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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 = http://www.mamicode.com/(T) in.readObject();>
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); } }
手写一个自己的LocalCache - 基于LinkedHashMap实现LRU
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