.memory_snapshots[:] = self.memory_snapshots[-max_size:] - - def render(self): - if not self.memory_snapshots: - return [] - - max_rss = max(self.memory_snapshots) - min_rss = min(self.memory_snapshots) - sum_rss = sum(self.memory_snapshots) - len_rss = len(self.memory_snapshots) - - return [ - "process_psutil_rss:max %d" % max_rss, - "process_psutil_rss:min %d" % min_rss, - "process_psutil_rss:total %d" % sum_rss, - "process_psutil_rss:count %d" % len_rss, - ] |