一、概述
HashMap是基于哈希表的Map接口实现的,它存储的是内容是键值对<key,value>映射,不保证映射的顺序
数据结构为链表散列,jdk1.8以后链表深度大于8会转为红黑树
HashMap的实例有两个参数影响性能,初始化容量initialCapacity(16)和loadFactor加载因子(0.75)
二、源码
1、属性
static final int DEFAULT_INITIAL_CAPACITY = 1 << 4;
map初始的容量16,之所以要是2的幂次,为了方便元素插入时使用位运算计算存放的位置(取模效率较低),也为了更方便扩容(避免扩容后重复处理哈希碰撞)
static final int MAXIMUM_CAPACITY = 1 << 30;
上限取了int类型最大的2的幂次
static final float DEFAULT_LOAD_FACTOR = 0.75f;
负载因子太小了浪费空间并且会发生更多次数的resize,太大了哈希冲突增加会导致性能不好,所以0.75只是一个折中的选择
static final int TREEIFY_THRESHOLD = 8;
当链表长度大于等于8时(且数组长度大于等于64),链表转为红黑树结构,之所以是8因为在负载因子为0.75的情况下(长度为length的数组放入0.75*length个元素),链表长度达到8的概率为0.00000006,非常小(一般只有分布非常不均匀的时候才会触发)
static final int UNTREEIFY_THRESHOLD = 6;
当红黑树个数小于等于6时,重新退化为链表,没有用7因为增加一个差值防止链表和红黑树频繁转换
static final int MIN_TREEIFY_CAPACITY = 64;
当哈希表容量大于等于64时才允许链表到红黑树的转换
2、构造方法
public HashMap() { //设置负载因子 this.loadFactor = DEFAULT_LOAD_FACTOR; // all other fields defaulted } public HashMap(int initialCapacity) { //设置初始大小和负载因子 this(initialCapacity, DEFAULT_LOAD_FACTOR); } public HashMap(int initialCapacity, float loadFactor) { //校验范围 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; //设置阈值(如果长度入参不是2的幂次,返回最接近的2的幂次) this.threshold = tableSizeFor(initialCapacity); } public HashMap(Map<? extends K, ? extends V> m) { this.loadFactor = DEFAULT_LOAD_FACTOR; //填充map putMapEntries(m, false); } final void putMapEntries(Map<? extends K, ? extends V> m, boolean evict) { int s = m.size(); if (s > 0) { if (table == null) { // pre-size float ft = ((float)s / loadFactor) + 1.0F; int t = ((ft < (float)MAXIMUM_CAPACITY) ? (int)ft : MAXIMUM_CAPACITY); if (t > threshold) threshold = tableSizeFor(t); } else if (s > threshold) //扩容 resize(); //将m中的所有元素添加至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、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; //table未初始化或者长度为0,进行扩容 if ((tab = table) == null || (n = tab.length) == 0) n = (tab = resize()).length; //通过位运算判断元素放在桶中的位置 if ((p = tab[i = (n - 1) & hash]) == null) tab[i] = newNode(hash, key, value, null); //发生碰撞,桶中已有元素 else { Node<K,V> e; K k; //比较key和hash相同,将要覆盖value if (p.hash == hash && ((k = p.key) == key || (key != null && key.equals(k)))) e = p; //如果是红黑树 else if (p instanceof TreeNode) e = ((TreeNode<K,V>)p).putTreeVal(this, tab, hash, key, value); //如果是链表 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; } } //告诉迭代器修改过数量了 ++modCount; //如果实际大小大于阈值就扩容 if (++size > threshold) resize(); //插入后回调 afterNodeInsertion(evict); return null; }
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; //如果table已经初始化,根据哈希寻找元素不为空 if ((tab = table) != null && (n = tab.length) > 0 && (first = tab[(n - 1) & hash]) != null) { //如果第一个元素就像等,数组 if (first.hash == hash && // always check first node ((k = first.key) == key || (key != null && key.equals(k)))) return first; if ((e = first.next) != null) { //如果是红黑树 if (first instanceof TreeNode) return ((TreeNode<K,V>)first).getTreeNode(hash, key); do { //在链表中查找 if (e.hash == hash && ((k = e.key) == key || (key != null && key.equals(k)))) return e; } while ((e = e.next) != null); } } return null; }
5、resize方法
final Node<K,V>[] resize() { // 当前table保存 Node<K,V>[] oldTab = table; // 保存table大小 int oldCap = (oldTab == null) ? 0 : oldTab.length; // 保存当前阈值 int oldThr = threshold; int newCap, newThr = 0; // 之前table大小大于0 if (oldCap > 0) { // 之前table大于最大容量 if (oldCap >= MAXIMUM_CAPACITY) { // 阈值为最大整形 threshold = Integer.MAX_VALUE; return oldTab; } // 容量翻倍,使用左移,效率更高 else if ((newCap = oldCap << 1) < MAXIMUM_CAPACITY && oldCap >= DEFAULT_INITIAL_CAPACITY) // 阈值翻倍 newThr = oldThr << 1; // double threshold } // 之前阈值大于0 else if (oldThr > 0) newCap = oldThr; // oldCap = 0并且oldThr = 0,使用缺省值(如使用HashMap()构造函数,之后再插入一个元素会调用resize函数,会进入这一步) else { newCap = DEFAULT_INITIAL_CAPACITY; newThr = (int)(DEFAULT_LOAD_FACTOR * DEFAULT_INITIAL_CAPACITY); } // 新阈值为0 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"}) // 初始化table Node<K,V>[] newTab = (Node<K,V>[])new Node[newCap]; table = newTab; // 之前的table已经初始化过 if (oldTab != null) { // 复制元素,重新进行hash 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; // 将同一桶中的元素根据(e.hash & oldCap)是否为0进行分割,分成两个不同的链表,完成rehash 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; }