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 的幂。
* <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数负数都取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，evict 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(也就是构建函数时调用的)，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，也就是存储 Map 的实体。
/** * 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 和 hash 方法：
hash 方法(每个 key 的 hash 是不会变的，这个无符号右移 16 位的操作可以减少冲突)：
/**
* Computes key.hashCode() and spreads (XORs) higher bits of hash
* to lower. Because the table uses power-of-two masking, sets of
* hashes that vary only in bits above the current mask will
* always collide. (Among known examples are sets of Float keys
* holding consecutive whole numbers in small tables.) So we
* apply a transform that spreads the impact of higher bits
* downward. There is a tradeoff between speed, utility, and
* quality of bit-spreading. Because many common sets of hashes
* are already reasonably distributed (so don't benefit from
* spreading), and because we use trees to handle large sets of
* collisions in bins, we just XOR some shifted bits in the
* cheapest possible way to reduce systematic lossage, as well as
* to incorporate impact of the highest bits that would otherwise
* never be used in index calculations because of table bounds.
*/
static final int hash(Object key) { int h; return (key == null) ? 0 : (h = key.hashCode()) ^ (h >>> 16); // 保留高16位，将高16位和低16位进行异或的结果作为低16位。 } public final int hashCode() { return Objects.hashCode(key) ^ Objects.hashCode(value); }
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 还未被初始化过或者数据被清空，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,因为是与操作(n是2的幂)，所以扩容对位置没有影响1 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; 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、我们再来看看常用的 get 和 set 方法(感觉没啥好解释的了)：
public V get(Object key) { Node<K,V> e; return (e = getNode(hash(key), key)) == null ? null : e.value; } public V put(K key, V value) { return putVal(hash(key), key, value, false, true); } 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) { if (first.hash == hash && // always check first node ((k = first.key) == key || (key != null && key.equals(k)))) return first; if ((e = first.next) != null) { 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; }
