大厂之路的第五篇 HashMap（散列表）
前面几篇我们介绍了两种线性表：顺序表和链表。这两种线性表它们各有优缺点：顺序表适合随机查找比较多的场景，而链表适合与需要频繁插入删除的场景。
那么，有没有一种集合查找也快插入删除也没那么耗时呢？答案是肯定的，它就是我们今天要介绍的 散列表 也称 哈希表。
HashMap是如何做到查找也快插入删除也快的呢？
老样子，我们还是到源码里面去一探究竟。
我们先看一下它的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[] tab; Node p; int n, i;
if ((tab = table) == null || (n = tab.length) == 0)
n = (tab = resize()).length;
if ((p = tab[i = (n - 1) & hash]) == null)
tab[i] = newNode(hash, key, value, null);
else {
Node e; K k;
if (p.hash == hash &&
((k = p.key) == key || (key != null && key.equals(k))))
e = p;
else if (p instanceof TreeNode)
e = ((TreeNode)p).putTreeVal(this, tab, hash, key, value);
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;
}
}
++modCount;
if (++size > threshold)
resize();
afterNodeInsertion(evict);
return null;
}
我们不难发现，这个函数里面又一个很重要的结构--Node,我们在分析LinkedList的源码过程中也有这么一个Node，不同的地方在于在HashMap中这个Node是以key，value即键值对的形式存在的。
ok，那我们重点来看一下Node这个内部类。
static class Node implements Map.Entry {
final int hash;
final K key;
V value;
Node next;
Node(int hash, K key, V value, Node 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;
}
}
interface Entry {
/**
* Returns the key corresponding to this entry.
*
* @return the key corresponding to this entry
* @throws IllegalStateException implementations may, but are not
* required to, throw this exception if the entry has been
* removed from the backing map.
*/
K getKey();
/**
* Returns the value corresponding to this entry. If the mapping
* has been removed from the backing map (by the iterator's
* remove operation), the results of this call are undefined.
*
* @return the value corresponding to this entry
* @throws IllegalStateException implementations may, but are not
* required to, throw this exception if the entry has been
* removed from the backing map.
*/
V getValue();
/**
* Replaces the value corresponding to this entry with the specified
* value (optional operation). (Writes through to the map.) The
* behavior of this call is undefined if the mapping has already been
* removed from the map (by the iterator's remove operation).
*
* @param value new value to be stored in this entry
* @return old value corresponding to the entry
* @throws UnsupportedOperationException if the put operation
* is not supported by the backing map
* @throws ClassCastException if the class of the specified value
* prevents it from being stored in the backing map
* @throws NullPointerException if the backing map does not permit
* null values, and the specified value is null
* @throws IllegalArgumentException if some property of this value
* prevents it from being stored in the backing map
* @throws IllegalStateException implementations may, but are not
* required to, throw this exception if the entry has been
* removed from the backing map.
*/
V setValue(V value);
/**
* Compares the specified object with this entry for equality.
* Returns true if the given object is also a map entry and
* the two entries represent the same mapping. More formally, two
* entries e1 and e2 represent the same mapping
* if
* (e1.getKey()==null ?
* e2.getKey()==null : e1.getKey().equals(e2.getKey())) &&
* (e1.getValue()==null ?
* e2.getValue()==null : e1.getValue().equals(e2.getValue()))
*
* This ensures that the equals method works properly across
* different implementations of the Map.Entry interface.
*
* @param o object to be compared for equality with this map entry
* @return true if the specified object is equal to this map
* entry
*/
boolean equals(Object o);
/**
* Returns the hash code value for this map entry. The hash code
* of a map entry e is defined to be:
* (e.getKey()==null ? 0 : e.getKey().hashCode()) ^
* (e.getValue()==null ? 0 : e.getValue().hashCode())
*
* This ensures that e1.equals(e2) implies that
* e1.hashCode()==e2.hashCode() for any two Entries
* e1 and e2, as required by the general
* contract of Object.hashCode.
*
* @return the hash code value for this map entry
* @see Object#hashCode()
* @see Object#equals(Object)
* @see #equals(Object)
*/
int hashCode();
/**
* Returns a comparator that compares {@link Map.Entry} in natural order on key.
*
* The returned comparator is serializable and throws {@link
* NullPointerException} when comparing an entry with a null key.
*
* @param the {@link Comparable} type of then map keys
* @param the type of the map values
* @return a comparator that compares {@link Map.Entry} in natural order on key.
* @see Comparable
* @since 1.8
*/
public static , V> Comparator> comparingByKey() {
return (Comparator> & Serializable)
(c1, c2) -> c1.getKey().compareTo(c2.getKey());
}
/**
* Returns a comparator that compares {@link Map.Entry} in natural order on value.
*
* The returned comparator is serializable and throws {@link
* NullPointerException} when comparing an entry with null values.
*
* @param the type of the map keys
* @param the {@link Comparable} type of the map values
* @return a comparator that compares {@link Map.Entry} in natural order on value.
* @see Comparable
* @since 1.8
*/
public static > Comparator> comparingByValue() {
return (Comparator> & Serializable)
(c1, c2) -> c1.getValue().compareTo(c2.getValue());
}
/**
* Returns a comparator that compares {@link Map.Entry} by key using the given
* {@link Comparator}.
*
* The returned comparator is serializable if the specified comparator
* is also serializable.
*
* @param the type of the map keys
* @param the type of the map values
* @param cmp the key {@link Comparator}
* @return a comparator that compares {@link Map.Entry} by the key.
* @since 1.8
*/
public static Comparator> comparingByKey(Comparator cmp) {
Objects.requireNonNull(cmp);
return (Comparator> & Serializable)
(c1, c2) -> cmp.compare(c1.getKey(), c2.getKey());
}
/**
* Returns a comparator that compares {@link Map.Entry} by value using the given
* {@link Comparator}.
*
* The returned comparator is serializable if the specified comparator
* is also serializable.
*
* @param the type of the map keys
* @param the type of the map values
* @param cmp the value {@link Comparator}
* @return a comparator that compares {@link Map.Entry} by the value.
* @since 1.8
*/
public static Comparator> comparingByValue(Comparator cmp) {
Objects.requireNonNull(cmp);
return (Comparator> & Serializable)
(c1, c2) -> cmp.compare(c1.getValue(), c2.getValue());
}
}
可以看出，主要的数据操作还是对这个Node的操作。
在分析HashMap的几个重要函数之前，我想先给大家介绍一下HashMap存储数据的主要形式，也就是说它的数据结构到底是怎样的。
下面这张图可以大概说明。为什么说是大概说明，因为在java8中HashMap引入了红黑树来取代链表。
现在我们再深入的探究HashMap的几个重要函数:
1.构造函数
public HashMap() {
this.loadFactor = DEFAULT_LOAD_FACTOR; // all other fields defaulted
}
public HashMap(int initialCapacity) {
this(initialCapacity, DEFAULT_LOAD_FACTOR);
}
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);
}
public HashMap(Map m) {
this.loadFactor = DEFAULT_LOAD_FACTOR;
putMapEntries(m, false);
}
在前三个构造函数中，主要是对HashMap两个重要的参数进行赋值。
一个是loadFactor:扩容因子，要求要小于1大于0，当容量达到阈值时，就需要对哈希表进行扩容，而这个阈值就是由当前容量和扩容因子共同决定的。
另一个是initialCapacity：即哈希表的初始容量。
我们看一下第三个构造函数中，有这么一行代码
this.threshold = tableSizeFor(initialCapacity);
我们仔细来看一下tableSizeFor这个函数:
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;
}
关于这个算法的解释，可以参考这篇文章，在这里我们就不重复的去做解释了。
接下来我们重点看第四个构造函数：
public HashMap(Map m) {
this.loadFactor = DEFAULT_LOAD_FACTOR;
putMapEntries(m, false);
}
final void putMapEntries(Map 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 e : m.entrySet()) {
K key = e.getKey();
V value = e.getValue();
putVal(hash(key), key, value, false, evict);
}
}
}
主要是看putMapEntries这个函数，先判断哈希表的大小是不是大于0，然后再判断table是不是为空，也就是我们的哈希表内有没有元素。如果是空的，那么还得像前面那三个构造函数一样先对必要的参数赋值；如果不为空判断要添加的哈希表的size是否达到了扩容阈值，如果达到了，就需要对哈希表进行扩容，也就是走resize函数。
final Node[] resize() {
Node[] oldTab = table;
int oldCap = (oldTab == null) ? 0 : oldTab.length;
int oldThr = threshold;
int newCap, newThr = 0;
if (oldCap > 0) {
if (oldCap >= MAXIMUM_CAPACITY) {
threshold = Integer.MAX_VALUE;
return oldTab;
}
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 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[] newTab = (Node[])new Node[newCap];
table = newTab;
if (oldTab != null) {
for (int j = 0; j < oldCap; ++j) {
Node 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)e).split(this, newTab, j, oldCap);
else { // preserve order
Node loHead = null, loTail = null;
Node hiHead = null, hiTail = null;
Node 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;
}
resize这个函数很重要，所以我们一行一行的来分析它。
首先，先判断旧的哈希表是否为空：如果不为空，根据原来的大小确定是否需要扩容，如果需要扩容的话则是将扩容阈值扩大到之前的2倍。如果为空，判断旧的扩容阈值是否大于0。如果大于0，则将其赋给新的哈希表容量；否则，新的哈希表容量则为默认容量即16，扩容阈值则为16*0.75即12.
第二步，经过上一轮赋值过后，判断新的扩容阈值是否等于0。如果等于0，则对新的扩容阈值进行赋值。最终也就确定的新的扩容阈值。
第三步，根据新的容量创建一个新的数组newTab,判断以前的哈希表中的table数组是否为空，如果不为空，对扩容前的哈希表的table进行遍历，
然后对table中每一个链表进行遍历。简单来说就是：遍历hashmap每个bucket里的链表，每个链表可能会被拆分成两个链表，不需要移动的元素置入loHead为首的链表，需要移动的元素置入hiHead为首的链表，然后分别分配给老的buket和新的buket。
上面就是整个resize或者叫rehash的过程，可能理解起来会比较困难，需要反复的去思考去验证。
resize的过程主要是两步，一步是扩容，一步是对扩容前的hash表的数据重新摆放位置的过程。
在resize之后，还要将即将添加的数据加入到新的哈希表当中去。主要是调用putVal这个函数。
final V putVal(int hash, K key, V value, boolean onlyIfAbsent,
boolean evict) {
Node[] tab; Node p; int n, i;
if ((tab = table) == null || (n = tab.length) == 0)
n = (tab = resize()).length;
if ((p = tab[i = (n - 1) & hash]) == null)
tab[i] = newNode(hash, key, value, null);
else {
Node e; K k;
if (p.hash == hash &&
((k = p.key) == key || (key != null && key.equals(k))))
e = p;
else if (p instanceof TreeNode)
e = ((TreeNode)p).putTreeVal(this, tab, hash, key, value);
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;
}
}
++modCount;
if (++size > threshold)
resize();
afterNodeInsertion(evict);
return null;
}
这个函数也很重要，我们还是一行一行的来解读它。
第一个if语句，判断添加前哈希表是否为空，如果为空则需要走一个resize的过程，上面我们已经分析过了。
第二步，计算hash并判断对应位置上是否有值，如果没有值则直接将新的Node存入指定位置；如果有值：1.判断key和hash是否相同，如果相同的话则将说明已经存在了指定的key，只需要更新对应value就行了。2.判断对应节点是不是红黑树，如果是红黑树则调用红黑树的对应方法（这一点咱们暂时带过，后面我们会单独分析红黑树的数据结构）3.如果以上两者都不满足，则遍历bucket对应的链表，要么找到对应的key，只需要更新value；要么找不到，则将数据添加入链表尾部。
以上两个函数是HashMap中最重要的两个函数，理解了这两个函数，那HashMap 其实也就理解的差不多了。
总结一下，HashMap主要的两个函数：resize和putval。
对于一个集合来说，最重要的操作就是增删改查。而在HashMap中，这几个操作必要的步骤都是先通过hash寻找到其在数组的位置，然后对比key和hash来找到对应的值。这个就是HashMap的关键。
所以，为什么说HashMap结合了顺序表和链表的缺点呢，因为它将顺序表的数组存储和链表的链式存储相结合，所以就具有了这两者的优点。
今天就分析到这了，晚安各位～～～