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- # -*- 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
- import logging
- import re
- logger = logging.getLogger(__name__)
- def flatten(items):
- """Flatten a list of lists
- Args:
- items: iterable[iterable[X]]
- Returns:
- list[X]: flattened list
- """
- return list(chain.from_iterable(items))
- class BaseMetric(object):
- """Base class for metrics which report a single value per label set
- """
- def __init__(self, name, labels=[], alternative_names=[]):
- """
- Args:
- name (str): principal name for this metric
- labels (list(str)): names of the labels which will be reported
- for this metric
- alternative_names (iterable(str)): list of alternative names for
- this metric. This can be useful to provide a migration path
- when renaming metrics.
- """
- self._names = [name] + list(alternative_names)
- 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):
- return '"%s"' % (_escape_label_value(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_for_labels(self, label_values, value):
- """Render this metric for a single set of labels
- Args:
- label_values (list[object]): values for each of the labels,
- (which get stringified).
- value: value of the metric at with these labels
- Returns:
- iterable[str]: rendered metric
- """
- rendered_labels = self._render_key(label_values)
- return (
- "%s%s %.12g" % (name, rendered_labels, value)
- for name in self._names
- )
- def render(self):
- """Render this metric
- Each metric is rendered as:
- name{label1="val1",label2="val2"} value
- https://prometheus.io/docs/instrumenting/exposition_formats/#text-format-details
- Returns:
- iterable[str]: rendered metrics
- """
- raise NotImplementedError()
- class CounterMetric(BaseMetric):
- """The simplest kind of metric; one that stores a monotonically-increasing
- value that counts events or running totals.
- Example use cases for Counters:
- - Number of requests processed
- - Number of items that were inserted into a queue
- - Total amount of data that a system has processed
- Counters can only go up (and be reset when the process restarts).
- """
- def __init__(self, *args, **kwargs):
- super(CounterMetric, self).__init__(*args, **kwargs)
- # dict[list[str]]: value for each set of label values. the keys are the
- # label values, in the same order as the labels in self.labels.
- #
- # (if the metric is a scalar, the (single) key is the empty tuple).
- 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(self):
- return flatten(
- self._render_for_labels(k, self.counts[k])
- for k in sorted(self.counts.keys())
- )
- class GaugeMetric(BaseMetric):
- """A metric that can go up or down
- """
- def __init__(self, *args, **kwargs):
- super(GaugeMetric, self).__init__(*args, **kwargs)
- # dict[list[str]]: value for each set of label values. the keys are the
- # label values, in the same order as the labels in self.labels.
- #
- # (if the metric is a scalar, the (single) key is the empty tuple).
- self.guages = {}
- def set(self, v, *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
- self.guages[values] = v
- def render(self):
- return flatten(
- self._render_for_labels(k, self.guages[k])
- for k in sorted(self.guages.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):
- try:
- value = self.callback()
- except Exception:
- logger.exception("Failed to render %s", self.name)
- return ["# FAILED to render " + self.name]
- if self.is_scalar():
- return list(self._render_for_labels([], value))
- return flatten(
- self._render_for_labels(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", "evicted_size", "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.evicted_size = 0
- self.size_callback = size_callback
- def inc_hits(self):
- self.hits += 1
- def inc_misses(self):
- self.misses += 1
- def inc_evictions(self, size=1):
- self.evicted_size += size
- 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),
- """%s:evicted_size{name="%s"} %d""" % (
- self.name, self.cache_name, self.evicted_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,
- ]
- def _escape_character(m):
- """Replaces a single character with its escape sequence.
- Args:
- m (re.MatchObject): A match object whose first group is the single
- character to replace
- Returns:
- str
- """
- c = m.group(1)
- if c == "\\":
- return "\\\\"
- elif c == "\"":
- return "\\\""
- elif c == "\n":
- return "\\n"
- return c
- def _escape_label_value(value):
- """Takes a label value and escapes quotes, newlines and backslashes
- """
- return re.sub(r"([\n\"\\])", _escape_character, str(value))
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