在并发量比较大的场景，如果采用直接访问数据库的方式，将会对数据库带来巨大的压力，严重的情况下可能会导致数据库不可用状态，并且时间的消耗也是不能容忍的，尤其对于某些获取起来比较昂贵的数据。在这种情况下，一般采用缓存的方式。将经常访问的热点数据提前加载到内存中，这样能够大大降低数据库的压力。

OSCache是一个开源的缓存框架，虽然现在已经停止维护了，但是对于OSCache的实现还是值得学习和借鉴的。下面通过OSCache的部分源码分析OSCache的设计思想。

缓存数据结构

通常缓存都是通过<K,V>这种数据结构存储，但缓存都是应用在多线程的场景下，需要保证线程安全。Java中可以选择HashTable、ConcurrentHashMap、synchronizedMap等。OSCache本质上使用的HashTable实现的，具体实现代码在com.opensymphony.oscache.base.algorithm.AbstractConcurrentReadCache中。由于HashTable在保证线程安全上采用的加锁整个数据块，因此，适合读多写少（mostly-concurrent reading, but exclusive writing）的场景（OSCache对原始的HashTable进行了优化，下面会讲到）。如果写很多的话，可以采用ConcurrentHashMap数据结构。

获得缓存内容

//从缓存中获取指定key对应的内容
public Object getFromCache(String key, int refreshPeriod, String cronExpiry) throws NeedsRefreshException {//首先尝试获取内容，如果获取不到，则新建一个CacheEntry对象CacheEntry cacheEntry = this.getCacheEntry(key, null, null);Object content = cacheEntry.getContent();CacheMapAccessEventType accessEventType = CacheMapAccessEventType.HIT;boolean reload = false;// 检查缓存是否过期，如果过期分为以下几种情况处理if (this.isStale(cacheEntry, refreshPeriod, cronExpiry)) {//获取更新状态，如果没有，新建一个同时引用计数默认为1EntryUpdateState updateState = getUpdateState(key);try {synchronized (updateState) {if (updateState.isAwaitingUpdate() || updateState.isCancelled()) {// 如果状态为等待刷新或者已经取消刷新，说明当前没有其他线程对其进行刷新操作// 因此这里启动刷新操作（这里会将状态更新为刷新中同时引用计数+1）updateState.startUpdate();//如果是新建的CacheEntry对象（即之前未缓存该key对应的对象）if (cacheEntry.isNew()) {//设置命中状态为未命中accessEventType = CacheMapAccessEventType.MISS;} else {//否则说明虽然命中了，但是需要刷新accessEventType = CacheMapAccessEventType.STALE_HIT;}} else if (updateState.isUpdating()) {// 如果更新状态为刷新中，说明另有一个线程正对该缓存对象执行刷新操作// 此时如果是新建的CacheEntry对象或者同步模式设置为true，那么该线程将阻塞//通过putInCache或者cancelUpdate可以让线程继续运行// 否则获取到的很有可能是脏数据if (cacheEntry.isNew() || blocking) {do {try {updateState.wait();} catch (InterruptedException e) {}} while (updateState.isUpdating());//如果更新状态变成了取消，说明另外一个线程取消了刷新缓存操作，那么让该线程尝试刷新if (updateState.isCancelled()) {//更新状态设置为更新中并将引用计数+1updateState.startUpdate();if (cacheEntry.isNew()) {accessEventType = CacheMapAccessEventType.MISS;} else {accessEventType = CacheMapAccessEventType.STALE_HIT;}} else if (updateState.isComplete()) {reload = true;} else {log.error("Invalid update state for cache entry " + key);}}} else {reload = true;}}} finally {//将引用计数-1同时检查如果引用计数=0，将updateState移除releaseUpdateState(updateState, key);}}// 如果该标志位为true，说明缓存一定刷新了if (reload) {cacheEntry = (CacheEntry) cacheMap.get(key);if (cacheEntry != null) {content = cacheEntry.getContent();} else {log.error("Could not reload cache entry after waiting for it to be rebuilt");}}dispatchCacheMapAccessEvent(accessEventType, cacheEntry, null);// 如果缓存不存在或者缓存过期将抛出需要刷新的异常if (accessEventType != CacheMapAccessEventType.HIT) {throw new NeedsRefreshException(content);}return content;}

从上面可以看到EntryUpdateState很关键。EntryUpdateState用来标记某个key对应的缓存的更新状态以及线程引用计数（可以理解为一个计数器），并且每一个key对应一个EntryUpdateState。如果缓存存在并且没有过期，EntryUpdateState为空。OSCache使用的HashTable相对于原始的HashTable在get操作中是没有synchronize关键字的，而为了防止并发问题，所以引入了EntryUpdateState这个数据结构。这样做的目的就是防止过多的使用synchronize，从而对性能不会造成很大的影响。

查看定义如下：

//默认public static final int NOT_YET_UPDATING = -1;public static final int UPDATE_IN_PROGRESS = 0;public static final int UPDATE_COMPLETE = 1;public static final int UPDATE_CANCELLED = 2;int state = NOT_YET_UPDATING;//引用计数private int nbConcurrentUses = 1;

这里的引用计数，代表了当前有多少线程在缓存更新或存入过程中进行访问。

通过上面从缓存获取指定key的代码可以发现一个问题：当缓存不存在或者缓存过期的情况下，都会抛出NeedsRefreshException的异常，在这种情况下，如果blocking设置为true（通常设置为true），其他访问的线程将处于阻塞状态，直到缓存更新完毕才会继续运行，倘若这里处理不当，将会导致死锁的发生。

因此在该异常产生时，需要进行缓存的刷新操作，官方给出了两种方法：

//第一种：with fail over
String myKey = "myKey";
String myValue;
int myRefreshPeriod = 1000;
try {// Get from the cachemyValue = (String) admin.getFromCache(myKey, myRefreshPeriod);
} catch (NeedsRefreshException nre) {try {// Get the value (probably by calling an EJB)myValue = "This is the content retrieved.";// Store in the cacheadmin.putInCache(myKey, myValue);} catch (Exception ex) {// We have the current content if we want fail-over.myValue = (String) nre.getCacheContent();// It is essential that cancelUpdate is called if the// cached content is not rebuiltadmin.cancelUpdate(myKey);}
}

//第二种：without fail over
String myKey = "myKey";
String myValue;
int myRefreshPeriod = 1000;
try {// Get from the cachemyValue = (String) admin.getFromCache(myKey, myRefreshPeriod);
} catch (NeedsRefreshException nre) {try {// Get the value (probably by calling an EJB)myValue = "This is the content retrieved.";// Store in the cacheadmin.putInCache(myKey, myValue);updated = true;} finally {if (!updated) {// It is essential that cancelUpdate is called if the// cached content could not be rebuiltadmin.cancelUpdate(myKey);}}
}

正如之前的代码，如果这里不调用putInCache或者cancelUpdate，其他访问该缓存的线程将会由于得不到资源始终处于阻塞状态，导致死锁的发生。因此这里是一个非常重要的关注点。只有调用了putInCache或者cancelUpdate方法，阻塞的线程才会开始运行。

下面看一下putInCache和cancelUpdate方法具体做了什么：

public void putInCache(String key, Object content, String[] groups, EntryRefreshPolicy policy, String origin) {CacheEntry cacheEntry = this.getCacheEntry(key, policy, origin);boolean isNewEntry = cacheEntry.isNew();// 首先判断缓存中是否已经存在if (!isNewEntry) {cacheEntry = new CacheEntry(key, policy);}cacheEntry.setContent(content);cacheEntry.setGroups(groups);cacheMap.put(key, cacheEntry);// 更新状态及引用计数，通知其它阻塞线程可以获取缓存了completeUpdate(key);//......//......}
}
protected void completeUpdate(String key) {EntryUpdateState state;synchronized (updateStates) {state = (EntryUpdateState) updateStates.get(key);if (state != null) {synchronized (state) {//更新状态为UPDATE_COMPLETE，引用计数-1int usageCounter = state.completeUpdate();//唤醒其它等待该缓存资源的线程state.notifyAll();checkEntryStateUpdateUsage(key, state, usageCounter);}} else {//如果putInCache方法直接调用(如不是因NeedRefreshException异常调用)这样EntryUpdateState将为null，不执行操作 }}}

cancelUpdate的逻辑和putInCache基本相同：

public void cancelUpdate(String key) {EntryUpdateState state;if (key != null) {synchronized (updateStates) {state = (EntryUpdateState) updateStates.get(key);if (state != null) {synchronized (state) {//更新状态为UPDATE_CANCELLED，引用计数-1int usageCounter = state.cancelUpdate();state.notify();checkEntryStateUpdateUsage(key, state, usageCounter);}} else {if (log.isErrorEnabled()) {log.error("internal error: expected to get a state from key [" + key + "]");}}}}}

从上面的代码可以看到，当发生需要刷新缓存（NeedsRefreshException）的异常时，需要通过putInCache（）方法进行缓存的更新或者cancelUpdate（）方法放弃刷新缓存，从而释放资源，唤醒其它阻塞的线程。

缓存淘汰（替换）策略

因为我们的内存不是无限的，缓存不可能无限的扩大，因此在缓存占满时，我们需要将缓存中一些“不重要”的内容剔除，从而腾出空间缓存新的内容。如何丈量这个“不重要”，就是我们需要考虑的缓存淘汰（替换）策略。

一般有以下策略：

1. Least Frequently Used（LFU）：计算每个缓存对象的使用频率，将频率最低的剔除；

2. Least Recently User（LRU）：最近最少使用，具体是将最近访问的内容始终放在最顶端，一直未访问或者最久未访问的内容放在最底端，当需要替换的时候，只需将最底端的剔除即可，这样可以使得最常访问的内容始终在缓存中，使用比较广泛，OSCache中默认也是采用该方法。LRU的这种特性，在Java中很容易通过LinkedHashMap实现，具体实现方法可以参考下面的介绍。

3. First in First out（FIFO）：先进先出。实现起来最为简单，但是不适用。

4. Random Cache：随机替换。

当然还有很多替换算法，这里就不一一列举了。仅就最常用的LRU算法进行介绍。

Java中的LinkedHashMap可以保持插入顺序或者访问顺序，对于第二个特性，跟LRU的机制很相似，因此，可以很简单的采用LinkedHashMap来实现LRU算法。

查看LinkedHashMap的定义，有下面一个参数：

final boolean accessOrder;

再看几个构造函数的定义：

public LinkedHashMap(int initialCapacity, float loadFactor) {super(initialCapacity, loadFactor);accessOrder = false;
}
public LinkedHashMap(int initialCapacity) {super(initialCapacity);accessOrder = false;
}
public LinkedHashMap() {super();accessOrder = false;
}
public LinkedHashMap(Map<? extends K, ? extends V> m) {super();accessOrder = false;putMapEntries(m, false);
}
public LinkedHashMap(int initialCapacity,float loadFactor,boolean accessOrder) {super(initialCapacity, loadFactor);this.accessOrder = accessOrder;
}

可以看到，除了最后一个构造函数，其余的accessOrder默认为false。当accessOrder为false时，LinkedHashMap保持插入顺序，而accessOrder如果为true，将保持访问顺序，因此这正是关键点。具体如何保持插入顺序或者访问顺序，可以参考LinkedHashMap的实现代码，并不复杂。

仅仅是保持访问顺序还不行，我们还要淘汰最近最少使用的对象。LinkedHashMap重写了父类HashMap的afterNodeInsertion方法：

void afterNodeInsertion(boolean evict) { // possibly remove eldestLinkedHashMap.Entry<K,V> first;if (evict && (first = head) != null && removeEldestEntry(first)) {K key = first.key;removeNode(hash(key), key, null, false, true);}}/*** Returns <tt>true</tt> if this map should remove its eldest entry.* This method is invoked by <tt>put</tt> and <tt>putAll</tt> after* inserting a new entry into the map.  It provides the implementor* with the opportunity to remove the eldest entry each time a new one* is added.  This is useful if the map represents a cache: it allows* the map to reduce memory consumption by deleting stale entries.** <p>Sample use: this override will allow the map to grow up to 100* entries and then delete the eldest entry each time a new entry is* added, maintaining a steady state of 100 entries.* <pre>*     private static final int MAX_ENTRIES = 100;**     protected boolean removeEldestEntry(Map.Entry eldest) {*        return size() &gt; MAX_ENTRIES;*     }* </pre>** <p>This method typically does not modify the map in any way,* instead allowing the map to modify itself as directed by its* return value.  It <i>is</i> permitted for this method to modify* the map directly, but if it does so, it <i>must</i> return* <tt>false</tt> (indicating that the map should not attempt any* further modification).  The effects of returning <tt>true</tt>* after modifying the map from within this method are unspecified.** <p>This implementation merely returns <tt>false</tt> (so that this* map acts like a normal map - the eldest element is never removed).** @param    eldest The least recently inserted entry in the map, or if*           this is an access-ordered map, the least recently accessed*           entry.  This is the entry that will be removed it this*           method returns <tt>true</tt>.  If the map was empty prior*           to the <tt>put</tt> or <tt>putAll</tt> invocation resulting*           in this invocation, this will be the entry that was just*           inserted; in other words, if the map contains a single*           entry, the eldest entry is also the newest.* @return   <tt>true</tt> if the eldest entry should be removed*           from the map; <tt>false</tt> if it should be retained.*/protected boolean removeEldestEntry(Map.Entry<K,V> eldest) {return false;}

removeEldestEntry方法默认返回false，即默认不移除。因此我们只要在这里加以判断：如果缓存已经占满，返回true，就可以将最近最少使用的对象移除了。因此，通过使用LinkedHashMap，仅需要非常简单的修改即可实现LRU算法。

下面附上LRU的实现代码：

import java.util.LinkedHashMap;
import java.util.Collection;
import java.util.Map;
import java.util.ArrayList;/*** An LRU cache, based on <code>LinkedHashMap</code>.** <p>* This cache has a fixed maximum number of elements (<code>cacheSize</code>).* If the cache is full and another entry is added, the LRU (least recently* used) entry is dropped.** <p>* This class is thread-safe. All methods of this class are synchronized.** <p>* Author: Christian d'Heureuse, Inventec Informatik AG, Zurich, Switzerland<br>* Multi-licensed: EPL / LGPL / GPL / AL / BSD.*/
public class LRUCache<K, V> {private static final float hashTableLoadFactor = 0.75f;private LinkedHashMap<K, V> map;private int cacheSize;/*** Creates a new LRU cache. 在该方法中，new LinkedHashMap<K,V>(hashTableCapacity,* hashTableLoadFactor, true)中，true代表使用访问顺序* * @param cacheSize*            the maximum number of entries that will be kept in this cache.*/public LRUCache(int cacheSize) {this.cacheSize = cacheSize;int hashTableCapacity = (int) Math.ceil(cacheSize / hashTableLoadFactor) + 1;map = new LinkedHashMap<K, V>(hashTableCapacity, hashTableLoadFactor,true) {// (an anonymous inner class)private static final long serialVersionUID = 1;@Overrideprotected boolean removeEldestEntry(Map.Entry<K, V> eldest) {return size() > LRUCache.this.cacheSize;}};}/*** Retrieves an entry from the cache.<br>* The retrieved entry becomes the MRU (most recently used) entry.* * @param key*            the key whose associated value is to be returned.* @return the value associated to this key, or null if no value with this*         key exists in the cache.*/public synchronized V get(K key) {return map.get(key);}/*** Adds an entry to this cache. The new entry becomes the MRU (most recently* used) entry. If an entry with the specified key already exists in the* cache, it is replaced by the new entry. If the cache is full, the LRU* (least recently used) entry is removed from the cache.* * @param key*            the key with which the specified value is to be associated.* @param value*            a value to be associated with the specified key.*/public synchronized void put(K key, V value) {map.put(key, value);}/*** Clears the cache.*/public synchronized void clear() {map.clear();}/*** Returns the number of used entries in the cache.* * @return the number of entries currently in the cache.*/public synchronized int usedEntries() {return map.size();}/*** Returns a <code>Collection</code> that contains a copy of all cache* entries.* * @return a <code>Collection</code> with a copy of the cache content.*/public synchronized Collection<Map.Entry<K, V>> getAll() {return new ArrayList<Map.Entry<K, V>>(map.entrySet());}
}

最后，缓存在使用过程中，需要考虑一致性问题。缓存的刷新就是为了保持一致性。具体如何去刷新，需要根据具体的使用场景进行设计。