通过分析Hashtable就知道，synchronized是针对整张Hash表的，即每次锁住整张表让线程独占，
ConcurrentHashMap允许多个修改操作并发进行，其关键在于使用了锁分离技术。它使用了多个锁来控制对hash表的不同部分进行的修改。
ConcurrentHashMap内部使用段(Segment)来表示这些不同的部分，每个段其实就是一个小的hash table，它们有自己的锁。只要多个修改操作发生在不同的段上，它们就可以并发进行。
有些方法需要跨段，比如size()和containsValue()，它们可能需要锁定整个表而而不仅仅是某个段，这需要按顺序锁定所有段，操作完毕后，又按顺序释放所有段的锁。这里“按顺序”是很重要的，否则极有可能出现死锁，
在ConcurrentHashMap内部，段数组是final的，并且其成员变量实际上也是final的，但是，仅仅是将数组声明为final的并不保证数组成员也是final的，这需要实现上的保证。这可以确保不会出现死锁，因为获得锁的顺序是固定的。
ConcurrentHashMap和Hashtable主要区别就是围绕着锁的粒度以及如何锁,可以简单理解成把一个大的HashTable分解成多个，形成了锁分离。
ConcurrentHashMap中主要实体类就是三个：ConcurrentHashMap（整个Hash表）,Segment（桶），HashEntry（节点）
ConcurrentHashMap是由Segment数组结构和HashEntry数组结构组成。
Segment是一种可重入锁ReentrantLock，在ConcurrentHashMap里扮演锁的角色，HashEntry则用于存储键值对数据。
一个ConcurrentHashMap里包含一个Segment数组，Segment的结构和HashMap类似，是一种数组和链表结构，
一个Segment里包含一个HashEntry数组，每个HashEntry是一个链表结构的元素，
每个Segment守护者一个HashEntry数组里的元素,当对HashEntry数组的数据进行修改时，必须首先获得它对应的Segment锁。
Get方法：
1.为输入的Key做Hash运算，得到hash值。
2.通过hash值，定位到对应的Segment对象
3.再次通过hash值，定位到Segment当中数组的具体位置。
Put方法：
1.为输入的Key做Hash运算，得到hash值。
2.通过hash值，定位到对应的Segment对象
3.获取可重入锁
4.再次通过hash值，定位到Segment当中数组的具体位置。
5.插入或覆盖HashEntry对象。
6.释放锁。
ConcurrentHashMap的Size方法是一个嵌套循环，大体逻辑如下：
1.遍历所有的Segment。
2.把Segment的元素数量累加起来。
3.把Segment的修改次数累加起来。
4.计算多次Size,判断所有Segment的总修改次数是否大于上一次的总修改次数。如果大于，说明统计过程中有修改，重新统计，尝试次数+1；如果不是。说明没有修改，统计结束。
5.如果尝试次数超过阈值，则对每一个Segment加锁，再重新统计。
6.再次判断所有Segment的总修改次数是否大于上一次的总修改次数。由于已经加锁，次数一定和上次相等。
7.释放锁，统计结束。
为了尽量不锁住所有Segment，首先乐观地假设Size过程中不会有修改。当尝试一定次数，才无奈转为悲观锁，锁住所有Segment保证强一致性。
JDK1.7版本：
public ConcurrentHashMap(int initialCapacity, float loadFactor, int concurrencyLevel) { if (!(loadFactor > 0) || initialCapacity < 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; } this.segmentShift = 32 - sshift; this.segmentMask = ssize - 1; if (initialCapacity > MAXIMUM_CAPACITY) initialCapacity = MAXIMUM_CAPACITY; int c = initialCapacity / ssize; if (c * ssize < initialCapacity) ++c; int cap = MIN_SEGMENT_TABLE_CAPACITY; while (cap < c) cap <<= 1; // create segments and segments[0] Segment<K,V> s0 = new Segment<K,V>(loadFactor, (int)(cap * loadFactor), (HashEntry<K,V>[])new HashEntry[cap]); Segment<K,V>[] ss = (Segment<K,V>[])new Segment[ssize]; UNSAFE.putOrderedObject(ss, SBASE, s0); // ordered write of segments[0] this.segments = ss; }
1.concurrencyLevel，含义，并发级别，并发度，在键不冲突的情况下，最多允许多少个线程同时访问数据不需要阻塞（理想情况下），我们应该知道，ConcurrentHashMap的基本实现原理就是引入Segment数据结构，将锁的粒度细化到Segment,也就是说，如果多个线程，同时操作多个key,如果这些key，分布在不同的Segment,那这些线程的操作互不影响，当然不需要加锁，提高性能。所以concurrencyLevel，就是要求告诉ConcurrentHashMap,我需要这么过个线程同时访问你而不产生锁冲突。
2.ssize，该变量的值等于ConcurrentHashMap中segment的长度，也就是 Segment[]的长度。该值取决于concurrencyLevel,其实就是小于concurrencyLevel的最大的2的幂，，比如concurrencyLevel=16,那 ssize=16,
如果 concurrencyLevel=12,ssize=8，因为ssize的长度为2的幂。
3.shift的值，看出来了没，其实就是 ssize 2 ^ shift,其实就是表示ssize需要的二进制位。
4.segmentMask、segmentShift ，这两个属性在该表达式中使用：(h >>> segmentShift) & segmentMask)，很明显，就是用来算Segment[]数组中的下标来的。意图segmentShift = 32 - sshift，也就是利用hash的高位与代表（ssize-1）来定位下标。// 如果默认，初始容量16,那么ssize=16, sshift=4 定位端 hash 无符号向右移多少28位，（总共32位），那就是使原本32-29位参与运算（高位）
5.cap,就是每个Segment中HashEntity[]的长度，大于【初始容量/segment长度】的最小2的幂。
分析到这里，ConcurrentHashMap就构建成功了，我们先重点关注一下Segment的数据结构。
Segment段的内部数据结构如下：
1）类的声明：static final class Segment<K,V> extends ReentrantLock implements Serializable
2）数据结构：
transient volatile HashEntry<K,V>[] table; // 内部键值对
transient int count; // 元素数量
transient int modCount; // 结构发生变化的次数
transient int threshold; // 扩容时的阔值
final float loadFactor; // 扩容因子，主要影响threshold，影响什么时候扩容
对上述结构，是否似曾相识，对了，就是它，HashMap；每个Segment其实就是一个HashMap;还有一个很关键点：Segment继承自ReentrantLock,也就是Segment本身就是一把锁。
get方法：
public V get(Object key) { Segment<K,V> s; // manually integrate access methods to reduce overhead HashEntry<K,V>[] tab; int h = hash(key); long u = (((h >>> segmentShift) & segmentMask) << SSHIFT) + SBASE; if ((s = (Segment<K,V>)UNSAFE.getObjectVolatile(segments, u)) != null && (tab = s.table) != null) { for (HashEntry<K,V> e = (HashEntry<K,V>) UNSAFE.getObjectVolatile (tab, ((long)(((tab.length - 1) & h)) << TSHIFT) + TBASE); e != null; e = e.next) { K k; if ((k = e.key) == key || (e.hash == h && key.equals(k))) return e.value; } } return null; }
put方法：
public V put(K key, V value) { Segment<K,V> s; if (value == null) throw new NullPointerException(); int hash = hash(key); int j = (hash >>> segmentShift) & segmentMask; if ((s = (Segment<K,V>)UNSAFE.getObject // nonvolatile; recheck (segments, (j << SSHIFT) + SBASE)) == null) // in ensureSegment s = ensureSegment(j); return s.put(key, hash, value, false); }
containsKey方法：
public boolean containsKey(Object key) { Segment<K,V> s; // same as get() except no need for volatile value read HashEntry<K,V>[] tab; int h = hash(key); long u = (((h >>> segmentShift) & segmentMask) << SSHIFT) + SBASE; if ((s = (Segment<K,V>)UNSAFE.getObjectVolatile(segments, u)) != null && (tab = s.table) != null) { for (HashEntry<K,V> e = (HashEntry<K,V>) UNSAFE.getObjectVolatile (tab, ((long)(((tab.length - 1) & h)) << TSHIFT) + TBASE); e != null; e = e.next) { K k; if ((k = e.key) == key || (e.hash == h && key.equals(k))) return true; } } return false; }
containsValue方法：
public boolean containsValue(Object value) { // Same idea as size() if (value == null) throw new NullPointerException(); final Segment<K,V>[] segments = this.segments; boolean found = false; long last = 0; int retries = -1; try { outer: for (;;) { if (retries++ == RETRIES_BEFORE_LOCK) { for (int j = 0; j < segments.length; ++j) ensureSegment(j).lock(); // force creation } long hashSum = 0L; int sum = 0; for (int j = 0; j < segments.length; ++j) { HashEntry<K,V>[] tab; Segment<K,V> seg = segmentAt(segments, j); if (seg != null && (tab = seg.table) != null) { for (int i = 0 ; i < tab.length; i++) { HashEntry<K,V> e; for (e = entryAt(tab, i); e != null; e = e.next) { V v = e.value; if (v != null && value.equals(v)) { found = true; break outer; } } } sum += seg.modCount; } } if (retries > 0 && sum == last) break; last = sum; } } finally { if (retries > RETRIES_BEFORE_LOCK) { for (int j = 0; j < segments.length; ++j) segmentAt(segments, j).unlock(); } } return found; }
size方法：
public int size() { // Try a few times to get accurate count. On failure due to // continuous async changes in table, resort to locking. final Segment<K,V>[] segments = this.segments; int size; boolean overflow; // true if size overflows 32 bits long sum; // sum of modCounts long last = 0L; // previous sum int retries = -1; // first iteration isn't retry try { for (;;) { if (retries++ == RETRIES_BEFORE_LOCK) { for (int j = 0; j < segments.length; ++j) ensureSegment(j).lock(); // force creation } sum = 0L; size = 0; overflow = false; for (int j = 0; j < segments.length; ++j) { Segment<K,V> seg = segmentAt(segments, j); if (seg != null) { sum += seg.modCount; int c = seg.count; if (c < 0 || (size += c) < 0) overflow = true; } } if (sum == last) break; last = sum; } } finally { if (retries > RETRIES_BEFORE_LOCK) { for (int j = 0; j < segments.length; ++j) segmentAt(segments, j).unlock(); } } return overflow ? Integer.MAX_VALUE : size; }
Segment类：
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 (via volatile * reads of segments and tables) without locking. This * requires replicating nodes when necessary during table * resizing, so the old lists can be traversed by readers * still using old version of table. * * This class defines only mutative methods requiring locking. * Except as noted, the methods of this class perform the * per-segment versions of ConcurrentHashMap methods. (Other * methods are integrated directly into ConcurrentHashMap * methods.) These mutative methods use a form of controlled * spinning on contention via methods scanAndLock and * scanAndLockForPut. These intersperse tryLocks with * traversals to locate nodes. The main benefit is to absorb * cache misses (which are very common for hash tables) while * obtaining locks so that traversal is faster once * acquired. We do not actually use the found nodes since they * must be re-acquired under lock anyway to ensure sequential * consistency of updates (and in any case may be undetectably * stale), but they will normally be much faster to re-locate. * Also, scanAndLockForPut speculatively creates a fresh node * to use in put if no node is found. */ private static final long serialVersionUID = 2249069246763182397L; /** * The maximum number of times to tryLock in a prescan before * possibly blocking on acquire in preparation for a locked * segment operation. On multiprocessors, using a bounded * number of retries maintains cache acquired while locating * nodes. */ static final int MAX_SCAN_RETRIES = Runtime.getRuntime().availableProcessors() > 1 ? 64 : 1; /** * The per-segment table. Elements are accessed via * entryAt/setEntryAt providing volatile semantics. */ transient volatile HashEntry<K,V>[] table; /** * The number of elements. Accessed only either within locks * or among other volatile reads that maintain visibility. */ transient int count; /** * The total number of mutative operations in this segment. * Even though this may overflows 32 bits, it provides * sufficient accuracy for stability checks in CHM isEmpty() * and size() methods. Accessed only either within locks or * among other volatile reads that maintain visibility. */ 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 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; Segment(float lf, int threshold, HashEntry<K,V>[] tab) { this.loadFactor = lf; this.threshold = threshold; this.table = tab; } final V put(K key, int hash, V value, boolean onlyIfAbsent) { HashEntry<K,V> node = tryLock() ? null : scanAndLockForPut(key, hash, value); V oldValue; try { HashEntry<K,V>[] tab = table; int index = (tab.length - 1) & hash; HashEntry<K,V> first = entryAt(tab, index); for (HashEntry<K,V> e = first;;) { if (e != null) { K k; if ((k = e.key) == key || (e.hash == hash && key.equals(k))) { oldValue = e.value; if (!onlyIfAbsent) { e.value = value; ++modCount; } break; } e = e.next; } else { if (node != null) node.setNext(first); else node = new HashEntry<K,V>(hash, key, value, first); int c = count + 1; if (c > threshold && tab.length < MAXIMUM_CAPACITY) rehash(node); else setEntryAt(tab, index, node); ++modCount; count = c; oldValue = null; break; } } } finally { unlock(); } return oldValue; } /** * Doubles size of table and repacks entries, also adding the * given node to new table */ @SuppressWarnings("unchecked") private void rehash(HashEntry<K,V> node) { /* * Reclassify nodes in each list to new table. 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 * concurrently traversing table. Entry accesses use plain * array indexing because they are followed by volatile * table write. */ HashEntry<K,V>[] oldTable = table; int oldCapacity = oldTable.length; int newCapacity = oldCapacity << 1; threshold = (int)(newCapacity * loadFactor); HashEntry<K,V>[] newTable = (HashEntry<K,V>[]) new HashEntry[newCapacity]; int sizeMask = newCapacity - 1; for (int i = 0; i < oldCapacity ; i++) { HashEntry<K,V> e = oldTable[i]; if (e != null) { HashEntry<K,V> next = e.next; int idx = e.hash & sizeMask; if (next == null) // Single node on list newTable[idx] = e; else { // Reuse 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 remaining nodes for (HashEntry<K,V> p = e; p != lastRun; p = p.next) { V v = p.value; int h = p.hash; int k = h & sizeMask; HashEntry<K,V> n = newTable[k]; newTable[k] = new HashEntry<K,V>(h, p.key, v, n); } } } } int nodeIndex = node.hash & sizeMask; // add the new node node.setNext(newTable[nodeIndex]); newTable[nodeIndex] = node; table = newTable; } /** * Scans for a node containing given key while trying to * acquire lock, creating and returning one if not found. Upon * return, guarantees that lock is held. UNlike in most * methods, calls to method equals are not screened: Since * traversal speed doesn't matter, we might as well help warm * up the associated code and accesses as well. * * @return a new node if key not found, else null */ private HashEntry<K,V> scanAndLockForPut(K key, int hash, V value) { HashEntry<K,V> first = entryForHash(this, hash); HashEntry<K,V> e = first; HashEntry<K,V> node = null; int retries = -1; // negative while locating node while (!tryLock()) { HashEntry<K,V> f; // to recheck first below if (retries < 0) { if (e == null) { if (node == null) // speculatively create node node = new HashEntry<K,V>(hash, key, value, null); retries = 0; } else if (key.equals(e.key)) retries = 0; else e = e.next; } else if (++retries > MAX_SCAN_RETRIES) { lock(); break; } else if ((retries & 1) == 0 && (f = entryForHash(this, hash)) != first) { e = first = f; // re-traverse if entry changed retries = -1; } } return node; } /** * Scans for a node containing the given key while trying to * acquire lock for a remove or replace operation. Upon * return, guarantees that lock is held. Note that we must * lock even if the key is not found, to ensure sequential * consistency of updates. */ private void scanAndLock(Object key, int hash) { // similar to but simpler than scanAndLockForPut HashEntry<K,V> first = entryForHash(this, hash); HashEntry<K,V> e = first; int retries = -1; while (!tryLock()) { HashEntry<K,V> f; if (retries < 0) { if (e == null || key.equals(e.key)) retries = 0; else e = e.next; } else if (++retries > MAX_SCAN_RETRIES) { lock(); break; } else if ((retries & 1) == 0 && (f = entryForHash(this, hash)) != first) { e = first = f; retries = -1; } } } /** * Remove; match on key only if value null, else match both. */ final V remove(Object key, int hash, Object value) { if (!tryLock()) scanAndLock(key, hash); V oldValue = null; try { HashEntry<K,V>[] tab = table; int index = (tab.length - 1) & hash; HashEntry<K,V> e = entryAt(tab, index); HashEntry<K,V> pred = null; while (e != null) { K k; HashEntry<K,V> next = e.next; if ((k = e.key) == key || (e.hash == hash && key.equals(k))) { V v = e.value; if (value == null || value == v || value.equals(v)) { if (pred == null) setEntryAt(tab, index, next); else pred.setNext(next); ++modCount; --count; oldValue = v; } break; } pred = e; e = next; } } finally { unlock(); } return oldValue; } final boolean replace(K key, int hash, V oldValue, V newValue) { if (!tryLock()) scanAndLock(key, hash); boolean replaced = false; try { HashEntry<K,V> e; for (e = entryForHash(this, hash); e != null; e = e.next) { K k; if ((k = e.key) == key || (e.hash == hash && key.equals(k))) { if (oldValue.equals(e.value)) { e.value = newValue; ++modCount; replaced = true; } break; } } } finally { unlock(); } return replaced; } final V replace(K key, int hash, V value) { if (!tryLock()) scanAndLock(key, hash); V oldValue = null; try { HashEntry<K,V> e; for (e = entryForHash(this, hash); e != null; e = e.next) { K k; if ((k = e.key) == key || (e.hash == hash && key.equals(k))) { oldValue = e.value; e.value = value; ++modCount; break; } } } finally { unlock(); } return oldValue; } final void clear() { lock(); try { HashEntry<K,V>[] tab = table; for (int i = 0; i < tab.length ; i++) setEntryAt(tab, i, null); ++modCount; count = 0; } finally { unlock(); } } }
ConcurrentHashMap中的可以同时多个get，一个put,在get的时候可能会与put的有冲突，在put赋值的时候，value可能为null,也可能读取到修改前，或后的值
如果读取到null，就进行加锁重新读取（readValueUnderLock），读取到修改前后的值是可以允许的。
http://www.jasongj.com/java/concurrenthashmap/
http://blog.csdn.net/prestigeding/article/details/53391264
http://www.infoq.com/cn/articles/ConcurrentHashMap/
http://www.cnblogs.com/ITtangtang/p/3948786.html