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// 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.
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// 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.
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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package jodd.cache;

import java.util.LinkedHashMap;
import java.util.Map;
import java.util.Iterator;


/**
 * LRU (least recently used) cache.
 *
 * 

* 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. *

* Implementation note: unfortunately, it was not possible to have onRemove callback method, * since LinkedHashMap has its removal methods private. *

* Summary for LRU: fast, adaptive, not scan resistant. */ public class LRUCache extends AbstractCacheMap { public LRUCache(final int cacheSize) { this(cacheSize, 0); } /** * Creates a new LRU cache. */ public LRUCache(final int cacheSize, final long timeout) { this.cacheSize = cacheSize; this.timeout = timeout; cacheMap = new LinkedHashMap>(cacheSize + 1, 1.0f, true) { @Override protected boolean removeEldestEntry(final Map.Entry eldest) { return LRUCache.this.removeEldestEntry(size()); } }; } /** * Removes the eldest entry if current cache size exceed cache size. */ protected boolean removeEldestEntry(final int currentSize) { if (cacheSize == 0) { return false; } return currentSize > cacheSize; } // ---------------------------------------------------------------- prune /** * Prune only expired objects, LinkedHashMap will take care of LRU if needed. */ @Override protected int pruneCache() { if (!isPruneExpiredActive()) { return 0; } int count = 0; Iterator> values = cacheMap.values().iterator(); while (values.hasNext()) { CacheObject co = values.next(); if (co.isExpired()) { values.remove(); onRemove(co.key, co.cachedObject); count++; } } return count; } }





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