# coding=utf-8 # # This file is part of Hypothesis, which may be found at # https://github.com/HypothesisWorks/hypothesis-python # # Most of this work is copyright (C) 2013-2018 David R. MacIver # (david@drmaciver.com), but it contains contributions by others. See # CONTRIBUTING.rst for a full list of people who may hold copyright, and # consult the git log if you need to determine who owns an individual # contribution. # # This Source Code Form is subject to the terms of the Mozilla Public License, # v. 2.0. If a copy of the MPL was not distributed with this file, You can # obtain one at http://mozilla.org/MPL/2.0/. # # END HEADER from __future__ import division, print_function, absolute_import import attr @attr.s(slots=True) class Entry(object): key = attr.ib() value = attr.ib() score = attr.ib() class GenericCache(object): """Generic supertype for cache implementations. Defines a dict-like mapping with a maximum size, where as well as mapping to a value, each key also maps to a score. When a write would cause the dict to exceed its maximum size, it first evicts the existing key with the smallest score, then adds the new key to the map. A key has the following lifecycle: 1. key is written for the first time, the key is given the score self.new_entry(key, value) 2. whenever an existing key is read or written, self.on_access(key, value, score) is called. This returns a new score for the key. 3. When a key is evicted, self.on_evict(key, value, score) is called. The cache will be in a valid state in all of these cases. Implementations are expected to implement new_entry and optionally on_access and on_evict to implement a specific scoring strategy. """ __slots__ = ('keys_to_indices', 'data', 'max_size') def __init__(self, max_size): self.max_size = max_size # Implementation: We store a binary heap of Entry objects in self.data, # with the heap property requiring that a parent's score is <= that of # its children. keys_to_index then maps keys to their index in the # heap. We keep these two in sync automatically - the heap is never # reordered without updating the index. self.keys_to_indices = {} self.data = [] def __len__(self): assert len(self.keys_to_indices) == len(self.data) return len(self.data) def __getitem__(self, key): i = self.keys_to_indices[key] result = self.data[i] self.on_access(result.key, result.value, result.score) self.__balance(i) return result.value def __setitem__(self, key, value): if self.max_size == 0: return evicted = None try: i = self.keys_to_indices[key] except KeyError: entry = Entry(key, value, self.new_entry(key, value)) if len(self.data) >= self.max_size: evicted = self.data[0] del self.keys_to_indices[evicted.key] i = 0 self.data[0] = entry else: i = len(self.data) self.data.append(entry) self.keys_to_indices[key] = i else: entry = self.data[i] assert entry.key == key entry.value = value entry.score = self.on_access(entry.key, entry.value, entry.score) self.__balance(i) if evicted is not None: if self.data[0] is not entry: assert evicted.score <= self.data[0].score self.on_evict(evicted.key, evicted.value, evicted.score) def clear(self): del self.data[:] self.keys_to_indices.clear() def __repr__(self): return '{%s}' % (', '.join( '%r: %r' % (e.key, e.value) for e in self.data),) def new_entry(self, key, value): """Called when a key is written that does not currently appear in the map. Returns the score to associate with the key. """ raise NotImplementedError() def on_access(self, key, value, score): """Called every time a key that is already in the map is read or written. Returns the new score for the key. """ return score def on_evict(self, key, value, score): """Called after a key has been evicted, with the score it had had at the point of eviction.""" pass def check_valid(self): """Debugging method for use in tests. Asserts that all of the cache's invariants hold. When everything is working correctly this should be an expensive no-op. """ for i, e in enumerate(self.data): assert self.keys_to_indices[e.key] == i for j in [i * 2 + 1, i * 2 + 2]: if j < len(self.data): assert e.score <= self.data[j].score, self.data def __swap(self, i, j): assert i < j assert self.data[j].score < self.data[i].score self.data[i], self.data[j] = self.data[j], self.data[i] self.keys_to_indices[self.data[i].key] = i self.keys_to_indices[self.data[j].key] = j def __balance(self, i): """When we have made a modification to the heap such that means that the heap property has been violated locally around i but previously held for all other indexes (and no other values have been modified), this fixes the heap so that the heap property holds everywhere.""" while i > 0: parent = (i - 1) // 2 if self.__out_of_order(parent, i): self.__swap(parent, i) i = parent else: break while True: children = [ j for j in (2 * i + 1, 2 * i + 2) if j < len(self.data) ] if len(children) == 2: children.sort(key=lambda j: self.data[j].score) for j in children: if self.__out_of_order(i, j): self.__swap(i, j) i = j break else: break def __out_of_order(self, i, j): """Returns True if the indices i, j are in the wrong order. i must be the parent of j. """ assert i == (j - 1) // 2 return self.data[j].score < self.data[i].score class LRUReusedCache(GenericCache): """The only concrete implementation of GenericCache we use outside of tests currently. Adopts a modified least-frequently used eviction policy: It evicts the key that has been used least recently, but it will always preferentially evict keys that have only ever been accessed once. Among keys that have been accessed more than once, it ignores the number of accesses. This retains most of the benefits of an LRU cache, but adds an element of scan-resistance to the process: If we end up scanning through a large number of keys without reusing them, this does not evict the existing entries in preference for the new ones. """ __slots__ = ('__tick',) def __init__(self, max_size, ): super(LRUReusedCache, self).__init__(max_size) self.__tick = 0 def tick(self): self.__tick += 1 return self.__tick def new_entry(self, key, value): return [1, self.tick()] def on_access(self, key, value, score): score[0] = 2 score[1] = self.tick() return score