forked-synapse/synapse/metrics/__init__.py

479 lines
15 KiB
Python

# Copyright 2015, 2016 OpenMarket Ltd
# Copyright 2022 The Matrix.org Foundation C.I.C.
#
# 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 itertools
import logging
import os
import platform
import threading
from typing import (
Callable,
Dict,
Generic,
Iterable,
Mapping,
Optional,
Sequence,
Set,
Tuple,
Type,
TypeVar,
Union,
cast,
)
import attr
from prometheus_client import CollectorRegistry, Counter, Gauge, Histogram, Metric
from prometheus_client.core import (
REGISTRY,
GaugeHistogramMetricFamily,
GaugeMetricFamily,
)
from twisted.python.threadpool import ThreadPool
# This module is imported for its side effects; flake8 needn't warn that it's unused.
import synapse.metrics._reactor_metrics # noqa: F401
from synapse.metrics._gc import MIN_TIME_BETWEEN_GCS, install_gc_manager
from synapse.metrics._twisted_exposition import MetricsResource, generate_latest
from synapse.metrics._types import Collector
from synapse.util import SYNAPSE_VERSION
logger = logging.getLogger(__name__)
METRICS_PREFIX = "/_synapse/metrics"
all_gauges: Dict[str, Collector] = {}
HAVE_PROC_SELF_STAT = os.path.exists("/proc/self/stat")
class _RegistryProxy:
@staticmethod
def collect() -> Iterable[Metric]:
for metric in REGISTRY.collect():
if not metric.name.startswith("__"):
yield metric
# A little bit nasty, but collect() above is static so a Protocol doesn't work.
# _RegistryProxy matches the signature of a CollectorRegistry instance enough
# for it to be usable in the contexts in which we use it.
# TODO Do something nicer about this.
RegistryProxy = cast(CollectorRegistry, _RegistryProxy)
@attr.s(slots=True, hash=True, auto_attribs=True)
class LaterGauge(Collector):
name: str
desc: str
labels: Optional[Sequence[str]] = attr.ib(hash=False)
# callback: should either return a value (if there are no labels for this metric),
# or dict mapping from a label tuple to a value
caller: Callable[
[], Union[Mapping[Tuple[str, ...], Union[int, float]], Union[int, float]]
]
def collect(self) -> Iterable[Metric]:
g = GaugeMetricFamily(self.name, self.desc, labels=self.labels)
try:
calls = self.caller()
except Exception:
logger.exception("Exception running callback for LaterGauge(%s)", self.name)
yield g
return
if isinstance(calls, (int, float)):
g.add_metric([], calls)
else:
for k, v in calls.items():
g.add_metric(k, v)
yield g
def __attrs_post_init__(self) -> None:
self._register()
def _register(self) -> None:
if self.name in all_gauges.keys():
logger.warning("%s already registered, reregistering" % (self.name,))
REGISTRY.unregister(all_gauges.pop(self.name))
REGISTRY.register(self)
all_gauges[self.name] = self
# `MetricsEntry` only makes sense when it is a `Protocol`,
# but `Protocol` can't be used as a `TypeVar` bound.
MetricsEntry = TypeVar("MetricsEntry")
class InFlightGauge(Generic[MetricsEntry], Collector):
"""Tracks number of things (e.g. requests, Measure blocks, etc) in flight
at any given time.
Each InFlightGauge will create a metric called `<name>_total` that counts
the number of in flight blocks, as well as a metrics for each item in the
given `sub_metrics` as `<name>_<sub_metric>` which will get updated by the
callbacks.
Args:
name
desc
labels
sub_metrics: A list of sub metrics that the callbacks will update.
"""
def __init__(
self,
name: str,
desc: str,
labels: Sequence[str],
sub_metrics: Sequence[str],
):
self.name = name
self.desc = desc
self.labels = labels
self.sub_metrics = sub_metrics
# Create a class which have the sub_metrics values as attributes, which
# default to 0 on initialization. Used to pass to registered callbacks.
self._metrics_class: Type[MetricsEntry] = attr.make_class(
"_MetricsEntry",
attrs={x: attr.ib(default=0) for x in sub_metrics},
slots=True,
)
# Counts number of in flight blocks for a given set of label values
self._registrations: Dict[
Tuple[str, ...], Set[Callable[[MetricsEntry], None]]
] = {}
# Protects access to _registrations
self._lock = threading.Lock()
self._register_with_collector()
def register(
self,
key: Tuple[str, ...],
callback: Callable[[MetricsEntry], None],
) -> None:
"""Registers that we've entered a new block with labels `key`.
`callback` gets called each time the metrics are collected. The same
value must also be given to `unregister`.
`callback` gets called with an object that has an attribute per
sub_metric, which should be updated with the necessary values. Note that
the metrics object is shared between all callbacks registered with the
same key.
Note that `callback` may be called on a separate thread.
"""
with self._lock:
self._registrations.setdefault(key, set()).add(callback)
def unregister(
self,
key: Tuple[str, ...],
callback: Callable[[MetricsEntry], None],
) -> None:
"""Registers that we've exited a block with labels `key`."""
with self._lock:
self._registrations.setdefault(key, set()).discard(callback)
def collect(self) -> Iterable[Metric]:
"""Called by prometheus client when it reads metrics.
Note: may be called by a separate thread.
"""
in_flight = GaugeMetricFamily(
self.name + "_total", self.desc, labels=self.labels
)
metrics_by_key = {}
# We copy so that we don't mutate the list while iterating
with self._lock:
keys = list(self._registrations)
for key in keys:
with self._lock:
callbacks = set(self._registrations[key])
in_flight.add_metric(key, len(callbacks))
metrics = self._metrics_class()
metrics_by_key[key] = metrics
for callback in callbacks:
callback(metrics)
yield in_flight
for name in self.sub_metrics:
gauge = GaugeMetricFamily(
"_".join([self.name, name]), "", labels=self.labels
)
for key, metrics in metrics_by_key.items():
gauge.add_metric(key, getattr(metrics, name))
yield gauge
def _register_with_collector(self) -> None:
if self.name in all_gauges.keys():
logger.warning("%s already registered, reregistering" % (self.name,))
REGISTRY.unregister(all_gauges.pop(self.name))
REGISTRY.register(self)
all_gauges[self.name] = self
class GaugeBucketCollector(Collector):
"""Like a Histogram, but the buckets are Gauges which are updated atomically.
The data is updated by calling `update_data` with an iterable of measurements.
We assume that the data is updated less frequently than it is reported to
Prometheus, and optimise for that case.
"""
__slots__ = (
"_name",
"_documentation",
"_bucket_bounds",
"_metric",
)
def __init__(
self,
name: str,
documentation: str,
buckets: Iterable[float],
registry: CollectorRegistry = REGISTRY,
):
"""
Args:
name: base name of metric to be exported to Prometheus. (a _bucket suffix
will be added.)
documentation: help text for the metric
buckets: The top bounds of the buckets to report
registry: metric registry to register with
"""
self._name = name
self._documentation = documentation
# the tops of the buckets
self._bucket_bounds = [float(b) for b in buckets]
if self._bucket_bounds != sorted(self._bucket_bounds):
raise ValueError("Buckets not in sorted order")
if self._bucket_bounds[-1] != float("inf"):
self._bucket_bounds.append(float("inf"))
# We initially set this to None. We won't report metrics until
# this has been initialised after a successful data update
self._metric: Optional[GaugeHistogramMetricFamily] = None
registry.register(self)
def collect(self) -> Iterable[Metric]:
# Don't report metrics unless we've already collected some data
if self._metric is not None:
yield self._metric
def update_data(self, values: Iterable[float]) -> None:
"""Update the data to be reported by the metric
The existing data is cleared, and each measurement in the input is assigned
to the relevant bucket.
"""
self._metric = self._values_to_metric(values)
def _values_to_metric(self, values: Iterable[float]) -> GaugeHistogramMetricFamily:
total = 0.0
bucket_values = [0 for _ in self._bucket_bounds]
for v in values:
# assign each value to a bucket
for i, bound in enumerate(self._bucket_bounds):
if v <= bound:
bucket_values[i] += 1
break
# ... and increment the sum
total += v
# now, aggregate the bucket values so that they count the number of entries in
# that bucket or below.
accumulated_values = itertools.accumulate(bucket_values)
return GaugeHistogramMetricFamily(
self._name,
self._documentation,
buckets=list(
zip((str(b) for b in self._bucket_bounds), accumulated_values)
),
gsum_value=total,
)
#
# Detailed CPU metrics
#
class CPUMetrics(Collector):
def __init__(self) -> None:
ticks_per_sec = 100
try:
# Try and get the system config
ticks_per_sec = os.sysconf("SC_CLK_TCK")
except (ValueError, TypeError, AttributeError):
pass
self.ticks_per_sec = ticks_per_sec
def collect(self) -> Iterable[Metric]:
if not HAVE_PROC_SELF_STAT:
return
with open("/proc/self/stat") as s:
line = s.read()
raw_stats = line.split(") ", 1)[1].split(" ")
user = GaugeMetricFamily("process_cpu_user_seconds_total", "")
user.add_metric([], float(raw_stats[11]) / self.ticks_per_sec)
yield user
sys = GaugeMetricFamily("process_cpu_system_seconds_total", "")
sys.add_metric([], float(raw_stats[12]) / self.ticks_per_sec)
yield sys
REGISTRY.register(CPUMetrics())
#
# Federation Metrics
#
sent_transactions_counter = Counter("synapse_federation_client_sent_transactions", "")
events_processed_counter = Counter("synapse_federation_client_events_processed", "")
event_processing_loop_counter = Counter(
"synapse_event_processing_loop_count", "Event processing loop iterations", ["name"]
)
event_processing_loop_room_count = Counter(
"synapse_event_processing_loop_room_count",
"Rooms seen per event processing loop iteration",
["name"],
)
# Used to track where various components have processed in the event stream,
# e.g. federation sending, appservice sending, etc.
event_processing_positions = Gauge("synapse_event_processing_positions", "", ["name"])
# Used to track the current max events stream position
event_persisted_position = Gauge("synapse_event_persisted_position", "")
# Used to track the received_ts of the last event processed by various
# components
event_processing_last_ts = Gauge("synapse_event_processing_last_ts", "", ["name"])
# Used to track the lag processing events. This is the time difference
# between the last processed event's received_ts and the time it was
# finished being processed.
event_processing_lag = Gauge("synapse_event_processing_lag", "", ["name"])
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()]),
SYNAPSE_VERSION,
" ".join([platform.system(), platform.release()]),
).set(1)
# 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)
)
__all__ = [
"Collector",
"MetricsResource",
"generate_latest",
"LaterGauge",
"InFlightGauge",
"GaugeBucketCollector",
"MIN_TIME_BETWEEN_GCS",
"install_gc_manager",
]