java集合之HashMap

JDK8

使用Node数组存储数据，数组每个Node是一个链表或者红黑树，当链表长度大于8时，会转换成红黑树，从大于8降到小于6时才转换成链表。

采用延迟初始化，当put第一个元素时，才初始化map

1,成员变量:

static final int DEFAULT_INITIAL_CAPACITY = 1 << 4; // aka 16static final int MAXIMUM_CAPACITY = 1 << 30;static final float DEFAULT_LOAD_FACTOR = 0.75f;static final int TREEIFY_THRESHOLD = 8;static final int UNTREEIFY_THRESHOLD = 6;static final int MIN_TREEIFY_CAPACITY = 64;

2,对于所有增删改查操作,map都会根据hash算法找到key在Node数组的索引，Hash算法计算结果越分散均匀，Hash碰撞的概率就越小，map的存取效率就会越高。下面先介绍hash算法:

static final int hash(Object key) {    int h;    return (key == null) ? 0 : (h = key.hashCode()) ^ (h >>> 16);}

使用高16位和低16位异或,能够保证在数组length较小时,高位的bit也能参与到hash的计算中.，计算结果更分散。

获得hash之后,h&(table.length-1)得到下标,因为容量总是2的n次方,所以等价于h%table.length,效率更高.

3.put增加元素操作:

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;    if ((tab = table) == null || (n = tab.length) == 0)        n = (tab = resize()).length;//扩容resize()    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);            return oldValue;        }    }    ++modCount;    if (++size > threshold)        resize();    afterNodeInsertion(evict);    return null;}

添加元素之前会检查是否需要扩容，扩容使用resize()方法。和1.7不同的是添加完元素后可能会将链表转变成红黑树。

4,扩容的方法

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) {        if (oldCap >= MAXIMUM_CAPACITY) {            threshold = Integer.MAX_VALUE;            return oldTab;        }        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        newCap = oldThr;    else {               // zero initial threshold signifies using defaults        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;    @SuppressWarnings({"rawtypes","unchecked"})        Node<K,V>[] newTab = (Node<K,V>[])new Node[newCap];    table = newTab;    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;}

和1.7不同，重hash后，找到新位置插入，新插入的节点放在头节点。

而1.8中，新位置只有两种可能,要么是原位置，要么是原位置移动2次幂的长度,遍历链表,根据新位置不同,分成两个链表,分别插到新位置。

5,删除方法

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;    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;        if (p.hash == hash &&            ((k = p.key) == key || (key != null && key.equals(k))))            node = p;        else if ((e = p.next) != null) {            if (p instanceof TreeNode)                node = ((TreeNode<K,V>)p).getTreeNode(hash, key);            else {                do {                    if (e.hash == hash &&                        ((k = e.key) == key ||                         (key != null && key.equals(k)))) {                        node = e;                        break;                    }                    p = e;                } while ((e = e.next) != null);            }        }        if (node != null && (!matchValue || (v = node.value) == value ||                             (value != null && value.equals(v)))) {            if (node instanceof TreeNode)                ((TreeNode<K,V>)node).removeTreeNode(this, tab, movable);            else if (node == p)                tab[index] = node.next;            else                p.next = node.next;            ++modCount;            --size;            afterNodeRemoval(node);            return node;        }    }    return null;}

删除元素也是先通过hash算法找到元素在数组的索引，然后搜索链表或者红黑树，通过hashcode和equals方法比较找到元素后删除。