// 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 com.fr.third.jodd.cache; import java.util.HashMap; import java.util.Iterator; /** * LFU (least frequently used) cache. Frequency is calculated as access count. This cache * is resistant on 'new usages scenario': when some object is removed from the cache, * access count of all items in cache is decreased by access count of removed value. * This allows new frequent elements to come into the cache. *

* Frequency of use data is kept on all items. The most frequently used items are kept in the cache. * Because of the bookkeeping requirements, cache access overhead increases logarithmically with cache size. * The advantage is that long term usage patterns are captured well, incidentally making the algorithm scan resistant; * the disadvantage, besides the larger access overhead, is that the algorithm doesn't adapt quickly to changing * usage patterns, and in particular doesn't help with temporally clustered accesses. *

* Summary for LFU: not fast, captures frequency of use, scan resistant. */ public class LFUCache extends AbstractCacheMap { public LFUCache(int maxSize) { this(maxSize, 0); } public LFUCache(int maxSize, long timeout) { this.cacheSize = maxSize; this.timeout = timeout; cacheMap = new HashMap>(maxSize + 1); } // ---------------------------------------------------------------- prune /** * Prunes expired and, if cache is still full, the LFU element(s) from the cache. * On LFU removal, access count is normalized to value which had removed object. * Returns the number of removed objects. */ @Override protected int pruneCache() { int count = 0; CacheObject comin = null; // remove expired items and find cached object with minimal access count Iterator> values = cacheMap.values().iterator(); while (values.hasNext()) { CacheObject co = values.next(); if (co.isExpired()) { values.remove(); onRemove(co.key, co.cachedObject); count++; continue; } if (comin == null) { comin = co; } else { if (co.accessCount < comin.accessCount) { comin = co; } } } if (!isFull()) { return count; } // decrease access count to all cached objects if (comin != null) { long minAccessCount = comin.accessCount; values = cacheMap.values().iterator(); while (values.hasNext()) { CacheObject co = values.next(); co.accessCount -= minAccessCount; if (co.accessCount <= 0) { values.remove(); onRemove(co.key, co.cachedObject); count++; } } } return count; } /** * Callback method invoked on cached object removal. * By default does nothing. */ protected void onRemove(K key, V cachedObject) { } }