JDK1.8 之后的HashMap

时间:2019-10-18
本文章向大家介绍JDK1.8 之后的HashMap,主要包括JDK1.8 之后的HashMap使用实例、应用技巧、基本知识点总结和需要注意事项,具有一定的参考价值,需要的朋友可以参考一下。

JDK1.7以前的HashMap

jdk1.7中,当冲突时,在冲突的地址上生成一个链表,将冲突的元素的key,通过equals进行比较,相同即覆盖,不同则添加到链表上,此时如果链表过长,效率就会大大降低,查找和添加操作的时间复杂度都为O(n);但是在jdk1.8中如果链表长度大于8,链表就会转化为 红黑树,时间复杂度也降为了O(logn),性能得到了很大的优化。

当红黑数节点小于等于6会重新转换为链表。 

 代码分析:

    /**
     *  默认初始容量为16,0000 0001 右移4位 0001 0000为16,主干数组的初始容量为16,而且这个数组
   *必须是2的倍数(后面说为什么是2的倍数)
     */
    static final int DEFAULT_INITIAL_CAPACITY = 1 << 4; // aka 16

    /**
     * 最大容量为2的30次方
       */
    static final int MAXIMUM_CAPACITY = 1 << 30;

    /**
     * 默认加载因子为0.75
     */
    static final float DEFAULT_LOAD_FACTOR = 0.75f;

    /**
     * 阈值,如果主干数组上的链表的长度大于8,链表转化为红黑树
     */
    static final int TREEIFY_THRESHOLD = 8;

    /**
     * hash表扩容后,如果发现某一个红黑树的长度小于6,则会重新退化为链表
     */
    static final int UNTREEIFY_THRESHOLD = 6;

    /**
     * 当hashmap容量大于64时,链表才能转成红黑树
     */
    static final int MIN_TREEIFY_CAPACITY = 64;
   /**
     * 临界值=主干数组容量*负载因子 DEFAULT_INITIAL_CAPACITY *DEFAULT_LOAD_FACTOR 
*/
    int threshold;

 HashMap构造方法:

//initialCapacity为初始容量,loadFactor为负载因子
public HashMap(int initialCapacity, float loadFactor) {
    //初始容量小于0,抛出非法数据异常
if (initialCapacity < 0) throw new IllegalArgumentException("Illegal initial capacity: " + initialCapacity);
    //初始容量最大为MAXIMUM_CAPACITY
if (initialCapacity > MAXIMUM_CAPACITY) initialCapacity = MAXIMUM_CAPACITY;
    //校验 loadFactor 合法性
if (loadFactor <= 0 || Float.isNaN(loadFactor)) throw new IllegalArgumentException("Illegal load factor: " + loadFactor); this.loadFactor = loadFactor;
    //将初始容量转成2次幂
this.threshold = tableSizeFor(initialCapacity); }
 //tableSizeFor的作用就是,如果传入A,当A大于0,小于定义的最大容量时,
  //  如果A是2次幂则返回A,否则将A转化为一个比A大且差距最小的2次幂。  
    //例如传入7返回8,传入8返回8,传入9返回16
 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;
    }
  //默认构造方法,负载因子为0.75,初始容量为DEFAULT_INITIAL_CAPACITY=16,初始 容量在第一次put时才会初始化

public HashMap() {
        this.loadFactor = DEFAULT_LOAD_FACTOR; // all other fields defaulted
    }

put方法: 

 static final int hash(Object key) {

int h;
        return (key == null) ? 0 : (h = key.hashCode()) ^ (h >>> 16);
    }
  public V put(K key, V value) {
        return putVal(hash(key), key, value, false, true);
    }
  

final V putVal(int hash, K key, V value, boolean onlyIfAbsent, boolean evict) { Node<K,V>[] tab; Node<K,V> p; int n, i;
//如果主干上的table为空,长度为0,调用resize方法,调整table的长度(
if ((tab = table) == null || (n = tab.length) == 0)

  /* 这里调用resize,其实就是第一次put时,对数组进行初始化。*/

            n = (tab = resize()).length;

      //存入的key 不存在 ,存入新增的key
        if ((p = tab[i = (n - 1) & hash]) == null)
            tab[i] = newNode(hash, key, value, null);
        else {
            Node<K,V> e; K k;
        //key存在
if (p.hash == hash &&((k = p.key) == key || (key != null && key.equals(k)))) e = p;
        //判断p是否是 红黑树节点
else if (p instanceof TreeNode) e = ((TreeNode<K,V>)p).putTreeVal(this, tab, hash, key, value); else {//p与新节点既不完全相同,p也不是treenode的实例 for (int binCount = 0; ; ++binCount) { if ((e = p.next) == null) { p.next = newNode(hash, key, value, null);
                //如果链表长度大于等于8
if (binCount >= TREEIFY_THRESHOLD - 1) // -1 for 1st
                  
//将链表转为红黑树 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

        //如果添加的元素产生了hash冲突,那么调用//put方法时,会将他在链表中他的上一个元素的值返回

V oldValue = e.value;
if (!onlyIfAbsent || oldValue == null)
                    e.value = value;
                afterNodeAccess(e);
                return oldValue;
            }
        }
        ++modCount;
    //
如果元素数量大于临界值,则进行扩容
if (++size > threshold)
            resize();
        afterNodeInsertion(evict);
        return null;
    }

转化为红黑树:

  final void treeifyBin(Node<K,V>[] tab, int hash) {
        int n, index; Node<K,V> e;
      // 数组长度小于64则,再次扩容 不转换为红黑树
        if (tab == null || (n = tab.length) < MIN_TREEIFY_CAPACITY)
            resize();
        else if ((e = tab[index = (n - 1) & hash]) != null) {
            TreeNode<K,V> hd = null, tl = null;
            do { //转化为红黑树
                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)
                hd.treeify(tab);
        }
    }    

resize的源码详解,扩容机制,单元素如何散列到新的数组中,链表中的元素如何散列到新的数组中,红黑树中的元素如何散列到新的数组中?

 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) {  //扩容执行分支
            if (oldCap >= MAXIMUM_CAPACITY) {   //当容量超过最大值时,临界值设置为int最大值
                threshold = Integer.MAX_VALUE;
                return oldTab;
            }
            else if ((newCap = oldCap << 1) < MAXIMUM_CAPACITY &&oldCap >= DEFAULT_INITIAL_CAPACITY) //扩容容量为2倍,临界值为2倍
                newThr = oldThr << 1;
        }
        else if (oldThr > 0) 
            newCap = oldThr;
        else {               
            newCap = DEFAULT_INITIAL_CAPACITY;
            newThr = (int)(DEFAULT_LOAD_FACTOR * DEFAULT_INITIAL_CAPACITY);
        }
        if (newThr == 0) {  
            float ft = (float)newCap * loadFactor;
            newThr = (newCap < MAXIMUM_CAPACITY && ft < (float)MAXIMUM_CAPACITY ?
                      (int)ft : Integer.MAX_VALUE);
        }
        threshold = newThr;    //将新的临界值赋值赋值给threshold 
        Node<K,V>[] newTab = (Node<K,V>[])new Node[newCap];
        table = newTab;   //新的数组赋值给table
 
        //扩容后,重新计算元素新的位置
        if (oldTab != null) {   //原数组
            for (int j = 0; j < oldCap; ++j) {   //通过原容量遍历原数组
                Node<K,V> e;
                if ((e = oldTab[j]) != null) {   //判断node是否为空,将j位置上的节点
                        oldTab[j] = null;
                    if (e.next == null)          //判断node上是否有链表
                        newTab[e.hash & (newCap - 1)] = e; //无链表,确定元素存放位置,
                    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; //尾节点的next设置为空 newTab[j] = loHead; } if (hiTail != null) { hiTail.next = null; //尾节点的next设置为空 newTab[j + oldCap] = hiHead; } } } } } return newTab; }
//红黑树
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; 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) {
//小于6 转化为 链表
if (lc <= UNTREEIFY_THRESHOLD) tab[index] = loHead.untreeify(map); else { 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 { tab[index + bit] = hiHead; if (loHead != null) hiHead.treeify(tab); } } }

原文地址:https://www.cnblogs.com/fanBlog/p/11692419.html