JDK8的HashMap源码学习笔记
正文:
概念
HashMap是数组+链表+红黑树实现的，红黑树是在JDK8中增加的，优化了链表过长的效率问题
HashMap
HashMap
泊松分布
HashMap源码注释有提到这个概念，泊松分布是单位时间内独立事件发生次数的概率分布，指数分布是独立事件的时间间隔的概率分布，可以参考阮一峰泊松分布博客
设计
方法参数
- 都会带hash值作为参数(通常由公共参数提供)，允许它们彼此调用而无需重新计算用户哈希代码。
全局变量
static final int DEFAULT_INITIAL_CAPACITY = 1 << 4; // 默认初始容量
static final int MAXIMUM_CAPACITY = 1 << 30; //最大容量(超出则扩为(2^31)-1)
static final float DEFAULT_LOAD_FACTOR = 0.75f; //默认加载因子
static final int TREEIFY_THRESHOLD = 8; //转树的阈值
static final int MIN_TREEIFY_CAPACITY = 64; //当桶数组容量小于该值时，优先进行扩容，而不是树化(容量大小会影响碰撞率)
static final int UNTREEIFY_THRESHOLD = 6; //红黑树转链表阈值(扩容时候红黑树拆分用到)
int threshold; //当前HashMap所能容纳键值对数量的最大值算threshold的方法：
static final int tableSizeFor(int cap) {
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;
}tableSizeFor()方法作用是算出大于或等于cap的最小2的幂，如2^5+1的结果则是2^6也就是64
hash()方法
static final int hash(Object key) {
int h;
return (key == null) ? 0 : (h = key.hashCode()) ^ (h >>> 16);
}
hash
hash
可以看到在JDK8的实现中，优化了高位运算的算法，自己的高半区和低半区做异或，减少了低位的碰撞率。
putVal()方法
final V putVal(int hash, K key, V value, boolean onlyIfAbsent,
boolean evict) {
Node<K,V>[] tab; Node<K,V> p; int n, i;
//初始化buckets table+为null扩容
if ((tab = table) == null || (n = tab.length) == 0)
n = (tab = resize()).length;
//位置为null直接插入新的节点
if ((p = tab[i = (n - 1) & hash]) == null)
tab[i] = newNode(hash, key, value, null);
else {
Node<K,V> e; K k;
//如果hash值相同并且key相同则直接覆盖
if (p.hash == hash &&
((k = p.key) == key || (key != null && key.equals(k))))
e = p;
//判断该链是不是红黑树，是的话走Tree版本的putVal
else if (p instanceof TreeNode)
e = ((TreeNode<K,V>)p).putTreeVal(this, tab, hash, key, value);
//否则则是链表
else {
for (int binCount = 0; ; ++binCount) {
//如果下个节点为空则放入下个节点
if ((e = p.next) == null) {
p.next = newNode(hash, key, value, null);
//链表长度大于8转换为红黑树进行处理
if (binCount >= TREEIFY_THRESHOLD - 1) // -1 for 1st
treeifyBin(tab, hash);
break;
}
//覆盖
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;
//超出当前容量最大值就扩容
if (++size > threshold)
resize();
afterNodeInsertion(evict);
return null;
}resize()方法
我们先来看下扩容机制，在HashMap中元素位置都是2的幂，接下来我们来看具体代码实现
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) {
//如果当前容量大于默认值2^30，则扩容至2^31-1
if (oldCap >= MAXIMUM_CAPACITY) {
threshold = Integer.MAX_VALUE;
return oldTab;
}
//double
else if ((newCap = oldCap << 1) < MAXIMUM_CAPACITY &&
oldCap >= DEFAULT_INITIAL_CAPACITY)
newThr = oldThr << 1; // double threshold
}
//initial capacity
else if (oldThr > 0) // initial capacity was placed in threshold
newCap = oldThr;
//默认构造方法初始化Cap
else { // zero initial threshold signifies using defaults
newCap = DEFAULT_INITIAL_CAPACITY;
newThr = (int)(DEFAULT_LOAD_FACTOR * DEFAULT_INITIAL_CAPACITY);
}
//newThr 为 0 时，按阈值计算公式进行计算
if (newThr == 0) {
float ft = (float)newCap * loadFactor;
newThr = (newCap < MAXIMUM_CAPACITY && ft < (float)MAXIMUM_CAPACITY ?
(int)ft : Integer.MAX_VALUE);
}
threshold = newThr;
@SuppressWarnings({"rawtypes","unchecked"})
Node<K,V>[] newTab = (Node<K,V>[])new Node[newCap];
table = newTab;
if (oldTab != null) {
//把每个bucket都移动到新的buckets中
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)
//对红黑树进行拆分
((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;
//遍历链表
do {
next = e.next;
//原位置(根据0和非0判断是否扩容)
if ((e.hash & oldCap) == 0) {
if (loTail == null)
loHead = e;
else
loTail.next = e;
loTail = e;
}
//原位置+oldCap
else {
if (hiTail == null)
hiHead = e;
else
hiTail.next = e;
hiTail = e;
}
} while ((e = next) != null);
//原位置的放到bucket里
if (loTail != null) {
loTail.next = null;
newTab[j] = loHead;
}
//原位置+oldCap的放到bucket里
if (hiTail != null) {
hiTail.next = null;
newTab[j + oldCap] = hiHead;
}
}
}
}
}
return newTab;
}split()方法
该方法是红黑树拆分方法，普通链表需要拆分，红黑树也同样需要拆分
final void split(HashMap<K,V> map, Node<K,V>[] tab, int index, int bit) {
TreeNode<K,V> b = this;
// Relink into lo and hi lists, preserving order
TreeNode<K,V> loHead = null, loTail = null;
TreeNode<K,V> hiHead = null, hiTail = null;
int lc = 0, hc = 0;
//对红黑树节点进行分组(同链表一个原理)
for (TreeNode<K,V> e = b, next; e != null; e = next) {
next = (TreeNode<K,V>)e.next;
e.next = null;
if ((e.hash & bit) == 0) {
if ((e.prev = loTail) == null)
loHead = e;
else
loTail.next = e;
loTail = e;
++lc;
}
else {
if ((e.prev = hiTail) == null)
hiHead = e;
else
hiTail.next = e;
hiTail = e;
++hc;
}
}
if (loHead != null) {
//如果 loHead 不为空，且链表长度小于等于 6，则将红黑树转成链表
if (lc <= UNTREEIFY_THRESHOLD)
tab[index] = loHead.untreeify(map);
else {
//原位置的放到bucket里
tab[index] = loHead;
if (hiHead != null) // (else is already treeified)
loHead.treeify(tab);
}
}
if (hiHead != null) {
if (hc <= UNTREEIFY_THRESHOLD)
tab[index + bit] = hiHead.untreeify(map);
else {
//原位置+oldCap的放到bucket里
tab[index + bit] = hiHead;
if (loHead != null)
hiHead.treeify(tab);
}
}
}untreeify()方法
此方法是将红黑树转为链表
final Node<K,V> untreeify(HashMap<K,V> map) {
Node<K,V> hd = null, tl = null;
for (Node<K,V> q = this; q != null; q = q.next) {
//转为Node
Node<K,V> p = map.replacementNode(q, null);
if (tl == null)
hd = p;
else
tl.next = p;
tl = p;
}
return hd;
}
Node<K,V> replacementNode(Node<K,V> p, Node<K,V> next) {
return new Node<>(p.hash, p.key, p.value, next);
}晚安~
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原始发表：2018-03-11，如有侵权请联系 cloudcommunity@tencent.com 删除
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