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.

1 files changed, 203 insertions, 0 deletions

diff --git a/synapse/metrics/_gc.py b/synapse/metrics/_gc.py
new file mode 100644
index 0000000000..2bc909efa0
--- /dev/null
+++ b/synapse/metrics/_gc.py
@@ -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())