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Java HashMap

代码基于 Jdk1.8

最近在工作用到Map等一系列的集合,于是,想仔细看一下其具体实现。

结构

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public class HashMap<K,V> extends AbstractMap<K,V>
implements Map<K,V>, Cloneable, Serializable

抽象类AbstractMap

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public abstract class AbstractMap<K,V> implements Map<K,V>

该类实现了Map接口,具体结构如下:

该类代码很简单,不再赘述。

序列化接口:Serializable

该接口没有什么好说的,但通过该接口,就解释了为什么HashMap总一些字段是用transient来修饰。

一旦变量被transient修饰,变量将不再是对象持久化的一部分,该变量内容在序列化后无法获得访问。

阅读JDK中类注释

HashMap是无序的

如果希望保持元素的输入顺序应该使用LinkedHashMap

除了非同步和允许使用null之外,HashMap与Hashtable基本一致。

此处的非同步指的是多线程访问,并至少一个线程修改HashMap结构。结构修改包括任何新增、删除映射,但仅仅修改HashMap中已存在项值得操作不属于结构修改。

初始容量与加载因子是影响HashMap的两个重要因素。

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public HashMap(int initialCapacity, float loadFactor)

初始容量默认值:

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/**
* The default initial capacity - MUST be a power of two.
*/
static final int DEFAULT_INITIAL_CAPACITY = 1 << 4; // aka 16

加载因子默认值:

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/**
* The load factor used when none specified in constructor.
*/
static final float DEFAULT_LOAD_FACTOR = 0.75f;

容量是HashMap在创建时“桶”的数量,而初始容量是哈希表在创建时分配的空间大小。加载因子是哈希表在其容量自动增加时能达到多满的衡量尺度(比如默认为0.75,即桶中数据达到3/4就不能再放数据了)。

默认的负载因子大小为0.75,也就是说,当一个map填满了75%的bucket时候,和其它集合类(如ArrayList等)一样,将会创建原来HashMap大小的两倍的bucket数组,来重新调整map的大小,并将原来的对象放入新的bucket数组中。这个过程叫作rehashing,因为它调用hash方法找到新的bucket位置。
当重新调整HashMap大小的时候,会存在条件竞争,因为如果两个线程都发现HashMap需要重新调整大小了,它们会同时试着调整大小。在调整大小的过程中,存储在链表中的元素的次序会反过来,因为移动到新的bucket位置的时候,HashMap并不会将元素放在链表的尾部,而是放在头部,这是为了避免尾部遍历(tail traversing)。如果条件竞争发生了,那么就死循环了。
所以 HashMap应该避免在多线程环境下使用。

默认0.75这是时间和空间成本上一种折衷:增大负载因子可以减少 Hash 表(就是那个 Entry 数组)所占用的内存空间,但会增加查询数据的时间开销,而查询是最频繁的的操作(HashMap 的 get() 与 put() 方法都要用到查询);减小负载因子会提高数据查询的性能,但会增加 Hash 表所占用的内存空间。

存储形式

链表形式存储?树形结构?

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* This map usually acts as a binned (bucketed) hash table, but
* when bins get too large, they are transformed into bins of
* TreeNodes, each structured similarly to those in
* java.util.TreeMap. Most methods try to use normal bins, but
* relay to TreeNode methods when applicable (simply by checking
* instanceof a node).

源码阅读

添加元素

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/**
* Associates the specified value with the specified key in this map.
* If the map previously contained a mapping for the key, the old
* value is replaced.
*
* @param key key with which the specified value is to be associated
* @param value value to be associated with the specified key
* @return the previous value associated with <tt>key</tt>, or
* <tt>null</tt> if there was no mapping for <tt>key</tt>.
* (A <tt>null</tt> return can also indicate that the map
* previously associated <tt>null</tt> with <tt>key</tt>.)
*/
public V put(K key, V value) {
return putVal(hash(key), key, value, false, true);
}

/**
* 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;
//hashmap第一次添加元素,调用resize()方法初始化table
if ((tab = table) == null || (n = tab.length) == 0)
n = (tab = resize()).length;
//通过与运算判断tab[hash]位置是否有值
//从newNode这里可以看出,hashmap中key value是以Node<K,V>实例的形式存放的
if ((p = tab[i = (n - 1) & hash]) == null)
tab[i] = newNode(hash, key, value, null);
//tab[i]有元素,则需要遍历结点后再添加
else {
Node<K,V> e; K k;
// hash、key均等,说明待插入元素和第一个元素相等,直接更新
if (p.hash == hash &&
((k = p.key) == key || (key != null && key.equals(k))))
e = p;
else if (p instanceof TreeNode)//如果p类型为TreeNode,调用树的添加元素方法(红黑树冲突插入)
e = ((TreeNode<K,V>)p).putTreeVal(this, tab, hash, key, value);
else {
//不是TreeNode,即为链表,遍历链表,查找给定关键字
for (int binCount = 0; ; ++binCount) {
if ((e = p.next) == null) {
//到达链表的尾端也没有找到key值相同的节点,则生成一个新的Node
p.next = newNode(hash, key, value, null);
//创建新节点后若超出树形化阈值,则转换为树形存储
if (binCount >= TREEIFY_THRESHOLD - 1) // -1 for 1st
treeifyBin(tab, hash);//当桶中链表的数量>=9的时候,底层则改为红黑树实现
break;
}
//如果找到关键字相同的结点
if (e.hash == hash &&
((k = e.key) == key || (key != null && key.equals(k))))
break;
//更新p指向下一个节点
p = e;
}
}
// e不为空,即map中存在要添加的关键字
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();//扩容
afterNodeInsertion(evict);
return null;
}

小注:
1、回调

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afterNodeAccess(e);
afterNodeInsertion(evict);

是为LinkedHashMap回调准备的。
2、计算hash值

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 /**
* 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);
}

‘>>>’:无符号右移,忽略符号位,空位都以0补齐

value >>> num – num 指定要移位值value 移动的位数。

即按二进制形式把所有的数字向右移动对应位数,低位移出(舍弃),高位的空位补零。对于正数来说和带符号右移相同,对于负数来说不同。

^异或:两个操作数的位中,相同则结果为0,不同则结果为1。

这也正好解释了为什么HashMap底层数组的长度总是 2 的 n 次方。因为这样(数组长度-1)正好相当于一个“低位掩码”。“异或”操作的结果就是散列值的高位全部归零,只保留低位值,用来做数组下标访问。
以初始长度16为例,16-1=15。
2进制表示是00000000 00000000 00001111。
和某hash值做“异或”操作如下,结果就是截取了最低的四位值。

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10100101 11000100 00100101
00000000 00000000 00001111
----------------------------------
00000000 00000000 00000101 //高位全部归零,只保留末四位

更详细的步骤如下:

3、存储结构


获取元素

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/**
* Returns the value to which the specified key is mapped,
* or {@code null} if this map contains no mapping for the key.
*
* <p>More formally, if this map contains a mapping from a key
* {@code k} to a value {@code v} such that {@code (key==null ? k==null :
* key.equals(k))}, then this method returns {@code v}; otherwise
* it returns {@code null}. (There can be at most one such mapping.)
*
* <p>A return value of {@code null} does not <i>necessarily</i>
* indicate that the map contains no mapping for the key; it's also
* possible that the map explicitly maps the key to {@code null}.
* The {@link #containsKey containsKey} operation may be used to
* distinguish these two cases.
*
* @see #put(Object, Object)
*/
public V get(Object key) {
Node<K,V> e;
return (e = getNode(hash(key), key)) == null ? null : e.value;
}

/**
* Implements Map.get and related methods
*
* @param hash hash for key
* @param key the key
* @return the node, or null if none
*/
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 &&
//hash & length-1 定位数组下标
(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) {
//第一个节点是TreeNode,则采用位桶+红黑树结构,
//调用TreeNode.getTreeNode(hash,key),
//遍历红黑树,得到节点的value
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;
}

树节点的查找:

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/**
* Calls find for root node.
*/
final TreeNode<K,V> getTreeNode(int h, Object k) {
return ((parent != null) ? root() : this).find(h, k, null);
}
/**
* Finds the node starting at root p with the given hash and key.
* The kc argument caches comparableClassFor(key) upon first use
* comparing keys.
*通过hash值的比较,递归的去遍历红黑树,
compareableClassFor(Class k):判断实例k对应的类是否实现了Comparable接口,如果实现了该接口并
在某些时候如果红黑树节点的元素are of the same "class C implements Comparable<C>" type
*利用他们的compareTo()方法来比较大小,这里需要通过反射机制来check他们到底是不是属于同一个类,是不是具有可比较性.
*/
final TreeNode<K,V> find(int h, Object k, Class<?> kc) {
TreeNode<K,V> p = this;
do {
int ph, dir; K pk;
TreeNode<K,V> pl = p.left, pr = p.right, q;
if ((ph = p.hash) > h)
p = pl;
else if (ph < h)
p = pr;
else if ((pk = p.key) == k || (k != null && k.equals(pk)))
return p;
else if (pl == null)
p = pr;
else if (pr == null)
p = pl;
else if ((kc != null ||
(kc = comparableClassFor(k)) != null) &&
(dir = compareComparables(kc, k, pk)) != 0)
p = (dir < 0) ? pl : pr;
else if ((q = pr.find(h, k, kc)) != null)
return q;
else
p = pl;
} while (p != null);
return null;
}

元素包含containsKey

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/**
* Returns <tt>true</tt> if this map contains a mapping for the
* specified key.
*
* @param key The key whose presence in this map is to be tested
* @return <tt>true</tt> if this map contains a mapping for the specified
* key.
*/
public boolean containsKey(Object key) {
return getNode(hash(key), key) != null;
}
/**
* Implements Map.get and related methods
*
* @param hash hash for key
* @param key the key
* @return the node, or null if none
*/
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 &&
//判断tab[hash]位置是否有值
(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);
//遍历寻找
do {
if (e.hash == hash &&
((k = e.key) == key || (key != null && key.equals(k))))
return e;
} while ((e = e.next) != null);
}
}
return null;
}
/**
* Calls find for root node.
*/
final TreeNode<K,V> getTreeNode(int h, Object k) {
return ((parent != null) ? root() : this).find(h, k, null);
}
/**
* Returns root of tree containing this node.
* 获取红黑树的根
*/
final TreeNode<K,V> root() {
for (TreeNode<K,V> r = this, p;;) {
if ((p = r.parent) == null)
return r;
r = p;
}
}
/**
* Finds the node starting at root p with the given hash and key.
* The kc argument caches comparableClassFor(key) upon first use
* comparing keys.
*/
final TreeNode<K,V> find(int h, Object k, Class<?> kc) {// k即key,kc为null
TreeNode<K,V> p = this;
do {
int ph, dir; K pk;
TreeNode<K,V> pl = p.left, pr = p.right, q;
if ((ph = p.hash) > h)// ph存当前节点hash
p = pl;
else if (ph < h) // 所查hash比当前节点hash大
p = pr;// 查右子树
else if ((pk = p.key) == k || (k != null && k.equals(pk)))
return p;// hash、key均相同,【找到了!】返回当前节点
else if (pl == null)// hash等,key不等,且当前节点的左节点null
p = pr;//查右子树
else if (pr == null)
p = pl;
//get->getTreeNode传递的kc为null。||逻辑或,短路运算,有真即可
// false || (false && ??)
else if ((kc != null ||
(kc = comparableClassFor(k)) != null) &&
(dir = compareComparables(kc, k, pk)) != 0)
p = (dir < 0) ? pl : pr;
else if ((q = pr.find(h, k, kc)) != null)
return q;
else
p = pl;
} while (p != null);
return null;
}

移除remove

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/**
* Removes the mapping for the specified key from this map if present.
*
* @param key key whose mapping is to be removed from the map
* @return the previous value associated with <tt>key</tt>, or
* <tt>null</tt> if there was no mapping for <tt>key</tt>.
* (A <tt>null</tt> return can also indicate that the map
* previously associated <tt>null</tt> with <tt>key</tt>.)
*/
public V remove(Object key) {
Node<K,V> e;
return (e = removeNode(hash(key), key, null, false, true)) == null ?
null : e.value;
}
/**
* Implements Map.remove and related methods
*
* @param hash hash for key
* @param key the key
* @param value the value to match if matchValue, else ignored
* @param matchValue if true only remove if value is equal
* @param movable if false do not move other nodes while removing
* @return the node, or null if none
*/
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 &&
//先比较内存地址,如果地址不一致,再调用equals进行比较
((k = p.key) == key || (key != null && key.equals(k))))
node = p;
else if ((e = p.next) != null) {
//如果是以红黑树处理冲突,则通过getTreeNode查找
if (p instanceof TreeNode)
node = ((TreeNode<K,V>)p).getTreeNode(hash, key);
else {
//如果是以链式的方式处理冲突,则通过遍历链表来寻找节点
do {
if (e.hash == hash &&
((k = e.key) == key ||
(key != null && key.equals(k)))) {
node = e;
break;
}
p = e;
} while ((e = e.next) != null);
}
}
//比对找到的key的value跟要删除的是否匹配
if (node != null && (!matchValue || (v = node.value) == value ||
(value != null && value.equals(v)))) {
if (node instanceof TreeNode)
((TreeNode<K,V>)node).removeTreeNode(this, tab, movable);
else if (node == p)
tab[index] = node.next;
else
p.next = node.next;
//已从结构上修改 此列表的次数
++modCount;
--size;
//回调
afterNodeRemoval(node);
return node;
}
}
return null;
}
/**
* Removes the given node, that must be present before this call.
* This is messier than typical red-black deletion code because we
* cannot swap the contents of an interior node with a leaf
* successor that is pinned by "next" pointers that are accessible
* independently during traversal. So instead we swap the tree
* linkages. If the current tree appears to have too few nodes,
* the bin is converted back to a plain bin. (The test triggers
* somewhere between 2 and 6 nodes, depending on tree structure).
*/
final void removeTreeNode(HashMap<K,V> map, Node<K,V>[] tab,
boolean movable) {
int n;
if (tab == null || (n = tab.length) == 0)
return;
int index = (n - 1) & hash;
TreeNode<K,V> first = (TreeNode<K,V>)tab[index], root = first, rl;
TreeNode<K,V> succ = (TreeNode<K,V>)next, pred = prev;
if (pred == null)
tab[index] = first = succ;
else
pred.next = succ;
if (succ != null)
succ.prev = pred;
if (first == null)
return;
if (root.parent != null)
root = root.root();
if (root == null || root.right == null ||
(rl = root.left) == null || rl.left == null) {
tab[index] = first.untreeify(map); // too small
return;
}
TreeNode<K,V> p = this, pl = left, pr = right, replacement;
if (pl != null && pr != null) {
TreeNode<K,V> s = pr, sl;
while ((sl = s.left) != null) // find successor
s = sl;
boolean c = s.red; s.red = p.red; p.red = c; // swap colors
TreeNode<K,V> sr = s.right;
TreeNode<K,V> pp = p.parent;
if (s == pr) { // p was s's direct parent
p.parent = s;
s.right = p;
}
else {
TreeNode<K,V> sp = s.parent;
if ((p.parent = sp) != null) {
if (s == sp.left)
sp.left = p;
else
sp.right = p;
}
if ((s.right = pr) != null)
pr.parent = s;
}
p.left = null;
if ((p.right = sr) != null)
sr.parent = p;
if ((s.left = pl) != null)
pl.parent = s;
if ((s.parent = pp) == null)
root = s;
else if (p == pp.left)
pp.left = s;
else
pp.right = s;
if (sr != null)
replacement = sr;
else
replacement = p;
}
else if (pl != null)
replacement = pl;
else if (pr != null)
replacement = pr;
else
replacement = p;
if (replacement != p) {
TreeNode<K,V> pp = replacement.parent = p.parent;
if (pp == null)
root = replacement;
else if (p == pp.left)
pp.left = replacement;
else
pp.right = replacement;
p.left = p.right = p.parent = null;
}

TreeNode<K,V> r = p.red ? root : balanceDeletion(root, replacement);

if (replacement == p) { // detach
TreeNode<K,V> pp = p.parent;
p.parent = null;
if (pp != null) {
if (p == pp.left)
pp.left = null;
else if (p == pp.right)
pp.right = null;
}
}
if (movable)
moveRootToFront(tab, r);
}

小结

在创建 HashMap 时根据实际需要适当地调整 load factor 的值;如果程序比较关心空间开销、内存比较紧张,可以适当地增加负载因子;如果程序比较关心时间开销,内存比较宽裕则可以适当的减少负载因子。通常情况下,程序员无需改变负载因子的值。

如果开始就知道 HashMap 会保存多个 key-value 对,可以在创建时就使用较大的初始化容量,如果 HashMap 中 Entry 的数量一直不会超过极限容量(capacity * load factor),HashMap 就无需调用 resize() 方法重新分配 table 数组,从而保证较好的性能。当然,开始就将初始容量设置太高可能会浪费空间(系统需要创建一个长度为 capacity 的 Entry 数组),因此创建 HashMap 时初始化容量设置也需要小心对待。

HashMap高性能需要以下几点:

  • 高效的hash算法
  • 保证hash值到内存地址(数组索引)的映射速度
  • 根据内存地址(数组索引)可以直接得到相应的值
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