483 lines
15 KiB
Python
483 lines
15 KiB
Python
# Copyright 2015, 2016 OpenMarket Ltd
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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 threading
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from functools import wraps
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from typing import (
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Any,
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Callable,
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Collection,
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Generic,
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Iterable,
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List,
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Optional,
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Type,
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TypeVar,
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Union,
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cast,
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overload,
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)
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from typing_extensions import Literal
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from synapse.config import cache as cache_config
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from synapse.util import caches
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from synapse.util.caches import CacheMetric, register_cache
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from synapse.util.caches.treecache import TreeCache, iterate_tree_cache_entry
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try:
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from pympler.asizeof import Asizer
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def _get_size_of(val: Any, *, recurse=True) -> int:
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"""Get an estimate of the size in bytes of the object.
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Args:
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val: The object to size.
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recurse: If true will include referenced values in the size,
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otherwise only sizes the given object.
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"""
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# Ignore singleton values when calculating memory usage.
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if val in ((), None, ""):
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return 0
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sizer = Asizer()
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sizer.exclude_refs((), None, "")
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return sizer.asizeof(val, limit=100 if recurse else 0)
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except ImportError:
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def _get_size_of(val: Any, *, recurse=True) -> int:
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return 0
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# Function type: the type used for invalidation callbacks
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FT = TypeVar("FT", bound=Callable[..., Any])
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# Key and Value type for the cache
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KT = TypeVar("KT")
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VT = TypeVar("VT")
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# a general type var, distinct from either KT or VT
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T = TypeVar("T")
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def enumerate_leaves(node, depth):
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if depth == 0:
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yield node
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else:
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for n in node.values():
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for m in enumerate_leaves(n, depth - 1):
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yield m
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class _Node:
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__slots__ = ["prev_node", "next_node", "key", "value", "callbacks", "memory"]
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def __init__(
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self,
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prev_node,
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next_node,
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key,
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value,
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callbacks: Collection[Callable[[], None]] = (),
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):
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self.prev_node = prev_node
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self.next_node = next_node
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self.key = key
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self.value = value
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# Set of callbacks to run when the node gets deleted. We store as a list
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# rather than a set to keep memory usage down (and since we expect few
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# entries per node, the performance of checking for duplication in a
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# list vs using a set is negligible).
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#
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# Note that we store this as an optional list to keep the memory
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# footprint down. Storing `None` is free as its a singleton, while empty
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# lists are 56 bytes (and empty sets are 216 bytes, if we did the naive
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# thing and used sets).
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self.callbacks = None # type: Optional[List[Callable[[], None]]]
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self.add_callbacks(callbacks)
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self.memory = 0
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if caches.TRACK_MEMORY_USAGE:
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self.memory = (
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_get_size_of(key)
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+ _get_size_of(value)
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+ _get_size_of(self.callbacks, recurse=False)
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+ _get_size_of(self, recurse=False)
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)
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self.memory += _get_size_of(self.memory, recurse=False)
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def add_callbacks(self, callbacks: Collection[Callable[[], None]]) -> None:
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"""Add to stored list of callbacks, removing duplicates."""
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if not callbacks:
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return
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if not self.callbacks:
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self.callbacks = []
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for callback in callbacks:
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if callback not in self.callbacks:
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self.callbacks.append(callback)
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def run_and_clear_callbacks(self) -> None:
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"""Run all callbacks and clear the stored list of callbacks. Used when
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the node is being deleted.
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"""
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if not self.callbacks:
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return
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for callback in self.callbacks:
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callback()
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self.callbacks = None
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class LruCache(Generic[KT, VT]):
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"""
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Least-recently-used cache, supporting prometheus metrics and invalidation callbacks.
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Supports del_multi only if cache_type=TreeCache
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If cache_type=TreeCache, all keys must be tuples.
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"""
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def __init__(
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self,
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max_size: int,
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cache_name: Optional[str] = None,
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cache_type: Type[Union[dict, TreeCache]] = dict,
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size_callback: Optional[Callable] = None,
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metrics_collection_callback: Optional[Callable[[], None]] = None,
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apply_cache_factor_from_config: bool = True,
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):
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"""
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Args:
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max_size: The maximum amount of entries the cache can hold
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cache_name: The name of this cache, for the prometheus metrics. If unset,
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no metrics will be reported on this cache.
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cache_type (type):
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type of underlying cache to be used. Typically one of dict
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or TreeCache.
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size_callback (func(V) -> int | None):
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metrics_collection_callback:
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metrics collection callback. This is called early in the metrics
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collection process, before any of the metrics registered with the
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prometheus Registry are collected, so can be used to update any dynamic
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metrics.
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Ignored if cache_name is None.
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apply_cache_factor_from_config (bool): If true, `max_size` will be
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multiplied by a cache factor derived from the homeserver config
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"""
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cache = cache_type()
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self.cache = cache # Used for introspection.
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self.apply_cache_factor_from_config = apply_cache_factor_from_config
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# Save the original max size, and apply the default size factor.
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self._original_max_size = max_size
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# We previously didn't apply the cache factor here, and as such some caches were
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# not affected by the global cache factor. Add an option here to disable applying
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# the cache factor when a cache is created
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if apply_cache_factor_from_config:
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self.max_size = int(max_size * cache_config.properties.default_factor_size)
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else:
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self.max_size = int(max_size)
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# register_cache might call our "set_cache_factor" callback; there's nothing to
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# do yet when we get resized.
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self._on_resize = None # type: Optional[Callable[[],None]]
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if cache_name is not None:
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metrics = register_cache(
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"lru_cache",
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cache_name,
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self,
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collect_callback=metrics_collection_callback,
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) # type: Optional[CacheMetric]
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else:
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metrics = None
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# this is exposed for access from outside this class
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self.metrics = metrics
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list_root = _Node(None, None, None, None)
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list_root.next_node = list_root
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list_root.prev_node = list_root
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lock = threading.Lock()
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def evict():
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while cache_len() > self.max_size:
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todelete = list_root.prev_node
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evicted_len = delete_node(todelete)
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cache.pop(todelete.key, None)
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if metrics:
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metrics.inc_evictions(evicted_len)
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def synchronized(f: FT) -> FT:
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@wraps(f)
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def inner(*args, **kwargs):
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with lock:
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return f(*args, **kwargs)
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return cast(FT, inner)
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cached_cache_len = [0]
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if size_callback is not None:
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def cache_len():
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return cached_cache_len[0]
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else:
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def cache_len():
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return len(cache)
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self.len = synchronized(cache_len)
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def add_node(key, value, callbacks: Collection[Callable[[], None]] = ()):
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prev_node = list_root
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next_node = prev_node.next_node
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node = _Node(prev_node, next_node, key, value, callbacks)
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prev_node.next_node = node
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next_node.prev_node = node
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cache[key] = node
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if size_callback:
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cached_cache_len[0] += size_callback(node.value)
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if caches.TRACK_MEMORY_USAGE and metrics:
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metrics.inc_memory_usage(node.memory)
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def move_node_to_front(node):
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prev_node = node.prev_node
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next_node = node.next_node
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prev_node.next_node = next_node
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next_node.prev_node = prev_node
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prev_node = list_root
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next_node = prev_node.next_node
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node.prev_node = prev_node
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node.next_node = next_node
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prev_node.next_node = node
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next_node.prev_node = node
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def delete_node(node):
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prev_node = node.prev_node
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next_node = node.next_node
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prev_node.next_node = next_node
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next_node.prev_node = prev_node
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deleted_len = 1
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if size_callback:
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deleted_len = size_callback(node.value)
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cached_cache_len[0] -= deleted_len
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node.run_and_clear_callbacks()
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if caches.TRACK_MEMORY_USAGE and metrics:
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metrics.dec_memory_usage(node.memory)
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return deleted_len
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@overload
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def cache_get(
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key: KT,
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default: Literal[None] = None,
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callbacks: Collection[Callable[[], None]] = ...,
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update_metrics: bool = ...,
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) -> Optional[VT]:
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...
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@overload
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def cache_get(
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key: KT,
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default: T,
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callbacks: Collection[Callable[[], None]] = ...,
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update_metrics: bool = ...,
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) -> Union[T, VT]:
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...
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@synchronized
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def cache_get(
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key: KT,
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default: Optional[T] = None,
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callbacks: Collection[Callable[[], None]] = (),
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update_metrics: bool = True,
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):
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node = cache.get(key, None)
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if node is not None:
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move_node_to_front(node)
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node.add_callbacks(callbacks)
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if update_metrics and metrics:
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metrics.inc_hits()
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return node.value
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else:
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if update_metrics and metrics:
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metrics.inc_misses()
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return default
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@synchronized
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def cache_set(key: KT, value: VT, callbacks: Iterable[Callable[[], None]] = ()):
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node = cache.get(key, None)
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if node is not None:
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# We sometimes store large objects, e.g. dicts, which cause
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# the inequality check to take a long time. So let's only do
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# the check if we have some callbacks to call.
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if value != node.value:
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node.run_and_clear_callbacks()
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# We don't bother to protect this by value != node.value as
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# generally size_callback will be cheap compared with equality
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# checks. (For example, taking the size of two dicts is quicker
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# than comparing them for equality.)
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if size_callback:
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cached_cache_len[0] -= size_callback(node.value)
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cached_cache_len[0] += size_callback(value)
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node.add_callbacks(callbacks)
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move_node_to_front(node)
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node.value = value
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else:
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add_node(key, value, set(callbacks))
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evict()
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@synchronized
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def cache_set_default(key: KT, value: VT) -> VT:
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node = cache.get(key, None)
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if node is not None:
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return node.value
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else:
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add_node(key, value)
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evict()
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return value
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@overload
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def cache_pop(key: KT, default: Literal[None] = None) -> Optional[VT]:
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...
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@overload
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def cache_pop(key: KT, default: T) -> Union[T, VT]:
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...
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@synchronized
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def cache_pop(key: KT, default: Optional[T] = None):
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node = cache.get(key, None)
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if node:
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delete_node(node)
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cache.pop(node.key, None)
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return node.value
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else:
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return default
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@synchronized
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def cache_del_multi(key: KT) -> None:
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"""
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This will only work if constructed with cache_type=TreeCache
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"""
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popped = cache.pop(key)
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if popped is None:
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return
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# for each deleted node, we now need to remove it from the linked list
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# and run its callbacks.
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for leaf in iterate_tree_cache_entry(popped):
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delete_node(leaf)
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@synchronized
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def cache_clear() -> None:
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list_root.next_node = list_root
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list_root.prev_node = list_root
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for node in cache.values():
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node.run_and_clear_callbacks()
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cache.clear()
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if size_callback:
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cached_cache_len[0] = 0
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if caches.TRACK_MEMORY_USAGE and metrics:
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metrics.clear_memory_usage()
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@synchronized
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def cache_contains(key: KT) -> bool:
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return key in cache
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self.sentinel = object()
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# make sure that we clear out any excess entries after we get resized.
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self._on_resize = evict
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self.get = cache_get
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self.set = cache_set
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self.setdefault = cache_set_default
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self.pop = cache_pop
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# `invalidate` is exposed for consistency with DeferredCache, so that it can be
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# invalidated by the cache invalidation replication stream.
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self.invalidate = cache_pop
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if cache_type is TreeCache:
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self.del_multi = cache_del_multi
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self.len = synchronized(cache_len)
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self.contains = cache_contains
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self.clear = cache_clear
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def __getitem__(self, key):
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result = self.get(key, self.sentinel)
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if result is self.sentinel:
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raise KeyError()
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else:
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return result
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def __setitem__(self, key, value):
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self.set(key, value)
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def __delitem__(self, key, value):
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result = self.pop(key, self.sentinel)
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if result is self.sentinel:
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raise KeyError()
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def __len__(self):
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return self.len()
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def __contains__(self, key):
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return self.contains(key)
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def set_cache_factor(self, factor: float) -> bool:
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"""
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Set the cache factor for this individual cache.
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This will trigger a resize if it changes, which may require evicting
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items from the cache.
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Returns:
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bool: Whether the cache changed size or not.
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"""
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if not self.apply_cache_factor_from_config:
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return False
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new_size = int(self._original_max_size * factor)
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if new_size != self.max_size:
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self.max_size = new_size
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if self._on_resize:
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self._on_resize()
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return True
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return False
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