concurrenthashmap
备注JDK版本1.8
HashMap
- 首先是构造方法，构造方法可传入初始容量和负载因子。传入的初始化容量会被修正为最接近的2的正次幂的值。
public HashMap(int initialCapacity, float loadFactor) {
if (initialCapacity < 0)
throw new illegalargumentException("Illegal initial capacity: " +
initialCapacity);
if (initialCapacity > MAXIMUM_CAPACITY) //查处最高容量值
initialCapacity = MAXIMUM_CAPACITY;
if (loadFactor <= 0 || Float.isNaN(loadFactor))// 负载因子值不合法
throw new IllegalArgumentException("Illegal load factor: " +
loadFactor);
this.loadFactor = loadFactor;// 负载因子
this.threshold = tableSizeFor(initialCapacity);// 返回最接近的2的正次幂，设置阀值，这里也做初始化table大小用
}
看完这段代码有个疑问loadFactor、threshold有啥用，别急继续往下看：
tableforsize
- put方法和resize方法如下：
final V putVal(int hash, K key, V value, boolean onlyIfAbsent,
boolean evict) {
Node<K,V>[] tab; Node<K,V> p; int n, i;
if ((tab = table) == null || (n = tab.length) == 0) // 没初始化过则初始化一下
n = (tab = resize()).length;
if ((p = tab[i = (n - 1) & hash]) == null) // 第一次设置新建一个
tab[i] = newNode(hash, key, value, null);
else {
Node<K,V> e; K k;
if (p.hash == hash &&
((k = p.key) == key || (key != null && key.equals(k)))) // 直接命中槽中节点
e = p;
else if (p instanceof TreeNode) // 这个节点已经膨胀成树了
e = ((TreeNode<K,V>)p).putTreeVal(this, tab, hash, key, value); // 插入节点
else { // 这个节点没有还很低调,撸这个链表
for (int binCount = 0; ; ++binCount) {
if ((e = p.next) == null) {
p.next = newNode(hash, key, value, null);
if (binCount >= TREEIFY_THRESHOLD - 1) // -1 for 1st
treeifyBin(tab, hash);// 超过8自我膨胀
break;
}
if (e.hash == hash &&
((k = e.key) == key || (key != null && key.equals(k))))
break;
p = e;
}
}
if (e != null) { // existing mAPPing for key
V oldValue = e.value;
if (!onlyIfAbsent || oldValue == null)
e.value = value;
afterNodeAccess(e);
return oldValue;
}
}
++modCount;
if (++size > threshold) // 超过阈值！弄你
resize();
afterNodeInsertion(evict);
return null;
}
final Node<K,V>[] resize() {
Node<K,V>[] oldTab = table;
int oldCap = (oldTab == null) ? 0 : oldTab.length; // 初始化的时候会是0
int oldThr = threshold; // 这里如果是初始化的话，会是设置的初始容量
int newCap, newThr = 0;
if (oldCap > 0) {
if (oldCap >= MAXIMUM_CAPACITY) {// 太大了不能在扩大了，直接改下阈值返回了
threshold = integer.MAX_VALUE;
return oldTab;
}
else if ((newCap = oldCap << 1) < MAXIMUM_CAPACITY &&
oldCap >= DEFAULT_INITIAL_CAPACITY) // 新的容量等于老的容量*2
newThr = oldThr << 1; // 新的阈值也等于老的阈值*2
}
else if (oldThr > 0) // 初始容量被设置为阈值
newCap = oldThr;// 设置新的容量大小，这里初始化的情况下直接设置为阈值
else { // 零初始阈值表示使用默认值
newCap = DEFAULT_INITIAL_CAPACITY;
newThr = (int)(DEFAULT_LOAD_FACTOR * DEFAULT_INITIAL_CAPACITY);
}
if (newThr == 0) {
// 计算新阈值
float ft = (float)newCap * loadFactor;
newThr = (newCap < MAXIMUM_CAPACITY && ft < (float)MAXIMUM_CAPACITY ?
(int)ft : Integer.MAX_VALUE);
}
threshold = newThr; // 设置新的阈值
@SuppressWarnings({"rawtypes","unchecked"})
Node<K,V>[] newTab = (Node<K,V>[])new Node[newCap]; // 创建新的hash表
table = newTab;
// 干啥它自己心里清楚
if (oldTab != null) {
for (int j = 0; j < oldCap; ++j) {
Node<K,V> e;
if ((e = oldTab[j]) != null) {
oldTab[j] = null;
if (e.next == null)
newTab[e.hash & (newCap - 1)] = e;
else if (e instanceof TreeNode)
((TreeNode<K,V>)e).split(this, newTab, j, oldCap);
else { // preserve order
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;
}
通过以上代码可以看出
- resize执行时如果还未初始化会使用threshold作为初始化table的size，然后threshold*loadFactor做真正的threshold
- 如果已经执行过初始化，新容量=老容量*2，新阈值=老阈值*2，并且将旧数组的内容重新hash到新数组中（这里1.8的代码虽然进行了改进不会出现类似1.7的死循环但是并发执行的话会产生一定的副作用）
LinkedHashMap
重所周知LinkedHashMap可以按照插入的顺序进行遍历，愿意是在newNode的时候将新节点连接到连表尾部实现的。
Node<K,V> newNode(int hash, K key, V value, Node<K,V> e) {
LinkedHashMap.Entry<K,V> p =
new LinkedHashMap.Entry<K,V>(hash, key, value, e);
linkNodeLast(p);
return p;
}
不过发现构造参数有一个accessOrder是干嘛的呢？
public LinkedHashMap(int initialCapacity,
float loadFactor,
boolean accessOrder) {
super(initialCapacity, loadFactor);
this.accessOrder = accessOrder;
}
看一下这个参数在什么时候会用到。
public V get(Object key) {
Node<K,V> e;
if ((e = getNode(hash(key), key)) == null)
return null;
if (accessOrder)
afterNodeAccess(e);
return e.value;
}
在get方法会调用afterNodeAccess这个方法
void afterNodeAccess(Node<K,V> e) { // move node to last
LinkedHashMap.Entry<K,V> last;
if (accessOrder && (last = tail) != e) {
LinkedHashMap.Entry<K,V> p =
(LinkedHashMap.Entry<K,V>)e, b = p.before, a = p.after;
p.after = null;
if (b == null)
head = a;
else
b.after = a;
if (a != null)
a.before = b;
else
last = b;
if (last == null)
head = p;
else {
p.before = last;
last.after = p;
}
tail = p;
++modCount;
}
}
afterNodeAccess是将这个节点从原来的链表中移动到链表尾部，这个方法在put的时候也会调用
final V putVal(int hash, K key, V value, boolean onlyIfAbsent,
boolean evict) {
Node<K,V>[] tab; Node<K,V> p; int n, i;
if ((tab = table) == null || (n = tab.length) == 0)
n = (tab = resize()).length;
if ((p = tab[i = (n - 1) & hash]) == null)
tab[i] = newNode(hash, key, value, null);
else {
Node<K,V> e; K k;
if (p.hash == hash &&
((k = p.key) == key || (key != null && key.equals(k))))
e = p;
else if (p instanceof TreeNode)
e = ((TreeNode<K,V>)p).putTreeVal(this, tab, hash, key, value);
else {
for (int binCount = 0; ; ++binCount) {
if ((e = p.next) == null) {
p.next = newNode(hash, key, value, null);
if (binCount >= TREEIFY_THRESHOLD - 1) // -1 for 1st
treeifyBin(tab, hash);
break;
}
if (e.hash == hash &&
((k = e.key) == key || (key != null && key.equals(k))))
break;
p = e;
}
}
if (e != null) { // existing mapping for key
V oldValue = e.value;
if (!onlyIfAbsent || oldValue == null)
e.value = value;
afterNodeAccess(e);// 这里
return oldValue;
}
}
++modCount;
if (++size > threshold)
resize();
afterNodeInsertion(evict);
return null;
}
也就是说这个afterNodeAccess参数导致了无论我们在get还是在set的时候都会将这个node放到链表尾部。这样链表就是按照最近使用顺序排序的了。
在加上afterNodeInsertion这个方法：
void afterNodeInsertion(boolean evict) { // possibly remove eldest
LinkedHashMap.Entry<K,V> first;
if (evict && (first = head) != null && removeEldestEntry(first)) {
K key = first.key;
removeNode(hash(key), key, null, false, true);
}
}
/**
* Returns <tt>true</tt> if this map should remove its eldest entry.
* This method is invoked by <tt>put</tt> and <tt>putAll</tt> after
* inserting a new entry into the map. It provides the implementor
* with the opportunity to remove the eldest entry each time a new one
* is added. This is useful if the map represents a cache: it allows
* the map to reduce memory consumption by deleting stale entries.
*
* <p>Sample use: this override will allow the map to grow up to 100
* entries and then delete the eldest entry each time a new entry is
* added, maintaining a steady state of 100 entries.
* <pre>
* private static final int MAX_ENTRIES = 100;
*
* protected boolean removeEldestEntry(Map.Entry eldest) {
* return size() > MAX_ENTRIES;
* }
* </pre>
*
* <p>This method typically does not modify the map in any way,
* instead allowing the map to modify itself as directed by its
* return value. It <i>is</i> permitted for this method to modify
* the map directly, but if it does so, it <i>must</i> return
* <tt>false</tt> (indicating that the map should not attempt any
* further modification). The effects of returning <tt>true</tt>
* after modifying the map from within this method are unspecified.
*
* <p>This implementation merely returns <tt>false</tt> (so that this
* map acts like a normal map - the eldest element is never removed).
*
* @param eldest The least recently inserted entry in the map, or if
* this is an access-ordered map, the least recently accessed
* entry. This is the entry that will be removed it this
* method returns <tt>true</tt>. If the map was empty prior
* to the <tt>put</tt> or <tt>putAll</tt> invocation resulting
* in this invocation, this will be the entry that was just
* inserted; in other words, if the map contains a single
* entry, the eldest entry is also the newest.
* @return <tt>true</tt> if the eldest entry should be removed
* from the map; <tt>false</tt> if it should be retained.
*/
protected boolean removeEldestEntry(Map.Entry<K,V> eldest) {
return false;
}
可以注意到removeEldestEntry方法的注释说，可以通过重写这个方法来实现最老访问条目的淘汰策略。比如可以做缓存的LRU策略。在accessOrder＝false情况下还可以做FIFO策略。
ConcurrentHashMap
首先看下put方法
final V putVal(K key, V value, boolean onlyIfAbsent) {
if (key == null || value == null) throw new NullPointerException();
int hash = spread(key.hashCode());
int binCount = 0;
for (Node<K,V>[] tab = table;;) {
Node<K,V> f; int n, i, fh;
if (tab == null || (n = tab.length) == 0)
tab = initTable();
else if ((f = taBAT(tab, i = (n - 1) & hash)) == null) {
if (casTabAt(tab, i, null,
new Node<K,V>(hash, key, value, null)))
break; // no lock when adding to empty bin
}
else if ((fh = f.hash) == MOVED)
tab = helpTransfer(tab, f);
else {
V oldVal = null;
synchronized (f) { // 锁住了放在数组里的节点，相当于对数组的每个空间加锁，有效提高并发数量
if (tabAt(tab, i) == f) {
if (fh >= 0) {
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) {
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) {
if (binCount >= TREEIFY_THRESHOLD)
treeifyBin(tab, i);
if (oldVal != null)
return oldVal;
break;
}
}
}
addCount(1L, binCount);// 注意这里
return null;
}
这个addCount方法用处很广可以看到几乎所有修改操作后都会调用，那么它是做什么的呢？
/**
* Adds to count, and if table is too small and not already
* resizing, initiates transfer. If already resizing, helps
* perform transfer if work is available. Rechecks occupancy
* after a transfer to see if another resize is already needed
* because resizings are lagging additions.
*
* @param x the count to add
* @param check if <0, don't check resize, if <= 1 only check if uncontended
*/
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();
}
}
}
可以看出ConcurrentHashMap内部维护了一个CounterCell数组，用来记录元素的个数，在看size源码：
/**
* {@inheritDoc}
*/
public int size() {
long n = sumCount();
return ((n < 0L) ? 0 :
(n > (long)Integer.MAX_VALUE) ? Integer.MAX_VALUE :
(int)n);
}
final long sumCount() {
CounterCell[] as = counterCells; CounterCell a;
long sum = baseCount;
if (as != null) {
for (int i = 0; i < as.length; ++i) {
if ((a = as[i]) != null)
sum += a.value;
}
}
return sum;
}
很明显获取size的时候是将所有countercell相加获得的，由于没有锁所以这个size方法是弱一致的
未完待续。。。
参考：
- HashMap
文章最后发布于: 2019-04-19 17:16:40
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