缓存淘汰算法--LRU算法(java代码实现)

LRU 
原理 
LRU（Least recently used，最近最少使用）算法根据数据的历史访问记录来进行淘汰数据，其核心思想是“如果数据最近被访问过，那么将来被访问的几率也更高”。

实现1 
最常见的实现是使用一个链表保存缓存数据，详细算法实现如下： 
 
1. 新数据插入到链表头部； 
2. 每当缓存命中（即缓存数据被访问），则将数据移到链表头部； 
3. 当链表满的时候，将链表尾部的数据丢弃。 
分析 
【命中率】 
当存在热点数据时，LRU的效率很好，但偶发性的、周期性的批量操作会导致LRU命中率急剧下降，缓存污染情况比较严重。 
【复杂度】 
实现简单。 
【代价】 
命中时需要遍历链表，找到命中的数据块索引，然后需要将数据移到头部。

import java.util.ArrayList;  
import java.util.Collection;  
import java.util.LinkedHashMap;  
import java.util.concurrent.locks.Lock;  
import java.util.concurrent.locks.ReentrantLock;  
import java.util.Map;  


/** 
 * 类说明：利用LinkedHashMap实现简单的缓存， 必须实现removeEldestEntry方法，具体参见JDK文档 
 *  
 * @author dennis 
 *  
 * @param <K> 
 * @param <V> 
 */ 
public class LRULinkedHashMap<K, V> extends LinkedHashMap<K, V> {  
    private final int maxCapacity;  

    private static final float DEFAULT_LOAD_FACTOR = 0.75f;  

    private final Lock lock = new ReentrantLock();  

    public LRULinkedHashMap(int maxCapacity) {  
        super(maxCapacity, DEFAULT_LOAD_FACTOR, true);  
        this.maxCapacity = maxCapacity;  
    }  

    @Override 
    protected boolean removeEldestEntry(java.util.Map.Entry<K, V> eldest) {  
        return size() > maxCapacity;  
    }  
    @Override 
    public boolean containsKey(Object key) {  
        try {  
            lock.lock();  
            return super.containsKey(key);  
        } finally {  
            lock.unlock();  
        }  
    }  


    @Override 
    public V get(Object key) {  
        try {  
            lock.lock();  
            return super.get(key);  
        } finally {  
            lock.unlock();  
        }  
    }  

    @Override 
    public V put(K key, V value) {  
        try {  
            lock.lock();  
            return super.put(key, value);  
        } finally {  
            lock.unlock();  
        }  
    }  

    public int size() {  
        try {  
            lock.lock();  
            return super.size();  
        } finally {  
            lock.unlock();  
        }  
    }  

    public void clear() {  
        try {  
            lock.lock();  
            super.clear();  
        } finally {  
            lock.unlock();  
        }  
    }  

    public Collection<Map.Entry<K, V>> getAll() {  
        try {  
            lock.lock();  
            return new ArrayList<Map.Entry<K, V>>(super.entrySet());  
        } finally {  
            lock.unlock();  
        }  
    }  
}  

实现2 
LRUCache的链表+HashMap实现 

传统意义的LRU算法是为每一个Cache对象设置一个计数器，每次Cache命中则给计数器+1，而Cache用完，需要淘汰旧内容，放置新内容时，就查看所有的计数器，并将最少使用的内容替换掉。

它的弊端很明显，如果Cache的数量少，问题不会很大， 但是如果Cache的空间过大，达到10W或者100W以上，一旦需要淘汰，则需要遍历所有计算器，其性能与资源消耗是巨大的。效率也就非常的慢了。 
它的原理： 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。 
上面说了这么多的理论， 下面用代码来实现一个LRU策略的缓存。 
非线程安全，若实现安全，则在响应的方法加锁。

import java.util.HashMap;
import java.util.Map.Entry;
import java.util.Set;


public class LRUCache<K, V> {

    private int currentCacheSize;
    private int CacheCapcity;
    private HashMap<K,CacheNode> caches;
    private CacheNode first;
    private CacheNode last;

    public LRUCache(int size){
        currentCacheSize = 0;
        this.CacheCapcity = size;
        caches = new HashMap<K,CacheNode>(size);
    }

    public void put(K k,V v){
        CacheNode node = caches.get(k);
        if(node == null){
            if(caches.size() >= CacheCapcity){

                caches.remove(last.key);
                removeLast();
            }
            node = new CacheNode();
            node.key = k;
        }
        node.value = v;
        moveToFirst(node);
        caches.put(k, node);
    }

    public Object  get(K k){
        CacheNode node = caches.get(k);
        if(node == null){
            return null;
        }
        moveToFirst(node);
        return node.value;
    }

    public Object remove(K k){
        CacheNode node = caches.get(k);
        if(node != null){
            if(node.pre != null){
                node.pre.next=node.next;
            }
            if(node.next != null){
                node.next.pre=node.pre;
            }
            if(node == first){
                first = node.next;
            }
            if(node == last){
                last = node.pre;
            }
        }

        return caches.remove(k);
    }

    public void clear(){
        first = null;
        last = null;
        caches.clear();
    }



    private void moveToFirst(CacheNode node){
        if(first == node){
            return;
        }
        if(node.next != null){
            node.next.pre = node.pre;
        }
        if(node.pre != null){
            node.pre.next = node.next;
        }
        if(node == last){
            last= last.pre;
        }
        if(first == null || last == null){
            first = last = node;
            return;
        }

        node.next=first;
        first.pre = node;
        first = node;
        first.pre=null;

    }

    private void removeLast(){
        if(last != null){
            last = last.pre;
            if(last == null){
                first = null;
            }else{
                last.next = null;
            }
        }
    }
    @Override
    public String toString(){
        StringBuilder sb = new StringBuilder();
        CacheNode node = first;
        while(node != null){
            sb.append(String.format("%s:%s ", node.key,node.value));
            node = node.next;
        }

        return sb.toString();
    }

    class CacheNode{
        CacheNode pre;
        CacheNode next;
        Object key;
        Object value;
        public CacheNode(){

        }
    }

    public static void main(String[] args) {

        LRUCache<Integer,String> lru = new LRUCache<Integer,String>(3);

        lru.put(1, "a");    // 1:a
        System.out.println(lru.toString());
        lru.put(2, "b");    // 2:b 1:a 
        System.out.println(lru.toString());
        lru.put(3, "c");    // 3:c 2:b 1:a 
        System.out.println(lru.toString());
        lru.put(4, "d");    // 4:d 3:c 2:b  
        System.out.println(lru.toString());
        lru.put(1, "aa");   // 1:aa 4:d 3:c  
        System.out.println(lru.toString());
        lru.put(2, "bb");   // 2:bb 1:aa 4:d
        System.out.println(lru.toString());
        lru.put(5, "e");    // 5:e 2:bb 1:aa
        System.out.println(lru.toString());
        lru.get(1);         // 1:aa 5:e 2:bb
        System.out.println(lru.toString());
        lru.remove(11);     // 1:aa 5:e 2:bb
        System.out.println(lru.toString());
        lru.remove(1);      //5:e 2:bb
        System.out.println(lru.toString());
        lru.put(1, "aaa");  //1:aaa 5:e 2:bb
        System.out.println(lru.toString());
    }

}

• LRU Cache的LinkedHashMap实现
• LRU Cache的链表+HashMap实现
• LinkedHashMap的FIFO实现
• 调用示例

LRU是Least Recently Used 的缩写，翻译过来就是“最近最少使用”，LRU缓存就是使用这种原理实现，简单的说就是缓存一定量的数据，当超过设定的阈值时就把一些过期的数据删除掉，比如我们缓存10000条数据，当数据小于10000时可以随意添加，当超过10000时就需要把新的数据添加进来，同时要把过期数据删除，以确保我们最大缓存10000条，那怎么确定删除哪条过期数据呢，采用LRU算法实现的话就是将最老的数据删掉，废话不多说，下面来说下Java版的LRU缓存实现

Java里面实现LRU缓存通常有两种选择，一种是使用LinkedHashMap，一种是自己设计数据结构，使用链表+HashMap

LRU Cache的LinkedHashMap实现

LinkedHashMap自身已经实现了顺序存储，默认情况下是按照元素的添加顺序存储，也可以启用按照访问顺序存储，即最近读取的数据放在最前面，最早读取的数据放在最后面，然后它还有一个判断是否删除最老数据的方法，默认是返回false，即不删除数据，我们使用LinkedHashMap实现LRU缓存的方法就是对LinkedHashMap实现简单的扩展，扩展方式有两种，一种是inheritance，一种是delegation，具体使用什么方式看个人喜好

//LinkedHashMap的一个构造函数，当参数accessOrder为true时，即会按照访问顺序排序，最近访问的放在最前，最早访问的放在后面
public LinkedHashMap(int initialCapacity, float loadFactor, boolean accessOrder) {
        super(initialCapacity, loadFactor);
        this.accessOrder = accessOrder;
}

//LinkedHashMap自带的判断是否删除最老的元素方法，默认返回false，即不删除老数据
//我们要做的就是重写这个方法，当满足一定条件时删除老数据
protected boolean removeEldestEntry(Map.Entry<K,V> eldest) {
        return false;
}

LRU缓存LinkedHashMap(inheritance)实现

采用inheritance方式实现比较简单，而且实现了Map接口，在多线程环境使用时可以使用 Collections.synchronizedMap()方法实现线程安全操作

package cn.lzrabbit.structure.lru;

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

/**
 * Created by liuzhao on 14-5-15.
 */
public class LRUCache2<K, V> extends LinkedHashMap<K, V> {
    private final int MAX_CACHE_SIZE;

    public LRUCache2(int cacheSize) {
        super((int) Math.ceil(cacheSize / 0.75) + 1, 0.75f, true);
        MAX_CACHE_SIZE = cacheSize;
    }

    @Override
    protected boolean removeEldestEntry(Map.Entry eldest) {
        return size() > MAX_CACHE_SIZE;
    }

    @Override
    public String toString() {
        StringBuilder sb = new StringBuilder();
        for (Map.Entry<K, V> entry : entrySet()) {
            sb.append(String.format("%s:%s ", entry.getKey(), entry.getValue()));
        }
        return sb.toString();
    }
}

 这样算是比较标准的实现吧，实际使用中这样写还是有些繁琐，更实用的方法时像下面这样写，省去了单独见一个类的麻烦

final int cacheSize = 100;
Map<String, String> map = new LinkedHashMap<String, String>((int) Math.ceil(cacheSize / 0.75f) + 1, 0.75f, true) {
    @Override
    protected boolean removeEldestEntry(Map.Entry<String, String> eldest) {
    return size() > cacheSize;
    }
};

 

LRU缓存LinkedHashMap(delegation)实现

delegation方式实现更加优雅一些，但是由于没有实现Map接口，所以线程同步就需要自己搞定了

package cn.lzrabbit.structure.lru;

import java.util.LinkedHashMap;
import java.util.Map;
import java.util.Set;

/**
 * Created by liuzhao on 14-5-13.
 */
public class LRUCache3<K, V> {

    private final int MAX_CACHE_SIZE;
    private final float DEFAULT_LOAD_FACTOR = 0.75f;
    LinkedHashMap<K, V> map;

    public LRUCache3(int cacheSize) {
        MAX_CACHE_SIZE = cacheSize;
        //根据cacheSize和加载因子计算hashmap的capactiy，+1确保当达到cacheSize上限时不会触发hashmap的扩容，
        int capacity = (int) Math.ceil(MAX_CACHE_SIZE / DEFAULT_LOAD_FACTOR) + 1;
        map = new LinkedHashMap(capacity, DEFAULT_LOAD_FACTOR, true) {
            @Override
            protected boolean removeEldestEntry(Map.Entry eldest) {
                return size() > MAX_CACHE_SIZE;
            }
        };
    }

    public synchronized void put(K key, V value) {
        map.put(key, value);
    }

    public synchronized V get(K key) {
        return map.get(key);
    }

    public synchronized void remove(K key) {
        map.remove(key);
    }

    public synchronized Set<Map.Entry<K, V>> getAll() {
        return map.entrySet();
    }

    public synchronized int size() {
        return map.size();
    }

    public synchronized void clear() {
        map.clear();
    }

    @Override
    public String toString() {
        StringBuilder sb = new StringBuilder();
        for (Map.Entry entry : map.entrySet()) {
            sb.append(String.format("%s:%s ", entry.getKey(), entry.getValue()));
        }
        return sb.toString();
    }
}

 LRU Cache的链表+HashMap实现

 注：此实现为非线程安全，若在多线程环境下使用需要在相关方法上添加synchronized以实现线程安全操作

package cn.lzrabbit.structure.lru;


import java.util.HashMap;

/**
 * Created by liuzhao on 14-5-12.
 */
public class LRUCache1<K, V> {

    private final int MAX_CACHE_SIZE;
    private Entry first;
    private Entry last;

    private HashMap<K, Entry<K, V>> hashMap;

    public LRUCache1(int cacheSize) {
        MAX_CACHE_SIZE = cacheSize;
        hashMap = new HashMap<K, Entry<K, V>>();
    }

    public void put(K key, V value) {
        Entry entry = getEntry(key);
        if (entry == null) {
            if (hashMap.size() >= MAX_CACHE_SIZE) {
                hashMap.remove(last.key);
                removeLast();
            }
            entry = new Entry();
            entry.key = key;
        }
        entry.value = value;
        moveToFirst(entry);
        hashMap.put(key, entry);
    }

    public V get(K key) {
        Entry<K, V> entry = getEntry(key);
        if (entry == null) return null;
        moveToFirst(entry);
        return entry.value;
    }

    public void remove(K key) {
        Entry entry = getEntry(key);
        if (entry != null) {
            if (entry.pre != null) entry.pre.next = entry.next;
            if (entry.next != null) entry.next.pre = entry.pre;
            if (entry == first) first = entry.next;
            if (entry == last) last = entry.pre;
        }
        hashMap.remove(key);
    }

    private void moveToFirst(Entry entry) {
        if (entry == first) return;
        if (entry.pre != null) entry.pre.next = entry.next;
        if (entry.next != null) entry.next.pre = entry.pre;
        if (entry == last) last = last.pre;

        if (first == null || last == null) {
            first = last = entry;
            return;
        }

        entry.next = first;
        first.pre = entry;
        first = entry;
        entry.pre = null;
    }

    private void removeLast() {
        if (last != null) {
            last = last.pre;
            if (last == null) first = null;
            else last.next = null;
        }
    }


    private Entry<K, V> getEntry(K key) {
        return hashMap.get(key);
    }

    @Override
    public String toString() {
        StringBuilder sb = new StringBuilder();
        Entry entry = first;
        while (entry != null) {
            sb.append(String.format("%s:%s ", entry.key, entry.value));
            entry = entry.next;
        }
        return sb.toString();
    }

    class Entry<K, V> {
        public Entry pre;
        public Entry next;
        public K key;
        public V value;
    }
}

LinkedHashMap的FIFO实现

FIFO是First Input First Output的缩写，也就是常说的先入先出，默认情况下LinkedHashMap就是按照添加顺序保存，我们只需重写下removeEldestEntry方法即可轻松实现一个FIFO缓存，简化版的实现代码如下

final int cacheSize = 5;
LinkedHashMap<Integer, String> lru = new LinkedHashMap<Integer, String>() {
    @Override
    protected boolean removeEldestEntry(Map.Entry<Integer, String> eldest) {
    return size() > cacheSize;
    }
};

调用示例

测试代码

package cn.lzrabbit.structure.lru;

import cn.lzrabbit.ITest;

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

/**
 * Created by liuzhao on 14-5-15.
 */
public class LRUCacheTest  {

    public static void main(String[] args) throws Exception {
        System.out.println("start...");

        lruCache1();
        lruCache2();
        lruCache3();
        lruCache4();
     
        System.out.println("over...");
    }
 

 static   void lruCache1() {
        System.out.println();
        System.out.println("===========================LRU 链表实现===========================");
        LRUCache1<Integer, String> lru = new LRUCache1(5);
        lru.put(1, "11");
        lru.put(2, "11");
        lru.put(3, "11");
        lru.put(4, "11");
        lru.put(5, "11");
        System.out.println(lru.toString());
        lru.put(6, "66");
        lru.get(2);
        lru.put(7, "77");
        lru.get(4);
        System.out.println(lru.toString());
        System.out.println();
    }


static   <T> void lruCache2() {
        System.out.println();
        System.out.println("===========================LRU LinkedHashMap(inheritance)实现===========================");
        LRUCache2<Integer, String> lru = new LRUCache2(5);
        lru.put(1, "11");
        lru.put(2, "11");
        lru.put(3, "11");
        lru.put(4, "11");
        lru.put(5, "11");
        System.out.println(lru.toString());
        lru.put(6, "66");
        lru.get(2);
        lru.put(7, "77");
        lru.get(4);
        System.out.println(lru.toString());
        System.out.println();
    }

  static  void lruCache3() {
        System.out.println();
        System.out.println("===========================LRU LinkedHashMap(delegation)实现===========================");
        LRUCache3<Integer, String> lru = new LRUCache3(5);
        lru.put(1, "11");
        lru.put(2, "11");
        lru.put(3, "11");
        lru.put(4, "11");
        lru.put(5, "11");
        System.out.println(lru.toString());
        lru.put(6, "66");
        lru.get(2);
        lru.put(7, "77");
        lru.get(4);
        System.out.println(lru.toString());
        System.out.println();
    }

  static  void lruCache4() {
        System.out.println();
        System.out.println("===========================FIFO LinkedHashMap默认实现===========================");
        final int cacheSize = 5;
        LinkedHashMap<Integer, String> lru = new LinkedHashMap<Integer, String>() {
            @Override
            protected boolean removeEldestEntry(Map.Entry<Integer, String> eldest) {
                return size() > cacheSize;
            }
        };
        lru.put(1, "11");
        lru.put(2, "11");
        lru.put(3, "11");
        lru.put(4, "11");
        lru.put(5, "11");
        System.out.println(lru.toString());
        lru.put(6, "66");
        lru.get(2);
        lru.put(7, "77");
        lru.get(4);
        System.out.println(lru.toString());
        System.out.println();
    }

}

运行结果

"C:\Program Files (x86)\Java\jdk1.6.0_10\bin\java" -Didea.launcher.port=7535 "-Didea.launcher.bin.path=C:\Program Files (x86)\JetBrains\IntelliJ IDEA 13.0.2\bin" -Dfile.encoding=UTF-8 -classpath "C:\Program Files (x86)\Java\jdk1.6.0_10\jre\lib\charsets.jar;C:\Program Files (x86)\Java\jdk1.6.0_10\jre\lib\deploy.jar;C:\Program Files (x86)\Java\jdk1.6.0_10\jre\lib\javaws.jar;C:\Program Files (x86)\Java\jdk1.6.0_10\jre\lib\jce.jar;C:\Program Files (x86)\Java\jdk1.6.0_10\jre\lib\jsse.jar;C:\Program Files (x86)\Java\jdk1.6.0_10\jre\lib\management-agent.jar;C:\Program Files (x86)\Java\jdk1.6.0_10\jre\lib\plugin.jar;C:\Program Files (x86)\Java\jdk1.6.0_10\jre\lib\resources.jar;C:\Program Files (x86)\Java\jdk1.6.0_10\jre\lib\rt.jar;C:\Program Files (x86)\Java\jdk1.6.0_10\jre\lib\ext\dnsns.jar;C:\Program Files (x86)\Java\jdk1.6.0_10\jre\lib\ext\localedata.jar;C:\Program Files (x86)\Java\jdk1.6.0_10\jre\lib\ext\sunjce_provider.jar;C:\Program Files (x86)\Java\jdk1.6.0_10\jre\lib\ext\sunmscapi.jar;C:\Program Files (x86)\Java\jdk1.6.0_10\jre\lib\ext\sunpkcs11.jar;D:\SVN\projects\Java\Java.Algorithm\target\test-classes;D:\SVN\projects\Java\Java.Algorithm\target\classes;C:\Program Files (x86)\JetBrains\IntelliJ IDEA 13.0.2\lib\idea_rt.jar" com.intellij.rt.execution.application.AppMain Main
start...

===========================LRU 链表实现===========================
5:11 4:11 3:11 2:11 1:11 
4:11 7:77 2:11 6:66 5:11 


===========================LRU LinkedHashMap(inheritance)实现===========================
1:11 2:11 3:11 4:11 5:11 
5:11 6:66 2:11 7:77 4:11 


===========================LRU LinkedHashMap(delegation)实现===========================
1:11 2:11 3:11 4:11 5:11 
5:11 6:66 2:11 7:77 4:11 


===========================FIFO LinkedHashMap默认实现===========================
{1=11, 2=11, 3=11, 4=11, 5=11}
{3=11, 4=11, 5=11, 6=66, 7=77}

over...

Process finished with exit code 0

 

"C:\Program Files (x86)\Java\jdk1.6.0_10\bin\java" -Didea.launcher.port=7535 "-Didea.launcher.bin.path=C:\Program Files (x86)\JetBrains\IntelliJ IDEA 13.0.2\bin" -Dfile.encoding=UTF-8 -classpath "C:\Program Files (x86)\Java\jdk1.6.0_10\jre\lib\charsets.jar;C:\Program Files (x86)\Java\jdk1.6.0_10\jre\lib\deploy.jar;C:\Program Files (x86)\Java\jdk1.6.0_10\jre\lib\javaws.jar;C:\Program Files (x86)\Java\jdk1.6.0_10\jre\lib\jce.jar;C:\Program Files (x86)\Java\jdk1.6.0_10\jre\lib\jsse.jar;C:\Program Files (x86)\Java\jdk1.6.0_10\jre\lib\management-agent.jar;C:\Program Files (x86)\Java\jdk1.6.0_10\jre\lib\plugin.jar;C:\Program Files (x86)\Java\jdk1.6.0_10\jre\lib\resources.jar;C:\Program Files (x86)\Java\jdk1.6.0_10\jre\lib\rt.jar;C:\Program Files (x86)\Java\jdk1.6.0_10\jre\lib\ext\dnsns.jar;C:\Program Files (x86)\Java\jdk1.6.0_10\jre\lib\ext\localedata.jar;C:\Program Files (x86)\Java\jdk1.6.0_10\jre\lib\ext\sunjce_provider.jar;C:\Program Files (x86)\Java\jdk1.6.0_10\jre\lib\ext\sunmscapi.jar;C:\Program Files (x86)\Java\jdk1.6.0_10\jre\lib\ext\sunpkcs11.jar;D:\SVN\projects\Java\Java.Algorithm\target\test-classes;D:\SVN\projects\Java\Java.Algorithm\target\classes;C:\Program Files (x86)\JetBrains\IntelliJ IDEA 13.0.2\lib\idea_rt.jar" com.intellij.rt.execution.application.AppMain Main
start...

===========================LRU 链表实现===========================
5:11 4:11 3:11 2:11 1:11 
4:11 7:77 2:11 6:66 5:11 


===========================LRU LinkedHashMap(inheritance)实现===========================
1:11 2:11 3:11 4:11 5:11 
5:11 6:66 2:11 7:77 4:11 


===========================LRU LinkedHashMap(delegation)实现===========================
1:11 2:11 3:11 4:11 5:11 
5:11 6:66 2:11 7:77 4:11 


===========================FIFO LinkedHashMap默认实现===========================
{1=11, 2=11, 3=11, 4=11, 5=11}
{3=11, 4=11, 5=11, 6=66, 7=77}

over...

Process finished with exit code 0

缓存淘汰算法--LRU算法

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原文地址：https://www.cnblogs.com/xuwc/p/9028216.html