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());
}
}
}