（转载）ConcurrentHaspLRUHashMap实现初探
一、 关于LRU
LRU 即 Least Rencetly Used（最近最少使用）缓存替换策略。在任何LRU算法中，它必定有以下两个策略组成：
1、 退化 策略。根据访问情况，对节点按热度进行排序（hot->cold），以便决定哪些节点是热节点（hot）的，哪些节点是冷节点（cold）的。这个退化的策略，一般按以下两种方式去处理：
l 非集中式。即每命中一次就进行退化操作。
非集中式的退化操作，往往由双向链表的方式去实现。每次命中之后就移动命中节点在链表中的位置。（位置靠前的就是hot的数据）。当然，复杂的策略中，有用queue数组进行hot分级等。
l 集中式。定期去进行退化操作。
在集中式的退化操作，常用的策略是：每次命中之后，记录一个时间戳、定时器时间点等等参数。由一个线程去扫描，定期清除老数据。
2、 清除 策略。即去掉那些cold的数据。
l 替换。这个在操作系统缓存中应该是一个常用的做法。
l 删除。删除掉数据，以腾出空间放新的数据。（因为内存是有限的）
二、 ConcurrentHashMap与LinkedHashMap
在JAVA中，LRU的原生实现是JDK中LinkedHashMap。LinkedHashMap继承自HashMap
【实现原理】 简单说就是HashMap的每个节点做一个双向链表。每次访问这个节点，就把该节点移动到双向链表的头部。满了以后，就从链表的尾部删除。但是LinkedHashMap并是非线程安全（其实现中，双向链表的操作是没有任何线程安全的措施的）。
对于线程安全的HashMap，在JDK中有ConcurrentHashMap原生支持。
【实现原理】采用锁分离机制，把一个HashMap分成多个segement，对每个segement的写操作上锁。同时，他的get（）操作是没有锁的，具体思想就是把每个hash槽中的链表的头节点置成final的。对hash槽中链表操作，只能从头部去处理。这样就不会有读不一致的情况出现。这个原理，最好还是看源码，比较清晰。
三、 ConcurrentLRUHashMap的实现方式一：直接包装LinkedHashMap。
即，在LinkedHashMap外层全部加锁。
典型代码：
public V get(Object key) {
lock.lock();
try {
return super.get(key);
}
finally {
lock.unlock();
}
}
对LinkedHashMap做包装，所有访问都是带锁委托给LinkedHashMap。这样虽然解决了多线程安全问题。但是，是以严重的性能消耗为代价代价。
四、 ConcurrentLRUHashMap实现方式二：直接改造ConcurrentHashMap
该方案主要是重写ConcurrentHashMap。
1、 给每个Entry加一个timestamp。
2、 每次get命中的话，修改时间戳。
3、 定时统计整个map的总量，如果总量大于某个阈值，则deadline往后推。同时，在put的时候，检查hash槽里面每个节点的时间戳，如果已经过期，就删除掉过期节点。
上述做法，删除操作分布在每次put操作中。所以，删除效率比较高。但是，由于时间片不可控，最终将导致内存爆炸的情况出现。
请看下面一种场景：
横坐标表示一个时间片。面积表示这个时间片里面节点数量。
假定节点命中率为50%（命中后，更新到命中时刻的时间片），每个时间片写入10条新数据。
我们可以在运行过程中，每个时间片定义一个更新一次deadline。在put数据的时候，我们可以检查hash槽中Entry是否过期，如果已经过期，则删掉过期数据。
对于deadline的计算，我们可以设置三个阈值(a<b<c)
a) totalCount<a deadline不变
b) a<totalCount<b deadline=deadline+cycle
c) b<totalCount<c deadline=deadline+2*cycle
d) totalCount>c deadline=currentTime
上述看似非常优雅的方案，却隐藏几个严重的问题：
1、 时间片的选择问题。
这个方案中，时间片的选择是一个比较困难的问题。因为，如果系统在一个时间片之内爆掉内存的话，系统将直接崩溃。
当然，这个问题，我们可以加外部限制得方式去控制
2、 deadline 之前的数据，不能很快删除。导致deaddata滞留，浪费大量的内存
假定 deadline之前的数据，约为总数据量的10%。因为删数据操作，只在put的时候。假定每个时间点的put操作，能覆盖20%的hash槽。这个10%*20%=2%，每个时间点，只能删除2%的过期数据。然后，随着时间的推移。这个过程必将趋于稳定。而这个趋于稳定后，内存消耗，至少是capacity的4-5倍。这样的消耗和浪费。是难以承受的。
这个方案，从实际测试来看，情况非常糟糕。所以最终还是放弃了。
五、 ConcurrentLRUHashMap实现方式三:分段实现锁分离+每个段内维护一份退化链表
【实现策略】：
1、锁分离机制。内部分成了多个segement，每个segement是独立加锁，相互不干扰。
2、每个segement内部维护一个双向链表(退化链表)。每次命中/添加，就把节点移动到退化链表头部。
3、每次put操作，通过hash，散到每个segement中，判断segment的容量是否到达阈值。 如果到达阈值，则删除退化链表中最末尾的节点。
【实现】
1、重新定义HashEntry<K,V>
static class HashEntry<K, V> {/*** 键*/final K key;/*** hash值*/final int hash;/*** 值*/volatile V value;/*** hash链指针*/final HashEntry<K, V> next;/*** 双向链表的下一个节点*/HashEntry<K, V> linknext;/*** 双向链表的下一个节点*/HashEntry<K, V> linkpref;/*** 死亡标记*/AtomicBoolean dead;}
2、定义segment
static final class Segment<K, V> extends ReentrantLock implements Serializable { private static final long serialVersionUID = 1L; transient int threshold; transient volatile int count; transient int modCount; transient volatile HashEntry<K, V>[] table; transient final HashEntry<K, V> header;// 头节点 }
3、 put操作
代码太长了，见附件吧
4、 get操作
V get(Object key, int hash) {
HashEntry<K, V> e = getFirst(hash);
// 遍历查找
while (e != null) {
if (e.hash == hash && key.equals(e.key)) {
V v = e.value;
// 把节点移动到头部。
moveNodeToHeader(e);
if (v != null)
return v;
// 在锁的情况读，必定能读到。
// tab[index] = new HashEntry<K,V>(key, hash, first, value)，
// value赋值和tab[index]赋值可能会重新排序，重新排序之后，可能会读空值
// 读到空值的话，在有锁的情况在再读一遍，一定能读！
return readValueUnderLock(e); // recheck
}
e = e.next;
}
return null;
六、 ConcurrentLRUHashMap实现方式四:
具体的做法是：
1、 对concurrentHashMap 每个节点加时间戳，每次命中只修改该节点的时间戳。
2、 集中式退化操作，每次命中并不进行退化操作。而是集中式进行退化操作（满的时候，或者时间到了）。
代码：
private static class CountableKey<K,V> implements Comparable<CountableKey<K, V>> {
public CountableKey(K key,V value) {
if (value == null) {
throw new NullPointerException("should not be null");
}
this.value = value;
this.key = key;
refreshTimeStamp();
}
public void refreshTimeStamp(){
timestamp.set(System.currentTimeMillis());
}
final V value;
final K key;
AtomicLong timestamp = new AtomicLong();
@Override
public int compareTo(CountableKey<K, V> o) {
long thisval = this.timestamp.get();
long anotherVal = o.timestamp.get();
return (thisval < anotherVal?-1:(thisval == anotherVal?0:1));
}
}
该方案的好处：
1、 快速执行get操作。get操作的时间是“concurrentHashMap的get时间+更新时间戳”的时间。
2、 put操作，一般的put操作的时间是“concurrentHashMap的put时间”，只要还未到达容量限制。而到达容量限制以后的，需要进行“退化，清理操作”+put的时间
该方案的 可能存在的问题：
1、 命中率，该算法的命中率同linkedHashMap
2、 清除 策略:
l 满了，执行清楚。缺点：1、会出现某个时刻，写操作卡死（如果正在等待清理的话）
l 定时执行。缺点：1、性能耗费。2、读不一致仍然无法避免。
七、 ConcurrentLRUHashMap实现方式的比较
本文只是抛砖引玉，希望能看到更多好多ConcurrentLRUHashMap的实现方式。由于能力有限。上文提到的第二种实现方式，在实际实现中并不能很好的退化，最终可能导致内存溢出。具体分析如下表
| 方式 | 方式一 | 方式二 | 方式三 | 方式四 |
| 性能 | 差 | 好 | 好 | 好 |
| 线程安全 | 绝对安全 | 安全 | 安全 | 安全 |
| 内存消耗 | 一般 | 很多 | 一般 | 一般 |
| 稳定性 | 稳定 | 不稳定 | 稳定 | 不稳定 |
总体来说，第三者性较好。
比较方式一和方式三：
源代码如下：
package com.googlecode.jue.util;
import java.io.IOException;
import java.io.Serializable;
import java.util.AbstractCollection;
import java.util.AbstractMap;
import java.util.AbstractSet;
import java.util.Collection;
import java.util.ConcurrentModificationException;
import java.util.Enumeration;
import java.util.Iterator;
import java.util.Map;
import java.util.NoSuchElementException;
import java.util.Set;
import java.util.concurrent.ConcurrentMap;
import java.util.concurrent.atomic.AtomicBoolean;
import java.util.concurrent.locks.ReentrantLock;
/**
* 基于ConcurrentHashMap修改的LRUMap
*
* @author noah
*
* @param <K>
* @param <V>
*/
public class ConcurrentLRUHashMap<K, V> extends AbstractMap<K, V> implements
ConcurrentMap<K, V>, Serializable {
/*
* The basic strategy is to subdivide the table among Segments, each of
* which itself is a concurrently readable hash table.
*/
/* ---------------- Constants -------------- */
/**
*
*/
private static final long serialVersionUID = -5031526786765467550L;
/**
* Segement默认最大数
*/
static final int DEFAULT_SEGEMENT_MAX_CAPACITY = 100;
/**
* The default load factor for this table, used when not otherwise specified
* in a constructor.
*/
static final float DEFAULT_LOAD_FACTOR = 0.75f;
/**
* The default concurrency level for this table, used when not otherwise
* specified in a constructor.
*/
static final int DEFAULT_CONCURRENCY_LEVEL = 16;
/**
* The maximum capacity, used if a higher value is implicitly specified by
* either of the constructors with arguments. MUST be a power of two <=
* 1<<30 to ensure that entries are indexable using ints.
*/
static final int MAXIMUM_CAPACITY = 1 << 30;
/**
* The maximum number of segments to allow; used to bound constructor
* arguments.
*/
static final int MAX_SEGMENTS = 1 << 16; // slightly conservative
/**
* Number of unsynchronized retries in size and containsValue methods before
* resorting to locking. This is used to avoid unbounded retries if tables
* undergo continuous modification which would make it impossible to obtain
* an accurate result.
*/
static final int RETRIES_BEFORE_LOCK = 2;
/* ---------------- Fields -------------- */
/**
* Mask value for indexing into segments. The upper bits of a key's hash
* code are used to choose the segment.
*/
final int segmentMask;
/**
* Shift value for indexing within segments.
*/
final int segmentShift;
/**
* The segments, each of which is a specialized hash table
*/
final Segment<K, V>[] segments;
transient Set<K> keySet;
transient Set<Map.Entry<K, V>> entrySet;
transient Collection<V> values;
/* ---------------- 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);
}
/**
* Returns the segment that should be used for key with given hash
*
* @param hash
* the hash code for the key
* @return the segment
*/
final Segment<K, V> segmentFor(int hash) {
return segments[(hash >>> segmentShift) & segmentMask];
}
/* ---------------- Inner Classes -------------- */
/**
* 修改原HashEntry，
*/
static final class HashEntry<K, V> {
/**
* 键
*/
final K key;
/**
* hash值
*/
final int hash;
/**
* 值
*/
volatile V value;
/**
* hash链指针
*/
final HashEntry<K, V> next;
/**
* 双向链表的下一个节点
*/
HashEntry<K, V> linkNext;
/**
* 双向链表的上一个节点
*/
HashEntry<K, V> linkPrev;
/**
* 死亡标记
*/
AtomicBoolean dead;
HashEntry(K key, int hash, HashEntry<K, V> next, V value) {
this.key = key;
this.hash = hash;
this.next = next;
this.value = value;
dead = new AtomicBoolean(false);
}
@SuppressWarnings("unchecked")
static final <K, V> HashEntry<K, V>[] newArray(int i) {
return new HashEntry[i];
}
}
/**
* 基于原Segment修改，内部实现一个双向列表
*
* @author noah
*
* @param <K>
* @param <V>
*/
static final class Segment<K, V> extends ReentrantLock implements Serializable {
/*
* Segments maintain a table of entry lists that are ALWAYS kept in a
* consistent state, so can be read without locking. Next fields of
* nodes are immutable (final). All list additions are performed at the
* front of each bin. This makes it easy to check changes, and also fast
* to traverse. When nodes would otherwise be changed, new nodes are
* created to replace them. This works well for hash tables since the
* bin lists tend to be short. (The average length is less than two for
* the default load factor threshold.)
*
* Read operations can thus proceed without locking, but rely on
* selected uses of volatiles to ensure that completed write operations
* performed by other threads are noticed. For most purposes, the
* "count" field, tracking the number of elements, serves as that
* volatile variable ensuring visibility. This is convenient because
* this field needs to be read in many read operations anyway:
*
* - All (unsynchronized) read operations must first read the "count"
* field, and should not look at table entries if it is 0.
*
* - All (synchronized) write operations should write to the "count"
* field after structurally changing any bin. The operations must not
* take any action that could even momentarily cause a concurrent read
* operation to see inconsistent data. This is made easier by the nature
* of the read operations in Map. For example, no operation can reveal
* that the table has grown but the threshold has not yet been updated,
* so there are no atomicity requirements for this with respect to
* reads.
*
* As a guide, all critical volatile reads and writes to the count field
* are marked in code comments.
*/
private static final long serialVersionUID = 2249069246763182397L;
/**
* The number of elements in this segment's region.
*/
transient volatile int count;
/**
* Number of updates that alter the size of the table. This is used
* during bulk-read methods to make sure they see a consistent snapshot:
* If modCounts change during a traversal of segments computing size or
* checking containsValue, then we might have an inconsistent view of
* state so (usually) must retry.
*/
transient int modCount;
/**
* The table is rehashed when its size exceeds this threshold. (The
* value of this field is always <tt>(int)(capacity *
* loadFactor)</tt>.)
*/
transient int threshold;
/**
* The per-segment table.
*/
transient volatile HashEntry<K, V>[] table;
/**
* The load factor for the hash table. Even though this value is same
* for all segments, it is replicated to avoid needing links to outer
* object.
*
* @serial
*/
final float loadFactor;
/**
* 头节点
*/
transient final HashEntry<K, V> header;
/**
* Segement最大容量
*/
final int maxCapacity;
Segment(int maxCapacity, float lf, ConcurrentLRUHashMap<K, V> lruMap) {
this.maxCapacity = maxCapacity;
loadFactor = lf;
setTable(HashEntry.<K, V> newArray(maxCapacity));
header = new HashEntry<K, V>(null, -1, null, null);
header.linkNext = header;
header.linkPrev = header;
}
@SuppressWarnings("unchecked")
static final <K, V> Segment<K, V>[] newArray(int i) {
return new Segment[i];
}
/**
* Sets table to new HashEntry array. Call only while holding lock or in
* constructor.
*/
void setTable(HashEntry<K, V>[] newTable) {
threshold = (int) (newTable.length * loadFactor);
table = newTable;
}
/**
* Returns properly casted first entry of bin for given hash.
*/
HashEntry<K, V> getFirst(int hash) {
HashEntry<K, V>[] tab = table;
return tab[hash & (tab.length - 1)];
}
/**
* Reads value field of an entry under lock. Called if value field ever
* appears to be null. This is possible only if a compiler happens to
* reorder a HashEntry initialization with its table assignment, which
* is legal under memory model but is not known to ever occur.
*/
V readValueUnderLock(HashEntry<K, V> e) {
lock();
try {
return e.value;
} finally {
unlock();
}
}
/* Specialized implementations of map methods */
V get(Object key, int hash) {
lock();
try {
if (count != 0) { // read-volatile
HashEntry<K, V> e = getFirst(hash);
while (e != null) {
if (e.hash == hash && key.equals(e.key)) {
V v = e.value;
// 将节点移动到头节点之前
moveNodeToHeader(e);
if (v != null)
return v;
return readValueUnderLock(e); // recheck
}
e = e.next;
}
}
return null;
} finally {
unlock();
}
}
/**
* 将节点移动到头节点之前
*
* @param entry
*/
void moveNodeToHeader(HashEntry<K, V> entry) {
// 先移除，然后插入到头节点的前面
removeNode(entry);
addBefore(entry, header);
}
/**
* 将第一个参数代表的节点插入到第二个参数代表的节点之前
*
* @param newEntry
* 需要插入的节点
* @param entry
* 被插入的节点
*/
void addBefore(HashEntry<K, V> newEntry, HashEntry<K, V> entry) {
newEntry.linkNext = entry;
newEntry.linkPrev = entry.linkPrev;
entry.linkPrev.linkNext = newEntry;
entry.linkPrev = newEntry;
}
/**
* 从双向链中删除该Entry
*
* @param entry
*/
void removeNode(HashEntry<K, V> entry) {
entry.linkPrev.linkNext = entry.linkNext;
entry.linkNext.linkPrev = entry.linkPrev;
}
boolean containsKey(Object key, int hash) {
lock();
try {
if (count != 0) { // read-volatile
HashEntry<K, V> e = getFirst(hash);
while (e != null) {
if (e.hash == hash && key.equals(e.key)) {
moveNodeToHeader(e);
return true;
}
e = e.next;
}
}
return false;
} finally {
unlock();
}
}
boolean containsValue(Object value) {
lock();
try {
if (count != 0) { // read-volatile
HashEntry<K, V>[] tab = table;
int len = tab.length;
for (int i = 0; i < len; i++) {
for (HashEntry<K, V> e = tab[i]; e != null; e = e.next) {
V v = e.value;
if (v == null) // recheck
v = readValueUnderLock(e);
if (value.equals(v)) {
moveNodeToHeader(e);
return true;
}
}
}
}
return false;
} finally {
unlock();
}
}
boolean replace(K key, int hash, V oldValue, V newValue) {
lock();
try {
HashEntry<K, V> e = getFirst(hash);
while (e != null && (e.hash != hash || !key.equals(e.key)))
e = e.next;
boolean replaced = false;
if (e != null && oldValue.equals(e.value)) {
replaced = true;
e.value = newValue;
// 移动到头部
moveNodeToHeader(e);
}
return replaced;
} finally {
unlock();
}
}
V replace(K key, int hash, V newValue) {
lock();
try {
HashEntry<K, V> e = getFirst(hash);
while (e != null && (e.hash != hash || !key.equals(e.key)))
e = e.next;
V oldValue = null;
if (e != null) {
oldValue = e.value;
e.value = newValue;
// 移动到头部
moveNodeToHeader(e);
}
return oldValue;
} finally {
unlock();
}
}
V put(K key, int hash, V value, boolean onlyIfAbsent) {
lock();
try {
int c = count;
if (c++ > threshold) // ensure capacity
rehash();
HashEntry<K, V>[] tab = table;
int index = hash & (tab.length - 1);
HashEntry<K, V> first = tab[index];
HashEntry<K, V> e = first;
while (e != null && (e.hash != hash || !key.equals(e.key)))
e = e.next;
V oldValue = null;
if (e != null) {
oldValue = e.value;
if (!onlyIfAbsent) {
e.value = value;
// 移动到头部
moveNodeToHeader(e);
}
} else {
oldValue = null;
++modCount;
HashEntry<K, V> newEntry = new HashEntry<K, V>(key, hash, first, value);
tab[index] = newEntry;
count = c; // write-volatile
// 添加到双向链
addBefore(newEntry, header);
// 判断是否达到最大值
removeEldestEntry();
}
return oldValue;
} finally {
unlock();
}
}
void rehash() {
HashEntry<K, V>[] oldTable = table;
int oldCapacity = oldTable.length;
if (oldCapacity >= MAXIMUM_CAPACITY)
return;
/*
* Reclassify nodes in each list to new Map. Because we are using
* power-of-two expansion, the elements from each bin must either
* stay at same index, or move with a power of two offset. We
* eliminate unnecessary node creation by catching cases where old
* nodes can be reused because their next fields won't change.
* Statistically, at the default threshold, only about one-sixth of
* them need cloning when a table doubles. The nodes they replace
* will be garbage collectable as soon as they are no longer
* referenced by any reader thread that may be in the midst of
* traversing table right now.
*/
HashEntry<K, V>[] newTable = HashEntry.newArray(oldCapacity << 1);
threshold = (int) (newTable.length * loadFactor);
int sizeMask = newTable.length - 1;
for (int i = 0; i < oldCapacity; i++) {
// We need to guarantee that any existing reads of old Map can
// proceed. So we cannot yet null out each bin.
HashEntry<K, V> e = oldTable[i];
if (e != null) {
HashEntry<K, V> next = e.next;
int idx = e.hash & sizeMask;
// Single node on list
if (next == null)
newTable[idx] = e;
else {
// Reuse trailing consecutive sequence at same slot
HashEntry<K, V> lastRun = e;
int lastIdx = idx;
for (HashEntry<K, V> last = next; last != null; last = last.next) {
int k = last.hash & sizeMask;
if (k != lastIdx) {
lastIdx = k;
lastRun = last;
}
}
newTable[lastIdx] = lastRun;
// Clone all remaining nodes
for (HashEntry<K, V> p = e; p != lastRun; p = p.next) {
int k = p.hash & sizeMask;
HashEntry<K, V> n = newTable[k];
HashEntry<K, V> newEntry = new HashEntry<K, V>(
p.key, p.hash, n, p.value);
// update by Noah
newEntry.linkNext = p.linkNext;
newEntry.linkPrev = p.linkPrev;
newTable[k] = newEntry;
}
}
}
}
table = newTable;
}
/**
* Remove; match on key only if value null, else match both.
*/
V remove(Object key, int hash, Object value) {
lock();
try {
int c = count - 1;
HashEntry<K, V>[] tab = table;
int index = hash & (tab.length - 1);
HashEntry<K, V> first = tab[index];
HashEntry<K, V> e = first;
while (e != null && (e.hash != hash || !key.equals(e.key)))
e = e.next;
V oldValue = null;
if (e != null) {
V v = e.value;
if (value == null || value.equals(v)) {
oldValue = v;
// All entries following removed node can stay
// in list, but all preceding ones need to be
// cloned.
++modCount;
HashEntry<K, V> newFirst = e.next;
for (HashEntry<K, V> p = first; p != e; p = p.next) {
newFirst = new HashEntry<K, V>(p.key, p.hash,
newFirst, p.value);
newFirst.linkNext = p.linkNext;
newFirst.linkPrev = p.linkPrev;
}
tab[index] = newFirst;
count = c; // write-volatile
// 移除节点
removeNode(e);
}
}
return oldValue;
} finally {
unlock();
}
}
/**
* 移除最旧元素
*/
void removeEldestEntry() {
if (count > this.maxCapacity) {
HashEntry<K, V> eldest = header.linkNext;
remove(eldest.key, eldest.hash, null);
}
}
void clear() {
if (count != 0) {
lock();
try {
HashEntry<K, V>[] tab = table;
for (int i = 0; i < tab.length; i++)
tab[i] = null;
++modCount;
count = 0; // write-volatile
} finally {
unlock();
}
}
}
}
/**
* 使用指定参数，创建一个ConcurrentLRUHashMap
*
* @param segementCapacity
* Segement最大容量
* @param loadFactor
* 加载因子
* @param concurrencyLevel
* 并发级别
*/
public ConcurrentLRUHashMap(int segementCapacity, float loadFactor,
int concurrencyLevel) {
if (!(loadFactor > 0) || segementCapacity < 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 = Segment.newArray(ssize);
for (int i = 0; i < this.segments.length; ++i)
this.segments[i] = new Segment<K, V>(segementCapacity, loadFactor, this);
}
/**
* 使用指定参数，创建一个ConcurrentLRUHashMap
*
* @param segementCapacity
* Segement最大容量
* @param loadFactor
* 加载因子
*/
public ConcurrentLRUHashMap(int segementCapacity, float loadFactor) {
this(segementCapacity, loadFactor, DEFAULT_CONCURRENCY_LEVEL);
}
/**
* 使用指定参数，创建一个ConcurrentLRUHashMap
*
* @param segementCapacity
* Segement最大容量
*/
public ConcurrentLRUHashMap(int segementCapacity) {
this(segementCapacity, DEFAULT_LOAD_FACTOR, DEFAULT_CONCURRENCY_LEVEL);
}
/**
* 使用默认参数，创建一个ConcurrentLRUHashMap，存放元素最大数默认为1000， 加载因子为0.75，并发级别16
*/
public ConcurrentLRUHashMap() {
this(DEFAULT_SEGEMENT_MAX_CAPACITY, DEFAULT_LOAD_FACTOR,
DEFAULT_CONCURRENCY_LEVEL);
}
/**
* Returns <tt>true</tt> if this map contains no key-value mappings.
*
* @return <tt>true</tt> if this map contains no key-value mappings
*/
public boolean isEmpty() {
final Segment<K, V>[] segments = this.segments;
/*
* We keep track of per-segment modCounts to avoid ABA problems in which
* an element in one segment was added and in another removed during
* traversal, in which case the table was never actually empty at any
* point. Note the similar use of modCounts in the size() and
* containsValue() methods, which are the only other methods also
* susceptible to ABA problems.
*/
int[] mc = new int[segments.length];
int mcsum = 0;
for (int i = 0; i < segments.length; ++i) {
if (segments[i].count != 0)
return false;
else
mcsum += mc[i] = segments[i].modCount;
}
// If mcsum happens to be zero, then we know we got a snapshot
// before any modifications at all were made. This is
// probably common enough to bother tracking.
if (mcsum != 0) {
for (int i = 0; i < segments.length; ++i) {
if (segments[i].count != 0 || mc[i] != segments[i].modCount)
return false;
}
}
return true;
}
/**
* Returns the number of key-value mappings in this map. If the map contains
* more than <tt>Integer.MAX_VALUE</tt> elements, returns
* <tt>Integer.MAX_VALUE</tt>.
*
* @return the number of key-value mappings in this map
*/
public int size() {
final Segment<K, V>[] segments = this.segments;
long sum = 0;
long check = 0;
int[] mc = new int[segments.length];
// Try a few times to get accurate count. On failure due to
// continuous async changes in table, resort to locking.
for (int k = 0; k < RETRIES_BEFORE_LOCK; ++k) {
check = 0;
sum = 0;
int mcsum = 0;
for (int i = 0; i < segments.length; ++i) {
sum += segments[i].count;
mcsum += mc[i] = segments[i].modCount;
}
if (mcsum != 0) {
for (int i = 0; i < segments.length; ++i) {
check += segments[i].count;
if (mc[i] != segments[i].modCount) {
check = -1; // force retry
break;
}
}
}
if (check == sum)
break;
}
if (check != sum) { // Resort to locking all segments
sum = 0;
for (int i = 0; i < segments.length; ++i)
segments[i].lock();
for (int i = 0; i < segments.length; ++i)
sum += segments[i].count;
for (int i = 0; i < segments.length; ++i)
segments[i].unlock();
}
if (sum > Integer.MAX_VALUE)
return Integer.MAX_VALUE;
else
return (int) sum;
}
/**
* Returns the value to which the specified key is mapped, or {@code null}
* if this map contains no mapping for the key.
*
* <p>
* More formally, if this map contains a mapping from a key {@code k} to a
* value {@code v} such that {@code key.equals(k)}, then this method returns
* {@code v}; otherwise it returns {@code null}. (There can be at most one
* such mapping.)
*
* @throws NullPointerException
* if the specified key is null
*/
public V get(Object key) {
int hash = hash(key.hashCode());
return segmentFor(hash).get(key, hash);
}
/**
* Tests if the specified object is a key in this table.
*
* @param key
* possible key
* @return <tt>true</tt> if and only if the specified object is a key in
* this table, as determined by the <tt>equals</tt> method;
* <tt>false</tt> otherwise.
* @throws NullPointerException
* if the specified key is null
*/
public boolean containsKey(Object key) {
int hash = hash(key.hashCode());
return segmentFor(hash).containsKey(key, hash);
}
/**
* Returns <tt>true</tt> if this map maps one or more keys to the specified
* value. Note: This method requires a full internal traversal of the hash
* table, and so is much slower than method <tt>containsKey</tt>.
*
* @param value
* value whose presence in this map is to be tested
* @return <tt>true</tt> if this map maps one or more keys to the specified
* value
* @throws NullPointerException
* if the specified value is null
*/
public boolean containsValue(Object value) {
if (value == null)
throw new NullPointerException();
// See explanation of modCount use above
final Segment<K, V>[] segments = this.segments;
int[] mc = new int[segments.length];
// Try a few times without locking
for (int k = 0; k < RETRIES_BEFORE_LOCK; ++k) {
int mcsum = 0;
for (int i = 0; i < segments.length; ++i) {
mcsum += mc[i] = segments[i].modCount;
if (segments[i].containsValue(value))
return true;
}
boolean cleanSweep = true;
if (mcsum != 0) {
for (int i = 0; i < segments.length; ++i) {
if (mc[i] != segments[i].modCount) {
cleanSweep = false;
break;
}
}
}
if (cleanSweep)
return false;
}
// Resort to locking all segments
for (int i = 0; i < segments.length; ++i)
segments[i].lock();
boolean found = false;
try {
for (int i = 0; i < segments.length; ++i) {
if (segments[i].containsValue(value)) {
found = true;
break;
}
}
} finally {
for (int i = 0; i < segments.length; ++i)
segments[i].unlock();
}
return found;
}
/**
* Legacy method testing if some key maps into the specified value in this
* table. This method is identical in functionality to
* {@link #containsValue}, and exists solely to ensure full compatibility
* with class {@link java.util.Hashtable}, which supported this method prior
* to introduction of the Java Collections framework.
*
* @param value
* a value to search for
* @return <tt>true</tt> if and only if some key maps to the <tt>value</tt>
* argument in this table as determined by the <tt>equals</tt>
* method; <tt>false</tt> otherwise
* @throws NullPointerException
* if the specified value is null
*/
public boolean contains(Object value) {
return containsValue(value);
}
/**
* Put一个键值，加Map锁
*/
public V put(K key, V value) {
if (value == null)
throw new NullPointerException();
int hash = hash(key.hashCode());
return segmentFor(hash).put(key, hash, value, false);
}
/**
* Put一个键值，如果该Key不存在的话
*/
public V putIfAbsent(K key, V value) {
if (value == null)
throw new NullPointerException();
int hash = hash(key.hashCode());
return segmentFor(hash).put(key, hash, value, true);
}
/**
* Copies all of the mappings from the specified map to this one. These
* mappings replace any mappings that this map had for any of the keys
* currently in the specified map.
*
* @param m
* mappings to be stored in this map
*/
public void putAll(Map<? extends K, ? extends V> m) {
for (Map.Entry<? extends K, ? extends V> e : m.entrySet())
put(e.getKey(), e.getValue());
}
/**
* Removes the key (and its corresponding value) from this map. This method
* does nothing if the key is not in the map.
*
* @param key
* the key that needs to be removed
* @return the previous value associated with <tt>key</tt>, or <tt>null</tt>
* if there was no mapping for <tt>key</tt>
* @throws NullPointerException
* if the specified key is null
*/
public V remove(Object key) {
int hash = hash(key.hashCode());
return segmentFor(hash).remove(key, hash, null);
}
/**
* {@inheritDoc}
*
* @throws NullPointerException
* if the specified key is null
*/
public boolean remove(Object key, Object value) {
int hash = hash(key.hashCode());
if (value == null)
return false;
return segmentFor(hash).remove(key, hash, value) != null;
}
/**
* {@inheritDoc}
*
* @throws NullPointerException
* if any of the arguments are null
*/
public boolean replace(K key, V oldValue, V newValue) {
if (oldValue == null || newValue == null)
throw new NullPointerException();
int hash = hash(key.hashCode());
return segmentFor(hash).replace(key, hash, oldValue, newValue);
}
/**
* {@inheritDoc}
*
* @return the previous value associated with the specified key, or
* <tt>null</tt> if there was no mapping for the key
* @throws NullPointerException
* if the specified key or value is null
*/
public V replace(K key, V value) {
if (value == null)
throw new NullPointerException();
int hash = hash(key.hashCode());
return segmentFor(hash).replace(key, hash, value);
}
/**
* Removes all of the mappings from this map.
*/
public void clear() {
for (int i = 0; i < segments.length; ++i)
segments[i].clear();
}
/**
* Returns a {@link Set} view of the keys contained in this map. The set is
* backed by the map, so changes to the map are reflected in the set, and
* vice-versa. The set supports element removal, which removes the
* corresponding mapping from this map, via the <tt>Iterator.remove</tt>,
* <tt>Set.remove</tt>, <tt>removeAll</tt>, <tt>retainAll</tt>, and
* <tt>clear</tt> operations. It does not support the <tt>add</tt> or
* <tt>addAll</tt> operations.
*
* <p>
* The view's <tt>iterator</tt> is a "weakly consistent" iterator that will
* never throw {@link ConcurrentModificationException}, and guarantees to
* traverse elements as they existed upon construction of the iterator, and
* may (but is not guaranteed to) reflect any modifications subsequent to
* construction.
*/
public Set<K> keySet() {
Set<K> ks = keySet;
return (ks != null) ? ks : (keySet = new KeySet());
}
/**
* Returns a {@link Collection} view of the values contained in this map.
* The collection is backed by the map, so changes to the map are reflected
* in the collection, and vice-versa. The collection supports element
* removal, which removes the corresponding mapping from this map, via the
* <tt>Iterator.remove</tt>, <tt>Collection.remove</tt>, <tt>removeAll</tt>,
* <tt>retainAll</tt>, and <tt>clear</tt> operations. It does not support
* the <tt>add</tt> or <tt>addAll</tt> operations.
*
* <p>
* The view's <tt>iterator</tt> is a "weakly consistent" iterator that will
* never throw {@link ConcurrentModificationException}, and guarantees to
* traverse elements as they existed upon construction of the iterator, and
* may (but is not guaranteed to) reflect any modifications subsequent to
* construction.
*/
public Collection<V> values() {
Collection<V> vs = values;
return (vs != null) ? vs : (values = new Values());
}
/**
* Returns a {@link Set} view of the mappings contained in this map. The set
* is backed by the map, so changes to the map are reflected in the set, and
* vice-versa. The set supports element removal, which removes the
* corresponding mapping from the map, via the <tt>Iterator.remove</tt>,
* <tt>Set.remove</tt>, <tt>removeAll</tt>, <tt>retainAll</tt>, and
* <tt>clear</tt> operations. It does not support the <tt>add</tt> or
* <tt>addAll</tt> operations.
*
* <p>
* The view's <tt>iterator</tt> is a "weakly consistent" iterator that will
* never throw {@link ConcurrentModificationException}, and guarantees to
* traverse elements as they existed upon construction of the iterator, and
* may (but is not guaranteed to) reflect any modifications subsequent to
* construction.
*/
public Set<Map.Entry<K, V>> entrySet() {
Set<Map.Entry<K, V>> es = entrySet;
return (es != null) ? es : (entrySet = new EntrySet());
}
/**
* Returns an enumeration of the keys in this table.
*
* @return an enumeration of the keys in this table
* @see #keySet()
*/
public Enumeration<K> keys() {
return new KeyIterator();
}
/**
* Returns an enumeration of the values in this table.
*
* @return an enumeration of the values in this table
* @see #values()
*/
public Enumeration<V> elements() {
return new ValueIterator();
}
/* ---------------- Iterator Support -------------- */
abstract class HashIterator {
int nextSegmentIndex;
int nextTableIndex;
HashEntry<K, V>[] currentTable;
HashEntry<K, V> nextEntry;
HashEntry<K, V> lastReturned;
HashIterator() {
nextSegmentIndex = segments.length - 1;
nextTableIndex = -1;
advance();
}
public boolean hasMoreElements() {
return hasNext();
}
final void advance() {
if (nextEntry != null && (nextEntry = nextEntry.next) != null)
return;
while (nextTableIndex >= 0) {
if ((nextEntry = currentTable[nextTableIndex--]) != null)
return;
}
while (nextSegmentIndex >= 0) {
Segment<K, V> seg = segments[nextSegmentIndex--];
if (seg.count != 0) {
currentTable = seg.table;
for (int j = currentTable.length - 1; j >= 0; --j) {
if ((nextEntry = currentTable[j]) != null) {
nextTableIndex = j - 1;
return;
}
}
}
}
}
public boolean hasNext() {
return nextEntry != null;
}
HashEntry<K, V> nextEntry() {
if (nextEntry == null)
throw new NoSuchElementException();
lastReturned = nextEntry;
advance();
return lastReturned;
}
public void remove() {
if (lastReturned == null)
throw new IllegalStateException();
ConcurrentLRUHashMap.this.remove(lastReturned.key);
lastReturned = null;
}
}
final class KeyIterator extends HashIterator implements Iterator<K>,
Enumeration<K> {
public K next() {
return super.nextEntry().key;
}
public K nextElement() {
return super.nextEntry().key;
}
}
final class ValueIterator extends HashIterator implements Iterator<V>,
Enumeration<V> {
public V next() {
return super.nextEntry().value;
}
public V nextElement() {
return super.nextEntry().value;
}
}
/**
* Custom Entry class used by EntryIterator.next(), that relays setValue
* changes to the underlying map.
*/
final class WriteThroughEntry extends AbstractMap.SimpleEntry<K, V> {
/**
*
*/
private static final long serialVersionUID = -2545938966452012894L;
WriteThroughEntry(K k, V v) {
super(k, v);
}
/**
* Set our entry's value and write through to the map. The value to
* return is somewhat arbitrary here. Since a WriteThroughEntry does not
* necessarily track asynchronous changes, the most recent "previous"
* value could be different from what we return (or could even have been
* removed in which case the put will re-establish). We do not and
* cannot guarantee more.
*/
public V setValue(V value) {
if (value == null)
throw new NullPointerException();
V v = super.setValue(value);
ConcurrentLRUHashMap.this.put(getKey(), value);
return v;
}
}
final class EntryIterator extends HashIterator implements
Iterator<Entry<K, V>> {
public Map.Entry<K, V> next() {
HashEntry<K, V> e = super.nextEntry();
return new WriteThroughEntry(e.key, e.value);
}
}
final class KeySet extends AbstractSet<K> {
public Iterator<K> iterator() {
return new KeyIterator();
}
public int size() {
return ConcurrentLRUHashMap.this.size();
}
public boolean contains(Object o) {
return ConcurrentLRUHashMap.this.containsKey(o);
}
public boolean remove(Object o) {
return ConcurrentLRUHashMap.this.remove(o) != null;
}
public void clear() {
ConcurrentLRUHashMap.this.clear();
}
}
final class Values extends AbstractCollection<V> {
public Iterator<V> iterator() {
return new ValueIterator();
}
public int size() {
return ConcurrentLRUHashMap.this.size();
}
public boolean contains(Object o) {
return ConcurrentLRUHashMap.this.containsValue(o);
}
public void clear() {
ConcurrentLRUHashMap.this.clear();
}
}
final class EntrySet extends AbstractSet<Map.Entry<K, V>> {
public Iterator<Map.Entry<K, V>> iterator() {
return new EntryIterator();
}
public boolean contains(Object o) {
if (!(o instanceof Map.Entry))
return false;
Map.Entry<?, ?> e = (Map.Entry<?, ?>) o;
V v = ConcurrentLRUHashMap.this.get(e.getKey());
return v != null && v.equals(e.getValue());
}
public boolean remove(Object o) {
if (!(o instanceof Map.Entry))
return false;
Map.Entry<?, ?> e = (Map.Entry<?, ?>) o;
return ConcurrentLRUHashMap.this.remove(e.getKey(), e.getValue());
}
public int size() {
return ConcurrentLRUHashMap.this.size();
}
public void clear() {
ConcurrentLRUHashMap.this.clear();
}
}
/* ---------------- Serialization Support -------------- */
/**
* Save the state of the <tt>ConcurrentHashMap</tt> instance to a stream
* (i.e., serialize it).
*
* @param s
* the stream
* @serialData the key (Object) and value (Object) for each key-value
* mapping, followed by a null pair. The key-value mappings are
* emitted in no particular order.
*/
private void writeObject(java.io.ObjectOutputStream s) throws IOException {
s.defaultWriteObject();
for (int k = 0; k < segments.length; ++k) {
Segment<K, V> seg = segments[k];
seg.lock();
try {
HashEntry<K, V>[] tab = seg.table;
for (int i = 0; i < tab.length; ++i) {
for (HashEntry<K, V> e = tab[i]; e != null; e = e.next) {
s.writeObject(e.key);
s.writeObject(e.value);
}
}
} finally {
seg.unlock();
}
}
s.writeObject(null);
s.writeObject(null);
}
/**
* Reconstitute the <tt>ConcurrentHashMap</tt> instance from a stream (i.e.,
* deserialize it).
*
* @param s
* the stream
*/
@SuppressWarnings("unchecked")
private void readObject(java.io.ObjectInputStream s) throws IOException,
ClassNotFoundException {
s.defaultReadObject();
// Initialize each segment to be minimally sized, and let grow.
for (int i = 0; i < segments.length; ++i) {
segments[i].setTable(new HashEntry[1]);
}
// Read the keys and values, and put the mappings in the table
for (;;) {
K key = (K) s.readObject();
V value = (V) s.readObject();
if (key == null)
break;
put(key, value);
}
}
}