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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
# 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):
"""Base class for metrics which report a single value per label set
"""
def __init__(self, name, labels=[]):
"""
Args:
name (str): principal name for this metric
labels (list(str)): names of the labels which will be reported
for this metric
"""
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: escape backslashes, quotes and newlines
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):
"""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 list).
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 %.12g" % (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,
]
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