// Copyright (c) 2003-present, Jodd Team (http://jodd.org) // All rights reserved. // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // 1. Redistributions of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // 2. Redistributions in binary form must reproduce the above copyright // notice, this list of conditions and the following disclaimer in the // documentation and/or other materials provided with the distribution. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. package jodd.cache; import java.util.LinkedHashMap; import java.util.Map; import java.util.Iterator; /** * LRU (least recently used) cache. * * <p> * Items are added to the cache as they are accessed; when the cache is full, the least recently used item is ejected. * This type of cache is typically implemented as a linked list, so that an item in cache, when it is accessed again, * can be moved back up to the head of the queue; items are ejected from the tail of the queue. Cache access overhead * is again constant time. This algorithm is simple and fast, and it has a significant advantage over FIFO in being * able to adapt somewhat to the data access pattern; frequently used items are less likely to be * ejected from the cache. The main disadvantage is that it can still get filled up with items that are * unlikely to be reaccessed soon; in particular, it can become useless in the face of scanning type accesses. * Nonetheless, this is by far the most frequently used caching algorithm. * <p> * Implementation note: unfortunately, it was not possible to have <code>onRemove</code> callback method, * since <code>LinkedHashMap</code> has its removal methods private. * <p> * Summary for LRU: fast, adaptive, not scan resistant. */ public class LRUCache<K, V> extends AbstractCacheMap<K, V> { public LRUCache(int cacheSize) { this(cacheSize, 0); } /** * Creates a new LRU cache. */ public LRUCache(int cacheSize, long timeout) { this.cacheSize = cacheSize; this.timeout = timeout; cacheMap = new LinkedHashMap<K, CacheObject<K,V>>(cacheSize + 1, 1.0f, true) { @Override protected boolean removeEldestEntry(Map.Entry eldest) { return LRUCache.this.removeEldestEntry(size()); } }; } /** * Removes the eldest entry if current cache size exceed cache size. */ protected boolean removeEldestEntry(int currentSize) { if (cacheSize == 0) { return false; } return currentSize > cacheSize; } // ---------------------------------------------------------------- prune /** * Prune only expired objects, <code>LinkedHashMap</code> will take care of LRU if needed. */ @Override protected int pruneCache() { if (!isPruneExpiredActive()) { return 0; } int count = 0; Iterator<CacheObject<K,V>> values = cacheMap.values().iterator(); while (values.hasNext()) { CacheObject<K,V> co = values.next(); if (co.isExpired()) { values.remove(); onRemove(co.key, co.cachedObject); count++; } } return count; } }