没人比我更懂 HashMap :)

哈，标题开个玩笑，0202 年的段子哈。

一、首先看一下 HashMap 的构造函数

/**
     * Constructs an empty <tt>HashMap</tt> with the specified initial
     * capacity and load factor.
     *
     * @param  initialCapacity the initial capacity
     * @param  loadFactor      the load factor
     * @throws IllegalArgumentException if the initial capacity is negative
     *         or the load factor is nonpositive
     */
    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); // 奇怪的是这里初始化阈值没有用到负载因子。
    }

第一个参数是初始化容量大小，第二个参数是负载因子。

对这两个参数有如下介绍：

* <p>An instance of <tt>HashMap</tt> has two parameters that affect its
 * performance: <i>initial capacity</i> and <i>load factor</i>.  The
 * <i>capacity</i> is the number of buckets in the hash table, and the initial
 * capacity is simply the capacity at the time the hash table is created.  The
 * <i>load factor</i> is a measure of how full the hash table is allowed to
 * get before its capacity is automatically increased.  When the number of
 * entries in the hash table exceeds the product of the load factor and the
 * current capacity, the hash table is <i>rehashed</i> (that is, internal data
 * structures are rebuilt) so that the hash table has approximately twice the
 * number of buckets.

机翻的意思就是：

HashMap 的实例有两个影响其性能的参数:初始容量和负载因子。

容量是哈希表中的桶数，初始容量就是创建哈希表时的容量。

负载因子是一种度量方法，用来衡量在自动增加哈希表的容量之前，哈希表允许达到的满度。

当哈希表中的条目数超过负载因子和当前容量的乘积时，哈希表将被重新哈希(即重新构建内部数据结构)，

这样哈希表的桶数大约是原来的两倍。

/**
 * The maximum capacity, used if a higher value is implicitly specified
 * by either of the constructors with arguments.
 * MUST be a power of two <= 1<<30.
 */
static final int MAXIMUM_CAPACITY = 1 << 30;

最大的容量是 2 的 30 次方，因为 int 类型最大值是 2 的 31 次方减一。容量还必须是 2 的次方数。

 * <p>This implementation provides constant-time performance for the basic
 * operations (<tt>get</tt> and <tt>put</tt>), assuming the hash function
 * disperses the elements properly among the buckets.  Iteration over
 * collection views requires time proportional to the "capacity" of the
 * <tt>HashMap</tt> instance (the number of buckets) plus its size (the number
 * of key-value mappings).  Thus, it's very important not to set the initial
 * capacity too high (or the load factor too low) if iteration performance is
 * important.

另外不要将容量设置太高，或者将负载因子设置太低，这都会影响性能。

// The next size value at which to resize (capacity * load factor).
int threshold;

阈值，等于容量和负载因子的乘积，如果 table.length 大于 阈值，就得进行 2 倍扩容。

接下来看看 tableSizeFor 方法，也就是计算阈值的：

/**
 * Returns a power of two size for the given target capacity.
 */
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;
}

翻译的意思是返回给定目标容量的 2 的幂，也就是大于且最接近给定目标容量的最小 2 的幂。

这段代码可能看着不太好理解，我们假设 n 的最高位的 1 在位置 i 上，>>> 表示无符号右移。

(>>> 和 >> 的区别就是前者高位不管正式负数都取0，后者正数取 0，负数取 1)。

右移一位再和原来的值进行或操作，那么结果位置 i 和 i-1 的值一定也为 1。

同理，最后结果一定是 0 ~ i 位都为 1，再加 1 的话，就是最接近给定值的最小2的幂。

另外如果 cap 为  0 的话，那么就是所有位都是 1 了，n 就小于 0，阈值就为 1。

现在我们在来看下另外的三个构造函数：

    /**
     * Constructs an empty <tt>HashMap</tt> with the specified initial
     * capacity and the default load factor (0.75).
     *
     * @param  initialCapacity the initial capacity.
     * @throws IllegalArgumentException if the initial capacity is negative.
     */
    public HashMap(int initialCapacity) {
        this(initialCapacity, DEFAULT_LOAD_FACTOR);
    }

    /**
     * Constructs an empty <tt>HashMap</tt> with the default initial capacity
     * (16) and the default load factor (0.75).
     */
    public HashMap() {
        this.loadFactor = DEFAULT_LOAD_FACTOR; // all other fields defaulted
    }

    /**
     * Constructs a new <tt>HashMap</tt> with the same mappings as the
     * specified <tt>Map</tt>.  The <tt>HashMap</tt> is created with
     * default load factor (0.75) and an initial capacity sufficient to
     * hold the mappings in the specified <tt>Map</tt>.
     *
     * @param   m the map whose mappings are to be placed in this map
     * @throws  NullPointerException if the specified map is null
     */
    public HashMap(Map<? extends K, ? extends V> m) {
        this.loadFactor = DEFAULT_LOAD_FACTOR;
        putMapEntries(m, false);
    }

对于前两个就不用说了，默认的负载因子为 0.75，为什么要取这个值呢？

 * <p>As a general rule, the default load factor (.75) offers a good
 * tradeoff between time and space costs.  Higher values decrease the
 * space overhead but increase the lookup cost (reflected in most of
 * the operations of the <tt>HashMap</tt> class, including
 * <tt>get</tt> and <tt>put</tt>).  The expected number of entries in
 * the map and its load factor should be taken into account when
 * setting its initial capacity, so as to minimize the number of
 * rehash operations.  If the initial capacity is greater than the
 * maximum number of entries divided by the load factor, no rehash
 * operations will ever occur.

机翻如下：

作为一般规则，默认的负载系数(.75)在时间和空间成本之间提供了一个很好的折衷。

较高的值减少了空间开销，但增加了查找成本(反映在HashMap类的大部分操作中，包括get和put)。

在设置初始容量时，应该考虑映射中的预期条目数及其负载因子，以便最小化重散列操作的数量。

如果初始容量大于最大条目数除以负载因子(初始容量和负载因子的乘积大于最大条目数)，则不会发生重新散列操作。

接下来看看 putMapEntries 方法，最初构造时为假，否则为真。

    /**
     * Implements Map.putAll and Map constructor.
     *
     * @param m the map
     * @param evict false when initially constructing this map, else
     * true (relayed to method afterNodeInsertion).
     */
    final void putMapEntries(Map<? extends K, ? extends V> m, boolean evict) {
        int s = m.size();
        if (s > 0) {
            if (table == null) { // pre-size // table 没有被初始化过(也就是构建函数是调用的)，就初始化一下阈值
                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)   // table 已经被初始化过了，长度大于阈值需要进行扩容处理
                resize();
            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);
            }
        }
    }

table 就是存储 Map 键值对的数组，并根据需要调整大小，长度总是2的幂。

    /**
     * The table, initialized on first use, and resized as
     * necessary. When allocated, length is always a power of two.
     * (We also tolerate length zero in some operations to allow
     * bootstrapping mechanics that are currently not needed.)
     */
    transient Node<K,V>[] table;

Node 就是一个静态内部类，先看看就行，这个我们稍后再作分析。

    /**
     * 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;
        }
    }

我们目前看的是构建时候调用，就只看构建时候走的逻辑。那么我们看下 putVal 方法：

    /**
     * Implements Map.put and related methods.
     *
     * @param hash hash for key
     * @param key the key
     * @param value the value to put
     * @param onlyIfAbsent if true, don't change existing value
     * @param evict if false, the table is in creation mode.
     * @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)  // table 还未被初始化过，进行初始化。
            n = (tab = resize()).length;
        if ((p = tab[i = (n - 1) & hash]) == null)  // 如果这个表里没有这个 key 的哈希，就把这个键值对存表里
            tab[i] = newNode(hash, key, value, null);
        else { // 如果表里已经有这个 key 的哈希了，再进行进一步的比对，判断是否存在
            Node<K,V> e; K k;
            if (p.hash == hash &&
                ((k = p.key) == key || (key != null && key.equals(k)))) // 先比对第一个节点的值
                e = p;
            else if (p instanceof TreeNode) // 如果第一个节点不同，判断是否是红黑树结构，进行比对
                e = ((TreeNode<K,V>)p).putTreeVal(this, tab, hash, key, value);
            else { // 说明是链表结构，进行比对
                for (int binCount = 0; ; ++binCount) {
                    if ((e = p.next) == null) {  // 如果找不到，那么就将这个键值对存进去，并判断是否到达了要转换成红黑树的条件
                        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) // 如果为 onlyIfAbsent 为 true，不改变现在的值
                    e.value = value;
                afterNodeAccess(e);
                return oldValue;
            }
        }
        ++modCount;   // 用于记录修改映射的数量，该字段用于使 HashMap 集合视图上的迭代器快速失效
        if (++size > threshold) // 说明找不到该 key 的键值对，就插入进去
            resize();
        afterNodeInsertion(evict);
        return null;
    }

如果 table 为空的话，我们来看下 resize 方法，蛮长的，初始化或者给表的长度加倍：

    /**
     * Initializes or doubles table size.  If null, allocates in
     * accord with initial capacity target held in field threshold.
     * Otherwise, because we are using power-of-two expansion, the
     * elements from each bin must either stay at same index, or move
     * with a power of two offset in the new table.
     *
     * @return the table
     */
    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) {    // 原来的 table 有值
            if (oldCap >= MAXIMUM_CAPACITY) { // 容量大于最大容量，阈值设为最大。
                threshold = Integer.MAX_VALUE;
                return oldTab;
            }
            else if ((newCap = oldCap << 1) < MAXIMUM_CAPACITY && // 新容量扩展为原来两倍并且小于最大容量大于初始容量，就把新新的阈值设为原来两倍。
                     oldCap >= DEFAULT_INITIAL_CAPACITY)
                newThr = oldThr << 1; // double threshold
        }
        else if (oldThr > 0) // initial capacity was placed in threshold // 原来的 table 已经初始化过，但是table 里没有数据，新容量等于原来的阈值。
            newCap = oldThr;
        else {               // zero initial threshold signifies using defaults // table 还没有初始化过，进行初始化。
            newCap = DEFAULT_INITIAL_CAPACITY;
            newThr = (int)(DEFAULT_LOAD_FACTOR * DEFAULT_INITIAL_CAPACITY);
        }
        if (newThr == 0) { // 原来的 table 已经初始化过，但是 table 里没有数据，计算一下新阈值。
            float ft = (float)newCap * loadFactor;
            newThr = (newCap < MAXIMUM_CAPACITY && ft < (float)MAXIMUM_CAPACITY ?
                      (int)ft : Integer.MAX_VALUE);
        }
        threshold = newThr;  // 设置阈值
        @SuppressWarnings({"rawtypes","unchecked"})
        Node<K,V>[] newTab = (Node<K,V>[])new Node[newCap];
        table = newTab;
        if (oldTab != null) {   // 如果原来 table 有值，就把值放进新的 table 里.
            for (int j = 0; j < oldCap; ++j) {
                Node<K,V> e;
                if ((e = oldTab[j]) != null) {
                    oldTab[j] = null;
                    if (e.next == null)  // 这个节点没有发生过冲突
                        newTab[e.hash & (newCap - 1)] = e;  // 重新散列元素位置
                    else if (e instanceof TreeNode) // 发生过冲突，并且是红黑树结构
                        ((TreeNode<K,V>)e).split(this, newTab, j, oldCap);
                    else { // preserve order  // 发生过冲突，是链表结构
                        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) {    // 分成两段链表，暂时不知道为什么。
                                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;
    }

二、未完待续......

三、ArrayList 扩容机制