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Simplify GC prometheus metrics (#11723) 

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.
This commit is contained in:
Richard van der Hoff 2022-01-13 14:35:52 +00:00 committed by GitHub
parent 10a88ba91c
commit 20c6d85c6e
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4 changed files with 209 additions and 160 deletions
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1
changelog.d/11723.misc Normal file
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@ -0,0 +1 @@
Simplify calculation of prometheus metrics for garbage collection.

3
synapse/app/_base.py
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@ -60,7 +60,7 @@ from synapse.events.spamcheck import load_legacy_spam_checkers
from synapse.events.third_party_rules import load_legacy_third_party_event_rules
from synapse.handlers.auth import load_legacy_password_auth_providers
from synapse.logging.context import PreserveLoggingContext
from synapse.metrics import register_threadpool
from synapse.metrics import install_gc_manager, register_threadpool
from synapse.metrics.background_process_metrics import wrap_as_background_process
from synapse.metrics.jemalloc import setup_jemalloc_stats
from synapse.types import ISynapseReactor
@ -159,6 +159,7 @@ def start_reactor(
change_resource_limit(soft_file_limit)
if gc_thresholds:
gc.set_threshold(*gc_thresholds)
install_gc_manager()
run_command()
# make sure that we run the reactor with the sentinel log context,

162
synapse/metrics/__init__.py
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@ -13,7 +13,6 @@
# limitations under the License.
import functools
import gc
import itertools
import logging
import os
@ -41,7 +40,6 @@ import attr
from prometheus_client import CollectorRegistry, Counter, Gauge, Histogram, Metric
from prometheus_client.core import (
REGISTRY,
CounterMetricFamily,
GaugeHistogramMetricFamily,
GaugeMetricFamily,
)
@ -56,13 +54,13 @@ from synapse.metrics._exposition import (
generate_latest,
start_http_server,
)
from synapse.metrics._gc import MIN_TIME_BETWEEN_GCS, install_gc_manager
from synapse.util.versionstring import get_version_string
logger = logging.getLogger(__name__)
METRICS_PREFIX = "/_synapse/metrics"
running_on_pypy = platform.python_implementation() == "PyPy"
all_gauges: "Dict[str, Union[LaterGauge, InFlightGauge]]" = {}
HAVE_PROC_SELF_STAT = os.path.exists("/proc/self/stat")
@ -369,121 +367,6 @@ class CPUMetrics:
REGISTRY.register(CPUMetrics())
#
# 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
if not running_on_pypy:
REGISTRY.register(GCCounts())
#
# 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())
#
# Twisted reactor metrics
@ -612,14 +495,6 @@ class ReactorLastSeenMetric:
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])
@ -658,34 +533,6 @@ def runUntilCurrentTimer(reactor: ReactorBase, func: F) -> F:
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)
@ -701,11 +548,6 @@ try:
# 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
@ -717,4 +559,6 @@ __all__ = [
"LaterGauge",
"InFlightGauge",
"GaugeBucketCollector",
"MIN_TIME_BETWEEN_GCS",
"install_gc_manager",
]

203
synapse/metrics/_gc.py Normal file
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@ -0,0 +1,203 @@
# 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())