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- Update black version to the latest
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619 lines
21 KiB
Python
619 lines
21 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright 2015, 2016 OpenMarket Ltd
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# Copyright 2018 New Vector 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 enum
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import functools
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import inspect
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import logging
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from typing import (
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Any,
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Callable,
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Generic,
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Iterable,
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Mapping,
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Optional,
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Sequence,
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Tuple,
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TypeVar,
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Union,
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cast,
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)
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from weakref import WeakValueDictionary
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from twisted.internet import defer
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from synapse.logging.context import make_deferred_yieldable, preserve_fn
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from synapse.util import unwrapFirstError
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from synapse.util.caches.deferred_cache import DeferredCache
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from synapse.util.caches.lrucache import LruCache
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logger = logging.getLogger(__name__)
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CacheKey = Union[Tuple, Any]
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F = TypeVar("F", bound=Callable[..., Any])
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class _CachedFunction(Generic[F]):
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invalidate = None # type: Any
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invalidate_all = None # type: Any
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invalidate_many = None # type: Any
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prefill = None # type: Any
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cache = None # type: Any
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num_args = None # type: Any
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__name__ = None # type: str
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# Note: This function signature is actually fiddled with by the synapse mypy
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# plugin to a) make it a bound method, and b) remove any `cache_context` arg.
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__call__ = None # type: F
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class _CacheDescriptorBase:
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def __init__(self, orig: Callable[..., Any], num_args, cache_context=False):
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self.orig = orig
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arg_spec = inspect.getfullargspec(orig)
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all_args = arg_spec.args
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if "cache_context" in all_args:
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if not cache_context:
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raise ValueError(
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"Cannot have a 'cache_context' arg without setting"
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" cache_context=True"
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)
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elif cache_context:
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raise ValueError(
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"Cannot have cache_context=True without having an arg"
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" named `cache_context`"
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)
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if num_args is None:
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num_args = len(all_args) - 1
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if cache_context:
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num_args -= 1
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if len(all_args) < num_args + 1:
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raise Exception(
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"Not enough explicit positional arguments to key off for %r: "
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"got %i args, but wanted %i. (@cached cannot key off *args or "
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"**kwargs)" % (orig.__name__, len(all_args), num_args)
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)
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self.num_args = num_args
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# list of the names of the args used as the cache key
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self.arg_names = all_args[1 : num_args + 1]
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# self.arg_defaults is a map of arg name to its default value for each
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# argument that has a default value
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if arg_spec.defaults:
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self.arg_defaults = dict(
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zip(all_args[-len(arg_spec.defaults) :], arg_spec.defaults)
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)
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else:
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self.arg_defaults = {}
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if "cache_context" in self.arg_names:
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raise Exception("cache_context arg cannot be included among the cache keys")
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self.add_cache_context = cache_context
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self.cache_key_builder = get_cache_key_builder(
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self.arg_names, self.arg_defaults
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)
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class _LruCachedFunction(Generic[F]):
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cache = None # type: LruCache[CacheKey, Any]
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__call__ = None # type: F
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def lru_cache(
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max_entries: int = 1000,
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cache_context: bool = False,
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) -> Callable[[F], _LruCachedFunction[F]]:
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"""A method decorator that applies a memoizing cache around the function.
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This is more-or-less a drop-in equivalent to functools.lru_cache, although note
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that the signature is slightly different.
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The main differences with functools.lru_cache are:
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(a) the size of the cache can be controlled via the cache_factor mechanism
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(b) the wrapped function can request a "cache_context" which provides a
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callback mechanism to indicate that the result is no longer valid
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(c) prometheus metrics are exposed automatically.
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The function should take zero or more arguments, which are used as the key for the
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cache. Single-argument functions use that argument as the cache key; otherwise the
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arguments are built into a tuple.
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Cached functions can be "chained" (i.e. a cached function can call other cached
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functions and get appropriately invalidated when they called caches are
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invalidated) by adding a special "cache_context" argument to the function
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and passing that as a kwarg to all caches called. For example:
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@lru_cache(cache_context=True)
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def foo(self, key, cache_context):
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r1 = self.bar1(key, on_invalidate=cache_context.invalidate)
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r2 = self.bar2(key, on_invalidate=cache_context.invalidate)
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return r1 + r2
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The wrapped function also has a 'cache' property which offers direct access to the
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underlying LruCache.
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"""
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def func(orig: F) -> _LruCachedFunction[F]:
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desc = LruCacheDescriptor(
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orig,
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max_entries=max_entries,
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cache_context=cache_context,
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)
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return cast(_LruCachedFunction[F], desc)
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return func
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class LruCacheDescriptor(_CacheDescriptorBase):
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"""Helper for @lru_cache"""
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class _Sentinel(enum.Enum):
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sentinel = object()
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def __init__(
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self,
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orig,
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max_entries: int = 1000,
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cache_context: bool = False,
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):
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super().__init__(orig, num_args=None, cache_context=cache_context)
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self.max_entries = max_entries
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def __get__(self, obj, owner):
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cache = LruCache(
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cache_name=self.orig.__name__,
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max_size=self.max_entries,
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) # type: LruCache[CacheKey, Any]
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get_cache_key = self.cache_key_builder
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sentinel = LruCacheDescriptor._Sentinel.sentinel
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@functools.wraps(self.orig)
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def _wrapped(*args, **kwargs):
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invalidate_callback = kwargs.pop("on_invalidate", None)
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callbacks = (invalidate_callback,) if invalidate_callback else ()
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cache_key = get_cache_key(args, kwargs)
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ret = cache.get(cache_key, default=sentinel, callbacks=callbacks)
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if ret != sentinel:
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return ret
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# Add our own `cache_context` to argument list if the wrapped function
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# has asked for one
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if self.add_cache_context:
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kwargs["cache_context"] = _CacheContext.get_instance(cache, cache_key)
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ret2 = self.orig(obj, *args, **kwargs)
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cache.set(cache_key, ret2, callbacks=callbacks)
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return ret2
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wrapped = cast(_CachedFunction, _wrapped)
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wrapped.cache = cache
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obj.__dict__[self.orig.__name__] = wrapped
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return wrapped
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class DeferredCacheDescriptor(_CacheDescriptorBase):
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"""A method decorator that applies a memoizing cache around the function.
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This caches deferreds, rather than the results themselves. Deferreds that
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fail are removed from the cache.
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The function is presumed to take zero or more arguments, which are used in
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a tuple as the key for the cache. Hits are served directly from the cache;
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misses use the function body to generate the value.
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The wrapped function has an additional member, a callable called
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"invalidate". This can be used to remove individual entries from the cache.
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The wrapped function has another additional callable, called "prefill",
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which can be used to insert values into the cache specifically, without
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calling the calculation function.
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Cached functions can be "chained" (i.e. a cached function can call other cached
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functions and get appropriately invalidated when they called caches are
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invalidated) by adding a special "cache_context" argument to the function
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and passing that as a kwarg to all caches called. For example::
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@cached(cache_context=True)
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def foo(self, key, cache_context):
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r1 = yield self.bar1(key, on_invalidate=cache_context.invalidate)
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r2 = yield self.bar2(key, on_invalidate=cache_context.invalidate)
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return r1 + r2
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Args:
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num_args (int): number of positional arguments (excluding ``self`` and
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``cache_context``) to use as cache keys. Defaults to all named
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args of the function.
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"""
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def __init__(
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self,
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orig,
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max_entries=1000,
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num_args=None,
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tree=False,
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cache_context=False,
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iterable=False,
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):
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super().__init__(orig, num_args=num_args, cache_context=cache_context)
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self.max_entries = max_entries
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self.tree = tree
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self.iterable = iterable
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def __get__(self, obj, owner):
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cache = DeferredCache(
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name=self.orig.__name__,
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max_entries=self.max_entries,
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keylen=self.num_args,
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tree=self.tree,
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iterable=self.iterable,
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) # type: DeferredCache[CacheKey, Any]
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get_cache_key = self.cache_key_builder
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@functools.wraps(self.orig)
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def _wrapped(*args, **kwargs):
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# If we're passed a cache_context then we'll want to call its invalidate()
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# whenever we are invalidated
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invalidate_callback = kwargs.pop("on_invalidate", None)
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cache_key = get_cache_key(args, kwargs)
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try:
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ret = cache.get(cache_key, callback=invalidate_callback)
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except KeyError:
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# Add our own `cache_context` to argument list if the wrapped function
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# has asked for one
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if self.add_cache_context:
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kwargs["cache_context"] = _CacheContext.get_instance(
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cache, cache_key
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)
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ret = defer.maybeDeferred(preserve_fn(self.orig), obj, *args, **kwargs)
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ret = cache.set(cache_key, ret, callback=invalidate_callback)
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return make_deferred_yieldable(ret)
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wrapped = cast(_CachedFunction, _wrapped)
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if self.num_args == 1:
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wrapped.invalidate = lambda key: cache.invalidate(key[0])
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wrapped.prefill = lambda key, val: cache.prefill(key[0], val)
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else:
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wrapped.invalidate = cache.invalidate
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wrapped.invalidate_many = cache.invalidate_many
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wrapped.prefill = cache.prefill
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wrapped.invalidate_all = cache.invalidate_all
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wrapped.cache = cache
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wrapped.num_args = self.num_args
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obj.__dict__[self.orig.__name__] = wrapped
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return wrapped
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class DeferredCacheListDescriptor(_CacheDescriptorBase):
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"""Wraps an existing cache to support bulk fetching of keys.
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Given a list of keys it looks in the cache to find any hits, then passes
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the list of missing keys to the wrapped function.
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Once wrapped, the function returns a Deferred which resolves to the list
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of results.
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"""
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def __init__(self, orig, cached_method_name, list_name, num_args=None):
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"""
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Args:
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orig (function)
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cached_method_name (str): The name of the cached method.
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list_name (str): Name of the argument which is the bulk lookup list
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num_args (int): number of positional arguments (excluding ``self``,
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but including list_name) to use as cache keys. Defaults to all
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named args of the function.
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"""
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super().__init__(orig, num_args=num_args)
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self.list_name = list_name
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self.list_pos = self.arg_names.index(self.list_name)
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self.cached_method_name = cached_method_name
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self.sentinel = object()
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if self.list_name not in self.arg_names:
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raise Exception(
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"Couldn't see arguments %r for %r."
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% (self.list_name, cached_method_name)
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)
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def __get__(self, obj, objtype=None):
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cached_method = getattr(obj, self.cached_method_name)
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cache = cached_method.cache # type: DeferredCache[CacheKey, Any]
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num_args = cached_method.num_args
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@functools.wraps(self.orig)
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def wrapped(*args, **kwargs):
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# If we're passed a cache_context then we'll want to call its
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# invalidate() whenever we are invalidated
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invalidate_callback = kwargs.pop("on_invalidate", None)
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arg_dict = inspect.getcallargs(self.orig, obj, *args, **kwargs)
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keyargs = [arg_dict[arg_nm] for arg_nm in self.arg_names]
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list_args = arg_dict[self.list_name]
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results = {}
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def update_results_dict(res, arg):
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results[arg] = res
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# list of deferreds to wait for
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cached_defers = []
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missing = set()
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# If the cache takes a single arg then that is used as the key,
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# otherwise a tuple is used.
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if num_args == 1:
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def arg_to_cache_key(arg):
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return arg
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else:
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keylist = list(keyargs)
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def arg_to_cache_key(arg):
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keylist[self.list_pos] = arg
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return tuple(keylist)
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for arg in list_args:
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try:
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res = cache.get(arg_to_cache_key(arg), callback=invalidate_callback)
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if not res.called:
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res.addCallback(update_results_dict, arg)
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cached_defers.append(res)
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else:
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results[arg] = res.result
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except KeyError:
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missing.add(arg)
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if missing:
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# we need a deferred for each entry in the list,
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# which we put in the cache. Each deferred resolves with the
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# relevant result for that key.
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deferreds_map = {}
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for arg in missing:
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deferred = defer.Deferred()
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deferreds_map[arg] = deferred
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key = arg_to_cache_key(arg)
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cache.set(key, deferred, callback=invalidate_callback)
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def complete_all(res):
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# the wrapped function has completed. It returns a
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# a dict. We can now resolve the observable deferreds in
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# the cache and update our own result map.
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for e in missing:
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val = res.get(e, None)
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deferreds_map[e].callback(val)
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results[e] = val
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def errback(f):
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# the wrapped function has failed. Invalidate any cache
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# entries we're supposed to be populating, and fail
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# their deferreds.
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for e in missing:
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key = arg_to_cache_key(e)
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cache.invalidate(key)
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deferreds_map[e].errback(f)
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# return the failure, to propagate to our caller.
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return f
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args_to_call = dict(arg_dict)
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args_to_call[self.list_name] = list(missing)
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cached_defers.append(
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defer.maybeDeferred(
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preserve_fn(self.orig), **args_to_call
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).addCallbacks(complete_all, errback)
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)
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if cached_defers:
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d = defer.gatherResults(cached_defers, consumeErrors=True).addCallbacks(
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lambda _: results, unwrapFirstError
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)
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return make_deferred_yieldable(d)
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else:
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return defer.succeed(results)
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obj.__dict__[self.orig.__name__] = wrapped
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return wrapped
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class _CacheContext:
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"""Holds cache information from the cached function higher in the calling order.
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Can be used to invalidate the higher level cache entry if something changes
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on a lower level.
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"""
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Cache = Union[DeferredCache, LruCache]
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_cache_context_objects = (
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WeakValueDictionary()
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) # type: WeakValueDictionary[Tuple[_CacheContext.Cache, CacheKey], _CacheContext]
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def __init__(self, cache: "_CacheContext.Cache", cache_key: CacheKey) -> None:
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self._cache = cache
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self._cache_key = cache_key
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def invalidate(self): # type: () -> None
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"""Invalidates the cache entry referred to by the context."""
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self._cache.invalidate(self._cache_key)
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@classmethod
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def get_instance(
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cls, cache: "_CacheContext.Cache", cache_key: CacheKey
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) -> "_CacheContext":
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"""Returns an instance constructed with the given arguments.
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A new instance is only created if none already exists.
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"""
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# We make sure there are no identical _CacheContext instances. This is
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# important in particular to dedupe when we add callbacks to lru cache
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# nodes, otherwise the number of callbacks would grow.
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return cls._cache_context_objects.setdefault(
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(cache, cache_key), cls(cache, cache_key)
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)
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def cached(
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max_entries: int = 1000,
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num_args: Optional[int] = None,
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tree: bool = False,
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cache_context: bool = False,
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iterable: bool = False,
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) -> Callable[[F], _CachedFunction[F]]:
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func = lambda orig: DeferredCacheDescriptor(
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orig,
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max_entries=max_entries,
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num_args=num_args,
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tree=tree,
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cache_context=cache_context,
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iterable=iterable,
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)
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return cast(Callable[[F], _CachedFunction[F]], func)
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|
|
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def cachedList(
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cached_method_name: str, list_name: str, num_args: Optional[int] = None
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) -> Callable[[F], _CachedFunction[F]]:
|
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"""Creates a descriptor that wraps a function in a `CacheListDescriptor`.
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|
|
|
Used to do batch lookups for an already created cache. A single argument
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is specified as a list that is iterated through to lookup keys in the
|
|
original cache. A new list consisting of the keys that weren't in the cache
|
|
get passed to the original function, the result of which is stored in the
|
|
cache.
|
|
|
|
Args:
|
|
cached_method_name: The name of the single-item lookup method.
|
|
This is only used to find the cache to use.
|
|
list_name: The name of the argument that is the list to use to
|
|
do batch lookups in the cache.
|
|
num_args: Number of arguments to use as the key in the cache
|
|
(including list_name). Defaults to all named parameters.
|
|
|
|
Example:
|
|
|
|
class Example:
|
|
@cached(num_args=2)
|
|
def do_something(self, first_arg):
|
|
...
|
|
|
|
@cachedList(do_something.cache, list_name="second_args", num_args=2)
|
|
def batch_do_something(self, first_arg, second_args):
|
|
...
|
|
"""
|
|
func = lambda orig: DeferredCacheListDescriptor(
|
|
orig,
|
|
cached_method_name=cached_method_name,
|
|
list_name=list_name,
|
|
num_args=num_args,
|
|
)
|
|
|
|
return cast(Callable[[F], _CachedFunction[F]], func)
|
|
|
|
|
|
def get_cache_key_builder(
|
|
param_names: Sequence[str], param_defaults: Mapping[str, Any]
|
|
) -> Callable[[Sequence[Any], Mapping[str, Any]], CacheKey]:
|
|
"""Construct a function which will build cache keys suitable for a cached function
|
|
|
|
Args:
|
|
param_names: list of formal parameter names for the cached function
|
|
param_defaults: a mapping from parameter name to default value for that param
|
|
|
|
Returns:
|
|
A function which will take an (args, kwargs) pair and return a cache key
|
|
"""
|
|
|
|
# By default our cache key is a tuple, but if there is only one item
|
|
# then don't bother wrapping in a tuple. This is to save memory.
|
|
|
|
if len(param_names) == 1:
|
|
nm = param_names[0]
|
|
|
|
def get_cache_key(args: Sequence[Any], kwargs: Mapping[str, Any]) -> CacheKey:
|
|
if nm in kwargs:
|
|
return kwargs[nm]
|
|
elif len(args):
|
|
return args[0]
|
|
else:
|
|
return param_defaults[nm]
|
|
|
|
else:
|
|
|
|
def get_cache_key(args: Sequence[Any], kwargs: Mapping[str, Any]) -> CacheKey:
|
|
return tuple(_get_cache_key_gen(param_names, param_defaults, args, kwargs))
|
|
|
|
return get_cache_key
|
|
|
|
|
|
def _get_cache_key_gen(
|
|
param_names: Iterable[str],
|
|
param_defaults: Mapping[str, Any],
|
|
args: Sequence[Any],
|
|
kwargs: Mapping[str, Any],
|
|
) -> Iterable[Any]:
|
|
"""Given some args/kwargs return a generator that resolves into
|
|
the cache_key.
|
|
|
|
This is essentially the same operation as `inspect.getcallargs`, but optimised so
|
|
that we don't need to inspect the target function for each call.
|
|
"""
|
|
|
|
# We loop through each arg name, looking up if its in the `kwargs`,
|
|
# otherwise using the next argument in `args`. If there are no more
|
|
# args then we try looking the arg name up in the defaults.
|
|
pos = 0
|
|
for nm in param_names:
|
|
if nm in kwargs:
|
|
yield kwargs[nm]
|
|
elif pos < len(args):
|
|
yield args[pos]
|
|
pos += 1
|
|
else:
|
|
yield param_defaults[nm]
|