264 lines
8.1 KiB
Python
264 lines
8.1 KiB
Python
# Copyright 2015, 2016 OpenMarket Ltd
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# Copyright 2019, 2020 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 collections
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import logging
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import typing
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from enum import Enum, auto
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from sys import intern
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from typing import Any, Callable, Dict, List, Optional, Sized, TypeVar
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import attr
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from prometheus_client import REGISTRY
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from prometheus_client.core import Gauge
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from synapse.config.cache import add_resizable_cache
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from synapse.util.metrics import DynamicCollectorRegistry
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logger = logging.getLogger(__name__)
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# Whether to track estimated memory usage of the LruCaches.
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TRACK_MEMORY_USAGE = False
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# We track cache metrics in a special registry that lets us update the metrics
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# just before they are returned from the scrape endpoint.
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CACHE_METRIC_REGISTRY = DynamicCollectorRegistry()
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caches_by_name: Dict[str, Sized] = {}
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cache_size = Gauge(
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"synapse_util_caches_cache_size", "", ["name"], registry=CACHE_METRIC_REGISTRY
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)
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cache_hits = Gauge(
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"synapse_util_caches_cache_hits", "", ["name"], registry=CACHE_METRIC_REGISTRY
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)
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cache_evicted = Gauge(
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"synapse_util_caches_cache_evicted_size",
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"",
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["name", "reason"],
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registry=CACHE_METRIC_REGISTRY,
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)
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cache_total = Gauge(
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"synapse_util_caches_cache", "", ["name"], registry=CACHE_METRIC_REGISTRY
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)
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cache_max_size = Gauge(
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"synapse_util_caches_cache_max_size", "", ["name"], registry=CACHE_METRIC_REGISTRY
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)
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cache_memory_usage = Gauge(
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"synapse_util_caches_cache_size_bytes",
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"Estimated memory usage of the caches",
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["name"],
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registry=CACHE_METRIC_REGISTRY,
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)
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response_cache_size = Gauge(
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"synapse_util_caches_response_cache_size",
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"",
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["name"],
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registry=CACHE_METRIC_REGISTRY,
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)
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response_cache_hits = Gauge(
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"synapse_util_caches_response_cache_hits",
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"",
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["name"],
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registry=CACHE_METRIC_REGISTRY,
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)
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response_cache_evicted = Gauge(
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"synapse_util_caches_response_cache_evicted_size",
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"",
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["name", "reason"],
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registry=CACHE_METRIC_REGISTRY,
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)
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response_cache_total = Gauge(
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"synapse_util_caches_response_cache", "", ["name"], registry=CACHE_METRIC_REGISTRY
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)
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# Register our custom cache metrics registry with the global registry
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REGISTRY.register(CACHE_METRIC_REGISTRY)
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class EvictionReason(Enum):
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size = auto()
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time = auto()
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invalidation = auto()
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@attr.s(slots=True, auto_attribs=True)
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class CacheMetric:
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_cache: Sized
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_cache_type: str
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_cache_name: str
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_collect_callback: Optional[Callable]
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hits: int = 0
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misses: int = 0
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eviction_size_by_reason: typing.Counter[EvictionReason] = attr.ib(
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factory=collections.Counter
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)
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memory_usage: Optional[int] = None
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def inc_hits(self) -> None:
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self.hits += 1
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def inc_misses(self) -> None:
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self.misses += 1
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def inc_evictions(self, reason: EvictionReason, size: int = 1) -> None:
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self.eviction_size_by_reason[reason] += size
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def inc_memory_usage(self, memory: int) -> None:
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if self.memory_usage is None:
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self.memory_usage = 0
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self.memory_usage += memory
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def dec_memory_usage(self, memory: int) -> None:
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assert self.memory_usage is not None
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self.memory_usage -= memory
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def clear_memory_usage(self) -> None:
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if self.memory_usage is not None:
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self.memory_usage = 0
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def describe(self) -> List[str]:
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return []
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def collect(self) -> None:
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try:
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if self._cache_type == "response_cache":
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response_cache_size.labels(self._cache_name).set(len(self._cache))
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response_cache_hits.labels(self._cache_name).set(self.hits)
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for reason in EvictionReason:
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response_cache_evicted.labels(self._cache_name, reason.name).set(
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self.eviction_size_by_reason[reason]
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)
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response_cache_total.labels(self._cache_name).set(
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self.hits + self.misses
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)
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else:
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cache_size.labels(self._cache_name).set(len(self._cache))
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cache_hits.labels(self._cache_name).set(self.hits)
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for reason in EvictionReason:
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cache_evicted.labels(self._cache_name, reason.name).set(
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self.eviction_size_by_reason[reason]
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)
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cache_total.labels(self._cache_name).set(self.hits + self.misses)
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max_size = getattr(self._cache, "max_size", None)
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if max_size:
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cache_max_size.labels(self._cache_name).set(max_size)
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if TRACK_MEMORY_USAGE:
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# self.memory_usage can be None if nothing has been inserted
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# into the cache yet.
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cache_memory_usage.labels(self._cache_name).set(
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self.memory_usage or 0
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)
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if self._collect_callback:
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self._collect_callback()
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except Exception as e:
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logger.warning("Error calculating metrics for %s: %s", self._cache_name, e)
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raise
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def register_cache(
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cache_type: str,
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cache_name: str,
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cache: Sized,
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collect_callback: Optional[Callable] = None,
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resizable: bool = True,
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resize_callback: Optional[Callable] = None,
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) -> CacheMetric:
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"""Register a cache object for metric collection and resizing.
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Args:
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cache_type: a string indicating the "type" of the cache. This is used
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only for deduplication so isn't too important provided it's constant.
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cache_name: name of the cache
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cache: cache itself, which must implement __len__(), and may optionally implement
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a max_size property
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collect_callback: If given, a function which is called during metric
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collection to update additional metrics.
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resizable: Whether this cache supports being resized, in which case either
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resize_callback must be provided, or the cache must support set_max_size().
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resize_callback: A function which can be called to resize the cache.
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Returns:
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CacheMetric: an object which provides inc_{hits,misses,evictions} methods
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"""
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if resizable:
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if not resize_callback:
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resize_callback = cache.set_cache_factor # type: ignore
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add_resizable_cache(cache_name, resize_callback)
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metric = CacheMetric(cache, cache_type, cache_name, collect_callback)
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metric_name = "cache_%s_%s" % (cache_type, cache_name)
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caches_by_name[cache_name] = cache
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CACHE_METRIC_REGISTRY.register_hook(metric_name, metric.collect)
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return metric
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KNOWN_KEYS = {
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key: key
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for key in (
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"auth_events",
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"content",
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"depth",
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"event_id",
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"hashes",
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"origin",
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"origin_server_ts",
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"prev_events",
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"room_id",
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"sender",
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"signatures",
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"state_key",
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"type",
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"unsigned",
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"user_id",
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)
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}
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T = TypeVar("T", Optional[str], str)
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def intern_string(string: T) -> T:
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"""Takes a (potentially) unicode string and interns it if it's ascii"""
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if string is None:
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return None
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try:
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return intern(string)
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except UnicodeEncodeError:
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return string
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def intern_dict(dictionary: Dict[str, Any]) -> Dict[str, Any]:
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"""Takes a dictionary and interns well known keys and their values"""
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return {
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KNOWN_KEYS.get(key, key): _intern_known_values(key, value)
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for key, value in dictionary.items()
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}
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def _intern_known_values(key: str, value: Any) -> Any:
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intern_keys = ("event_id", "room_id", "sender", "user_id", "type", "state_key")
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if key in intern_keys:
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return intern_string(value)
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return value
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