手写一个自己的LocalCache - 基于LinkedHashMap实现LRU
功能目标
实现一个全局范围的LocalCache，各个业务点使用自己的Namespace对LocalCache进行逻辑分区，所以在LocalCache中进行读写采用的key为(namespace+(分隔符)+数据key)，如存在以下的一对keyValue : NameToAge,Troy -> 23 。要求LocalCache线程安全，且LocalCache中总keyValue数量可控，提供清空，调整大小，dump到本地文件等一系列操作。
用LinkedHashMap实现LRU Map
LinkedHashMap提供了键值对的储存功能，且可根据其支持访问排序的特性来模拟LRU算法。简单来说，LinkedHashMap在访问已存在元素或插入新元素时，会将该元素放置在链表的尾部，所以在链表头部的元素是最近最少未使用的元素，而这正是LRU算法的描述。由于其底层基于链表实现，所以对于元素的移动和插入操作性能表现优异。我们将利用一个LinkedHashMap实现一个线程安全的LRU Map。
LRU Map的实现
public class LRUMap<T> extends LinkedHashMap<String, SoftReference<T>> implements Externalizable {
private static final long serialVersionUID = -7076355612133906912L;
/** The maximum size of the cache. */
private int maxCacheSize;
/* lock for map */
private final Lock lock = new ReentrantLock();
/**
* 默认构造函数，LRUMap的大小为Integer.MAX_VALUE
*/
public LRUMap() {
super();
maxCacheSize = Integer.MAX_VALUE;
}
/**
* Constructs a new, empty cache with the specified maximum size.
*/
public LRUMap(int size) {
super(size + 1, 1f, true);
maxCacheSize = size;
}
/**
* 让LinkHashMap支持LRU，如果Map的大小超过了预定值，则返回true，LinkedHashMap自身实现返回
* fasle，即永远不删除元素
*/
@Override
protected boolean removeEldestEntry(Map.Entry<String, SoftReference<T>> eldest) {
boolean tmp = (size() > maxCacheSize);
return tmp;
}
public T addEntry(String key, T entry) {
try {
SoftReference<T> sr_entry = new SoftReference<T>(entry);
// add entry to hashmap
lock.lock();
put(key, sr_entry);
}
finally {
lock.unlock();
}
return entry;
}
public T getEntry(String key) {
SoftReference<T> sr_entry;
try {
lock.lock();
if ((sr_entry = get(key)) == null)
return null;
// if soft reference is null then the entry has been
// garbage collected and so the key should be removed also.
if (sr_entry.get() == null) {
remove(key);
return null;
}
}
finally {
lock.unlock();
}
return sr_entry.get();
}
@Override
public SoftReference<T> remove(Object key) {
try {
lock.lock();
return super.remove(key);
}
finally {
lock.unlock();
}
}
@Override
public synchronized void clear() {
super.clear();
}
public void writeExternal(ObjectOutput out) throws IOException {
Iterator<Map.Entry<String, SoftReference<T>>> i = (size() > 0) ? entrySet().iterator() : null;
// Write out size
out.writeInt(size());
// Write out keys and values
if (i != null) {
while (i.hasNext()) {
Map.Entry<String, SoftReference<T>> e = i.next();
if (e != null && e.getValue() != null && e.getValue().get() != null) {
out.writeObject(e.getKey());
out.writeObject(e.getValue().get());
}
}
}
}
public void readExternal(ObjectInput in) throws IOException, ClassNotFoundException {
// Read in size
int size = in.readInt();
// Read the keys and values, and put the mappings in the Map
for (int i = 0; i < size; i++) {
String key = (String) in.readObject();
@SuppressWarnings("unchecked")
T value = (T) in.readObject();
addEntry(key, value);
}
}
}
LocalCache设计
如果在LocalCache中只使用一个LRU Map，将产生性能问题：1. 单个LinkedHashMap中元素数量太多 2. 高并发下读写锁限制。
所以可以在LocalCache中使用多个LRU Map，并使用key 来 hash到某个LRU Map上，以此来提高在单个LinkedHashMap中检索的速度以及提高整体并发度。
LocalCache实现
这里hash选用了Wang/Jenkins hash算法。实现Hash的方式参考了ConcurrentHashMap的实现。
public class LocalCache{
private final int size;
/**
* 本地缓存最大容量
*/
static final int MAXIMUM_CAPACITY = 1 << 30;
/**
* 本地缓存支持最大的分区数
*/
static final int MAX_SEGMENTS = 1 << 16; // slightly conservative
/**
* 本地缓存存储的LRUMap数组
*/
LRUMap<CacheObject>[] segments;
/**
* Mask value for indexing into segments. The upper bits of a key's hash
* code are used to choose the segment.
*/
int segmentMask;
/**
* Shift value for indexing within segments.
*/
int segmentShift;
/**
*
* 计数器重置阀值
*/
private static final int MAX_LOOKUP = 100000000;
/**
* 用于重置计数器的锁，防止多次重置计数器
*/
private final Lock lock = new ReentrantLock();
/**
* Number of requests made to lookup a cache entry.
*/
private AtomicLong lookup = new AtomicLong(0);
/**
* Number of successful requests for cache entries.
*/
private AtomicLong found = new AtomicLong(0);
public LocalCacheServiceImpl(int size) {
this.size = size;
}
public CacheObject get(String key) {
if (StringUtils.isBlank(key)) {
return null;
}
// 增加计数器
lookup.incrementAndGet();
// 如果必要重置计数器
if (lookup.get() > MAX_LOOKUP) {
if (lock.tryLock()) {
try {
lookup.set(0);
found.set(0);
}
finally {
lock.unlock();
}
}
}
int hash = hash(key.hashCode());
CacheObject ret = segmentFor(hash).getEntry(key);
if (ret != null)
found.incrementAndGet();
return ret;
}
public void remove(String key) {
if (StringUtils.isBlank(key)) {
return;
}
int hash = hash(key.hashCode());
segmentFor(hash).remove(key);
return;
}
public void put(String key, CacheObject val) {
if (StringUtils.isBlank(key) || val == null) {
return;
}
int hash = hash(key.hashCode());
segmentFor(hash).addEntry(key, val);
return;
}
public synchronized void clearCache() {
for (int i = 0; i < segments.length; ++i)
segments[i].clear();
}
public synchronized void reload() throws Exception {
clearCache();
init();
}
public synchronized void dumpLocalCache() throws Exception {
for (int i = 0; i < segments.length; ++i) {
String tmpDir = System.getProperty("java.io.tmpdir");
String fileName = tmpDir + File.separator + "localCache-dump-file" + i + ".cache";
File file = new File(fileName);
ObjectUtils.objectToFile(segments[i], file);
}
}
@SuppressWarnings("unchecked")
public synchronized void restoreLocalCache() throws Exception {
for (int i = 0; i < segments.length; ++i) {
String tmpDir = System.getProperty("java.io.tmpdir");
String fileName = tmpDir + File.separator + "localCache-dump-file" + i + ".cache";
File file = new File(fileName);
LRUMap<CacheObject> lruMap = (LRUMap<CacheObject>) ObjectUtils.fileToObject(file);
if (lruMap != null) {
Set<Entry<String, SoftReference<CacheObject>>> set = lruMap.entrySet();
Iterator<Entry<String, SoftReference<CacheObject>>> it = set.iterator();
while (it.hasNext()) {
Entry<String, SoftReference<CacheObject>> entry = it.next();
if (entry.getValue() != null && entry.getValue().get() != null)
segments[i].addEntry(entry.getKey(), entry.getValue().get());
}
}
}
}
/**
* 本地缓存命中次数，在计数器RESET的时刻可能会出现0的命中率
*/
public int getHitRate() {
long query = lookup.get();
return query == 0 ? 0 : (int) ((found.get() * 100) / query);
}
/**
* 本地缓存访问次数，在计数器RESET时可能会出现0的查找次数
*/
public long getCount() {
return lookup.get();
}
public int size() {
final LRUMap<CacheObject>[] segments = this.segments;
long sum = 0;
for (int i = 0; i < segments.length; ++i) {
sum += segments[i].size();
}
if (sum > Integer.MAX_VALUE)
return Integer.MAX_VALUE;
else
return (int) sum;
}
/**
* Returns the segment that should be used for key with given hash
*
* @param hash
* the hash code for the key
* @return the segment
*/
final LRUMap<CacheObject> segmentFor(int hash) {
return segments[(hash >>> segmentShift) & segmentMask];
}
/* ---------------- Small Utilities -------------- */
/**
* Applies a supplemental hash function to a given hashCode, which defends
* against poor quality hash functions. This is critical because
* ConcurrentHashMap uses power-of-two length hash tables, that otherwise
* encounter collisions for hashCodes that do not differ in lower or upper
* bits.
*/
private static int hash(int h) {
// Spread bits to regularize both segment and index locations,
// using variant of single-word Wang/Jenkins hash.
h += (h << 15) ^ 0xffffcd7d;
h ^= (h >>> 10);
h += (h << 3);
h ^= (h >>> 6);
h += (h << 2) + (h << 14);
return h ^ (h >>> 16);
}
@SuppressWarnings("unchecked")
public void init() throws Exception {
int concurrencyLevel = 16;
int capacity = size;
if (capacity < 0 || concurrencyLevel <= 0)
throw new IllegalArgumentException();
if (concurrencyLevel > MAX_SEGMENTS)
concurrencyLevel = MAX_SEGMENTS;
// Find power-of-two sizes best matching arguments
int sshift = 0;
int ssize = 1;
while (ssize < concurrencyLevel) {
++sshift;
ssize <<= 1;
}
segmentShift = 32 - sshift;
segmentMask = ssize - 1;
this.segments = new LRUMap[ssize];
if (capacity > MAXIMUM_CAPACITY)
capacity = MAXIMUM_CAPACITY;
int c = capacity / ssize;
if (c * ssize < capacity)
++c;
int cap = 1;
while (cap < c)
cap <<= 1;
cap >>= 1;
for (int i = 0; i < this.segments.length; ++i)
this.segments[i] = new LRUMap<CacheObject>(cap);
}
}