jdk1.8之后的HashMap源码分析：
1、类的属性
//序列号
private static final long serialVersionUID = 362498820763181265L;
//默认初始容量为16
static final int DEFAULT_INITIAL_CAPACITY = 1 << 4;
//最大容量
static final int MAXIMUM_CAPACITY = 1 << 30;
//默认装载因子
static final float DEFAULT_LOAD_FACTOR = 0.75f;
//超过该值，使用红黑树
static final int TREEIFY_THRESHOLD = 8;
//小于该值，转化为链表
static final int UNTREEIFY_THRESHOLD = 6;
//转化为红黑树时table的最小大小
static final int MIN_TREEIFY_CAPACITY = 64;
//存储元素的数组，大小总为2的幂次方
transient Node<K,V>[] table;
//存放具体元素的集合
transient Set<Map.Entry<K,V>> entrySet;
// 存放元素的个数，注意这个不等于数组的长度。
transient int size;
// 每次扩容和更改map结构的计数器
transient int modCount;
// 临界值 当实际大小(容量*填充因子)超过临界值时，会进行扩容
int threshold;
// 加载因子
final float loadFactor1.1 loadFactor加载因子
loadFactor加载因子是控制数组存放数据的疏密程度，loadFactor越趋近于1，那么 数组中存放的数据(entry)也就越多，也就越密。
loadFactor太大导致查找元素效率低，太小导致数组的利用率低，存放的数据会很分散。loadFactor的默认值为0.75f是官方给出的一个比较好的临界值。
给定的默认容量为 16，负载因子为 0.75。Map 在使用过程中不断的往里面存放数据，当数量达到了 16 * 0.75 = 12 就需要将当前 16 的容量进行扩容，而扩容这个过程涉及到 rehash、复制数据等操作，所以非常消耗性能。
1.2 threshold
threshold = capacity * loadFactor，是衡量数组是否需要扩容的标准，当数组存储元素数目大于阈值就需要扩容。
2、数据结构
2.1 node节点
static class Node<K,V> implements Map.Entry<K,V> {
//哈希值，存放元素时用来比较的
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;
}
//重写
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;
}
}2.2 红黑树节点
static final class TreeNode<K,V> extends LinkedHashMap.Entry<K,V> {
TreeNode<K,V> parent; // red-black tree links
TreeNode<K,V> left;
TreeNode<K,V> right;
TreeNode<K,V> prev; // needed to unlink next upon deletion
boolean red; //判断颜色
TreeNode(int hash, K key, V val, Node<K,V> next) {
super(hash, key, val, next);
}
/**
* Returns root of tree containing this node.
*/
final TreeNode<K,V> root() {
for (TreeNode<K,V> r = this, p;;) {
if ((p = r.parent) == null)
return r;
r = p;
}
}
}3、源码分析
3.1 构造函数
// 默认构造函数。
public HashMap() {
this.loadFactor = DEFAULT_LOAD_FACTOR; // all other fields defaulted
}
// 包含另一个“Map”的构造函数
public HashMap(Map<? extends K, ? extends V> m) {
this.loadFactor = DEFAULT_LOAD_FACTOR;
putMapEntries(m, false);//下面会分析到这个方法
}
// 指定“容量大小”的构造函数
public HashMap(int initialCapacity) {
this(initialCapacity, DEFAULT_LOAD_FACTOR);
}
// 指定“容量大小”和“加载因子”的构造函数
public HashMap(int initialCapacity, float loadFactor) {
if (initialCapacity < 0)
throw new IllegalArgumentException("Illegal initial capacity: " + initialCapacity);
if (initialCapacity > MAXIMUM_CAPACITY)
initialCapacity = MAXIMUM_CAPACITY;
if (loadFactor <= 0 || Float.isNaN(loadFactor))
throw new IllegalArgumentException("Illegal load factor: " + loadFactor);
this.loadFactor = loadFactor;
this.threshold = tableSizeFor(initialCapacity);
}3.2 putMapEntries方法
final void putMapEntries(Map<? extends K, ? extends V> m, boolean evict) {
int s = m.size();
if (s > 0) {
//table未初始化
if (table == null) { // pre-size
//计算给定负载因子下需要的元素容量
float ft = ((float)s / loadFactor) + 1.0F;
int t = ((ft < (float)MAXIMUM_CAPACITY) ?
(int)ft : MAXIMUM_CAPACITY);
//如果得到的t大于阈值，则初始化阈值
if (t > threshold)
threshold = tableSizeFor(t);
}
//如果大于阈值，则进行扩容
else if (s > threshold)
resize();
//将m的元素添加到该hashMap中
for (Map.Entry<? extends K, ? extends V> e : m.entrySet()) {
K key = e.getKey();
V value = e.getValue();
putVal(hash(key), key, value, false, evict);
}
}
}3.3 put方法
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;
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
//转化为红黑树，其中会判断table长度是否小于MIN_TREEIFY_CAPACITY，如果小于
//则扩容，不转为红黑树
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
V oldValue = e.value;
if (!onlyIfAbsent || oldValue == null)
e.value = value;
afterNodeAccess(e);
return oldValue;
}
}
++modCount;
if (++size > threshold)
resize();
//插入后回调
afterNodeInsertion(evict);
return null;
}3.4 get方法
public V get(Object key) {
Node<K,V> e;
return (e = getNode(hash(key), key)) == null ? null : e.value;
}
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) {
// 数组元素相等
if (first.hash == hash && // always check first node
((k = first.key) == key || (key != null && key.equals(k))))
return first;
// 桶中不止一个节点
if ((e = first.next) != null) {
// 在树中get
if (first instanceof TreeNode)
return ((TreeNode<K,V>)first).getTreeNode(hash, key);
// 在链表中get
do {
if (e.hash == hash &&
((k = e.key) == key || (key != null && key.equals(k))))
return e;
} while ((e = e.next) != null);
}
}
return null;
}3.5 resize
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) {
// 超过最大值就不再扩充了，就只好随你碰撞去吧
if (oldCap >= MAXIMUM_CAPACITY) {
threshold = Integer.MAX_VALUE;
return oldTab;
}
// 没超过最大值，就扩充为原来的2倍
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 {
// signifies using defaults
newCap = DEFAULT_INITIAL_CAPACITY;
newThr = (int)(DEFAULT_LOAD_FACTOR * DEFAULT_INITIAL_CAPACITY);
}
// 计算新的resize上限
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)
((TreeNode<K,V>)e).split(this, newTab, j, oldCap);
else {
Node<K,V> loHead = null, loTail = null;
Node<K,V> hiHead = null, hiTail = null;
Node<K,V> next;
do {
next = e.next;
// 原索引
if ((e.hash & oldCap) == 0) {
if (loTail == null)
loHead = e;
else
loTail.next = e;
loTail = e;
}
// 原索引+oldCap
else {
if (hiTail == null)
hiHead = e;
else
hiTail.next = e;
hiTail = e;
}
} while ((e = next) != null);
// 原索引放到bucket里
if (loTail != null) {
loTail.next = null;
newTab[j] = loHead;
}
// 原索引+oldCap放到bucket里
if (hiTail != null) {
hiTail.next = null;
newTab[j + oldCap] = hiHead;
}
}
}
}
}
return newTab;
}
参考
1、https://snailclimb.gitee.io/javaguide/#/docs/java/collection/HashMap
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