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Commit f258deff authored by Amber Brown's avatar Amber Brown
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remove old metrics libs

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synapse/metrics/metric.py deleted 100644 → 0
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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))
synapse/metrics/process_collector.py deleted 100644 → 0
+ 0
− 122
View file @ ab5e8889
# -*- 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.
import os
TICKS_PER_SEC = 100
BYTES_PER_PAGE = 4096
HAVE_PROC_STAT = os.path.exists("/proc/stat")
HAVE_PROC_SELF_STAT = os.path.exists("/proc/self/stat")
HAVE_PROC_SELF_LIMITS = os.path.exists("/proc/self/limits")
HAVE_PROC_SELF_FD = os.path.exists("/proc/self/fd")
# Field indexes from /proc/self/stat, taken from the proc(5) manpage
STAT_FIELDS = {
"utime": 14,
"stime": 15,
"starttime": 22,
"vsize": 23,
"rss": 24,
}
stats = {}
# In order to report process_start_time_seconds we need to know the
# machine's boot time, because the value in /proc/self/stat is relative to
# this
boot_time = None
if HAVE_PROC_STAT:
with open("/proc/stat") as _procstat:
for line in _procstat:
if line.startswith("btime "):
boot_time = int(line.split()[1])
def update_resource_metrics():
if HAVE_PROC_SELF_STAT:
global stats
with open("/proc/self/stat") as s:
line = s.read()
# line is PID (command) more stats go here ...
raw_stats = line.split(") ", 1)[1].split(" ")
for (name, index) in STAT_FIELDS.iteritems():
# subtract 3 from the index, because proc(5) is 1-based, and
# we've lost the first two fields in PID and COMMAND above
stats[name] = int(raw_stats[index - 3])
def _count_fds():
# Not every OS will have a /proc/self/fd directory
if not HAVE_PROC_SELF_FD:
return 0
return len(os.listdir("/proc/self/fd"))
def register_process_collector(process_metrics):
process_metrics.register_collector(update_resource_metrics)
if HAVE_PROC_SELF_STAT:
process_metrics.register_callback(
"cpu_user_seconds_total",
lambda: float(stats["utime"]) / TICKS_PER_SEC
)
process_metrics.register_callback(
"cpu_system_seconds_total",
lambda: float(stats["stime"]) / TICKS_PER_SEC
)
process_metrics.register_callback(
"cpu_seconds_total",
lambda: (float(stats["utime"] + stats["stime"])) / TICKS_PER_SEC
)
process_metrics.register_callback(
"virtual_memory_bytes",
lambda: int(stats["vsize"])
)
process_metrics.register_callback(
"resident_memory_bytes",
lambda: int(stats["rss"]) * BYTES_PER_PAGE
)
process_metrics.register_callback(
"start_time_seconds",
lambda: boot_time + int(stats["starttime"]) / TICKS_PER_SEC
)
if HAVE_PROC_SELF_FD:
process_metrics.register_callback(
"open_fds",
lambda: _count_fds()
)
if HAVE_PROC_SELF_LIMITS:
def _get_max_fds():
with open("/proc/self/limits") as limits:
for line in limits:
if not line.startswith("Max open files "):
continue
# Line is Max open files $SOFT $HARD
return int(line.split()[3])
return None
process_metrics.register_callback(
"max_fds",
lambda: _get_max_fds()
)
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