Java的hashmap和concurrenthashmap探险
HashMap
先看一下构造器吧。HashMap中有一个无参构造器，其中什么都没有干。
/**
* Constructs an empty <tt>HashMap</tt> with the default initial capacity
* (16) and the default load factor (0.75).
*/
public HashMap() {
this.loadFactor = DEFAULT_LOAD_FACTOR; // all other fields defaulted
}
之后来看一下被用的也非常多的带初始容量的构造器。
/**
* Constructs an empty <tt>HashMap</tt> with the specified initial
* capacity and load factor.
*
* @param initialCapacity the initial capacity
* @param loadFactor the load factor
* @throws IllegalArgumentException if the initial capacity is negative
* or the load factor is nonpositive
*/
public HashMap(int initialCapacity, float loadFactor) {
if (initialCapacity < 0)
throw new IllegalArgumentException("Illegal initial capacity: " +
initialCapacity);
if (initialCapacity > MAXIMUM_CAPACITY)
initialCapacity = MAXIMUM_CAPACITY;
if (loadFactor <= 0 || Float.isNaN(loadFactor))
throw new IllegalArgumentException("Illegal load factor: " +
loadFactor);
this.loadFactor = loadFactor;
//这里比较奇怪的用threshold存储了初始容量，后面resize的时候threshold就会变成真正的阈值了
this.threshold = tableSizeFor(initialCapacity);
}
/**
* The load factor used when none specified in constructor.
*/
static final float DEFAULT_LOAD_FACTOR = 0.75f;
/**
* Constructs an empty <tt>HashMap</tt> with the specified initial
* capacity and the default load factor (0.75).
*
* @param initialCapacity the initial capacity.
* @throws IllegalArgumentException if the initial capacity is negative.
*/
public HashMap(int initialCapacity) {
this(initialCapacity, DEFAULT_LOAD_FACTOR);
}
其中默认的加载因子是0.75。其中tableSizeFor用于计算分配的表的大小：
/**
* Returns a power of two size for the given target capacity.
*/
//计算一个最小的2^k，满足2^k>=cap
static final int tableSizeFor(int cap) {
//这里的或运算实际上是将n的最高位1之后的所有位都设置为1
int n = cap - 1;
n |= n >>> 1;
n |= n >>> 2;
n |= n >>> 4;
n |= n >>> 8;
n |= n >>> 16;
return (n < 0) ? 1 : (n >= MAXIMUM_CAPACITY) ? MAXIMUM_CAPACITY : n + 1;
}
可以发现上面提到的构造器都没有涉及到数组的分配，因此创建哈希表是非常轻量级的操作。
下面看一下用的最多的put和get操作。
public V put(K key, V value) {
return putVal(hash(key), key, value, false, true);
}
其中hash方法实际上对key的哈希值再次进行了哈希，来保证在哈希表较小的情况下，键的哈希值的高16位也能被使用上。
static final int hash(Object key) {
int h;
return (key == null) ? 0 : (h = key.hashCode()) ^ (h >>> 16);
}
接下来看一下相当复杂的putVal方法。
/**
* The bin count threshold for using a tree rather than list for a
* bin. Bins are converted to trees when adding an element to a
* bin with at least this many nodes. The value must be greater
* than 2 and should be at least 8 to mesh with assumptions in
* tree removal about conversion back to plain bins upon
* shrinkage.
*/
static final int TREEIFY_THRESHOLD = 8;
/**
* Implements Map.put and related methods.
*
* @param hash hash for key
* @param key the key
* @param value the value to put
* @param onlyIfAbsent if true, don't change existing value
* @param evict if false, the table is in creation mode.
* @return previous value, or null if none
*/
final V putVal(int hash, K key, V value, boolean onlyIfAbsent,
boolean evict) {
Node<K,V>[] tab; Node<K,V> p; int n, i;
//由于创建哈希表的时候不会直接分配数组，这里会初始化数组
if ((tab = table) == null || (n = tab.length) == 0)
//这里通过resize方法进行缩放
n = (tab = resize()).length;
//如果链表是空的
if ((p = tab[i = (n - 1) & hash]) == null)
tab[i] = newNode(hash, key, value, null);
else {
Node<K,V> e; K k;
//如果链表头就是我们要找的key
if (p.hash == hash &&
((k = p.key) == key || (key != null && key.equals(k))))
e = p;
else if (p instanceof TreeNode)
//如果链表已经转换成了平衡树
e = ((TreeNode<K,V>)p).putTreeVal(this, tab, hash, key, value);
else {
//遍历链表
for (int binCount = 0; ; ++binCount) {
if ((e = p.next) == null) {
//到了末尾，我们就加入一个新结点
p.next = newNode(hash, key, value, null);
if (binCount >= TREEIFY_THRESHOLD - 1) // -1 for 1st
//这里比较特殊，如果在扫描链表的时候，发现链表很长，这时候会将链表转换成平衡树
treeifyBin(tab, hash);
break;
}
//找到了，就退出
if (e.hash == hash &&
((k = e.key) == key || (key != null && key.equals(k))))
break;
p = e;
}
}
if (e != null) { // existing mapping for key
//已经找到了话，就简单修改一下值即可
V oldValue = e.value;
if (!onlyIfAbsent || oldValue == null)
e.value = value;
//这里有些后置事件
afterNodeAccess(e);
return oldValue;
}
}
++modCount;
if (++size > threshold)
resize();
afterNodeInsertion(evict);
return null;
}
上面应该能看到一些后置事件，这些事件在LinkedHashMap中会被使用，我们可以继承LinkedHashMap，很容易实现像LRU，LFU等算法。
// Callbacks to allow LinkedHashMap post-actions
void afterNodeAccess(Node<K,V> p) { }
void afterNodeInsertion(boolean evict) { }
void afterNodeRemoval(Node<K,V> p) { }
上面的resize方法会缩放哈希表。
/**
* The default initial capacity - MUST be a power of two.
*/
static final int DEFAULT_INITIAL_CAPACITY = 1 << 4; // aka 16
/**
* Initializes or doubles table size. If null, allocates in
* accord with initial capacity target held in field threshold.
* Otherwise, 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 in the new table.
*
* @return the table
*/
final Node<K,V>[] resize() {
Node<K,V>[] oldTab = table;
int oldCap = (oldTab == null) ? 0 : oldTab.length;
int oldThr = threshold;
int newCap, newThr = 0;
if (oldCap > 0) {
//容量达到限度，那只能修改阈值了（这时候翻倍会变成负数）
if (oldCap >= MAXIMUM_CAPACITY) {
threshold = Integer.MAX_VALUE;
return oldTab;
}
//允许翻倍，就将newThr也翻一倍
else if ((newCap = oldCap << 1) < MAXIMUM_CAPACITY &&
oldCap >= DEFAULT_INITIAL_CAPACITY)
newThr = oldThr << 1; // double threshold
}
else if (oldThr > 0) // initial capacity was placed in threshold
//这时候tab应该还没有初始化，我们直接设置为阈值
newCap = oldThr;
else {
//阈值都没设置，就是用默认容量16，同时调整阈值
// zero initial threshold signifies using defaults
newCap = DEFAULT_INITIAL_CAPACITY;
newThr = (int)(DEFAULT_LOAD_FACTOR * DEFAULT_INITIAL_CAPACITY);
}
if (newThr == 0) {
//阈值没有被设置，手动设置一下
//一旦元素数目达到阈值就需要扩容了
float ft = (float)newCap * loadFactor;
newThr = (newCap < MAXIMUM_CAPACITY && ft < (float)MAXIMUM_CAPACITY ?
(int)ft : Integer.MAX_VALUE);
}
threshold = newThr;
@SuppressWarnings({"rawtypes","unchecked"})
//创建新的tab
Node<K,V>[] newTab = (Node<K,V>[])new Node[newCap];
table = newTab;
if (oldTab != null) {
//在老tab非null的情况下，这时候还需要我们复制一下entry
for (int j = 0; j < oldCap; ++j) {
Node<K,V> e;
if ((e = oldTab[j]) != null) {
//重新进行哈希
oldTab[j] = null;
if (e.next == null)
//只有一个元素
newTab[e.hash & (newCap - 1)] = e;
else if (e instanceof TreeNode)
//这边需要进行切分，由于是平衡树，因此可以将其划分为两颗树
((TreeNode<K,V>)e).split(this, newTab, j, oldCap);
else { // preserve order
//手动划分为两段，并且保持原来的顺序
Node<K,V> loHead = null, loTail = null;
Node<K,V> hiHead = null, hiTail = null;
Node<K,V> next;
do {
next = e.next;
if ((e.hash & oldCap) == 0) {
if (loTail == null)
loHead = e;
else
loTail.next = e;
loTail = e;
}
else {
if (hiTail == null)
hiHead = e;
else
hiTail.next = e;
hiTail = e;
}
} while ((e = next) != null);
if (loTail != null) {
loTail.next = null;
newTab[j] = loHead;
}
if (hiTail != null) {
hiTail.next = null;
newTab[j + oldCap] = hiHead;
}
}
}
}
}
return newTab;
}
至于newNode是个啥，只是一个简单的工厂方法。用工厂方法的好处就是子类中可以返回一个Node的子类。
// Create a regular (non-tree) node
Node<K,V> newNode(int hash, K key, V value, Node<K,V> next) {
return new Node<>(hash, key, value, next);
}
看一下putTree具体干了啥。
/**
* Tree version of putVal.
*/
final TreeNode<K,V> putTreeVal(HashMap<K,V> map, Node<K,V>[] tab,
int h, K k, V v) {
Class<?> kc = null;
boolean searched = false;
//找到树根
TreeNode<K,V> root = (parent != null) ? root() : this;
for (TreeNode<K,V> p = root;;) {
//向下二分找
int dir, ph; K pk;
//先根据哈希值搜
if ((ph = p.hash) > h)
dir = -1;
else if (ph < h)
dir = 1;
//如果哈希值相同，就看看关键字是否找到了
else if ((pk = p.key) == k || (k != null && k.equals(pk)))
return p;
//还没找到，说明发生了哈希碰撞，且关键字不可比较，就暴力搜索
else if ((kc == null &&
(kc = comparableClassFor(k)) == null) ||
(dir = compareComparables(kc, k, pk)) == 0) {
if (!searched) {
TreeNode<K,V> q, ch;
searched = true;
//暴力搜索左右结点
if (((ch = p.left) != null &&
(q = ch.find(h, k, kc)) != null) ||
((ch = p.right) != null &&
(q = ch.find(h, k, kc)) != null))
//找到了
return q;
}
//还没找到，但是必须分个搞下
dir = tieBreakOrder(k, pk);
}
TreeNode<K,V> xp = p;
//到底了就新建结点
if ((p = (dir <= 0) ? p.left : p.right) == null) {
Node<K,V> xpn = xp.next;
TreeNode<K,V> x = map.newTreeNode(h, k, v, xpn);
if (dir <= 0)
xp.left = x;
else
xp.right = x;
xp.next = x;
x.parent = x.prev = xp;
if (xpn != null)
((TreeNode<K,V>)xpn).prev = x;
//插入
moveRootToFront(tab, balanceInsertion(root, x));
return null;
}
}
}
其中root()方法会找到根结点。
/**
* Returns root of tree containing this node.
*/
final TreeNode<K,V> root() {
for (TreeNode<K,V> r = this, p;;) {
if ((p = r.parent) == null)
return r;
r = p;
}
}
如果发生了哈希碰撞的前提下，且无法通过比较来获得进一步的信息，这时候会调用方法对进行暴力搜索。
/**
* Finds the node starting at root p with the given hash and key.
* The kc argument caches comparableClassFor(key) upon first use
* comparing keys.
*/
final TreeNode<K,V> find(int h, Object k, Class<?> kc) {
TreeNode<K,V> p = this;
do {
int ph, dir; K pk;
TreeNode<K,V> pl = p.left, pr = p.right, q;
//先哈希比较
if ((ph = p.hash) > h)
p = pl;
else if (ph < h)
p = pr;
//不行就看看是不是找到了
else if ((pk = p.key) == k || (k != null && k.equals(pk)))
return p;
//如果左结点为空
else if (pl == null)
p = pr;
//如果右结点为肯呢个
else if (pr == null)
p = pl;
//左右结点都不空，则看一下能不能获得进一步的信息
else if ((kc != null ||
(kc = comparableClassFor(k)) != null) &&
(dir = compareComparables(kc, k, pk)) != 0)
//有信息，选择左右
p = (dir < 0) ? pl : pr;
//实在不行就先右结点找
else if ((q = pr.find(h, k, kc)) != null)
return q;
else
//走投无路选择左节点
p = pl;
} while (p != null);
return null;
}
其中comparableClassFor方法用于判断关键字k是否实现了Comparable<K>接口，如果实现了就返回其类型，否则返回null。
/**
* Returns x's Class if it is of the form "class C implements
* Comparable<C>", else null.
*/
static Class<?> comparableClassFor(Object x) {
if (x instanceof Comparable) {
Class<?> c; Type[] ts, as; Type t; ParameterizedType p;
if ((c = x.getClass()) == String.class) // bypass checks
return c;
if ((ts = c.getGenericInterfaces()) != null) {
for (int i = 0; i < ts.length; ++i) {
if (((t = ts[i]) instanceof ParameterizedType) &&
((p = (ParameterizedType)t).getRawType() ==
Comparable.class) &&
(as = p.getActualTypeArguments()) != null &&
as.length == 1 && as[0] == c) // type arg is c
return c;
}
}
}
return null;
}
之后会用compareComparables来比较目标关键字和树结点的关键字。
/**
* Returns k.compareTo(x) if x matches kc (k's screened comparable
* class), else 0.
*/
@SuppressWarnings({"rawtypes","unchecked"}) // for cast to Comparable
//如果能比较，就进行比较，否则返回0
static int compareComparables(Class<?> kc, Object k, Object x) {
return (x == null || x.getClass() != kc ? 0 :
((Comparable)k).compareTo(x));
}
在暴力都找不到的情况下，就会触发tieBreakOrder方法。
/**
* Tie-breaking utility for ordering insertions when equal
* hashCodes and non-comparable. We don't require a total
* order, just a consistent insertion rule to maintain
* equivalence across rebalancings. Tie-breaking further than
* necessary simplifies testing a bit.
*/
static int tieBreakOrder(Object a, Object b) {
int d;
//否则就比较关键字的名称
if (a == null || b == null ||
(d = a.getClass().getName().
compareTo(b.getClass().getName())) == 0)
//实在不行就比较它们的地址
d = (System.identityHashCode(a) <= System.identityHashCode(b) ?
-1 : 1);
return d;
}
顺便看一下链表超长的情况下，链表会被转换为平衡树：
/**
* Replaces all linked nodes in bin at index for given hash unless
* table is too small, in which case resizes instead.
*/
final void treeifyBin(Node<K,V>[] tab, int hash) {
int n, index; Node<K,V> e;
//如果tab长度太小了，那么这时候缩放的成本低，且能够减少链表的长度
if (tab == null || (n = tab.length) < MIN_TREEIFY_CAPACITY)
resize();
else if ((e = tab[index = (n - 1) & hash]) != null) {
//不然转平衡树
//hd为头节点，tl为尾节点
TreeNode<K,V> hd = null, tl = null;
//以链表的方式创建一棵树
do {
//创建一个新的节点
TreeNode<K,V> p = replacementTreeNode(e, null);
if (tl == null)
hd = p;
else {
p.prev = tl;
tl.next = p;
}
tl = p;
} while ((e = e.next) != null);
if ((tab[index] = hd) != null)
//最后需要平衡化
hd.treeify(tab);
}
}
// For treeifyBin
TreeNode<K,V> replacementTreeNode(Node<K,V> p, Node<K,V> next) {
return new TreeNode<>(p.hash, p.key, p.value, next);
}
其实了解了put的流程，get就非常简单了。
public V get(Object key) {
Node<K,V> e;
return (e = getNode(hash(key), key)) == null ? null : e.value;
}
final Node<K,V> getNode(int hash, Object key) {
Node<K,V>[] tab; Node<K,V> first, e; int n; K k;
//表为空，就不用找了
if ((tab = table) != null && (n = tab.length) > 0 &&
(first = tab[(n - 1) & hash]) != null) {
//如果是第一个，恭喜
if (first.hash == hash && // always check first node
((k = first.key) == key || (key != null && key.equals(k))))
return first;
//往后找
if ((e = first.next) != null) {
//如果已经转成平衡树了
if (first instanceof TreeNode)
return ((TreeNode<K,V>)first).getTreeNode(hash, key);
do {
//非平衡树，就链表找好了
if (e.hash == hash &&
((k = e.key) == key || (key != null && key.equals(k))))
return e;
} while ((e = e.next) != null);
}
}
//找不到
return null;
}
接下来看一下remove方法的实现。
public V remove(Object key) {
Node<K,V> e;
return (e = removeNode(hash(key), key, null, false, true)) == null ?
null : e.value;
}
final Node<K,V> removeNode(int hash, Object key, Object value,
boolean matchValue, boolean movable) {
Node<K,V>[] tab; Node<K,V> p; int n, index;
//表为空，啥都不用做
if ((tab = table) != null && (n = tab.length) > 0 &&
(p = tab[index = (n - 1) & hash]) != null) {
Node<K,V> node = null, e; K k; V v;
//如果是链表头
if (p.hash == hash &&
((k = p.key) == key || (key != null && key.equals(k))))
node = p;
else if ((e = p.next) != null) {
//红黑树
if (p instanceof TreeNode)
node = ((TreeNode<K,V>)p).getTreeNode(hash, key);
else {
//链表
do {
if (e.hash == hash &&
((k = e.key) == key ||
(key != null && key.equals(k)))) {
node = e;
break;
}
p = e;
} while ((e = e.next) != null);
}
}
//找到了
if (node != null && (!matchValue || (v = node.value) == value ||
(value != null && value.equals(v)))) {
if (node instanceof TreeNode)
//红黑树删除
((TreeNode<K,V>)node).removeTreeNode(this, tab, movable);
else if (node == p)
//链表删除
tab[index] = node.next;
else
//链表删除
p.next = node.next;
//修改版本
++modCount;
--size;
afterNodeRemoval(node);
return node;
}
}
//找不到
return null;
}
可以发现remove是不会缩小表的。
以上就是哈希表的实现了。
ConcurrentHashMap
ConcurrentHashMap与HashMap相比，构造器会有一个额外的参数concurrencyLevel，其指定预期同时更新的线程数。
/**
* @param concurrencyLevel the estimated number of concurrently
* updating threads. The implementation may use this value as
* a sizing hint.
*/
public ConcurrentHashMap(int initialCapacity,
float loadFactor, int concurrencyLevel) {
if (!(loadFactor > 0.0f) || initialCapacity < 0 || concurrencyLevel <= 0)
throw new IllegalArgumentException();
if (initialCapacity < concurrencyLevel) // Use at least as many bins
//槽数至少有concurrencyLevel
initialCapacity = concurrencyLevel; // as estimated threads
long size = (long)(1.0 + (long)initialCapacity / loadFactor);
int cap = (size >= (long)MAXIMUM_CAPACITY) ?
MAXIMUM_CAPACITY : tableSizeFor((int)size);
this.sizeCtl = cap;
}
继续从put入手：
public V put(K key, V value) {
return putVal(key, value, false);
}
final V putVal(K key, V value, boolean onlyIfAbsent) {
//哈希表支持null作为key，但是ConcurrentHashMap是不支持的
if (key == null || value == null) throw new NullPointerException();
//这里的spread与HashMap中的hash方法类似
int hash = spread(key.hashCode());
//binCount为槽中存的元素的类型，1为链表，2为二叉树
int binCount = 0;
//这个循环很奇怪
for (Node<K,V>[] tab = table;;) {
Node<K,V> f; int n, i, fh;
if (tab == null || (n = tab.length) == 0)
//在表为空的情况下需要做初始化
tab = initTable();
//如果表头为空
else if ((f = tabAt(tab, i = (n - 1) & hash)) == null) {
//既然没有元素，就CAS抢占
if (casTabAt(tab, i, null,
new Node<K,V>(hash, key, value, null)))
break; // no lock when adding to empty bin
}
//如果哈希表处在扩展的过程中
else if ((fh = f.hash) == MOVED)
tab = helpTransfer(tab, f);
else {
V oldVal = null;
//对链表头进行加锁
synchronized (f) {
//第一个元素没有被修改
if (tabAt(tab, i) == f) {
if (fh >= 0) {
//哈希值非负
binCount = 1;
for (Node<K,V> e = f;; ++binCount) {
K ek;
if (e.hash == hash &&
((ek = e.key) == key ||
(ek != null && key.equals(ek)))) {
oldVal = e.val;
if (!onlyIfAbsent)
e.val = value;
break;
}
Node<K,V> pred = e;
if ((e = e.next) == null) {
pred.next = new Node<K,V>(hash, key,
value, null);
break;
}
}
}
else if (f instanceof TreeBin) {
//如果是二叉树
Node<K,V> p;
binCount = 2;
if ((p = ((TreeBin<K,V>)f).putTreeVal(hash, key,
value)) != null) {
oldVal = p.val;
if (!onlyIfAbsent)
p.val = value;
}
}
}
}
if (binCount != 0) {
//这里如果链表过长，就转二叉树
if (binCount >= TREEIFY_THRESHOLD)
treeifyBin(tab, i);
if (oldVal != null)
return oldVal;
break;
}
}
}
//这个是干啥的
addCount(1L, binCount);
return null;
}
在表为空的情况下会通过initTable方法进行初始化
/**
* Table initialization and resizing control. When negative, the
* table is being initialized or resized: -1 for initialization,
* else -(1 + the number of active resizing threads). Otherwise,
* when table is null, holds the initial table size to use upon
* creation, or 0 for default. After initialization, holds the
* next element count value upon which to resize the table.
*/
//表示下一次扩展的阈值，在表初始化时，值为-1
private transient volatile int sizeCtl;
/**
* Initializes table, using the size recorded in sizeCtl.
*/
private final Node<K,V>[] initTable() {
Node<K,V>[] tab; int sc;
//ConcurrentHashMap中基本所有成员都加上了volatile字段，因此可以认为每次读到的都是最新的数据
while ((tab = table) == null || tab.length == 0) {
if ((sc = sizeCtl) < 0)
//在初始化，等等
Thread.yield(); // lost initialization race; just spin
else if (U.compareAndSwapInt(this, SIZECTL, sc, -1)) {
//这里通过CAS操作来抢锁
try {
if ((tab = table) == null || tab.length == 0) {
//双重检查
int n = (sc > 0) ? sc : DEFAULT_CAPACITY;
@SuppressWarnings("unchecked")
Node<K,V>[] nt = (Node<K,V>[])new Node<?,?>[n];
table = tab = nt;
//这里阈值始终为n-0.25n=0.75n
sc = n - (n >>> 2);
}
} finally {
sizeCtl = sc;
}
break;
}
}
return tab;
}
可以发现ConcurrentHashMap中并不会直接取数组tab中的元素，因为数组元素的访问不能保证读到最新的数据（尽管数组用volatile来修饰，但是这仅意味着能正确获得tab对象存储的数组地址而已）。ConcurrentHashMap中专门提供了一个叫做tabAt的方法用来访问数组中的元素。我们来一窥究竟：
static final <K,V> Node<K,V> tabAt(Node<K,V>[] tab, int i) {
return (Node<K,V>)U.getObjectVolatile(tab, ((long)i << ASHIFT) + ABASE);
}
可以发现ConcurrentHashMap中使用的是Unsafe中的getObjectVolatile来实现从数组中取元素。其中ASHIFT表示数组的每个元素占用$2^{ASHIFT}$个字节，数组第一个元素的地址距离数组地址（对象前面一块内存需要存储对象头，后面才是真正的有效内存）的偏移量存储在$ABASE$中。这两个字段在static代码块中初始化。
private static final long ABASE;
private static final int ASHIFT;
static {
ABASE = U.arrayBaseOffset(ak);
int scale = U.arrayIndexScale(ak);
if ((scale & (scale - 1)) != 0)
//数组元素大小不是2的幂
throw new Error("data type scale not a power of two");
//求log
ASHIFT = 31 - Integer.numberOfLeadingZeros(scale);
}
这里学习到了如何取数组中最新的元素。获得了新知识。
在slot为空的情况，这时候可以通过casTabAt抢占位置。
static final <K,V> boolean casTabAt(Node<K,V>[] tab, int i,
Node<K,V> c, Node<K,V> v) {
return U.compareAndSwapObject(tab, ((long)i << ASHIFT) + ABASE, c, v);
}
所以既然作为支持并发的数据结构，好像在扩张的时候插入新元素也没有什么稀奇的。这时候helpTransfer会被调用。
/**
* Helps transfer if a resize is in progress.
*/
//这里当前线程会加入从老的tab移动node到新的tab的过程，加速扩容
final Node<K,V>[] helpTransfer(Node<K,V>[] tab, Node<K,V> f) {
Node<K,V>[] nextTab; int sc;
if (tab != null && (f instanceof ForwardingNode) &&
(nextTab = ((ForwardingNode<K,V>)f).nextTable) != null) {
int rs = resizeStamp(tab.length);
//如果还处于扩容中
while (nextTab == nextTable && table == tab &&
(sc = sizeCtl) < 0) {
if ((sc >>> RESIZE_STAMP_SHIFT) != rs || sc == rs + 1 ||
sc == rs + MAX_RESIZERS || transferIndex <= 0)
break;
if (U.compareAndSwapInt(this, SIZECTL, sc, sc + 1)) {
//层层筛选终于获得了帮助扩容的资格，其余线程就只能继续自旋了
transfer(tab, nextTab);
break;
}
}
return nextTab;
}
return table;
}
其中ForwardingNode是一种特殊的结点，其继承了普通的Node，但是会存储缩放后的新的tab。
/**
* A node inserted at head of bins during transfer operations.
*/
static final class ForwardingNode<K,V> extends Node<K,V> {
final Node<K,V>[] nextTable;
ForwardingNode(Node<K,V>[] tab) {
//这里仅将哈希值设置为MOVED
super(MOVED, null, null, null);
this.nextTable = tab;
}
Node<K,V> find(int h, Object k) {
// loop to avoid arbitrarily deep recursion on forwarding nodes
outer: for (Node<K,V>[] tab = nextTable;;) {
Node<K,V> e; int n;
//如果没有找到链表
if (k == null || tab == null || (n = tab.length) == 0 ||
(e = tabAt(tab, (n - 1) & h)) == null)
return null;
for (;;) {
int eh; K ek;
//找到了
if ((eh = e.hash) == h &&
((ek = e.key) == k || (ek != null && k.equals(ek))))
return e;
//如果这个元素也是一个ForwardingNode，扩容的时候再度扩容？
if (eh < 0) {
if (e instanceof ForwardingNode) {
tab = ((ForwardingNode<K,V>)e).nextTable;
//这里跳到外边
continue outer;
}
else
//递归查找
return e.find(h, k);
}
//到了结尾
if ((e = e.next) == null)
return null;
}
}
}
}
扩容的时候计算的resizeStamp会指示当前的容量信息。
/**
* Returns the stamp bits for resizing a table of size n.
* Must be negative when shifted left by RESIZE_STAMP_SHIFT.
*/
static final int resizeStamp(int n) {
return Integer.numberOfLeadingZeros(n) | (1 << (RESIZE_STAMP_BITS - 1));
}
在扩容完成后，会执行一个叫做addCount的函数。不好意思，我看不懂。
private final void addCount(long x, int check) {
CounterCell[] as; long b, s;
if ((as = counterCells) != null ||
!U.compareAndSwapLong(this, BASECOUNT, b = baseCount, s = b + x)) {
CounterCell a; long v; int m;
boolean uncontended = true;
if (as == null || (m = as.length - 1) < 0 ||
(a = as[ThreadLocalRandom.getProbe() & m]) == null ||
!(uncontended =
U.compareAndSwapLong(a, CELLVALUE, v = a.value, v + x))) {
fullAddCount(x, uncontended);
return;
}
if (check <= 1)
return;
s = sumCount();
}
if (check >= 0) {
Node<K,V>[] tab, nt; int n, sc;
while (s >= (long)(sc = sizeCtl) && (tab = table) != null &&
(n = tab.length) < MAXIMUM_CAPACITY) {
int rs = resizeStamp(n);
if (sc < 0) {
if ((sc >>> RESIZE_STAMP_SHIFT) != rs || sc == rs + 1 ||
sc == rs + MAX_RESIZERS || (nt = nextTable) == null ||
transferIndex <= 0)
break;
if (U.compareAndSwapInt(this, SIZECTL, sc, sc + 1))
transfer(tab, nt);
}
else if (U.compareAndSwapInt(this, SIZECTL, sc,
(rs << RESIZE_STAMP_SHIFT) + 2))
transfer(tab, null);
s = sumCount();
}
}
}
再来看看读取方法。
public V get(Object key) {
Node<K,V>[] tab; Node<K,V> e, p; int n, eh; K ek;
int h = spread(key.hashCode());
if ((tab = table) != null && (n = tab.length) > 0 &&
(e = tabAt(tab, (n - 1) & h)) != null) {
//直接是第一个元素
if ((eh = e.hash) == h) {
if ((ek = e.key) == key || (ek != null && key.equals(ek)))
return e.val;
}
else if (eh < 0)
//如果处于扩容中或者是转成二叉树了
//委托给node来进行搜索
return (p = e.find(h, key)) != null ? p.val : null;
//遍历查找
while ((e = e.next) != null) {
if (e.hash == h &&
((ek = e.key) == key || (ek != null && key.equals(ek))))
return e.val;
}
}
return null;
}
最后按惯例看一下remove方法。其和put方法非常类似。
/**
* 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 {@code key}, or
* {@code null} if there was no mapping for {@code key}
* @throws NullPointerException if the specified key is null
*/
public V remove(Object key) {
return replaceNode(key, null, null);
}
/**
* Implementation for the four public remove/replace methods:
* Replaces node value with v, conditional upon match of cv if
* non-null. If resulting value is null, delete.
*/
final V replaceNode(Object key, V value, Object cv) {
int hash = spread(key.hashCode());
for (Node<K,V>[] tab = table;;) {
Node<K,V> f; int n, i, fh;
if (tab == null || (n = tab.length) == 0 ||
(f = tabAt(tab, i = (n - 1) & hash)) == null)
break;
else if ((fh = f.hash) == MOVED)
tab = helpTransfer(tab, f);
else {
V oldVal = null;
boolean validated = false;
synchronized (f) {
if (tabAt(tab, i) == f) {
if (fh >= 0) {
validated = true;
for (Node<K,V> e = f, pred = null;;) {
K ek;
if (e.hash == hash &&
((ek = e.key) == key ||
(ek != null && key.equals(ek)))) {
V ev = e.val;
if (cv == null || cv == ev ||
(ev != null && cv.equals(ev))) {
oldVal = ev;
if (value != null)
e.val = value;
else if (pred != null)
pred.next = e.next;
else
setTabAt(tab, i, e.next);
}
break;
}
pred = e;
if ((e = e.next) == null)
break;
}
}
else if (f instanceof TreeBin) {
validated = true;
TreeBin<K,V> t = (TreeBin<K,V>)f;
TreeNode<K,V> r, p;
if ((r = t.root) != null &&
(p = r.findTreeNode(hash, key, null)) != null) {
V pv = p.val;
if (cv == null || cv == pv ||
(pv != null && cv.equals(pv))) {
oldVal = pv;
if (value != null)
p.val = value;
else if (t.removeTreeNode(p))
setTabAt(tab, i, untreeify(t.first));
}
}
}
}
}
if (validated) {
if (oldVal != null) {
if (value == null)
addCount(-1L, -1);
return oldVal;
}
break;
}
}
}
return null;
}