synapse-old/synapse/util/caches/lrucache.py

483 lines
15 KiB
Python

# 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.
import threading
from functools import wraps
from typing import (
Any,
Callable,
Collection,
Generic,
Iterable,
List,
Optional,
Type,
TypeVar,
Union,
cast,
overload,
)
from typing_extensions import Literal
from synapse.config import cache as cache_config
from synapse.util import caches
from synapse.util.caches import CacheMetric, register_cache
from synapse.util.caches.treecache import TreeCache, iterate_tree_cache_entry
try:
from pympler.asizeof import Asizer
def _get_size_of(val: Any, *, recurse=True) -> int:
"""Get an estimate of the size in bytes of the object.
Args:
val: The object to size.
recurse: If true will include referenced values in the size,
otherwise only sizes the given object.
"""
# Ignore singleton values when calculating memory usage.
if val in ((), None, ""):
return 0
sizer = Asizer()
sizer.exclude_refs((), None, "")
return sizer.asizeof(val, limit=100 if recurse else 0)
except ImportError:
def _get_size_of(val: Any, *, recurse=True) -> int:
return 0
# Function type: the type used for invalidation callbacks
FT = TypeVar("FT", bound=Callable[..., Any])
# Key and Value type for the cache
KT = TypeVar("KT")
VT = TypeVar("VT")
# a general type var, distinct from either KT or VT
T = TypeVar("T")
def enumerate_leaves(node, depth):
if depth == 0:
yield node
else:
for n in node.values():
for m in enumerate_leaves(n, depth - 1):
yield m
class _Node:
__slots__ = ["prev_node", "next_node", "key", "value", "callbacks", "memory"]
def __init__(
self,
prev_node,
next_node,
key,
value,
callbacks: Collection[Callable[[], None]] = (),
):
self.prev_node = prev_node
self.next_node = next_node
self.key = key
self.value = value
# Set of callbacks to run when the node gets deleted. We store as a list
# rather than a set to keep memory usage down (and since we expect few
# entries per node, the performance of checking for duplication in a
# list vs using a set is negligible).
#
# Note that we store this as an optional list to keep the memory
# footprint down. Storing `None` is free as its a singleton, while empty
# lists are 56 bytes (and empty sets are 216 bytes, if we did the naive
# thing and used sets).
self.callbacks = None # type: Optional[List[Callable[[], None]]]
self.add_callbacks(callbacks)
self.memory = 0
if caches.TRACK_MEMORY_USAGE:
self.memory = (
_get_size_of(key)
+ _get_size_of(value)
+ _get_size_of(self.callbacks, recurse=False)
+ _get_size_of(self, recurse=False)
)
self.memory += _get_size_of(self.memory, recurse=False)
def add_callbacks(self, callbacks: Collection[Callable[[], None]]) -> None:
"""Add to stored list of callbacks, removing duplicates."""
if not callbacks:
return
if not self.callbacks:
self.callbacks = []
for callback in callbacks:
if callback not in self.callbacks:
self.callbacks.append(callback)
def run_and_clear_callbacks(self) -> None:
"""Run all callbacks and clear the stored list of callbacks. Used when
the node is being deleted.
"""
if not self.callbacks:
return
for callback in self.callbacks:
callback()
self.callbacks = None
class LruCache(Generic[KT, VT]):
"""
Least-recently-used cache, supporting prometheus metrics and invalidation callbacks.
Supports del_multi only if cache_type=TreeCache
If cache_type=TreeCache, all keys must be tuples.
"""
def __init__(
self,
max_size: int,
cache_name: Optional[str] = None,
cache_type: Type[Union[dict, TreeCache]] = dict,
size_callback: Optional[Callable] = None,
metrics_collection_callback: Optional[Callable[[], None]] = None,
apply_cache_factor_from_config: bool = True,
):
"""
Args:
max_size: The maximum amount of entries the cache can hold
cache_name: The name of this cache, for the prometheus metrics. If unset,
no metrics will be reported on this cache.
cache_type (type):
type of underlying cache to be used. Typically one of dict
or TreeCache.
size_callback (func(V) -> int | None):
metrics_collection_callback:
metrics collection callback. This is called early in the metrics
collection process, before any of the metrics registered with the
prometheus Registry are collected, so can be used to update any dynamic
metrics.
Ignored if cache_name is None.
apply_cache_factor_from_config (bool): If true, `max_size` will be
multiplied by a cache factor derived from the homeserver config
"""
cache = cache_type()
self.cache = cache # Used for introspection.
self.apply_cache_factor_from_config = apply_cache_factor_from_config
# Save the original max size, and apply the default size factor.
self._original_max_size = max_size
# We previously didn't apply the cache factor here, and as such some caches were
# not affected by the global cache factor. Add an option here to disable applying
# the cache factor when a cache is created
if apply_cache_factor_from_config:
self.max_size = int(max_size * cache_config.properties.default_factor_size)
else:
self.max_size = int(max_size)
# register_cache might call our "set_cache_factor" callback; there's nothing to
# do yet when we get resized.
self._on_resize = None # type: Optional[Callable[[],None]]
if cache_name is not None:
metrics = register_cache(
"lru_cache",
cache_name,
self,
collect_callback=metrics_collection_callback,
) # type: Optional[CacheMetric]
else:
metrics = None
# this is exposed for access from outside this class
self.metrics = metrics
list_root = _Node(None, None, None, None)
list_root.next_node = list_root
list_root.prev_node = list_root
lock = threading.Lock()
def evict():
while cache_len() > self.max_size:
todelete = list_root.prev_node
evicted_len = delete_node(todelete)
cache.pop(todelete.key, None)
if metrics:
metrics.inc_evictions(evicted_len)
def synchronized(f: FT) -> FT:
@wraps(f)
def inner(*args, **kwargs):
with lock:
return f(*args, **kwargs)
return cast(FT, inner)
cached_cache_len = [0]
if size_callback is not None:
def cache_len():
return cached_cache_len[0]
else:
def cache_len():
return len(cache)
self.len = synchronized(cache_len)
def add_node(key, value, callbacks: Collection[Callable[[], None]] = ()):
prev_node = list_root
next_node = prev_node.next_node
node = _Node(prev_node, next_node, key, value, callbacks)
prev_node.next_node = node
next_node.prev_node = node
cache[key] = node
if size_callback:
cached_cache_len[0] += size_callback(node.value)
if caches.TRACK_MEMORY_USAGE and metrics:
metrics.inc_memory_usage(node.memory)
def move_node_to_front(node):
prev_node = node.prev_node
next_node = node.next_node
prev_node.next_node = next_node
next_node.prev_node = prev_node
prev_node = list_root
next_node = prev_node.next_node
node.prev_node = prev_node
node.next_node = next_node
prev_node.next_node = node
next_node.prev_node = node
def delete_node(node):
prev_node = node.prev_node
next_node = node.next_node
prev_node.next_node = next_node
next_node.prev_node = prev_node
deleted_len = 1
if size_callback:
deleted_len = size_callback(node.value)
cached_cache_len[0] -= deleted_len
node.run_and_clear_callbacks()
if caches.TRACK_MEMORY_USAGE and metrics:
metrics.dec_memory_usage(node.memory)
return deleted_len
@overload
def cache_get(
key: KT,
default: Literal[None] = None,
callbacks: Collection[Callable[[], None]] = ...,
update_metrics: bool = ...,
) -> Optional[VT]:
...
@overload
def cache_get(
key: KT,
default: T,
callbacks: Collection[Callable[[], None]] = ...,
update_metrics: bool = ...,
) -> Union[T, VT]:
...
@synchronized
def cache_get(
key: KT,
default: Optional[T] = None,
callbacks: Collection[Callable[[], None]] = (),
update_metrics: bool = True,
):
node = cache.get(key, None)
if node is not None:
move_node_to_front(node)
node.add_callbacks(callbacks)
if update_metrics and metrics:
metrics.inc_hits()
return node.value
else:
if update_metrics and metrics:
metrics.inc_misses()
return default
@synchronized
def cache_set(key: KT, value: VT, callbacks: Iterable[Callable[[], None]] = ()):
node = cache.get(key, None)
if node is not None:
# We sometimes store large objects, e.g. dicts, which cause
# the inequality check to take a long time. So let's only do
# the check if we have some callbacks to call.
if value != node.value:
node.run_and_clear_callbacks()
# We don't bother to protect this by value != node.value as
# generally size_callback will be cheap compared with equality
# checks. (For example, taking the size of two dicts is quicker
# than comparing them for equality.)
if size_callback:
cached_cache_len[0] -= size_callback(node.value)
cached_cache_len[0] += size_callback(value)
node.add_callbacks(callbacks)
move_node_to_front(node)
node.value = value
else:
add_node(key, value, set(callbacks))
evict()
@synchronized
def cache_set_default(key: KT, value: VT) -> VT:
node = cache.get(key, None)
if node is not None:
return node.value
else:
add_node(key, value)
evict()
return value
@overload
def cache_pop(key: KT, default: Literal[None] = None) -> Optional[VT]:
...
@overload
def cache_pop(key: KT, default: T) -> Union[T, VT]:
...
@synchronized
def cache_pop(key: KT, default: Optional[T] = None):
node = cache.get(key, None)
if node:
delete_node(node)
cache.pop(node.key, None)
return node.value
else:
return default
@synchronized
def cache_del_multi(key: KT) -> None:
"""
This will only work if constructed with cache_type=TreeCache
"""
popped = cache.pop(key)
if popped is None:
return
# for each deleted node, we now need to remove it from the linked list
# and run its callbacks.
for leaf in iterate_tree_cache_entry(popped):
delete_node(leaf)
@synchronized
def cache_clear() -> None:
list_root.next_node = list_root
list_root.prev_node = list_root
for node in cache.values():
node.run_and_clear_callbacks()
cache.clear()
if size_callback:
cached_cache_len[0] = 0
if caches.TRACK_MEMORY_USAGE and metrics:
metrics.clear_memory_usage()
@synchronized
def cache_contains(key: KT) -> bool:
return key in cache
self.sentinel = object()
# make sure that we clear out any excess entries after we get resized.
self._on_resize = evict
self.get = cache_get
self.set = cache_set
self.setdefault = cache_set_default
self.pop = cache_pop
# `invalidate` is exposed for consistency with DeferredCache, so that it can be
# invalidated by the cache invalidation replication stream.
self.invalidate = cache_pop
if cache_type is TreeCache:
self.del_multi = cache_del_multi
self.len = synchronized(cache_len)
self.contains = cache_contains
self.clear = cache_clear
def __getitem__(self, key):
result = self.get(key, self.sentinel)
if result is self.sentinel:
raise KeyError()
else:
return result
def __setitem__(self, key, value):
self.set(key, value)
def __delitem__(self, key, value):
result = self.pop(key, self.sentinel)
if result is self.sentinel:
raise KeyError()
def __len__(self):
return self.len()
def __contains__(self, key):
return self.contains(key)
def set_cache_factor(self, factor: float) -> bool:
"""
Set the cache factor for this individual cache.
This will trigger a resize if it changes, which may require evicting
items from the cache.
Returns:
bool: Whether the cache changed size or not.
"""
if not self.apply_cache_factor_from_config:
return False
new_size = int(self._original_max_size * factor)
if new_size != self.max_size:
self.max_size = new_size
if self._on_resize:
self._on_resize()
return True
return False