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