JDK1.8源码解读——HashMap源码
从类的Doc文档注释中,我们可以得出HashMap的一些特性:
1 无序 允许为null 非同步
2 底层由哈希表实现
3 初始容量和负载因子对HashMap的影响很大
成员变量:
/**默认初始值,必须是二的倍数 */
static final int DEFAULT_INITIAL_CAPACITY = 1 << 4;
/** 最大容量 */
static final int MAXIMUM_CAPACITY = 1 << 30;
/**默认的负载因子 */
static final float DEFAULT_LOAD_FACTOR = 0.75f;
/**阈值,一个哈希桶被添加到TREEIFY_THRESHOLD个节点的时候,桶中的链表会被转化为红黑二叉树 */
static final int TREEIFY_THRESHOLD = 8;
/** 阈值,将红黑二叉树转化成链表(红黑二叉树为了保持平衡,要进行左旋,右旋,换色,消耗资源)*/
static final int UNTREEIFY_THRESHOLD = 6;
/**桶可能被树化为树形结构的最小容量*/
static final int MIN_TREEIFY_CAPACITY = 64;
存储结构:
HashMap内部包含了一个Node类型的数组table,Node即为 JDK1.8之前的Entry
transient Node<K,V>[] table;
/**
* Basic hash bin node, used for most entries. (See below for
* TreeNode subclass, and in LinkedHashMap for its Entry subclass.)
*/
static class Node<K,V> implements Map.Entry<K,V> {
final int hash;
final K key;
V value;
Node<K,V> next;
Node(int hash, K key, V value, Node<K,V> next) {
this.hash = hash;
this.key = key;
this.value = value;
this.next = next;
}
public final K getKey() { return key; }
public final V getValue() { return value; }
public final String toString() { return key + "=" + value; }
public final int hashCode() {
return Objects.hashCode(key) ^ Objects.hashCode(value);
}
public final V setValue(V newValue) {
V oldValue = value;
value = newValue;
return oldValue;
}
public final boolean equals(Object o) {
if (o == this)
return true;
if (o instanceof Map.Entry) {
Map.Entry<?,?> e = (Map.Entry<?,?>)o;
if (Objects.equals(key, e.getKey()) &&
Objects.equals(value, e.getValue()))
return true;
}
return false;
}
}
结论:
Node里存储的是键值对。包含四个字段,从next字段我们可以看出Node是一个链表。
即table数组中每个位置被当成一个桶,一个桶用来存放一个链表Node。
HashMap使用拉链法来解决冲突(ThreadLocalMap则是采用线性探测法),同一个链表存放key的哈希值相同的Node
1 put方法(HashMap的核心):
public V put(K key, V value) {
return putVal(hash(key), key, value, false, true);
}
hash后的key,key Value ,两个参数
首先,看一下hash方法
static final int hash(Object key) {
int h;
return (key == null) ? 0 : (h = key.hashCode()) ^ (h >>> 16);
}
结论:
int 一共32位,这里作者将其分成前16位和后16位。取key的hashcode,与keyhashCode的高16位做异或。这里是将低16位与高16位做运算,得到的值实际上是高位和低位的结合,这就增加了随机性。在一定程度上减少了散列冲突的发生。这里hashCode是调用的Object中的hashCode,即引用对象的hashcode与其内存地址有关
/**
* Implements Map.put and related methods
*
* @param key的哈希值
* @param key 键
* @param value 要放入的值
* @param onlyIfAbsent true 则不改变现有值,默认是false
* @param evict 如果是false,则表处于创建模式,默认是true
* @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;
if ((tab = table) == null || (n = tab.length) == 0)
n = (tab = resize()).length;
//如果散列表为null,则初始化散列表
if ((p = tab[i = (n - 1) & hash]) == null)
tab[i] = newNode(hash, key, value, null);
//将对象放入散列表的时,默认初始容量是16,也就是说,要放到table数组的0-15的位置上,所以通过tab[i = (n - 1) & hash]来确定数组下标位置,n如果为奇数,则n-1为偶数,0与任何数的&运算都是0,会造成一部分的内存浪费(下标最后一位为0的位置存不会存值)
//如果没有发生散列冲突,则直接将新建的Entry对象放入table数组中
else {
Node<K,V> e; K k;
if (p.hash == hash &&
((k = p.key) == key || (key != null && key.equals(k))))
e = p;
//如果发生了散列冲突,就先记录下发生冲突的Node
else if (p instanceof TreeNode)
e = ((TreeNode<K,V>)p).putTreeVal(this, tab, hash, key, value);
//如果红黑树结构,则调用红合数的插入方法
else {
for (int binCount = 0; ; ++binCount) {
if ((e = p.next) == null) {
//如果遍历链表没有发现该此节点,就插入链表的尾部(尾插法,1.8之前都是头插法)
p.next = newNode(hash, key, value, null);
//如果插入后链表长度大于8,则转换为红黑树
if (binCount >= TREEIFY_THRESHOLD - 1) // -1 for 1st
treeifyBin(tab, hash);
break;
}
//如果key在链表中已经存在,则退出循环
if (e.hash == hash &&
((k = e.key) == key || (key != null && key.equals(k))))
break;
p = e;
}
}
//如果key在链表中已经存在,则修改其原先的key值,并且返回老的值
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;
}
2 resize()方法:是HashMap的扩容方法,新的容量是旧的容量的两倍,需要注意的是,扩容操作同样需要把 oldTable 的所有键值对重新插入 newTable 中,因此这一步是很费时的。
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;
}
3 get方法
public V get(Object key) {
Node<K,V> e;
return (e = getNode(hash(key), key)) == null ? null : e.value;
}
通过key计算哈希值,调用getNode()来获取对应的value
/**
* Implements Map.get and related methods
*
* @param hash hash for key
* @param key the key
* @return the node, or null if none
*/
final Node<K,V> getNode(int hash, Object key) {
Node<K,V>[] tab; Node<K,V> first, e; int n; K k;
if ((tab = table) != null && (n = tab.length) > 0 &&
(first = tab[(n - 1) & hash]) != null) {
//判断计算出来的hash值是否在散列表上
if (first.hash == hash && // always check first node
((k = first.key) == key || (key != null && key.equals(k))))
return first;
//检查第一个位置,如果在桶的首位就可以被找到,那就直接返回
if ((e = first.next) != null) {
//否则则在红黑树中或者遍历链表寻找
if (first instanceof TreeNode)
return ((TreeNode<K,V>)first).getTreeNode(hash, key);
do {
if (e.hash == hash &&
((k = e.key) == key || (key != null && key.equals(k))))
return e;
} while ((e = e.next) != null);
}
}
return null;
}
4 remove方法:从此映射中删除指定键的映射(如果存在)
public V remove(Object key) {
Node<K,V> e;
return (e = removeNode(hash(key), key, null, false, true)) == null ?
null : e.value;
}
通过key计算哈希值,调用removeNode()来删除节点
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) {
//同get,判断计算出来的hash值是否在散列表上
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;
//先查找首位,如果可以找到,就记录下来
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);
}
}
//不是在首位,就去红黑树或者链表中查找,如果可以找到就记录下来
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;
//找到了对应的节点,并且value值对应上了,那么就可以删除了,这里也分三种情况,在链表,在红黑树,在桶的首位
}
}
return null;
}
参考资料:
jdk1.8源码
https://github.com/CyC2018/CS-Notes/blob/master/notes/Java 容器.md#hashmap
大牛博客
https://blog.csdn.net/qq_33256688/article/details/79938886
HashMap到底是插入链表头部还是尾部
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