一、概念
类定义：
public class HashMap<K,V> extends AbstractMap<K,V>
implements Map<K,V>, Cloneable, Serializable
- 继承了AbstractMap抽象类，实现了Map接口，拥有一组Map通用的操作。
- 实现了Cloneable接口，可进行浅层次拷贝。
- 实现了Serializable接口，可进行序列化。
特点：
- 允许键及值为空对象。
- 非线程安全类，可通过Collections.synchronizedMap(new HashMap())获得线程安全的HashMap。
- 不保证插入顺序，也不保证顺序不随时间变化。
二、使用
//TestHashMap
public class TestHashMap {
private static final String TAG = "TestHashMap";
private Map<String, Integer> map = new HashMap<>();
public void testPut() {
map.put("Android", 1);
map.put(null, null);
map.put("网络协议", 3);
map.put("数据结构与算法", 4);
Log.d(TAG, "zwm, put map: " + map);
Log.d(TAG, "zwm, get key: null, value: " + map.get(null));
}
public void testPutAll() {
Map<String, Integer> tempMap = new HashMap<>();
tempMap.put("Android", 100);
tempMap.put("Java", 200);
tempMap.put("网络协议", 300);
tempMap.put("数据结构与算法", 400);
map.putAll(tempMap);
Log.d(TAG, "zwm, putAll map: " + map);
}
public void testGet() {
String key = "数据结构与算法";
int value = map.get(key);
Log.d(TAG, "zwm, get key: " + key + ", value: " + value);
}
public void testRemove() {
String key = "网络协议";
int value = map.remove(key);
Log.d(TAG, "zwm, remove key: " + key + ", value: " + value);
}
public void testContains() {
String key = "Android";
boolean result = map.containsKey(key);
Log.d(TAG, "zwm, containsKey: " + key + ", result: " + result);
Integer value = 200;
result = map.containsValue(value);
Log.d(TAG, "zwm, containsValue: " + value + ", result: " + result);
}
public void testKeySet() {
Set<String> keySet = map.keySet();
Log.d(TAG, "zwm, keySet: " + keySet);
}
public void testValues() {
Collection<Integer> values = map.values();
Log.d(TAG, "zwm, values: " + values);
}
public void testEntrySet() {
Set<Map.Entry<String, Integer>> entrySet = map.entrySet();
for(Map.Entry<String, Integer> entry : entrySet) {
Log.d(TAG, "zwm, for entry, key: " + entry.getKey() + ", value: " + entry.getValue());
}
Iterator iterator = entrySet.iterator();
while (iterator.hasNext()) {
Map.Entry entry = (Map.Entry)iterator.next();
Log.d(TAG, "zwm, iterator entry, key: " + entry.getKey() + ", value: " + entry.getValue());
}
}
public void testClear() {
map.clear();
Log.d(TAG, "zwm, clear map: " + map);
}
public void testSize() {
Log.d(TAG, "zwm, size: " + map.size());
}
public void testEmpty() {
Log.d(TAG, "zwm, isEmpty: " + map.isEmpty());
}
}
//测试代码
private void testMethod() {
Log.d(TAG, "zwm, testMethod");
TestHashMap testHashMap = new TestHashMap();
testHashMap.testPut();
testHashMap.testPutAll();
testHashMap.testGet();
testHashMap.testRemove();
testHashMap.testContains();
testHashMap.testKeySet();
testHashMap.testValues();
testHashMap.testEntrySet();
testHashMap.testSize();
testHashMap.testClear();
testHashMap.testEmpty();
}
//输出log
2019-07-30 11:27:28.646 zwm, testMethod
2019-07-30 11:27:28.648 zwm, put map: {null=null, 数据结构与算法=4, 网络协议=3, Android=1}
2019-07-30 11:27:28.648 zwm, get key: null, value: null
2019-07-30 11:27:28.648 zwm, putAll map: {null=null, Java=200, 数据结构与算法=400, 网络协议=300, Android=100}
2019-07-30 11:27:28.649 zwm, get key: 数据结构与算法, value: 400
2019-07-30 11:27:28.649 zwm, remove key: 网络协议, value: 300
2019-07-30 11:27:28.649 zwm, containsKey: Android, result: true
2019-07-30 11:27:28.649 zwm, containsValue: 200, result: true
2019-07-30 11:27:28.649 zwm, keySet: [null, Java, 数据结构与算法, Android]
2019-07-30 11:27:28.650 zwm, values: [null, 200, 400, 100]
2019-07-30 11:27:28.650 zwm, for entry, key: null, value: null
2019-07-30 11:27:28.650 zwm, for entry, key: Java, value: 200
2019-07-30 11:27:28.650 zwm, for entry, key: 数据结构与算法, value: 400
2019-07-30 11:27:28.650 zwm, for entry, key: Android, value: 100
2019-07-30 11:27:28.650 zwm, iterator entry, key: null, value: null
2019-07-30 11:27:28.650 zwm, iterator entry, key: Java, value: 200
2019-07-30 11:27:28.651 zwm, iterator entry, key: 数据结构与算法, value: 400
2019-07-30 11:27:28.651 zwm, iterator entry, key: Android, value: 100
2019-07-30 11:27:28.651 zwm, size: 4
2019-07-30 11:27:28.651 zwm, clear map: {}
2019-07-30 11:27:28.651 zwm, isEmpty: true
三、原理
重要参数
//HashMap的初始容量为16，HashMap的容量指的是存储元素的数组大小，即桶的数量，必须为2的幂
static final int DEFAULT_INITIAL_CAPACITY = 1 << 4;
//HashMap的最大容量为2的30次方，若传入的容量过大，将被最大值替换
static final int MAXIMUM_CAPACITY = 1 << 30;
//HashMap的负载因子，当size等于桶的数量*DEFAULT_LOAD_FACTOR时，就需要对HashMap进行扩容，扩容操作就是把桶的数量*2
//当DEFAULT_LOAD_FACTOR较小时，桶的利用率较低，但发生哈希冲突的概率也较小
//当DEFAULT_LOAD_FACTOR较大时，桶的利用率较高，但发生哈希冲突的概率也较大
static final float DEFAULT_LOAD_FACTOR = 0.75f;
//当某一个桶中链表的长度大于等于8时，链表结构会转换成红黑树结构
static final int TREEIFY_THRESHOLD = 8;
//当红黑树中的结点数量小于等于6时，红黑树结构会转变为链表结构
static final int UNTREEIFY_THRESHOLD = 6;
//当Node数组容量大于等于64的前提下，如果某一个桶中链表长度大于等于8，则会将链表结构转换成红黑树结构
static final int MIN_TREEIFY_CAPACITY = 64;
数组元素 & 链表结点
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() { //结点哈希值的计算方法：key的哈希值异或上value的哈希值
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) { //重写equals方法
if (o == this) //对象相等
return true;
if (o instanceof Map.Entry) { //对象为Map.Entry实例
Map.Entry<?,?> e = (Map.Entry<?,?>)o;
if (Objects.equals(key, e.getKey()) &&
Objects.equals(value, e.getValue())) //key跟value的值都相等
return true;
}
return false;
}
}
红黑树结点
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;
TreeNode(int hash, K key, V val, Node<K,V> next) {
super(hash, key, val, next);
}
...
}
构造函数
public HashMap() { //无参构造器
this.loadFactor = DEFAULT_LOAD_FACTOR; // all other fields defaulted
}
public HashMap(int initialCapacity) { //指定容量大小，负载因子使用默认值
this(initialCapacity, DEFAULT_LOAD_FACTOR);
}
public HashMap(int initialCapacity, float loadFactor) { //指定容量大小与负载因子
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);
}
public HashMap(Map<? extends K, ? extends V> m) { //将传入的Map参数中的全部元素逐个添加到HashMap中
this.loadFactor = DEFAULT_LOAD_FACTOR;
putMapEntries(m, false);
}
static final int tableSizeFor(int cap) { //返回一个大于等于且最接近cap的2的幂次方整数。例如给定9，返回2的4次方（即返回16）
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;
}
分析tableSizeFor(int cap)方法：
假定cap形式为00..01XXXXXXX，（X代表可为0也可为1，X前面的1为从最高位开始第一个为1的那一位)）
第一步： n |= n >>> 1; 也就是n变为n与n右移一位之后异或后的值，即
n: 00..01XXXXXXX
n>>>1: 00..001XXXXXX
新n： 00..011XXXXXX
第二步： n |= n >>> 2; 也就是n变成n与n右移两位之后异或的值，即
n: 00..011XXXXXX
n>>>2: 00..00011XXXX
新n: 00..01111XXXX
后面步骤类似。
这个算法不断地把第一个1后面的位全部变成1。
本例由00..01XXXXXXX —> 00..011111111，最后再返回n+1（2的幂次方）。
public V put(K key, V value)
//插入方法，传递key值跟value值
public V put(K key, V value) {
return putVal(hash(key), key, value, false, true);
}
//将key值的哈希值异或该值右移16位后的值，得到新的哈希值
//key值可以为空，此时哈希值为0
static final int hash(Object key) {
int h;
return (key == null) ? 0 : (h = key.hashCode()) ^ (h >>> 16);
}
//插入数据
final V putVal(int hash, K key, V value, boolean onlyIfAbsent,
boolean evict) {
Node<K,V>[] tab; Node<K,V> p; int n, i;
//若哈希表的数组为空，则通过resize()方法创建
if ((tab = table) == null || (n = tab.length) == 0)
n = (tab = resize()).length;
//计算插入的位置索引：(n - 1) & hash
if ((p = tab[i = (n - 1) & hash]) == null) //不存在哈希冲突
//插入结点
tab[i] = newNode(hash, key, value, null);
else { //存在哈希冲突
Node<K,V> e; K k;
//判断索引位置的结点的key值是否与要插入数据的key值相同
if (p.hash == hash &&
((k = p.key) == key || (key != null && key.equals(k)))) //如果key值相同，则用新value值覆盖旧value值
e = p;
else if (p instanceof TreeNode) //如果key值不同，且索引位置的结点为红黑树结点，则在红黑树中插入或更新键值对
e = ((TreeNode<K,V>)p).putTreeVal(this, tab, hash, key, value);
else { //如果key值不同，且索引位置的结点为链表结点，则在链表中插入或更新键值对
for (int binCount = 0; ; ++binCount) {
//如果未找到相同的key值，则插入到表尾
//如果链表结点数达到阈值，则将链表转换为红黑树
if ((e = p.next) == null) {
p.next = newNode(hash, key, value, null);
if (binCount >= TREEIFY_THRESHOLD - 1) // -1 for 1st
treeifyBin(tab, hash);
break;
}
//如果找到相同的key值，则用新value值覆盖旧value值，并返回旧值
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;
}
}
//插入成功后，如果实际存在的键值对数量size大于阈值，则调用resize()方法进行扩容
++modCount;
if (++size > threshold)
resize();
afterNodeInsertion(evict);
return null;
}
//向红黑树插入或更新数据
final TreeNode<K,V> putTreeVal(HashMap<K,V> map, Node<K,V>[] tab, int h, K k, V v) {
Class<?> kc = null;
boolean searched = false;
TreeNode<K,V> root = (parent != null) ? root() : this;
for (TreeNode<K,V> p = root;;) {
int dir, ph; K pk;
if ((ph = p.hash) > h)
dir = -1;
else if (ph < h)
dir = 1;
else if ((pk = p.key) == k || (k != null && k.equals(pk)))
return p;
else if ((kc == null &&
(kc = comparableClassFor(k)) == null) ||
(dir = compareComparables(kc, k, pk)) == 0) {
if (!searched) {
TreeNode<K,V> q, ch;
searched = true;
if (((ch = p.left) != null &&
(q = ch.find(h, k, kc)) != null) ||
((ch = p.right) != null &&
(q = ch.find(h, k, kc)) != null))
return q;
}
dir = tieBreakOrder(k, pk);
}
TreeNode<K,V> xp = p;
if ((p = (dir <= 0) ? p.left : p.right) == null) {
Node<K,V> xpn = xp.next;
TreeNode<K,V> x = map.newTreeNode(h, k, v, xpn);
if (dir <= 0)
xp.left = x;
else
xp.right = x;
xp.next = x;
x.parent = x.prev = xp;
if (xpn != null)
((TreeNode<K,V>)xpn).prev = x;
moveRootToFront(tab, balanceInsertion(root, x));
return null;
}
}
}
final void treeifyBin(Node<K,V>[] tab, int hash)
//将链表转成红黑树
final void treeifyBin(Node<K,V>[] tab, int hash) {
int n, index; Node<K,V> e;
if (tab == null || (n = tab.length) < MIN_TREEIFY_CAPACITY) //链表转成红黑树的条件为数组最小容量大于等于MIN_TREEIFY_CAPACITY
resize();
else if ((e = tab[index = (n - 1) & hash]) != null) {
TreeNode<K,V> hd = null, tl = null;
do {
TreeNode<K,V> p = replacementTreeNode(e, null);
if (tl == null)
hd = p;
else {
p.prev = tl;
tl.next = p;
}
tl = p;
} while ((e = e.next) != null);
if ((tab[index] = hd) != null)
hd.treeify(tab);
}
}
final Node<K,V>[] resize()
//对哈希数组进行扩容
final Node<K,V>[] resize() {
Node<K,V>[] oldTab = table; //扩容前的数组
int oldCap = (oldTab == null) ? 0 : oldTab.length; //扩容前的数组的容量
int oldThr = threshold; //扩容前的数组的阈值
int newCap, newThr = 0;
if (oldCap > 0) {
if (oldCap >= MAXIMUM_CAPACITY) { //若扩容前数组的容量超过最大值，则不再进行扩容
threshold = Integer.MAX_VALUE;
return oldTab;
}
else if ((newCap = oldCap << 1) < MAXIMUM_CAPACITY &&
oldCap >= DEFAULT_INITIAL_CAPACITY)
newThr = oldThr << 1; //扩大为原来的两倍
}
else if (oldThr > 0) // initial capacity was placed in threshold
newCap = oldThr;
else { // zero initial threshold signifies using defaults
newCap = DEFAULT_INITIAL_CAPACITY;
newThr = (int)(DEFAULT_LOAD_FACTOR * DEFAULT_INITIAL_CAPACITY);
}
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) {
//把每个桶都移动到新的桶中
for (int j = 0; j < oldCap; ++j) {
Node<K,V> e;
if ((e = oldTab[j]) != null) {
oldTab[j] = null;
if (e.next == null) //如果只有单个结点
newTab[e.hash & (newCap - 1)] = e;
else if (e instanceof TreeNode) //如果是红黑树结点
((TreeNode<K,V>)e).split(this, newTab, j, oldCap);
else { //如果是链表结点
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) {
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;
}
//新索引：e.hash & (newCap - 1)
//扩容后，若哈希值新增参与运算的位为0，那么元素在扩容后位置为：原位置
//扩容后，若哈希值新增参与运算的位为1，那么元素在扩容后位置为：原位置 + 扩容前的旧容量
public V get(Object key)
//根据key值获取value值
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) {
if (first.hash == hash && //先检查第一个结点看是否满足要求
((k = first.key) == key || (key != null && key.equals(k))))
return first;
if ((e = first.next) != null) {
if (first instanceof TreeNode) //如果是红黑树结点，则到红黑树中找
return ((TreeNode<K,V>)first).getTreeNode(hash, key);
do { //如果是链表结点，则遍历链表
if (e.hash == hash &&
((k = e.key) == key || (key != null && key.equals(k))))
return e;
} while ((e = e.next) != null);
}
}
return null;
}
public void putAll(Map<? extends K, ? extends V> m)
//将指定Map中的键值对复制到此Map中
public void putAll(Map<? extends K, ? extends V> m) {
putMapEntries(m, true);
}
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)
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); //调用putVal方法进行插入操作
}
}
}
public V remove(Object key)
//删除键值对
public V remove(Object key) {
Node<K,V> e;
return (e = removeNode(hash(key), key, null, false, true)) == null ?
null : e.value;
}
final Node<K,V> removeNode(int hash, Object key, Object value,
boolean matchValue, boolean movable) {
Node<K,V>[] tab; Node<K,V> p; int n, index;
if ((tab = table) != null && (n = tab.length) > 0 &&
(p = tab[index = (n - 1) & hash]) != null) {
Node<K,V> node = null, e; K k; V v;
if (p.hash == hash &&
((k = p.key) == key || (key != null && key.equals(k))))
node = p;
else if ((e = p.next) != null) {
if (p instanceof TreeNode) //找到要删除的红黑树结点
node = ((TreeNode<K,V>)p).getTreeNode(hash, key);
else { //找到要删除的链表结点
do {
if (e.hash == hash &&
((k = e.key) == key ||
(key != null && key.equals(k)))) {
node = e;
break;
}
p = e;
} while ((e = e.next) != null);
}
}
if (node != null && (!matchValue || (v = node.value) == value ||
(value != null && value.equals(v)))) {
if (node instanceof TreeNode) //如果要删除的是红黑树结点
((TreeNode<K,V>)node).removeTreeNode(this, tab, movable);
else if (node == p) //如果要删除的是索引所在的结点
tab[index] = node.next;
else //如果要删除的是链表结点
p.next = node.next;
++modCount;
--size;
afterNodeRemoval(node);
return node;
}
}
return null;
}
public boolean containsKey(Object key)
//判断是否存在相应的键
public boolean containsKey(Object key) {
return getNode(hash(key), key) != null;
}
public boolean containsValue(Object value)
//判断是否存在相应的值
public boolean containsValue(Object value) {
Node<K,V>[] tab; V v;
if ((tab = table) != null && size > 0) {
for (int i = 0; i < tab.length; ++i) {
for (Node<K,V> e = tab[i]; e != null; e = e.next) {
if ((v = e.value) == value ||
(value != null && value.equals(v)))
return true;
}
}
}
return false;
}
public Set<K> keySet()
//获取key的Set集合
public Set<K> keySet() {
Set<K> ks;
return (ks = keySet) == null ? (keySet = new KeySet()) : ks;
}
public Collection<V> values()
//获取value的Collection集合
public Collection<V> values() {
Collection<V> vs;
return (vs = values) == null ? (values = new Values()) : vs;
}
public Set<Map.Entry<K,V>> entrySet()
//获取Entry的Set集合
public Set<Map.Entry<K,V>> entrySet() {
Set<Map.Entry<K,V>> es;
return (es = entrySet) == null ? (entrySet = new EntrySet()) : es;
}
public void clear()
//清空数组
public void clear() {
Node<K,V>[] tab;
modCount++;
if ((tab = table) != null && size > 0) {
size = 0;
for (int i = 0; i < tab.length; ++i)
tab[i] = null;
}
}
public int size()
//获取HashMap元素个数
public int size() {
return size;
}
public boolean isEmpty()
//判断HashMap是否为空
public boolean isEmpty() {
return size == 0;
}
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