LRU缓存介绍与实现 (Java)

LRU缓存：

LRU缓存利用了这样的一种思想。LRU是Least Recently Used 的缩写，翻译过来就是“最近最少使用”，也就是说，LRU缓存把最近最少使用的数据移除，让给最新读取的数据。而往往最常读取的，也是读取次数最多的，所以，利用LRU缓存，我们能够提高系统的performance.

实现：

要实现LRU缓存，我们首先要用到一个类 LinkedHashMap。用这个类有两大好处：一是它本身已经实现了按照访问顺序的存储，也就是说，最近读取的会放在最前面，最最不常读取的会放在最后（当然，它也可以实现按照插入顺序存储）。第二，LinkedHashMap本身有一个方法用于判断是否需要移除最不常读取的数，但是，原始方法默认不需要移除（这是，LinkedHashMap相当于一个linkedlist），所以，我们需要override这样一个方法，使得当缓存里存放的数据个数超过规定个数后，就把最不常用的移除掉。LinkedHashMap的API写得很清楚，推荐大家可以先读一下。

 1 import java.util.LinkedHashMap;  
 2 import java.util.Collection;  
 3 import java.util.Map;  
 4 import java.util.ArrayList;  
 5   
 6 /** 
 7 * An LRU cache, based on <code>LinkedHashMap</code>. 
 8 * 
 9 * <p> 
10 * This cache has a fixed maximum number of elements (<code>cacheSize</code>). 
11 * If the cache is full and another entry is added, the LRU (least recently used) entry is dropped. 
12 * 
13 * <p> 
14 * This class is thread-safe. All methods of this class are synchronized. 
15 * 
16 * <p> 
17 * Author: Christian d'Heureuse, Inventec Informatik AG, Zurich, Switzerland<br> 
18 * Multi-licensed: EPL / LGPL / GPL / AL / BSD. 
19 */  
20 public class LRUCache<K,V> {  
21   
22 private static final float   hashTableLoadFactor = 0.75f;  
23   
24 private LinkedHashMap<K,V>   map;  
25 private int                  cacheSize;  
26   
27 /** 
28 * Creates a new LRU cache. 
29 * @param cacheSize the maximum number of entries that will be kept in this cache. 
30 */  
31 public LRUCache (int cacheSize) {  
32    this.cacheSize = cacheSize;  
33    int hashTableCapacity = (int)Math.ceil(cacheSize / hashTableLoadFactor) + 1;  
34    map = new LinkedHashMap<K,V>(hashTableCapacity, hashTableLoadFactor, true) {  
35       // (an anonymous inner class)  
36       private static final long serialVersionUID = 1;  
37       @Override protected boolean removeEldestEntry (Map.Entry<K,V> eldest) {  
38          return size() > LRUCache.this.cacheSize; }}; }  
39   
40 /** 
41 * Retrieves an entry from the cache.<br> 
42 * The retrieved entry becomes the MRU (most recently used) entry. 
43 * @param key the key whose associated value is to be returned. 
44 * @return    the value associated to this key, or null if no value with this key exists in the cache. 
45 */  
46 public synchronized V get (K key) {  
47    return map.get(key); }  
48   
49 /** 
50 * Adds an entry to this cache. 
51 * The new entry becomes the MRU (most recently used) entry. 
52 * If an entry with the specified key already exists in the cache, it is replaced by the new entry. 
53 * If the cache is full, the LRU (least recently used) entry is removed from the cache. 
54 * @param key    the key with which the specified value is to be associated. 
55 * @param value  a value to be associated with the specified key. 
56 */  
57 public synchronized void put (K key, V value) {  
58    map.put (key, value); }  
59   
60 /** 
61 * Clears the cache. 
62 */  
63 public synchronized void clear() {  
64    map.clear(); }  
65   
66 /** 
67 * Returns the number of used entries in the cache. 
68 * @return the number of entries currently in the cache. 
69 */  
70 public synchronized int usedEntries() {  
71    return map.size(); }  
72   
73 /** 
74 * Returns a <code>Collection</code> that contains a copy of all cache entries. 
75 * @return a <code>Collection</code> with a copy of the cache content. 
76 */  
77 public synchronized Collection<Map.Entry<K,V>> getAll() {  
78    return new ArrayList<Map.Entry<K,V>>(map.entrySet()); }  
79   
80 } // end class LRUCache  
81 ------------------------------------------------------------------------------------------  
82 // Test routine for the LRUCache class.  
83 public static void main (String[] args) {  
84    LRUCache<String,String> c = new LRUCache<String, String>(3);  
85    c.put ("1", "one");                           // 1  
86    c.put ("2", "two");                           // 2 1  
87    c.put ("3", "three");                         // 3 2 1  
88    c.put ("4", "four");                          // 4 3 2  
89    if (c.get("2") == null) throw new Error();    // 2 4 3  
90    c.put ("5", "five");                          // 5 2 4  
91    c.put ("4", "second four");                   // 4 5 2  
92    // Verify cache content.  
93    if (c.usedEntries() != 3)              throw new Error();  
94    if (!c.get("4").equals("second four")) throw new Error();  
95    if (!c.get("5").equals("five"))        throw new Error();  
96    if (!c.get("2").equals("two"))         throw new Error();  
97    // List cache content.  
98    for (Map.Entry<String, String> e : c.getAll())  
99       System.out.println (e.getKey() + " : " + e.getValue()); }

代码出自：http://www.source-code.biz/snippets/java/6.htm

在博客 http://gogole.iteye.com/blog/692103 里，作者使用的是双链表 + hashtable 的方式实现的。如果在面试题里考到如何实现LRU，考官一般会要求使用双链表 + hashtable 的方式。所以，我把原文的部分内容摘抄如下：

双链表 + hashtable实现原理：

将Cache的所有位置都用双连表连接起来，当一个位置被命中之后，就将通过调整链表的指向，将该位置调整到链表头的位置，新加入的Cache直接加到链表头中。这样，在多次进行Cache操作后，最近被命中的，就会被向链表头方向移动，而没有命中的，而想链表后面移动，链表尾则表示最近最少使用的Cache。当需要替换内容时候，链表的最后位置就是最少被命中的位置，我们只需要淘汰链表最后的部分即可。

  1 public class LRUCache {  
  2       
  3     private int cacheSize;  
  4     private Hashtable<Object, Entry> nodes;//缓存容器  
  5     private int currentSize;  
  6     private Entry first;//链表头  
  7     private Entry last;//链表尾  
  8       
  9     public LRUCache(int i) {  
 10         currentSize = 0;  
 11         cacheSize = i;  
 12         nodes = new Hashtable<Object, Entry>(i);//缓存容器  
 13     }  
 14       
 15     /** 
 16      * 获取缓存中对象,并把它放在最前面 
 17      */  
 18     public Entry get(Object key) {  
 19         Entry node = nodes.get(key);  
 20         if (node != null) {  
 21             moveToHead(node);  
 22             return node;  
 23         } else {  
 24             return null;  
 25         }  
 26     }  
 27       
 28     /** 
 29      * 添加 entry到hashtable, 并把entry  
 30      */  
 31     public void put(Object key, Object value) {  
 32         //先查看hashtable是否存在该entry, 如果存在，则只更新其value  
 33         Entry node = nodes.get(key);  
 34           
 35         if (node == null) {  
 36             //缓存容器是否已经超过大小.  
 37             if (currentSize >= cacheSize) {  
 38                 nodes.remove(last.key);  
 39                 removeLast();  
 40             } else {  
 41                 currentSize++;  
 42             }             
 43             node = new Entry();  
 44         }  
 45         node.value = value;  
 46         //将最新使用的节点放到链表头，表示最新使用的.  
 47         moveToHead(node);  
 48         nodes.put(key, node);  
 49     }  
 50   
 51     /** 
 52      * 将entry删除, 注意：删除操作只有在cache满了才会被执行 
 53      */  
 54     public void remove(Object key) {  
 55         Entry node = nodes.get(key);  
 56         //在链表中删除  
 57         if (node != null) {  
 58             if (node.prev != null) {  
 59                 node.prev.next = node.next;  
 60             }  
 61             if (node.next != null) {  
 62                 node.next.prev = node.prev;  
 63             }  
 64             if (last == node)  
 65                 last = node.prev;  
 66             if (first == node)  
 67                 first = node.next;  
 68         }  
 69         //在hashtable中删除  
 70         nodes.remove(key);  
 71     }  
 72   
 73     /** 
 74      * 删除链表尾部节点，即使用最后 使用的entry 
 75      */  
 76     private void removeLast() {  
 77         //链表尾不为空,则将链表尾指向null. 删除连表尾（删除最少使用的缓存对象）  
 78         if (last != null) {  
 79             if (last.prev != null)  
 80                 last.prev.next = null;  
 81             else  
 82                 first = null;  
 83             last = last.prev;  
 84         }  
 85     }  
 86       
 87     /** 
 88      * 移动到链表头，表示这个节点是最新使用过的 
 89      */  
 90     private void moveToHead(Entry node) {  
 91         if (node == first)  
 92             return;  
 93         if (node.prev != null)  
 94             node.prev.next = node.next;  
 95         if (node.next != null)  
 96             node.next.prev = node.prev;  
 97         if (last == node)  
 98             last = node.prev;  
 99         if (first != null) {  
100             node.next = first;  
101             first.prev = node;  
102         }  
103         first = node;  
104         node.prev = null;  
105         if (last == null)  
106             last = first;  
107     }  
108     /* 
109      * 清空缓存 
110      */  
111     public void clear() {  
112         first = null;  
113         last = null;  
114         currentSize = 0;  
115     }  
116   
117 }  
118   
119 class Entry {  
120     Entry prev;//前一节点  
121     Entry next;//后一节点  
122     Object value;//值  
123     Object key;//键  
124 }

转自：http://blog.csdn.net/beiyeqingteng/article/details/7010411