看了下Java里面有HashMap、Hashtable、HashSet三种hash集合的实现源码，这里总结下，理解错误的地方还望指正
int hash = key.hashCode();
int index = (hash & 0x7FFFFFFF) % tab.length;
HashMap计算hash对key的hashcode进行了二次hash，以获得更好的散列值，然后对table数组长度取摸
static int hash(int h) {
// This function ensures that hashCodes that differ only by
// constant multiples at each bit position have a bounded
// number of collisions (approximately 8 at default load factor).
h ^= (h >>> 20) ^ (h >>> 12);
return h ^ (h >>> 7) ^ (h >>> 4);
}
static int indexFor(int h, int length) {
return h & (length-1);
}
除开HashMap和Hashtable外，还有一个hash集合HashSet，有所区别的是HashSet不是key value结构，仅仅是存储不重复的元素，相当于简化版的HashMap，只是包含HashMap中的key而已
通过查看源码也证实了这一点，HashSet内部就是使用HashMap实现，只不过HashSet里面的HashMap所有的value都是同一个Object而已，因此HashSet也是非线程安全的，至于HashSet和Hashtable的区别，HashSet就是个简化的HashMap的，所以你懂的
下面是HashSet几个主要方法的实现
private transient HashMap<E,Object> map;
private static final Object PRESENT = new Object();
public HashSet() {
map = new HashMap<E,Object>();
}
public boolean contains(Object o) {
return map.containsKey(o);
}
public boolean add(E e) {
return map.put(e, PRESENT)==null;
}
public boolean add(E e) {
return map.put(e, PRESENT)==null;
}
public boolean remove(Object o) {
return map.remove(o)==PRESENT;
}
public void clear() {
map.clear();
}
HashMap和Hashtable的底层实现都是数组+链表结构实现的，这点上完全一致
添加、删除、获取元素时都是先计算hash，根据hash和table.length计算index也就是table数组的下标，然后进行相应操作，下面以HashMap为例说明下它的简单实现
/**
* HashMap的默认初始容量 必须为2的n次幂
*/
static final int DEFAULT_INITIAL_CAPACITY = 16;
/**
* HashMap的最大容量，可以认为是int的最大值
*/
static final int MAXIMUM_CAPACITY = 1 << 30;
/**
* 默认的加载因子
*/
static final float DEFAULT_LOAD_FACTOR = 0.75f;
/**
* HashMap用来存储数据的数组
*/
transient Entry[] table;
/**
* Constructs an empty <tt>HashMap</tt> with the default initial capacity
* (16) and the default load factor (0.75).
*/
public HashMap() {
this.loadFactor = DEFAULT_LOAD_FACTOR;
threshold = (int)(DEFAULT_INITIAL_CAPACITY * DEFAULT_LOAD_FACTOR);
table = new Entry[DEFAULT_INITIAL_CAPACITY];
init();
}
public V put(K key, V value) {
if (key == null)
return putForNullKey(value); //处理null值
int hash = hash(key.hashCode());//计算hash
int i = indexFor(hash, table.length);//计算在数组中的存储位置
//遍历table[i]位置的链表，查找相同的key，若找到则使用新的value替换掉原来的oldValue并返回oldValue
for (Entry<K,V> e = table[i]; e != null; e = e.next) {
Object k;
if (e.hash == hash && ((k = e.key) == key || key.equals(k))) {
V oldValue = e.value;
e.value = value;
e.recordAccess(this);
return oldValue;
}
}
//若没有在table[i]位置找到相同的key，则添加key到table[i]位置，新的元素总是在table[i]位置的第一个元素，原来的元素后移
modCount++;
addEntry(hash, key, value, i);
return null;
}
void addEntry(int hash, K key, V value, int bucketIndex) {
//添加key到table[bucketIndex]位置，新的元素总是在table[bucketIndex]的第一个元素，原来的元素后移
Entry<K,V> e = table[bucketIndex];
table[bucketIndex] = new Entry<K,V>(hash, key, value, e);
//判断元素个数是否达到了临界值，若已达到临界值则扩容，table长度翻倍
if (size++ >= threshold)
resize(2 * table.length);
}
public V get(Object key) {
if (key == null)
return getForNullKey();//处理null值
int hash = hash(key.hashCode());//计算hash
//在table[index]遍历查找key，若找到则返回value，找不到返回null
for (Entry<K,V> e = table[indexFor(hash, table.length)];
e != null;
e = e.next) {
Object k;
if (e.hash == hash && ((k = e.key) == key || key.equals(k)))
return e.value;
}
return null;
}
public V remove(Object key) {
Entry<K,V> e = removeEntryForKey(key);
return (e == null ? null : e.value);
}
final Entry<K,V> removeEntryForKey(Object key) {
int hash = (key == null) ? 0 : hash(key.hashCode());
int i = indexFor(hash, table.length);
Entry<K,V> prev = table[i];
Entry<K,V> e = prev;
while (e != null) {
Entry<K,V> next = e.next;
Object k;
if (e.hash == hash &&
((k = e.key) == key || (key != null && key.equals(k)))) {
modCount++;
size--;
if (prev == e)
table[i] = next;
else
prev.next = next;
e.recordRemoval(this);
return e;
}
prev = e;
e = next;
}
return e;
}
void resize(int newCapacity) {
Entry[] oldTable = table;
int oldCapacity = oldTable.length;
if (oldCapacity == MAXIMUM_CAPACITY) {
threshold = Integer.MAX_VALUE;
return;
}
Entry[] newTable = new Entry[newCapacity];
transfer(newTable);
table = newTable;
threshold = (int)(newCapacity * loadFactor);
}
void transfer(Entry[] newTable) {
Entry[] src = table;
int newCapacity = newTable.length;
for (int j = 0; j < src.length; j++) {
Entry<K,V> e = src[j];
if (e != null) {
src[j] = null;
do {
Entry<K,V> next = e.next;
//重新对每个元素计算index int i = indexFor(e.hash, newCapacity);
e.next = newTable[i];
newTable[i] = e;
e = next;
} while (e != null);
}
}
}
public void clear() {
modCount++;
Entry[] tab = table;
for (int i = 0; i < tab.length; i++)
tab[i] = null;
size = 0;
}
public boolean containsKey(Object key) {
return getEntry(key) != null;
}
final Entry<K,V> getEntry(Object key) {
int hash = (key == null) ? 0 : hash(key.hashCode());
for (Entry<K,V> e = table[indexFor(hash, table.length)];
e != null;
e = e.next) {
Object k;
if (e.hash == hash &&
((k = e.key) == key || (key != null && key.equals(k))))
return e;
}
return null;
}
containsValue方法就比较粗暴了，就是直接遍历所有元素直到找到value，由此可见HashMap的containsValue方法本质上和普通数组和list的contains方法没什么区别，你别指望它会像containsKey那么高效
public boolean containsValue(Object value) {
if (value == null)
return containsNullValue();
Entry[] tab = table;
for (int i = 0; i < tab.length ; i++)
for (Entry e = tab[i] ; e != null ; e = e.next)
if (value.equals(e.value))
return true;
return false;
}
indexFor中的h & (length-1)就相当于h%length，用于计算index也就是在table数组中的下标
hash方法是对hashcode进行二次散列，以获得更好的散列值
为了更好理解这里我们可以把这两个方法简化为 int index= key.hashCode()/table.length,以put中的方法为例可以这样替换
int hash = hash(key.hashCode());//计算hash
int i = indexFor(hash, table.length);//计算在数组中的存储位置
//上面这两行可以这样简化
int i = key.key.hashCode()%table.length;
static int hash(int h) {
// This function ensures that hashCodes that differ only by
// constant multiples at each bit position have a bounded
// number of collisions (approximately 8 at default load factor).
h ^= (h >>> 20) ^ (h >>> 12);
return h ^ (h >>> 7) ^ (h >>> 4);
}
static int indexFor(int h, int length) {
return h & (length-1);
}
为了加深理解，我个人实现了一个简化版本的HashMap，注意哦，仅仅是简化版的功能并不完善，仅供参考
package cn.lzrabbit.structure;
/**
* Created by rabbit on 14-5-4.
*/
public class MyHashMap {
//默认初始化大小 16
private static final int DEFAULT_INITIAL_CAPACITY = 16;
//默认负载因子 0.75
private static final float DEFAULT_LOAD_FACTOR = 0.75f;
//临界值
private int threshold;
//元素个数
private int size;
//扩容次数
private int resize;
private HashEntry[] table;
public MyHashMap() {
table = new HashEntry[DEFAULT_INITIAL_CAPACITY];
threshold = (int) (DEFAULT_INITIAL_CAPACITY * DEFAULT_LOAD_FACTOR);
size = 0;
}
private int index(Object key) {
//根据key的hashcode和table长度取模计算key在table中的位置
return key.hashCode() % table.length;
}
public void put(Object key, Object value) {
//key为null时需要特殊处理，为简化实现忽略null值
if (key == null) return;
int index = index(key);
//遍历index位置的entry，若找到重复key则更新对应entry的值，然后返回
HashEntry entry = table[index];
while (entry != null) {
if (entry.getKey().hashCode() == key.hashCode() && (entry.getKey() == key || entry.getKey().equals(key))) {
entry.setValue(value);
return;
}
entry = entry.getNext();
}
//若index位置没有entry或者未找到重复的key，则将新key添加到table的index位置
add(index, key, value);
}
private void add(int index, Object key, Object value) {
//将新的entry放到table的index位置第一个，若原来有值则以链表形式存放
HashEntry entry = new HashEntry(key, value, table[index]);
table[index] = entry;
//判断size是否达到临界值，若已达到则进行扩容，将table的capacicy翻倍
if (size++ >= threshold) {
resize(table.length * 2);
}
}
private void resize(int capacity) {
if (capacity <= table.length) return;
HashEntry[] newTable = new HashEntry[capacity];
//遍历原table，将每个entry都重新计算hash放入newTable中
for (int i = 0; i < table.length; i++) {
HashEntry old = table[i];
while (old != null) {
HashEntry next = old.getNext();
int index = index(old.getKey());
old.setNext(newTable[index]);
newTable[index] = old;
old = next;
}
}
//用newTable替table
table = newTable;
//修改临界值
threshold = (int) (table.length * DEFAULT_LOAD_FACTOR);
resize++;
}
public Object get(Object key) {
//这里简化处理，忽略null值
if (key == null) return null;
HashEntry entry = getEntry(key);
return entry == null ? null : entry.getValue();
}
public HashEntry getEntry(Object key) {
HashEntry entry = table[index(key)];
while (entry != null) {
if (entry.getKey().hashCode() == key.hashCode() && (entry.getKey() == key || entry.getKey().equals(key))) {
return entry;
}
entry = entry.getNext();
}
return null;
}
public void remove(Object key) {
if (key == null) return;
int index = index(key);
HashEntry pre = null;
HashEntry entry = table[index];
while (entry != null) {
if (entry.getKey().hashCode() == key.hashCode() && (entry.getKey() == key || entry.getKey().equals(key))) {
if (pre == null) table[index] = entry.getNext();
else pre.setNext(entry.getNext());
//如果成功找到并删除，修改size
size--;
return;
}
pre = entry;
entry = entry.getNext();
}
}
public boolean containsKey(Object key) {
if (key == null) return false;
return getEntry(key) != null;
}
public int size() {
return this.size;
}
public void clear() {
for (int i = 0; i < table.length; i++) {
table[i] = null;
}
this.size = 0;
}
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append(String.format("size:%s capacity:%s resize:%s\n\n", size, table.length, resize));
for (HashEntry entry : table) {
while (entry != null) {
sb.append(entry.getKey() + ":" + entry.getValue() + "\n");
entry = entry.getNext();
}
}
return sb.toString();
}
}
class HashEntry {
private final Object key;
private Object value;
private HashEntry next;
public HashEntry(Object key, Object value, HashEntry next) {
this.key = key;
this.value = value;
this.next = next;
}
public Object getKey() {
return key;
}
public Object getValue() {
return value;
}
public void setValue(Object value) {
this.value = value;
}
public HashEntry getNext() {
return next;
}
public void setNext(HashEntry next) {
this.next = next;
}
}
原文：http://www.cnblogs.com/esther-qing/p/6480777.html