205 lines
7.0 KiB
Python
205 lines
7.0 KiB
Python
# Copyright 2015-2022 The Matrix.org Foundation C.I.C.
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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 gc
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import logging
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import platform
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import time
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from typing import Iterable
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from prometheus_client.core import (
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REGISTRY,
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CounterMetricFamily,
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Gauge,
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GaugeMetricFamily,
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Histogram,
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Metric,
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)
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from twisted.internet import task
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from synapse.metrics._types import Collector
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"""Prometheus metrics for garbage collection"""
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logger = logging.getLogger(__name__)
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# The minimum time in seconds between GCs for each generation, regardless of the current GC
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# thresholds and counts.
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MIN_TIME_BETWEEN_GCS = (1.0, 10.0, 30.0)
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running_on_pypy = platform.python_implementation() == "PyPy"
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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(Collector):
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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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def install_gc_manager() -> None:
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"""Disable automatic GC, and replace it with a task that runs every 100ms
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This means that (a) we can limit how often GC runs; (b) we can get some metrics
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about GC activity.
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It does nothing on PyPy.
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"""
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if running_on_pypy:
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return
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REGISTRY.register(GCCounts())
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gc.disable()
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# The time (in seconds since the epoch) of the last time we did a GC for each generation.
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_last_gc = [0.0, 0.0, 0.0]
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def _maybe_gc() -> None:
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# Check if we need to do a manual GC (since its been disabled), and do
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# one if necessary. Note we go in reverse order as e.g. a gen 1 GC may
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# promote an object into gen 2, and we don't want to handle the same
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# object multiple times.
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threshold = gc.get_threshold()
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counts = gc.get_count()
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end = time.time()
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for i in (2, 1, 0):
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# We check if we need to do one based on a straightforward
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# comparison between the threshold and count. We also do an extra
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# check to make sure that we don't a GC too often.
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if threshold[i] < counts[i] and MIN_TIME_BETWEEN_GCS[i] < end - _last_gc[i]:
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if i == 0:
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logger.debug("Collecting gc %d", i)
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else:
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logger.info("Collecting gc %d", i)
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start = time.time()
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unreachable = gc.collect(i)
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end = time.time()
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_last_gc[i] = end
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gc_time.labels(i).observe(end - start)
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gc_unreachable.labels(i).set(unreachable)
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gc_task = task.LoopingCall(_maybe_gc)
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gc_task.start(0.1)
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#
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# PyPy GC / memory metrics
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#
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class PyPyGCStats(Collector):
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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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