mirror of
https://mau.dev/maunium/synapse.git
synced 2024-08-23 18:56:10 +00:00
![Richard van der Hoff](/assets/img/avatar_default.png)
Rather than hooking into the reactor loop, just add a timed task that runs every 100 ms to do the garbage collection. Part 1 of a quest to simplify the reactor monkey-patching.
204 lines
6.9 KiB
Python
204 lines
6.9 KiB
Python
# Copyright 2015-2022 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 gc
|
|
import logging
|
|
import platform
|
|
import time
|
|
from typing import Iterable
|
|
|
|
from prometheus_client.core import (
|
|
REGISTRY,
|
|
CounterMetricFamily,
|
|
Gauge,
|
|
GaugeMetricFamily,
|
|
Histogram,
|
|
Metric,
|
|
)
|
|
|
|
from twisted.internet import task
|
|
|
|
"""Prometheus metrics for garbage collection"""
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
# 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)
|
|
|
|
running_on_pypy = platform.python_implementation() == "PyPy"
|
|
|
|
#
|
|
# Python GC metrics
|
|
#
|
|
|
|
gc_unreachable = Gauge("python_gc_unreachable_total", "Unreachable GC objects", ["gen"])
|
|
gc_time = Histogram(
|
|
"python_gc_time",
|
|
"Time taken to GC (sec)",
|
|
["gen"],
|
|
buckets=[
|
|
0.0025,
|
|
0.005,
|
|
0.01,
|
|
0.025,
|
|
0.05,
|
|
0.10,
|
|
0.25,
|
|
0.50,
|
|
1.00,
|
|
2.50,
|
|
5.00,
|
|
7.50,
|
|
15.00,
|
|
30.00,
|
|
45.00,
|
|
60.00,
|
|
],
|
|
)
|
|
|
|
|
|
class GCCounts:
|
|
def collect(self) -> Iterable[Metric]:
|
|
cm = GaugeMetricFamily("python_gc_counts", "GC object counts", labels=["gen"])
|
|
for n, m in enumerate(gc.get_count()):
|
|
cm.add_metric([str(n)], m)
|
|
|
|
yield cm
|
|
|
|
|
|
def install_gc_manager() -> None:
|
|
"""Disable automatic GC, and replace it with a task that runs every 100ms
|
|
|
|
This means that (a) we can limit how often GC runs; (b) we can get some metrics
|
|
about GC activity.
|
|
|
|
It does nothing on PyPy.
|
|
"""
|
|
|
|
if running_on_pypy:
|
|
return
|
|
|
|
REGISTRY.register(GCCounts())
|
|
|
|
gc.disable()
|
|
|
|
# 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]
|
|
|
|
def _maybe_gc() -> None:
|
|
# 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()
|
|
end = time.time()
|
|
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)
|
|
|
|
gc_task = task.LoopingCall(_maybe_gc)
|
|
gc_task.start(0.1)
|
|
|
|
|
|
#
|
|
# PyPy GC / memory metrics
|
|
#
|
|
|
|
|
|
class PyPyGCStats:
|
|
def collect(self) -> Iterable[Metric]:
|
|
|
|
# @stats is a pretty-printer object with __str__() returning a nice table,
|
|
# plus some fields that contain data from that table.
|
|
# unfortunately, fields are pretty-printed themselves (i. e. '4.5MB').
|
|
stats = gc.get_stats(memory_pressure=False) # type: ignore
|
|
# @s contains same fields as @stats, but as actual integers.
|
|
s = stats._s # type: ignore
|
|
|
|
# also note that field naming is completely braindead
|
|
# and only vaguely correlates with the pretty-printed table.
|
|
# >>>> gc.get_stats(False)
|
|
# Total memory consumed:
|
|
# GC used: 8.7MB (peak: 39.0MB) # s.total_gc_memory, s.peak_memory
|
|
# in arenas: 3.0MB # s.total_arena_memory
|
|
# rawmalloced: 1.7MB # s.total_rawmalloced_memory
|
|
# nursery: 4.0MB # s.nursery_size
|
|
# raw assembler used: 31.0kB # s.jit_backend_used
|
|
# -----------------------------
|
|
# Total: 8.8MB # stats.memory_used_sum
|
|
#
|
|
# Total memory allocated:
|
|
# GC allocated: 38.7MB (peak: 41.1MB) # s.total_allocated_memory, s.peak_allocated_memory
|
|
# in arenas: 30.9MB # s.peak_arena_memory
|
|
# rawmalloced: 4.1MB # s.peak_rawmalloced_memory
|
|
# nursery: 4.0MB # s.nursery_size
|
|
# raw assembler allocated: 1.0MB # s.jit_backend_allocated
|
|
# -----------------------------
|
|
# Total: 39.7MB # stats.memory_allocated_sum
|
|
#
|
|
# Total time spent in GC: 0.073 # s.total_gc_time
|
|
|
|
pypy_gc_time = CounterMetricFamily(
|
|
"pypy_gc_time_seconds_total",
|
|
"Total time spent in PyPy GC",
|
|
labels=[],
|
|
)
|
|
pypy_gc_time.add_metric([], s.total_gc_time / 1000)
|
|
yield pypy_gc_time
|
|
|
|
pypy_mem = GaugeMetricFamily(
|
|
"pypy_memory_bytes",
|
|
"Memory tracked by PyPy allocator",
|
|
labels=["state", "class", "kind"],
|
|
)
|
|
# memory used by JIT assembler
|
|
pypy_mem.add_metric(["used", "", "jit"], s.jit_backend_used)
|
|
pypy_mem.add_metric(["allocated", "", "jit"], s.jit_backend_allocated)
|
|
# memory used by GCed objects
|
|
pypy_mem.add_metric(["used", "", "arenas"], s.total_arena_memory)
|
|
pypy_mem.add_metric(["allocated", "", "arenas"], s.peak_arena_memory)
|
|
pypy_mem.add_metric(["used", "", "rawmalloced"], s.total_rawmalloced_memory)
|
|
pypy_mem.add_metric(["allocated", "", "rawmalloced"], s.peak_rawmalloced_memory)
|
|
pypy_mem.add_metric(["used", "", "nursery"], s.nursery_size)
|
|
pypy_mem.add_metric(["allocated", "", "nursery"], s.nursery_size)
|
|
# totals
|
|
pypy_mem.add_metric(["used", "totals", "gc"], s.total_gc_memory)
|
|
pypy_mem.add_metric(["allocated", "totals", "gc"], s.total_allocated_memory)
|
|
pypy_mem.add_metric(["used", "totals", "gc_peak"], s.peak_memory)
|
|
pypy_mem.add_metric(["allocated", "totals", "gc_peak"], s.peak_allocated_memory)
|
|
yield pypy_mem
|
|
|
|
|
|
if running_on_pypy:
|
|
REGISTRY.register(PyPyGCStats())
|