Java集合分析6
散列
[toc]
散列
一般想法
每个key都能完美的映射到表中.
散列函数
不存在完美的映射, 需要一个高效的函数 p = f(key). f称为散列函数. 通常, 关键字是一个字符串, 散列函数需要很好的选择
把字符串中字符的ASCII码或UniCode码的值加起来
//缺陷, 如果表很大, 函数将会不好分配, 例如key最多8个字符, 那么散列函数最多分配 127 * 8 = 1016 之间. public static int hash(String key, int tableSize) { int hashVal = 0; for(int i = 0; i < key.length; i ++) { hashVal += key.charAt(i); } return hashVal % tableSize; }一个稍微好的散列函数
public static int hash(String key, int tableSize) { int hashVal = 0; for(int i = 0; i < key.length; i ++) { hashVal = 37 + hashVal + key.charAt(i); } hashVal %= tableSize; //允许溢出 if(hashVal < 0) { hashVal += tableSize; } return hashVal; }Hornor 法则:有一些关于多项式求值的问题。对于多项式求值问题，我们最容易想到的算法是求出每一项的值然后把所求的值累加起来，这种算法的时间和空间效率都不高，对于数据规模不大的题目来说由于其直观、简单很容易被大家采纳，可一旦数据规模过大时，这种算法就显得无能为力了，下面介绍一种解决这类求值问题的高效算法――霍纳法则。在中国，霍纳法则也被称为秦九韶算法。
Java中HashCode的实现
/**
* Returns a hash code for this string. The hash code for a
* {@code String} object is computed as
* <blockquote><pre>
* s[0]*31^(n-1) + s[1]*31^(n-2) + ... + s[n-1] //多项式求和
* </pre></blockquote>
* using {@code int} arithmetic, where {@code s[i]} is the
* <i>i</i>th character of the string, {@code n} is the length of
* the string, and {@code ^} indicates exponentiation.
* (The hash value of the empty string is zero.)
*
* @return a hash code value for this object.
*/
public int hashCode() {
int h = hash;
if (h == 0 && value.length > 0) {
char val[] = value;
for (int i = 0; i < value.length; i++) {
h = 31 * h + val[i];
}
hash = h;
}
return h;
一个有趣的讨论
Why does Java’s hashCode() in String use 31 as a multiplier?
结论推测为早期的JVM优化31作为乘子可以优化 i* 31 = (i << 5) - i , 不过随着现代编译器的性能优化, 这个可能也不算是一个非常大的问题.
解决冲突: 分离链法
将散列到同一个值的所有元素保存到一个表中.
HashMap的源码解读
基本构成
public class HashMap<K,V> extends AbstractMap<K,V>
implements Map<K,V>, Cloneable, Serializable{}
//继承自AbstractMap实现了Map的接口
//节点 理论上是一个链表中的节点.
static class Node<K,V> implements Map.Entry<K,V> {
final int hash;
final K key;
V value;
Node<K,V> next; //冲突 桶中指向下一个节点.
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;
}
/**
Key 和value 都相等才相等. (比较两个节点.)
*/
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;
}
}
//TreeNode 待定 后续讲到Java8 将桶优化为红黑树再详细说明.
//定义的一些常量
/**
* The load factor for the hash table.
* 加载因子是表示Hsah表中元素的填满的程度.若:加载因子越大,填满的元素越多,好处是,空间利用率高了,但:冲突的机会加大了.反之,加载因子越小,填满的元 素越少,好处是:冲突的机会减小了,但:空间浪费多了.
* @serial
*/
final float loadFactor;
/**
* The next size value at which to resize (capacity * load factor).
*
* @serial
*/
// (The javadoc description is true upon serialization.
// Additionally, if the table array has not been allocated, this
// field holds the initial array capacity, or zero signifying
// DEFAULT_INITIAL_CAPACITY.)
int threshold;
//定义的一些常量
/**
* The default initial capacity - MUST be a power of two. 默认的初始化容量 必须是2的指数个
*/
static final int DEFAULT_INITIAL_CAPACITY = 1 << 4; // aka 16
/**
* 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. 最大容量
*/
static final int MAXIMUM_CAPACITY = 1 << 30;
/*
1 << 32 = 1
1 << 31 = -2147483648
1 << 30 = 1073741824
*/
/**
* The bin count threshold for using a tree rather than list for a
* bin. Bins are converted to trees when adding an element to a
* bin with at least this many nodes. The value must be greater
* than 2 and should be at least 8 to mesh with assumptions in
* tree removal about conversion back to plain bins upon
* shrinkage.
桶的形态发生改变的阈值. 链表向树形态变换的临界值
*/
static final int TREEIFY_THRESHOLD = 8;
/**
桶的形态发生改变的阈值. 树向链表形态变换的临界值
* The bin count threshold for untreeifying a (split) bin during a
* resize operation. Should be less than TREEIFY_THRESHOLD, and at
* most 6 to mesh with shrinkage detection under removal.
*/
static final int UNTREEIFY_THRESHOLD = 6;
/**
TODO explain
* The smallest table capacity for which bins may be treeified.
* (Otherwise the table is resized if too many nodes in a bin.)
* Should be at least 4 * TREEIFY_THRESHOLD to avoid conflicts
* between resizing and treeification thresholds.
*/
static final int MIN_TREEIFY_CAPACITY = 64;
/*
* Holds cached entrySet(). Note that AbstractMap fields are used
* for keySet() and values().
*/
transient Set<Map.Entry<K,V>> entrySet;
/**
HashMap的桶
* 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;
/**
* The number of key-value mappings contained in this map.
*/
transient int size;
/**
* The number of times this HashMap has been structurally modified
* Structural modifications are those that change the number of mappings in
* the HashMap or otherwise modify its internal structure (e.g.,
* rehash). This field is used to make iterators on Collection-views of
* the HashMap fail-fast. (See ConcurrentModificationException).
*/
transient int modCount;
构造函数
/**
* Constructs an empty <tt>HashMap</tt> with the specified initial
* capacity and load factor.
*
* @param initialCapacity the initial capacity 2的指数次幂个.
* @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);
}
/**
* 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); // 3/4 * 16 = 12个
}
public HashMap() {
this.loadFactor = DEFAULT_LOAD_FACTOR; // all other fields defaulted
}
public HashMap(Map<? extends K, ? extends V> m) {
this.loadFactor = DEFAULT_LOAD_FACTOR;
putMapEntries(m, false);
}
hash函数的原理解释, 可以参考hash函数的原理解读
/**
* 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);
}
工具
/**
* Returns a power of two size for the given target capacity.
此方法用与找到大于等于给定cap最接近的指数幂
*/
static final int tableSizeFor(int cap) {
/*
首先，为什么要对cap做减1操作。int n = cap - 1;
这是为了防止，cap已经是2的幂。如果cap已经是2的幂， 又没有执行这个减1操作，则执行完后面的几条无符号右移操作之后，返回的capacity将是这个cap的2倍。如果不懂，要看完后面的几个无符号右移之后再回来看看。
*/
int n = cap - 1;
n |= n >>> 1;
n |= n >>> 2;
n |= n >>> 4;
n |= n >>> 8;
n |= n >>> 16;
/*
注意，容量最大也就是32bit的正数，因此最后n |= n >>> 16; ，最多也就32个1（但是这已经是负数了。在执行tableSizeFor之前，对initialCapacity做了判断，如果大于MAXIMUM_CAPACITY(2 ^ 30)，则取MAXIMUM_CAPACITY。如果等于MAXIMUM_CAPACITY(2 ^ 30)，会执行移位操作。所以这里面的移位操作之后，最大30个1，不会大于等于MAXIMUM_CAPACITY。30个1，加1之后得2 ^ 30）
*/
return (n < 0) ? 1 : (n >= MAXIMUM_CAPACITY) ? MAXIMUM_CAPACITY : n + 1;
}
/**
* 初始化或者扩容两倍
* 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;
//old capacity
int oldCap = (oldTab == null) ? 0 : oldTab.length;
int oldThr = threshold;
//
int newCap, newThr = 0;
//如果旧容量大于0
if (oldCap > 0) {
//如果旧容量大于等于最大容量。
if (oldCap >= MAXIMUM_CAPACITY) {
//
threshold = Integer.MAX_VALUE;
return oldTab;
}
//如果(newCap = oldCap 的2倍)< 最大容量并且 oldCap >= 初始化容量
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<K,V>[] newTab = (Node<K,V>[])new Node[newCap];
table = newTab;
if (oldTab != null) {
//将oldTab赋值给newTab
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)
// 将红黑树拆分成链表.
((TreeNode<K,V>)e).split(this, newTab, j, oldCap);
else { // preserve order
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;
}
增
/**
* 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) {
//桶， 这个就是存储元素的桶 = table （field of class）
Node<K,V>[] tab;
Node<K,V> p;
int n, i;
//如果
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<K,V> 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<K,V>)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;
}