MyBatis的二级缓存以及装饰器模式运用
目录
-
Mybatis中如何配置二级缓存
-
Cache解析处理过程
- Cache支持的过期策略
- 装饰器模式
- 装饰器源码
Mybatis中如何配置二级缓存
基于注解配置缓存
@CacheNamespace(blocking=true)
public interface PersonMapper {
@Select("select id, firstname, lastname from person")
public List<Person> findAll();
}
基于XML配置缓存
<mapper namespace="org.apache.ibatis.submitted.cacheorder.Mapper2"> <cache/> </mapper>
Cache解析处理过程
为什么配置了一个<cache/>就可以使用缓存了呢？通过下面的源码可以发现，缓存配置是有默认值的
private void cacheElement(XNode context) throws Exception {
if (context != null) {
//获取配置的type值，默认值为PERPETUAL
String type = context.getStringAttribute("type", "PERPETUAL");
//获取type的class
Class<? extends Cache> typeClass = typeAliasRegistry.resolveAlias(type);
//获取配置过期策略，默认值为LRU
String eviction = context.getStringAttribute("eviction", "LRU");
Class<? extends Cache> evictionClass = typeAliasRegistry.resolveAlias(eviction);
//获取配置的刷新间隔
Long flushInterval = context.getLongAttribute("flushInterval");
//获取配置的缓存大小
Integer size = context.getIntAttribute("size");
//是否配置了只读，默认为false
boolean readWrite = !context.getBooleanAttribute("readOnly", false);
//是否配置了阻塞，默认为false
boolean blocking = context.getBooleanAttribute("blocking", false);
Properties props = context.getChildrenAsProperties();
builderAssistant.useNewCache(typeClass, evictionClass, flushInterval, size, readWrite, blocking, props);
}
}
Cache支持的过期策略
typeAliasRegistry.registerAlias("FIFO", FifoCache.class);
typeAliasRegistry.registerAlias("LRU", LruCache.class);
typeAliasRegistry.registerAlias("SOFT", SoftCache.class);
typeAliasRegistry.registerAlias("WEAK", WeakCache.class);
缓存的基本实现
public class PerpetualCache implements Cache {
//缓存ID
private final String id;
//缓存
private Map<Object, Object> cache = new HashMap<Object, Object>();
public PerpetualCache(String id) {
this.id = id;
}
@Override
public String getId() {
return id;
}
@Override
public int getSize() {
return cache.size();
}
@Override
public void putObject(Object key, Object value) {
cache.put(key, value);
}
@Override
public Object getObject(Object key) {
return cache.get(key);
}
@Override
public Object removeObject(Object key) {
return cache.remove(key);
}
@Override
public void clear() {
cache.clear();
}
@Override
public ReadWriteLock getReadWriteLock() {
return null;
}
@Override
public boolean equals(Object o) {
if (getId() == null) {
throw new CacheException("Cache instances require an ID.");
}
if (this == o) {
return true;
}
if (!(o instanceof Cache)) {
return false;
}
Cache otherCache = (Cache) o;
return getId().equals(otherCache.getId());
}
@Override
public int hashCode() {
if (getId() == null) {
throw new CacheException("Cache instances require an ID.");
}
return getId().hashCode();
}
}
在Mybatis中是不是根据不通的过期策略都创建不通都缓存呢？实际上Mybatis的所有Cache算法都是基于装饰器模式对PerpetualCache扩展增加功能。下面简单介绍一下装饰器（Decorator）模式以及在Mybatis装饰器实现源码
装饰器模式
装饰器模式以客户透明对方式动态给一个对象附加上更多的责任。换言之，客户端并不会觉得对象在装饰前和装饰后有什么不同。装饰器模式可以在不创造更多子类的情况下对对象对功能加以扩展。
装饰器模式常常被称为包裹模式，就是因为每一个装饰器实现类，都是将下一个装饰器或真实实现包裹起来。这样做可以将真实实现简化逻辑，同时更容易扩展新功能。
Mybatis装饰器
private Cache setStandardDecorators(Cache cache) {
try {
MetaObject metaCache = SystemMetaObject.forObject(cache);
if (size != null && metaCache.hasSetter("size")) {
metaCache.setValue("size", size);
}
if (clearInterval != null) {
//在cache基础上加上ScheduledCache装饰器
cache = new ScheduledCache(cache);
((ScheduledCache) cache).setClearInterval(clearInterval);
}
//如果配置只读则增加序列化功能
if (readWrite) {
//增加序列化装饰器
cache = new SerializedCache(cache);
}
//增加日志装饰器
cache = new LoggingCache(cache);
//增加同步装饰器
cache = new SynchronizedCache(cache);
if (blocking) {
//增加阻塞读装饰器
cache = new BlockingCache(cache);
}
return cache;
} catch (Exception e) {
throw new CacheException("Error building standard cache decorators. Cause: " + e, e);
}
}
简单阻塞装饰器， 当前缓存中不存在时对缓存缓存的key加锁，其它线程就只能一直等到这个元素保存到缓存中由于对每个key都保存了锁对象，如果在大量查询中使用可能存在OOM都风险
public class BlockingCache implements Cache {
//超时时间
private long timeout;
//委派代表
private final Cache delegate;
//缓存key和锁的映射关系
private final ConcurrentHashMap<Object, ReentrantLock> locks;
public BlockingCache(Cache delegate) {
this.delegate = delegate;
this.locks = new ConcurrentHashMap<Object, ReentrantLock>();
}
//获取ID，直接委派给delegate处理
@Override
public String getId() {
return delegate.getId();
}
@Override
public int getSize() {
return delegate.getSize();
}
//放置缓存，结束后释放锁； 注意在方缓存前是没有加锁的
//该处设置是和获取缓存有很大关系
@Override
public void putObject(Object key, Object value) {
try {
delegate.putObject(key, value);
} finally {
releaseLock(key);
}
}
@Override
public Object getObject(Object key) {
//获取锁
acquireLock(key);
//获取缓存数据
Object value = delegate.getObject(key);
//如果缓存数据存在则释放锁，否则返回，注意，此时锁没有释放；下一个线程获取的时候是没有办法
//获取锁，只能等待；记住 put结束的时候会释放锁，这里就是为什么put之前没有获取锁，但是结束后要释放锁的原因
if (value != null) {
releaseLock(key);
}
return value;
}
@Override
public Object removeObject(Object key) {
// despite of its name, this method is called only to release locks
releaseLock(key);
return null;
}
@Override
public void clear() {
delegate.clear();
}
@Override
public ReadWriteLock getReadWriteLock() {
return null;
}
private ReentrantLock getLockForKey(Object key) {
ReentrantLock lock = new ReentrantLock();
ReentrantLock previous = locks.putIfAbsent(key, lock);
return previous == null ? lock : previous;
}
private void acquireLock(Object key) {
Lock lock = getLockForKey(key);
if (timeout > 0) {
try {
boolean acquired = lock.tryLock(timeout, TimeUnit.MILLISECONDS);
if (!acquired) {
throw new CacheException("Couldn't get a lock in " + timeout + " for the key " + key + " at the cache " + delegate.getId());
}
} catch (InterruptedException e) {
throw new CacheException("Got interrupted while trying to acquire lock for key " + key, e);
}
} else {
lock.lock();
}
}
private void releaseLock(Object key) {
ReentrantLock lock = locks.get(key);
if (lock.isHeldByCurrentThread()) {
lock.unlock();
}
}
public long getTimeout() {
return timeout;
}
public void setTimeout(long timeout) {
this.timeout = timeout;
}
}
LRU缓存，LRU算法主要通过LinkedHashMap实现，实现简单明了
public class LruCache implements Cache {
private final Cache delegate;
//key映射表
private Map<Object, Object> keyMap;
//最老的key
private Object eldestKey;
public LruCache(Cache delegate) {
this.delegate = delegate;
setSize(1024);
}
@Override
public String getId() {
return delegate.getId();
}
@Override
public int getSize() {
return delegate.getSize();
}
public void setSize(final int size) {
//使用LinedListHashMap实现LRU， accessOrder=true 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
keyMap = new LinkedHashMap<Object, Object>(size, .75F, true) {
private static final long serialVersionUID = 4267176411845948333L;
@Override
protected boolean removeEldestEntry(Map.Entry<Object, Object> eldest) {
//如果当前大小已经超过1024则删除最老元素
boolean tooBig = size() > size;
if (tooBig) {
//将最老元素赋值给eldestKey
eldestKey = eldest.getKey();
}
return tooBig;
}
};
}
@Override
public void putObject(Object key, Object value) {
delegate.putObject(key, value);
cycleKeyList(key);
}
@Override
public Object getObject(Object key) {
//每次反问都会触发keyMap的排序
keyMap.get(key);
return delegate.getObject(key);
}
@Override
public Object removeObject(Object key) {
return delegate.removeObject(key);
}
@Override
public void clear() {
delegate.clear();
keyMap.clear();
}
@Override
public ReadWriteLock getReadWriteLock() {
return null;
}
private void cycleKeyList(Object key) {
//将当前key放入keyMap
keyMap.put(key, key);
//如果最老的key不为null则清除最老的key的缓存
if (eldestKey != null) {
delegate.removeObject(eldestKey);
eldestKey = null;
}
}
}
FIFO缓存，通过LinkedList实现了FIFO算法
public class FifoCache implements Cache {
private final Cache delegate;
//双端队列
private final Deque<Object> keyList;
private int size;
public FifoCache(Cache delegate) {
this.delegate = delegate;
this.keyList = new LinkedList<Object>();
this.size = 1024;
}
@Override
public String getId() {
return delegate.getId();
}
@Override
public int getSize() {
return delegate.getSize();
}
public void setSize(int size) {
this.size = size;
}
@Override
public void putObject(Object key, Object value) {
cycleKeyList(key);
delegate.putObject(key, value);
}
@Override
public Object getObject(Object key) {
return delegate.getObject(key);
}
@Override
public Object removeObject(Object key) {
return delegate.removeObject(key);
}
@Override
public void clear() {
delegate.clear();
keyList.clear();
}
@Override
public ReadWriteLock getReadWriteLock() {
return null;
}
private void cycleKeyList(Object key) {
//将当前key添加到队尾
keyList.addLast(key);
//如果key的队列长度超过限制则删除队首的key以及缓存
if (keyList.size() > size) {
Object oldestKey = keyList.removeFirst();
delegate.removeObject(oldestKey);
}
}
}
序列化缓存装饰器，对于缓存对象进行了序列化和反序列化避免了值引用问题
/**
* 序列化缓存
*/
public class SerializedCache implements Cache {
private final Cache delegate;
public SerializedCache(Cache delegate) {
this.delegate = delegate;
}
@Override
public String getId() {
return delegate.getId();
}
@Override
public int getSize() {
return delegate.getSize();
}
//将数据添加到缓存对时候对数据进行序列化保存
@Override
public void putObject(Object key, Object object) {
//如果对象为null或者实现了Serializable接口的对象需要进行序列化，否则抛出异常
if (object == null || object instanceof Serializable) {
delegate.putObject(key, serialize((Serializable) object));
} else {
throw new CacheException("SharedCache failed to make a copy of a non-serializable object: " + object);
}
}
//从缓存中获取数据对数据进行一次反序列化
@Override
public Object getObject(Object key) {
//从缓存中获取对象
Object object = delegate.getObject(key);
//如果对象为null则直接返回null,否则返回反序列化后对象
return object == null ? null : deserialize((byte[]) object);
}
@Override
public Object removeObject(Object key) {
return delegate.removeObject(key);
}
@Override
public void clear() {
delegate.clear();
}
@Override
public ReadWriteLock getReadWriteLock() {
return null;
}
@Override
public int hashCode() {
return delegate.hashCode();
}
@Override
public boolean equals(Object obj) {
return delegate.equals(obj);
}
//对数据进行序列化
private byte[] serialize(Serializable value) {
try {
ByteArrayOutputStream bos = new ByteArrayOutputStream();
ObjectOutputStream oos = new ObjectOutputStream(bos);
oos.writeObject(value);
oos.flush();
oos.close();
return bos.toByteArray();
} catch (Exception e) {
throw new CacheException("Error serializing object. Cause: " + e, e);
}
}
//对数据进行反序列化
private Serializable deserialize(byte[] value) {
Serializable result;
try {
ByteArrayInputStream bis = new ByteArrayInputStream(value);
ObjectInputStream ois = new CustomObjectInputStream(bis);
result = (Serializable) ois.readObject();
ois.close();
} catch (Exception e) {
throw new CacheException("Error deserializing object. Cause: " + e, e);
}
return result;
}
public static class CustomObjectInputStream extends ObjectInputStream {
public CustomObjectInputStream(InputStream in) throws IOException {
super(in);
}
@Override
protected Class<?> resolveClass(ObjectStreamClass desc) throws IOException, ClassNotFoundException {
return Resources.classForName(desc.getName());
}
}
}
软引用缓存装饰器，很好的一个实现方式，在弱引用缓存装饰器中同样使用了该方式实现
public class SoftCache implements Cache {
//强引用队列，包装队列中的元素不会被垃圾回收
private final Deque<Object> hardLinksToAvoidGarbageCollection;
//引用队列，被回收对象在添加到引用队列中
private final ReferenceQueue<Object> queueOfGarbageCollectedEntries;
//被包装的实现
private final Cache delegate;
//保存强引用的数量
private int numberOfHardLinks;
public SoftCache(Cache delegate) {
this.delegate = delegate;
this.numberOfHardLinks = 256;
this.hardLinksToAvoidGarbageCollection = new LinkedList<Object>();
this.queueOfGarbageCollectedEntries = new ReferenceQueue<Object>();
}
@Override
public String getId() {
return delegate.getId();
}
@Override
public int getSize() {
//返回删除被回收的对象后缓存的大小
removeGarbageCollectedItems();
return delegate.getSize();
}
public void setSize(int size) {
this.numberOfHardLinks = size;
}
@Override
public void putObject(Object key, Object value) {
//删除被回收对象缓存
removeGarbageCollectedItems();
//将当前键值对包装成SoftEntry存入缓存
delegate.putObject(key, new SoftEntry(key, value, queueOfGarbageCollectedEntries));
}
@Override
public Object getObject(Object key) {
Object result = null;
//从缓存中获取软引用对象
SoftReference<Object> softReference = (SoftReference<Object>) delegate.getObject(key);
//如果软引用不为null,则获取引用对象中的真实的对象
if (softReference != null) {
result = softReference.get();
//如果真实的对象为null,则标示该对象已经被垃圾回收了，则删除缓存
if (result == null) {
delegate.removeObject(key);
} else { //真实对象不为null
synchronized (hardLinksToAvoidGarbageCollection) {
//将对象添加早强引用的对头
hardLinksToAvoidGarbageCollection.addFirst(result);
//如果强引用队列达到阈值则删除队尾元素
if (hardLinksToAvoidGarbageCollection.size() > numberOfHardLinks) {
hardLinksToAvoidGarbageCollection.removeLast();
}
}
}
}
return result;
}
@Override
public Object removeObject(Object key) {
//删除对象前需要执行删除垃圾回收对象
removeGarbageCollectedItems();
return delegate.removeObject(key);
}
@Override
public void clear() {
synchronized (hardLinksToAvoidGarbageCollection) {
hardLinksToAvoidGarbageCollection.clear();
}
removeGarbageCollectedItems();
delegate.clear();
}
@Override
public ReadWriteLock getReadWriteLock() {
return null;
}
//删除被垃圾回收对象
private void removeGarbageCollectedItems() {
SoftEntry sv;
while ((sv = (SoftEntry) queueOfGarbageCollectedEntries.poll()) != null) {
delegate.removeObject(sv.key);
}
}
private static class SoftEntry extends SoftReference<Object> {
private final Object key;
SoftEntry(Object key, Object value, ReferenceQueue<Object> garbageCollectionQueue) {
super(value, garbageCollectionQueue);
this.key = key;
}
}
}
定期清空缓存装饰器，在该装饰器中并不是使用主动失效方式，而是使用懒惰方式。
/**
* 定期清空缓存的装饰器，其清空缓存的策略使用的是懒惰清空方式
* 在 getSize,putObject, getObject removeObject的时候会触发清空检查
*
*/
public class ScheduledCache implements Cache {
private final Cache delegate;
//刷新间隔
protected long clearInterval;
//最后一次清空缓存时间
protected long lastClear;
public ScheduledCache(Cache delegate) {
this.delegate = delegate;
this.clearInterval = 60 * 60 * 1000; // 1 hour
this.lastClear = System.currentTimeMillis();
}
public void setClearInterval(long clearInterval) {
this.clearInterval = clearInterval;
}
@Override
public String getId() {
return delegate.getId();
}
@Override
public int getSize() {
clearWhenStale();
return delegate.getSize();
}
@Override
public void putObject(Object key, Object object) {
clearWhenStale();
delegate.putObject(key, object);
}
@Override
public Object getObject(Object key) {
return clearWhenStale() ? null : delegate.getObject(key);
}
@Override
public Object removeObject(Object key) {
clearWhenStale();
return delegate.removeObject(key);
}
@Override
public void clear() {
lastClear = System.currentTimeMillis();
delegate.clear();
}
@Override
public ReadWriteLock getReadWriteLock() {
return null;
}
@Override
public int hashCode() {
return delegate.hashCode();
}
@Override
public boolean equals(Object obj) {
return delegate.equals(obj);
}
private boolean clearWhenStale() {
//如果当前时间减去最后一次刷新时间大于刷新间隔则需要晴空缓存
if (System.currentTimeMillis() - lastClear > clearInterval) {
clear();
return true;
}
return false;
}
}
