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#
# This file is licensed under the Affero General Public License (AGPL) version 3.
#
# Copyright (C) 2023 New Vector, Ltd
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# See the GNU Affero General Public License for more details:
# <https://www.gnu.org/licenses/agpl-3.0.html>.
#
# Originally licensed under the Apache License, Version 2.0:
# <http://www.apache.org/licenses/LICENSE-2.0>.
#
# [This file includes modifications made by New Vector Limited]
#
#
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
from synapse.metrics._types import Collector
"""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(Collector):
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(Collector):
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())
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