LinkedHashMap 和 LRU算法实现

个人觉得LinkedHashMap 存在的意义就是为了实现 LRU 算法。

public class LinkedHashMap<K,V> extends HashMap<K,V>
    implements Map<K,V>
{
    public LinkedHashMap(int initialCapacity,
                         float loadFactor,
                         boolean accessOrder) {
        super(initialCapacity, loadFactor);
        this.accessOrder = accessOrder;
    }
    ....

1、LinkedHashMap 的 <K,V>用HashMap存储。

2、LinkedHashMap 的Key 用双向链表维护。

　　当用get 和 set 方法的时候，内部维护key的双向链表的结构顺序会变动。

3、accessOrder：false 基于插入顺序  true  基于访问顺序（get一个元素后，这个元素被加到最后，使用了LRU  最近最少被使用的调度算法）。

4、removeEldestEntry方法，考虑清楚是否要重载。如果最大容量固定，则需要重载，否则表现为自适应。

protected boolean removeEldestEntry(Map.Entry<K,V> eldest) {
        return false;
    }

最简单的LRU算法实现

update1:第二个实现，读操作不必要采用独占锁，缓存显然是读多于写，读的时候一开始用独占锁是考虑到要递增计数和更新时间戳要加锁，不过这两个变量都是采用原子变量，因此也不必采用独占锁，修改为读写锁。
update2:一个错误，老是写错关键字啊，LRUCache的maxCapacity应该声明为volatile，而不是transient。
   
   最简单的LRU算法实现，就是利用jdk的LinkedHashMap，覆写其中的removeEldestEntry(Map.Entry)方法即可，如下所示：

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();
        }
    }
}

    如果你去看LinkedHashMap的源码可知，LRU算法是通过双向链表来实现，当某个位置被命中，通过调整链表的指向将该位置调整到头位置，新加入的内容直接放在链表头，如此一来，最近被命中的内容就向链表头移动，需要替换时，链表最后的位置就是最近最少使用的位置。
    LRU算法还可以通过计数来实现，缓存存储的位置附带一个计数器，当命中时将计数器加1，替换时就查找计数最小的位置并替换，结合访问时间戳来实现。这种算法比较适合缓存数据量较小的场景，显然，遍历查找计数最小位置的时间复杂度为O(n)。我实现了一个，结合了访问时间戳，当最小计数大于MINI_ACESS时(这个参数的调整对命中率有较大影响），就移除最久没有被访问的项：

package net.rubyeye.codelib.util.concurrency.cache;

import java.io.Serializable;
import java.util.ArrayList;
import java.util.Collection;
import java.util.HashMap;
import java.util.Iterator;
import java.util.Map;
import java.util.Set;
import java.util.concurrent.atomic.AtomicInteger;
import java.util.concurrent.atomic.AtomicLong;
import java.util.concurrent.locks.Lock;
import java.util.concurrent.locks.ReadWriteLock;
import java.util.concurrent.locks.ReentrantLock;
import java.util.concurrent.locks.ReentrantReadWriteLock;

/**
 * 
 * @author dennis 类说明：当缓存数目不多时，才用缓存计数的传统LRU算法
 * @param <K>
 * @param <V>
 */
public class LRUCache<K, V> implements Serializable {

    private static final int DEFAULT_CAPACITY = 100;

    protected Map<K, ValueEntry> map;

    private final ReadWriteLock lock = new ReentrantReadWriteLock();

    private final Lock readLock = lock.readLock();

    private final Lock writeLock = lock.writeLock();

    private final volatile int maxCapacity;  //保持可见性

    public static int MINI_ACCESS = 5;

    public LRUCache() {
        this(DEFAULT_CAPACITY);
    }

    public LRUCache(int capacity) {
        if (capacity <= 0)
            throw new RuntimeException("缓存容量不得小于0");
        this.maxCapacity = capacity;
        this.map = new HashMap<K, ValueEntry>(maxCapacity);
    }

    public boolean ContainsKey(K key) {
        try {
            readLock.lock();
            return this.map.containsKey(key);
        } finally {
            readLock.unlock();
        }
    }

    public V put(K key, V value) {
        try {
            writeLock.lock();
            if ((map.size() > maxCapacity - 1) && !map.containsKey(key)) {
                // System.out.println("开始");
                Set<Map.Entry<K, ValueEntry>> entries = this.map.entrySet();
                removeRencentlyLeastAccess(entries);
            }
            ValueEntry new_value = new ValueEntry(value);
            ValueEntry old_value = map.put(key, new_value);
            if (old_value != null) {
                new_value.count = old_value.count;
                return old_value.value;
            } else
                return null;
        } finally {
            writeLock.unlock();
        }
    }

    /**
     * 移除最近最少访问
     */
    protected void removeRencentlyLeastAccess(
            Set<Map.Entry<K, ValueEntry>> entries) {
        // 最小使用次数
        long least = 0;
        // 访问时间最早
        long earliest = 0;
        K toBeRemovedByCount = null;
        K toBeRemovedByTime = null;
        Iterator<Map.Entry<K, ValueEntry>> it = entries.iterator();
        if (it.hasNext()) {
            Map.Entry<K, ValueEntry> valueEntry = it.next();
            least = valueEntry.getValue().count.get();
            toBeRemovedByCount = valueEntry.getKey();
            earliest = valueEntry.getValue().lastAccess.get();
            toBeRemovedByTime = valueEntry.getKey();
        }
        while (it.hasNext()) {
            Map.Entry<K, ValueEntry> valueEntry = it.next();
            if (valueEntry.getValue().count.get() < least) {
                least = valueEntry.getValue().count.get();
                toBeRemovedByCount = valueEntry.getKey();
            }
            if (valueEntry.getValue().lastAccess.get() < earliest) {
                earliest = valueEntry.getValue().count.get();
                toBeRemovedByTime = valueEntry.getKey();
            }
        }
        // System.out.println("remove:" + toBeRemoved);
        // 如果最少使用次数大于MINI_ACCESS，那么移除访问时间最早的项(也就是最久没有被访问的项）
        if (least > MINI_ACCESS) {
            map.remove(toBeRemovedByTime);
        } else {
            map.remove(toBeRemovedByCount);
        }
    }

    public V get(K key) {
        try {
            readLock.lock();
            V value = null;
            ValueEntry valueEntry = map.get(key);
            if (valueEntry != null) {
                // 更新访问时间戳
                valueEntry.updateLastAccess();
                // 更新访问次数
                valueEntry.count.incrementAndGet();
                value = valueEntry.value;
            }
            return value;
        } finally {
            readLock.unlock();
        }
    }

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

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

    public long getCount(K key) {
        try {
            readLock.lock();
            ValueEntry valueEntry = map.get(key);
            if (valueEntry != null) {
                return valueEntry.count.get();
            }
            return 0;
        } finally {
            readLock.unlock();
        }
    }

    public Collection<Map.Entry<K, V>> getAll() {
        try {
            readLock.lock();
            Set<K> keys = map.keySet();
            Map<K, V> tmp = new HashMap<K, V>();
            for (K key : keys) {
                tmp.put(key, map.get(key).value);
            }
            return new ArrayList<Map.Entry<K, V>>(tmp.entrySet());
        } finally {
            readLock.unlock();
        }
    }

    class ValueEntry implements Serializable {
        private V value;

        private AtomicLong count;

        private AtomicLong lastAccess;

        public ValueEntry(V value) {
            this.value = value;
            this.count = new AtomicLong(0);
            lastAccess = new AtomicLong(System.nanoTime());
        }

        public void updateLastAccess() {
            this.lastAccess.set(System.nanoTime());
        }

    }
}

参考：

简单LRU算法实现缓存-update2