public class Test {public static void main(String[] args) {Map<String, Integer> map = new HashMap<String, Integer>();map.put("语文", 1);map.put("数学", 2);map.put("英语", 3);map.put("历史", 4);map.put("政治", 5);map.put("地理", 6);map.put("生物", 7);map.put("化学", 8);for (Map.Entry<String, Integer> entry : map.entrySet()) {System.out.println(entry.getKey() + ": " + entry.getValue());}}
}

政治: 5
生物: 7
历史: 4
数学: 2
化学: 8
语文: 1
英语: 3
地理: 6

Hash table based implementation of the Map interface.  This implementation provides all of the optional map operations, and permits
null values and the null key.  (The HashMap class is roughly equivalent to Hashtable, except that it is unsynchronized and permits nulls.)
This class makes no guarantees as to the order of the map; in particular, it does not guarantee that the order will remain constant over time.

The capacity 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 load factor 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 rehashed
(that is, internal data structures are rebuilt) so that the hash table has approximately twice the number of buckets.

public V put(K key, V value) {// 对key的hashCode()做hashreturn 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;// tab为空则创建if ((tab = table) == null || (n = tab.length) == 0)n = (tab = resize()).length;// 计算index，并对null做处理if ((p = tab[i = (n - 1) & hash]) == null)tab[i] = newNode(hash, key, value, null);else {Node<K,V> e; K k;// 节点存在if (p.hash == hash &&((k = p.key) == key || (key != null && key.equals(k))))e = p;// 该链为树else if (p instanceof TreeNode)e = ((TreeNode<K,V>)p).putTreeVal(this, tab, hash, key, value);// 该链为链表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;}}// 写入if (e != null) { // existing mapping for keyV oldValue = e.value;if (!onlyIfAbsent || oldValue == null)e.value = value;afterNodeAccess(e);return oldValue;}}++modCount;// 超过load factor*current capacity，resizeif (++size > threshold)resize();afterNodeInsertion(evict);return null;
}

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) {// 直接命中if (first.hash == hash && // always check first node((k = first.key) == key || (key != null && key.equals(k))))return first;// 未命中if ((e = first.next) != null) {// 在树中getif (first instanceof TreeNode)return ((TreeNode<K,V>)first).getTreeNode(hash, key);// 在链表中getdo {if (e.hash == hash &&((k = e.key) == key || (key != null && key.equals(k))))return e;} while ((e = e.next) != null);}}return null;
}

static final int hash(Object key) {int h;return (key == null) ? 0 : (h = key.hashCode()) ^ (h >>> 16);
}

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.

(n - 1) & hash

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.

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 thresholdnewCap = oldThr;else {               // zero initial threshold signifies using defaultsnewCap = 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 { // preserve orderNode<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;elseloTail.next = e;loTail = e;}// 原索引+oldCapelse {if (hiTail == null)hiHead = e;elsehiTail.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;
}

    以Entry[]数组实现的哈希桶数组，用Key的哈希值取模桶数组的大小可得到数组下标。插入元素时，如果两条Key落在同一个桶(比如哈希值1和17取模16后都属于第一个哈希桶)，Entry用一个next属性实现多个Entry以单向链表存放，后入桶的Entry将next指向桶当前的Entry。查找哈希值为17的key时，先定位到第一个哈希桶，然后以链表遍历桶里所有元素，逐个比较其key值。当Entry数量达到桶数量的75%时(很多文章说使用的桶数量达到了75%，但看代码不是)，会成倍扩容桶数组，并重新分配所有原来的Entry，所以这里也最好有个预估值。取模用位运算(hash & (arrayLength-1))会比较快，所以数组的大小永远是2的N次方， 你随便给一个初始值比如17会转为32。默认第一次放入元素时的初始值是16。iterator()时顺着哈希桶数组来遍历，看起来是个乱序。在JDK8里，新增默认为8的閥值，当一个桶里的Entry超过閥值，就不以单向链表而以红黑树来存放以加快Key的查找速度。