721 lines
22 KiB
Python
721 lines
22 KiB
Python
# Copyright 2015, 2016 OpenMarket Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import functools
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import gc
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import itertools
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import logging
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import os
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import platform
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import threading
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import time
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from typing import (
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Any,
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Callable,
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Dict,
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Generic,
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Iterable,
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Mapping,
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Optional,
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Sequence,
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Set,
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Tuple,
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Type,
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TypeVar,
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Union,
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cast,
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)
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import attr
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from prometheus_client import CollectorRegistry, Counter, Gauge, Histogram, Metric
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from prometheus_client.core import (
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REGISTRY,
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CounterMetricFamily,
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GaugeHistogramMetricFamily,
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GaugeMetricFamily,
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)
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from twisted.internet import reactor
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from twisted.internet.base import ReactorBase
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from twisted.python.threadpool import ThreadPool
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import synapse
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from synapse.metrics._exposition import (
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MetricsResource,
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generate_latest,
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start_http_server,
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)
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from synapse.util.versionstring import get_version_string
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logger = logging.getLogger(__name__)
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METRICS_PREFIX = "/_synapse/metrics"
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running_on_pypy = platform.python_implementation() == "PyPy"
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all_gauges: "Dict[str, Union[LaterGauge, InFlightGauge]]" = {}
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HAVE_PROC_SELF_STAT = os.path.exists("/proc/self/stat")
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class RegistryProxy:
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@staticmethod
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def collect() -> Iterable[Metric]:
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for metric in REGISTRY.collect():
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if not metric.name.startswith("__"):
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yield metric
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@attr.s(slots=True, hash=True)
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class LaterGauge:
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name = attr.ib(type=str)
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desc = attr.ib(type=str)
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labels = attr.ib(hash=False, type=Optional[Iterable[str]])
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# callback: should either return a value (if there are no labels for this metric),
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# or dict mapping from a label tuple to a value
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caller = attr.ib(
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type=Callable[
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[], Union[Mapping[Tuple[str, ...], Union[int, float]], Union[int, float]]
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]
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)
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def collect(self) -> Iterable[Metric]:
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g = GaugeMetricFamily(self.name, self.desc, labels=self.labels)
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try:
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calls = self.caller()
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except Exception:
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logger.exception("Exception running callback for LaterGauge(%s)", self.name)
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yield g
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return
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if isinstance(calls, (int, float)):
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g.add_metric([], calls)
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else:
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for k, v in calls.items():
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g.add_metric(k, v)
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yield g
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def __attrs_post_init__(self) -> None:
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self._register()
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def _register(self) -> None:
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if self.name in all_gauges.keys():
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logger.warning("%s already registered, reregistering" % (self.name,))
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REGISTRY.unregister(all_gauges.pop(self.name))
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REGISTRY.register(self)
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all_gauges[self.name] = self
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# `MetricsEntry` only makes sense when it is a `Protocol`,
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# but `Protocol` can't be used as a `TypeVar` bound.
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MetricsEntry = TypeVar("MetricsEntry")
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class InFlightGauge(Generic[MetricsEntry]):
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"""Tracks number of things (e.g. requests, Measure blocks, etc) in flight
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at any given time.
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Each InFlightGauge will create a metric called `<name>_total` that counts
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the number of in flight blocks, as well as a metrics for each item in the
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given `sub_metrics` as `<name>_<sub_metric>` which will get updated by the
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callbacks.
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Args:
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name
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desc
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labels
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sub_metrics: A list of sub metrics that the callbacks will update.
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"""
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def __init__(
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self,
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name: str,
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desc: str,
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labels: Sequence[str],
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sub_metrics: Sequence[str],
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):
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self.name = name
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self.desc = desc
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self.labels = labels
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self.sub_metrics = sub_metrics
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# Create a class which have the sub_metrics values as attributes, which
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# default to 0 on initialization. Used to pass to registered callbacks.
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self._metrics_class: Type[MetricsEntry] = attr.make_class(
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"_MetricsEntry", attrs={x: attr.ib(0) for x in sub_metrics}, slots=True
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)
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# Counts number of in flight blocks for a given set of label values
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self._registrations: Dict[
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Tuple[str, ...], Set[Callable[[MetricsEntry], None]]
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] = {}
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# Protects access to _registrations
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self._lock = threading.Lock()
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self._register_with_collector()
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def register(
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self,
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key: Tuple[str, ...],
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callback: Callable[[MetricsEntry], None],
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) -> None:
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"""Registers that we've entered a new block with labels `key`.
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`callback` gets called each time the metrics are collected. The same
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value must also be given to `unregister`.
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`callback` gets called with an object that has an attribute per
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sub_metric, which should be updated with the necessary values. Note that
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the metrics object is shared between all callbacks registered with the
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same key.
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Note that `callback` may be called on a separate thread.
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"""
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with self._lock:
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self._registrations.setdefault(key, set()).add(callback)
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def unregister(
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self,
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key: Tuple[str, ...],
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callback: Callable[[MetricsEntry], None],
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) -> None:
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"""Registers that we've exited a block with labels `key`."""
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with self._lock:
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self._registrations.setdefault(key, set()).discard(callback)
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def collect(self) -> Iterable[Metric]:
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"""Called by prometheus client when it reads metrics.
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Note: may be called by a separate thread.
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"""
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in_flight = GaugeMetricFamily(
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self.name + "_total", self.desc, labels=self.labels
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)
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metrics_by_key = {}
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# We copy so that we don't mutate the list while iterating
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with self._lock:
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keys = list(self._registrations)
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for key in keys:
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with self._lock:
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callbacks = set(self._registrations[key])
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in_flight.add_metric(key, len(callbacks))
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metrics = self._metrics_class()
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metrics_by_key[key] = metrics
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for callback in callbacks:
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callback(metrics)
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yield in_flight
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for name in self.sub_metrics:
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gauge = GaugeMetricFamily(
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"_".join([self.name, name]), "", labels=self.labels
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)
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for key, metrics in metrics_by_key.items():
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gauge.add_metric(key, getattr(metrics, name))
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yield gauge
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def _register_with_collector(self) -> None:
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if self.name in all_gauges.keys():
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logger.warning("%s already registered, reregistering" % (self.name,))
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REGISTRY.unregister(all_gauges.pop(self.name))
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REGISTRY.register(self)
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all_gauges[self.name] = self
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class GaugeBucketCollector:
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"""Like a Histogram, but the buckets are Gauges which are updated atomically.
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The data is updated by calling `update_data` with an iterable of measurements.
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We assume that the data is updated less frequently than it is reported to
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Prometheus, and optimise for that case.
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"""
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__slots__ = (
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"_name",
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"_documentation",
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"_bucket_bounds",
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"_metric",
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)
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def __init__(
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self,
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name: str,
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documentation: str,
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buckets: Iterable[float],
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registry: CollectorRegistry = REGISTRY,
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):
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"""
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Args:
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name: base name of metric to be exported to Prometheus. (a _bucket suffix
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will be added.)
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documentation: help text for the metric
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buckets: The top bounds of the buckets to report
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registry: metric registry to register with
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"""
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self._name = name
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self._documentation = documentation
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# the tops of the buckets
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self._bucket_bounds = [float(b) for b in buckets]
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if self._bucket_bounds != sorted(self._bucket_bounds):
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raise ValueError("Buckets not in sorted order")
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if self._bucket_bounds[-1] != float("inf"):
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self._bucket_bounds.append(float("inf"))
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# We initially set this to None. We won't report metrics until
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# this has been initialised after a successful data update
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self._metric: Optional[GaugeHistogramMetricFamily] = None
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registry.register(self)
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def collect(self) -> Iterable[Metric]:
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# Don't report metrics unless we've already collected some data
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if self._metric is not None:
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yield self._metric
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def update_data(self, values: Iterable[float]) -> None:
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"""Update the data to be reported by the metric
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The existing data is cleared, and each measurement in the input is assigned
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to the relevant bucket.
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"""
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self._metric = self._values_to_metric(values)
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def _values_to_metric(self, values: Iterable[float]) -> GaugeHistogramMetricFamily:
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total = 0.0
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bucket_values = [0 for _ in self._bucket_bounds]
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for v in values:
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# assign each value to a bucket
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for i, bound in enumerate(self._bucket_bounds):
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if v <= bound:
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bucket_values[i] += 1
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break
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# ... and increment the sum
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total += v
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# now, aggregate the bucket values so that they count the number of entries in
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# that bucket or below.
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accumulated_values = itertools.accumulate(bucket_values)
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return GaugeHistogramMetricFamily(
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self._name,
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self._documentation,
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buckets=list(
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zip((str(b) for b in self._bucket_bounds), accumulated_values)
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),
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gsum_value=total,
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)
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#
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# Detailed CPU metrics
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#
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class CPUMetrics:
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def __init__(self) -> None:
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ticks_per_sec = 100
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try:
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# Try and get the system config
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ticks_per_sec = os.sysconf("SC_CLK_TCK")
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except (ValueError, TypeError, AttributeError):
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pass
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self.ticks_per_sec = ticks_per_sec
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def collect(self) -> Iterable[Metric]:
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if not HAVE_PROC_SELF_STAT:
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return
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with open("/proc/self/stat") as s:
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line = s.read()
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raw_stats = line.split(") ", 1)[1].split(" ")
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user = GaugeMetricFamily("process_cpu_user_seconds_total", "")
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user.add_metric([], float(raw_stats[11]) / self.ticks_per_sec)
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yield user
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sys = GaugeMetricFamily("process_cpu_system_seconds_total", "")
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sys.add_metric([], float(raw_stats[12]) / self.ticks_per_sec)
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yield sys
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REGISTRY.register(CPUMetrics())
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#
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# Python GC metrics
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#
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gc_unreachable = Gauge("python_gc_unreachable_total", "Unreachable GC objects", ["gen"])
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gc_time = Histogram(
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"python_gc_time",
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"Time taken to GC (sec)",
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["gen"],
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buckets=[
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0.0025,
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0.005,
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0.01,
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0.025,
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0.05,
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0.10,
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0.25,
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0.50,
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1.00,
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2.50,
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5.00,
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7.50,
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15.00,
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30.00,
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45.00,
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60.00,
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],
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)
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class GCCounts:
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def collect(self) -> Iterable[Metric]:
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cm = GaugeMetricFamily("python_gc_counts", "GC object counts", labels=["gen"])
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for n, m in enumerate(gc.get_count()):
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cm.add_metric([str(n)], m)
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yield cm
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if not running_on_pypy:
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REGISTRY.register(GCCounts())
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#
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# PyPy GC / memory metrics
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#
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class PyPyGCStats:
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def collect(self) -> Iterable[Metric]:
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# @stats is a pretty-printer object with __str__() returning a nice table,
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# plus some fields that contain data from that table.
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# unfortunately, fields are pretty-printed themselves (i. e. '4.5MB').
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stats = gc.get_stats(memory_pressure=False) # type: ignore
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# @s contains same fields as @stats, but as actual integers.
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s = stats._s # type: ignore
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# also note that field naming is completely braindead
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# and only vaguely correlates with the pretty-printed table.
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# >>>> gc.get_stats(False)
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# Total memory consumed:
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# GC used: 8.7MB (peak: 39.0MB) # s.total_gc_memory, s.peak_memory
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# in arenas: 3.0MB # s.total_arena_memory
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# rawmalloced: 1.7MB # s.total_rawmalloced_memory
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# nursery: 4.0MB # s.nursery_size
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# raw assembler used: 31.0kB # s.jit_backend_used
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# -----------------------------
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# Total: 8.8MB # stats.memory_used_sum
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#
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# Total memory allocated:
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# GC allocated: 38.7MB (peak: 41.1MB) # s.total_allocated_memory, s.peak_allocated_memory
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# in arenas: 30.9MB # s.peak_arena_memory
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# rawmalloced: 4.1MB # s.peak_rawmalloced_memory
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# nursery: 4.0MB # s.nursery_size
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# raw assembler allocated: 1.0MB # s.jit_backend_allocated
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# -----------------------------
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# Total: 39.7MB # stats.memory_allocated_sum
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#
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# Total time spent in GC: 0.073 # s.total_gc_time
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pypy_gc_time = CounterMetricFamily(
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"pypy_gc_time_seconds_total",
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"Total time spent in PyPy GC",
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labels=[],
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)
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pypy_gc_time.add_metric([], s.total_gc_time / 1000)
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yield pypy_gc_time
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pypy_mem = GaugeMetricFamily(
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"pypy_memory_bytes",
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"Memory tracked by PyPy allocator",
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labels=["state", "class", "kind"],
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)
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# memory used by JIT assembler
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pypy_mem.add_metric(["used", "", "jit"], s.jit_backend_used)
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pypy_mem.add_metric(["allocated", "", "jit"], s.jit_backend_allocated)
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# memory used by GCed objects
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pypy_mem.add_metric(["used", "", "arenas"], s.total_arena_memory)
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pypy_mem.add_metric(["allocated", "", "arenas"], s.peak_arena_memory)
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pypy_mem.add_metric(["used", "", "rawmalloced"], s.total_rawmalloced_memory)
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pypy_mem.add_metric(["allocated", "", "rawmalloced"], s.peak_rawmalloced_memory)
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pypy_mem.add_metric(["used", "", "nursery"], s.nursery_size)
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pypy_mem.add_metric(["allocated", "", "nursery"], s.nursery_size)
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# totals
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pypy_mem.add_metric(["used", "totals", "gc"], s.total_gc_memory)
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pypy_mem.add_metric(["allocated", "totals", "gc"], s.total_allocated_memory)
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pypy_mem.add_metric(["used", "totals", "gc_peak"], s.peak_memory)
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pypy_mem.add_metric(["allocated", "totals", "gc_peak"], s.peak_allocated_memory)
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yield pypy_mem
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if running_on_pypy:
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REGISTRY.register(PyPyGCStats())
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#
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# Twisted reactor metrics
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#
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tick_time = Histogram(
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"python_twisted_reactor_tick_time",
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"Tick time of the Twisted reactor (sec)",
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buckets=[0.001, 0.002, 0.005, 0.01, 0.025, 0.05, 0.1, 0.2, 0.5, 1, 2, 5],
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)
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pending_calls_metric = Histogram(
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"python_twisted_reactor_pending_calls",
|
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"Pending calls",
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buckets=[1, 2, 5, 10, 25, 50, 100, 250, 500, 1000],
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)
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#
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# Federation Metrics
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#
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sent_transactions_counter = Counter("synapse_federation_client_sent_transactions", "")
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events_processed_counter = Counter("synapse_federation_client_events_processed", "")
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event_processing_loop_counter = Counter(
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"synapse_event_processing_loop_count", "Event processing loop iterations", ["name"]
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)
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event_processing_loop_room_count = Counter(
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"synapse_event_processing_loop_room_count",
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"Rooms seen per event processing loop iteration",
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["name"],
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)
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# Used to track where various components have processed in the event stream,
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# e.g. federation sending, appservice sending, etc.
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event_processing_positions = Gauge("synapse_event_processing_positions", "", ["name"])
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# Used to track the current max events stream position
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event_persisted_position = Gauge("synapse_event_persisted_position", "")
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# Used to track the received_ts of the last event processed by various
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# components
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event_processing_last_ts = Gauge("synapse_event_processing_last_ts", "", ["name"])
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# Used to track the lag processing events. This is the time difference
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# between the last processed event's received_ts and the time it was
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# finished being processed.
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event_processing_lag = Gauge("synapse_event_processing_lag", "", ["name"])
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event_processing_lag_by_event = Histogram(
|
|
"synapse_event_processing_lag_by_event",
|
|
"Time between an event being persisted and it being queued up to be sent to the relevant remote servers",
|
|
["name"],
|
|
)
|
|
|
|
# Build info of the running server.
|
|
build_info = Gauge(
|
|
"synapse_build_info", "Build information", ["pythonversion", "version", "osversion"]
|
|
)
|
|
build_info.labels(
|
|
" ".join([platform.python_implementation(), platform.python_version()]),
|
|
get_version_string(synapse),
|
|
" ".join([platform.system(), platform.release()]),
|
|
).set(1)
|
|
|
|
last_ticked = time.time()
|
|
|
|
# 3PID send info
|
|
threepid_send_requests = Histogram(
|
|
"synapse_threepid_send_requests_with_tries",
|
|
documentation="Number of requests for a 3pid token by try count. Note if"
|
|
" there is a request with try count of 4, then there would have been one"
|
|
" each for 1, 2 and 3",
|
|
buckets=(1, 2, 3, 4, 5, 10),
|
|
labelnames=("type", "reason"),
|
|
)
|
|
|
|
threadpool_total_threads = Gauge(
|
|
"synapse_threadpool_total_threads",
|
|
"Total number of threads currently in the threadpool",
|
|
["name"],
|
|
)
|
|
|
|
threadpool_total_working_threads = Gauge(
|
|
"synapse_threadpool_working_threads",
|
|
"Number of threads currently working in the threadpool",
|
|
["name"],
|
|
)
|
|
|
|
threadpool_total_min_threads = Gauge(
|
|
"synapse_threadpool_min_threads",
|
|
"Minimum number of threads configured in the threadpool",
|
|
["name"],
|
|
)
|
|
|
|
threadpool_total_max_threads = Gauge(
|
|
"synapse_threadpool_max_threads",
|
|
"Maximum number of threads configured in the threadpool",
|
|
["name"],
|
|
)
|
|
|
|
|
|
def register_threadpool(name: str, threadpool: ThreadPool) -> None:
|
|
"""Add metrics for the threadpool."""
|
|
|
|
threadpool_total_min_threads.labels(name).set(threadpool.min)
|
|
threadpool_total_max_threads.labels(name).set(threadpool.max)
|
|
|
|
threadpool_total_threads.labels(name).set_function(lambda: len(threadpool.threads))
|
|
threadpool_total_working_threads.labels(name).set_function(
|
|
lambda: len(threadpool.working)
|
|
)
|
|
|
|
|
|
class ReactorLastSeenMetric:
|
|
def collect(self) -> Iterable[Metric]:
|
|
cm = GaugeMetricFamily(
|
|
"python_twisted_reactor_last_seen",
|
|
"Seconds since the Twisted reactor was last seen",
|
|
)
|
|
cm.add_metric([], time.time() - last_ticked)
|
|
yield cm
|
|
|
|
|
|
REGISTRY.register(ReactorLastSeenMetric())
|
|
|
|
# The minimum time in seconds between GCs for each generation, regardless of the current GC
|
|
# thresholds and counts.
|
|
MIN_TIME_BETWEEN_GCS = (1.0, 10.0, 30.0)
|
|
|
|
# The time (in seconds since the epoch) of the last time we did a GC for each generation.
|
|
_last_gc = [0.0, 0.0, 0.0]
|
|
|
|
|
|
F = TypeVar("F", bound=Callable[..., Any])
|
|
|
|
|
|
def runUntilCurrentTimer(reactor: ReactorBase, func: F) -> F:
|
|
@functools.wraps(func)
|
|
def f(*args: Any, **kwargs: Any) -> Any:
|
|
now = reactor.seconds()
|
|
num_pending = 0
|
|
|
|
# _newTimedCalls is one long list of *all* pending calls. Below loop
|
|
# is based off of impl of reactor.runUntilCurrent
|
|
for delayed_call in reactor._newTimedCalls:
|
|
if delayed_call.time > now:
|
|
break
|
|
|
|
if delayed_call.delayed_time > 0:
|
|
continue
|
|
|
|
num_pending += 1
|
|
|
|
num_pending += len(reactor.threadCallQueue)
|
|
start = time.time()
|
|
ret = func(*args, **kwargs)
|
|
end = time.time()
|
|
|
|
# record the amount of wallclock time spent running pending calls.
|
|
# This is a proxy for the actual amount of time between reactor polls,
|
|
# since about 25% of time is actually spent running things triggered by
|
|
# I/O events, but that is harder to capture without rewriting half the
|
|
# reactor.
|
|
tick_time.observe(end - start)
|
|
pending_calls_metric.observe(num_pending)
|
|
|
|
# Update the time we last ticked, for the metric to test whether
|
|
# Synapse's reactor has frozen
|
|
global last_ticked
|
|
last_ticked = end
|
|
|
|
if running_on_pypy:
|
|
return ret
|
|
|
|
# Check if we need to do a manual GC (since its been disabled), and do
|
|
# one if necessary. Note we go in reverse order as e.g. a gen 1 GC may
|
|
# promote an object into gen 2, and we don't want to handle the same
|
|
# object multiple times.
|
|
threshold = gc.get_threshold()
|
|
counts = gc.get_count()
|
|
for i in (2, 1, 0):
|
|
# We check if we need to do one based on a straightforward
|
|
# comparison between the threshold and count. We also do an extra
|
|
# check to make sure that we don't a GC too often.
|
|
if threshold[i] < counts[i] and MIN_TIME_BETWEEN_GCS[i] < end - _last_gc[i]:
|
|
if i == 0:
|
|
logger.debug("Collecting gc %d", i)
|
|
else:
|
|
logger.info("Collecting gc %d", i)
|
|
|
|
start = time.time()
|
|
unreachable = gc.collect(i)
|
|
end = time.time()
|
|
|
|
_last_gc[i] = end
|
|
|
|
gc_time.labels(i).observe(end - start)
|
|
gc_unreachable.labels(i).set(unreachable)
|
|
|
|
return ret
|
|
|
|
return cast(F, f)
|
|
|
|
|
|
try:
|
|
# Ensure the reactor has all the attributes we expect
|
|
reactor.seconds # type: ignore
|
|
reactor.runUntilCurrent # type: ignore
|
|
reactor._newTimedCalls # type: ignore
|
|
reactor.threadCallQueue # type: ignore
|
|
|
|
# runUntilCurrent is called when we have pending calls. It is called once
|
|
# per iteratation after fd polling.
|
|
reactor.runUntilCurrent = runUntilCurrentTimer(reactor, reactor.runUntilCurrent) # type: ignore
|
|
|
|
# We manually run the GC each reactor tick so that we can get some metrics
|
|
# about time spent doing GC,
|
|
if not running_on_pypy:
|
|
gc.disable()
|
|
except AttributeError:
|
|
pass
|
|
|
|
|
|
__all__ = [
|
|
"MetricsResource",
|
|
"generate_latest",
|
|
"start_http_server",
|
|
"LaterGauge",
|
|
"InFlightGauge",
|
|
"GaugeBucketCollector",
|
|
]
|