synapse-product/synapse/util/caches/expiringcache.py
Erik Johnston f85b6ca494 Speed up cache size calculation
Instead of calculating the size of the cache repeatedly, which can take
a long time now that it can use a callback, instead cache the size and
update that on insertion and deletion.

This requires changing the cache descriptors to have two caches, one for
pending deferreds and the other for the actual values. There's no reason
to evict from the pending deferreds as they won't take up any more
memory.
2017-01-17 11:18:13 +00:00

139 lines
4.2 KiB
Python

# -*- coding: utf-8 -*-
# Copyright 2015, 2016 OpenMarket Ltd
#
# 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.
from synapse.util.caches import register_cache
from collections import OrderedDict
import logging
logger = logging.getLogger(__name__)
class ExpiringCache(object):
def __init__(self, cache_name, clock, max_len=0, expiry_ms=0,
reset_expiry_on_get=False, iterable=False):
"""
Args:
cache_name (str): Name of this cache, used for logging.
clock (Clock)
max_len (int): Max size of dict. If the dict grows larger than this
then the oldest items get automatically evicted. Default is 0,
which indicates there is no max limit.
expiry_ms (int): How long before an item is evicted from the cache
in milliseconds. Default is 0, indicating items never get
evicted based on time.
reset_expiry_on_get (bool): If true, will reset the expiry time for
an item on access. Defaults to False.
iterable (bool): If true, the size is calculated by summing the
sizes of all entries, rather than the number of entries.
"""
self._cache_name = cache_name
self._clock = clock
self._max_len = max_len
self._expiry_ms = expiry_ms
self._reset_expiry_on_get = reset_expiry_on_get
self._cache = OrderedDict()
self.metrics = register_cache(cache_name, self)
self.iterable = iterable
self._size_estimate = 0
def start(self):
if not self._expiry_ms:
# Don't bother starting the loop if things never expire
return
def f():
self._prune_cache()
self._clock.looping_call(f, self._expiry_ms / 2)
def __setitem__(self, key, value):
now = self._clock.time_msec()
self._cache[key] = _CacheEntry(now, value)
if self.iterable:
self._size_estimate += len(value)
# Evict if there are now too many items
while self._max_len and len(self) > self._max_len:
_key, value = self._cache.popitem(last=False)
if self.iterable:
self._size_estimate -= len(value.value)
def __getitem__(self, key):
try:
entry = self._cache[key]
self.metrics.inc_hits()
except KeyError:
self.metrics.inc_misses()
raise
if self._reset_expiry_on_get:
entry.time = self._clock.time_msec()
return entry.value
def get(self, key, default=None):
try:
return self[key]
except KeyError:
return default
def _prune_cache(self):
if not self._expiry_ms:
# zero expiry time means don't expire. This should never get called
# since we have this check in start too.
return
begin_length = len(self)
now = self._clock.time_msec()
keys_to_delete = set()
for key, cache_entry in self._cache.items():
if now - cache_entry.time > self._expiry_ms:
keys_to_delete.add(key)
for k in keys_to_delete:
value = self._cache.pop(k)
if self.iterable:
self._size_estimate -= len(value.value)
logger.debug(
"[%s] _prune_cache before: %d, after len: %d",
self._cache_name, begin_length, len(self)
)
def __len__(self):
if self.iterable:
return self._size_estimate
else:
return len(self._cache)
class _CacheEntry(object):
def __init__(self, time, value):
self.time = time
self.value = value