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Remove unused @lru_cache
decorator (#13595)
* Remove unused `@lru_cache` decorator Spotted this working on something else. Co-authored-by: David Robertson <davidr@element.io>
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3 changed files with 5 additions and 140 deletions
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@ -12,7 +12,6 @@
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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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@ -146,109 +145,6 @@ class _CacheDescriptorBase:
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)
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class _LruCachedFunction(Generic[F]):
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cache: LruCache[CacheKey, Any]
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__call__: F
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def lru_cache(
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*, max_entries: int = 1000, 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callback mechanism to indicate that the result is no longer valid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: Callable[..., Any],
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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__(
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orig, num_args=None, uncached_args=None, cache_context=cache_context
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)
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self.max_entries = max_entries
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def __get__(self, obj: Optional[Any], owner: Optional[Type]) -> Callable[..., Any]:
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cache: LruCache[CacheKey, Any] = LruCache(
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cache_name=self.name,
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max_size=self.max_entries,
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)
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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: Any, **kwargs: Any) -> Any:
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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.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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