学习笔记：三数组实现的HashMap

  网上的一个HashMap代码，用三个数组实现，不同于jdk中的实现方式。处理哈希冲突是采用二次哈希（再哈希）的策略，学习了一把，个别地方可能没有理解到位。写了一些注释，如果有错误，敬请指出。

public final class LongHashMap {

    
    protected long table[];//存放键,类型为long,应该是用于特殊场所
    protected Object values[];//存放值
    protected byte state[];//state[i]=0,1,2表示table[i]与values[i]没有使用,已经使用,已删除
    protected int freeEntries;//空闲的空间数

    protected int distinct;//当前存了多少对键值
 
    protected int lowWaterMark;//当LongHashMap存放的键值对少于此数时,将重新调整(再哈希）
    protected int highWaterMark;//当LongHashMap存放的键值对大于此数时,将重新调整,由容量和装载因子决定

   
    protected double minLoadFactor;//最小装载因子
    protected double maxLoadFactor;//最大装载因子

   // 如果元素放得太满，就必须进行rehash(再哈希)。再哈希使空间增大，并将原有的对象重新导入新的LongHashMap中，
   //而原始的LongHashMap被删除。loadfactor(装载因子)决定何时要对LongHashMap进行再哈希。
    protected static final int DEFAULT_CAPACITY = 277;//缺省的容量,一个素数
    protected static final double DEFAULT_MIN_LOAD_FACTOR = 0.2;//缺省的最小的装载因子
    protected static final double DEFAULT_MAX_LOAD_FACTOR = 0.6;//缺省的最大的装载因子

    protected static final byte FREE = 0;
    protected static final byte FULL = 1;
    protected static final byte REMOVED = 2;

   //用缺省的容量构建HashMap
    public LongHashMap() {
        this(DEFAULT_CAPACITY);
    }

   //构造函数
    public LongHashMap(int initialCapacity) {
        this(initialCapacity, DEFAULT_MIN_LOAD_FACTOR, DEFAULT_MAX_LOAD_FACTOR);
    }

   //构造函数
    public LongHashMap(int initialCapacity, double minLoadFactor, double maxLoadFactor) {
        setUp(initialCapacity,minLoadFactor,maxLoadFactor);
    }

    //使用指定的初始化容量,最小装载因子,最大装载因子构建LongHashMap
    protected void setUp(int initialCapacity, double minLoadFactor, double maxLoadFactor) {
        if (initialCapacity < 0) {//参数检查
            throw new IllegalArgumentException(
                "Initial Capacity must not be less than zero: "+ initialCapacity
            );
        }
        if (minLoadFactor < 0.0 || minLoadFactor >= 1.0) {
                throw new IllegalArgumentException(
                    "Illegal minLoadFactor: "+ minLoadFactor
                );
        }
        if (maxLoadFactor <= 0.0 || maxLoadFactor >= 1.0) {
                throw new IllegalArgumentException(
                    "Illegal maxLoadFactor: "+ maxLoadFactor
                );
        }
        if (minLoadFactor >= maxLoadFactor) {
                throw new IllegalArgumentException(
                    "Illegal minLoadFactor: " + minLoadFactor +
                    " and maxLoadFactor: " + maxLoadFactor
                );
        }

        int capacity = initialCapacity;
        capacity = nextPrime(capacity);//程序将调整初始化容量,使之为素数
      
        if (capacity==0) {
            capacity=1;
        }

        this.table = new long[capacity];//关键字数组
        this.values = new Object[capacity];//值数组
        this.state = new byte[capacity];//状态数组

       
        this.minLoadFactor = minLoadFactor;
        
        if (capacity == LARGEST_PRIME) this.maxLoadFactor = 1.0;
        else this.maxLoadFactor = maxLoadFactor;

        this.distinct = 0;//开始时,LongHashMap中没有存入键值对
        this.freeEntries = capacity; // 开始时空闲的空间数=容量
       
        this.lowWaterMark = 0;

        // Math.min(capacity-2, (int) (capacity * maxLoadFactor));
        this.highWaterMark = chooseHighWaterMark(capacity, this.maxLoadFactor);
    }

     
     //扩容量,一个素数
     private int chooseGrowCapacity(int size, double minLoad, double maxLoad) {
        return nextPrime(Math.max(size+1, (int) ((4*size / (3*minLoad+maxLoad)))));
    }

     public boolean put(long key, Object value) {//在LongHashMap中存放键值对
        int i = indexOfInsertion(key);
        if (i<0) { 
            i = -i -1;
            this.values[i]=value;
            return false;
        }
         if (this.distinct > this.highWaterMark) {//当存的健值对超过highWaterMark时,将重新构建LongHashMap
            int newCapacity = chooseGrowCapacity(
                    this.distinct+1,
                    this.minLoadFactor,
                    this.maxLoadFactor
                );
            rehash(newCapacity);//用新容量重新构造
            return put(key, value);
        }

        this.table[i]=key;
        this.values[i]=value;
        if (this.state[i]==FREE) this.freeEntries--;//剩余空间少了一个
        this.state[i]=FULL;
        this.distinct++;//当前存放的键值对数目加1

        if (this.freeEntries < 1) {
            int newCapacity = chooseGrowCapacity(
                    this.distinct+1,
                    this.minLoadFactor,
                    this.maxLoadFactor
                );
            rehash(newCapacity);//用新容量重新构造
        }

        return true;
    }
  

         //求关键字key的索引值,用于插入(添加)
    private final int indexOfInsertion(long key) {
        final long tab[] = table;
        final byte stat[] = state;
        final int length = tab.length;
        //这就是哈希函数了
        final int hash = ((int)(key ^ (key >> 32))) & 0x7FFFFFFF;
        int i = hash % length;

         //发生哈希冲突时,用于再哈希探测的步长
        int decrement = (hash) % (length-2);
      
        if (decrement == 0) decrement = 1;
        //stat[i]有三种情况
        while (stat[i] == FULL && tab[i] != key) {//第一种,发生哈希冲突,往前探测
            i -= decrement;
            if (i<0) i+=length;
        }

        if (stat[i] == REMOVED) {//第二种,此位置原来的键值对已删除
           
            int j = i;
            //有意思，已删除的位置并不用来存新的
            while (stat[i] != FREE && (stat[i] == REMOVED || tab[i] != key)) {
                i -= decrement;
                if (i<0) i+=length;
            }
            if (stat[i] == FREE) i = j;
        }

        if (stat[i] == FULL) {//第三种,这种情况会出现吗?
          
            return -i-1;
        }
        //第三种,stat[i]=FREE
       
        return i;
    }

     //删除key的value
    public boolean removeKey(long key) {
        int i = indexOfKey(key);//获取关键字的索引
        if (i<0) return false; 

        this.state[i]=REMOVED;//作删除标记
        this.values[i]=null; //注意：table[i]并没有置为null
        this.distinct--;

        if (this.distinct < this.lowWaterMark) {//存放的键值对少于lowWaterMark,重新调整
                int newCapacity = chooseShrinkCapacity(
                        this.distinct,
                        this.minLoadFactor,
                        this.maxLoadFactor
                    );
         rehash(newCapacity);//用新容量重新构造
        }

        return true;
    }


      public final Object get(long key) {//获取关键字对应的值
        int i = indexOfKey(key);
        if (i<0) {
            return null;
        }
        else {
            return values[i];
        }
    }

    private final int indexOfKey(long key) {//求关键字之索引值,用于查找
        final long tab[] = table;
        final byte stat[] = state;
        final int length = tab.length;
        //这个是哈希函数
        final int hash = ((int)(key ^ (key >> 32))) & 0x7FFFFFFF;
        int i = hash % length;//得到了关键字的索引值
       
        //用于再哈希探测的步长
        int decrement = (hash) % (length-2);//减量
  
        if (decrement == 0) decrement = 1;

        while (stat[i] != FREE && (stat[i] == REMOVED || tab[i] != key)) {
            i -= decrement;//往前找
            if (i<0) i+=length;
        }

        if (stat[i] == FREE) return -1; // 没有找到
        return i; //找到了,返回索引值
    }

  
      public void clear() {//清空
        for (int i=0; i<state.length; i++) {
            state[i] = FREE;
        }
        for (int i=0; i<values.length-1; i++) {
            values[i] = null;
        }

        this.distinct = 0;
        this.freeEntries = table.length; 
        trimToSize();//清空以后，容量不能太大，这里重新调整，以节约空间
    }

     public void trimToSize() {
       int newCapacity = nextPrime((int)(1 + 1.2*size()));
        if (table.length > newCapacity) {
            rehash(newCapacity);
        }
    }



    //是否包含key
    public boolean containsKey(long key) {
        return indexOfKey(key) >= 0;
    }

    //是否包含value
    public boolean containsValue(Object value) {
        return indexOfValue(value) >= 0;
    }

  
    public void ensureCapacity(int minCapacity) {//确保容量不小于minCapacity
        if (table.length < minCapacity) {
            int newCapacity = nextPrime(minCapacity);
            rehash(newCapacity);//再哈希
        }
    }


    protected int indexOfValue(Object value) {//获取值的索引
        final Object val[] = values;
        final byte stat[] = state;

        for (int i=stat.length; --i >= 0;) {
            if (stat[i]==FULL && val[i]==value) return i;
        }

        return -1; // not found
    }

   //获取value的第一个键值,可能的多个
    public long keyOf(Object value) {
        int i = indexOfValue(value);
        if (i<0) return Long.MIN_VALUE;
        return table[i];
    }

 
    public long[] keys() {//所有的键
        long[] elements = new long[distinct];
        long[] tab = table;
        byte[] stat = state;
        int j=0;
        for (int i = tab.length ; i-- > 0 ;) {
            if (stat[i]==FULL) {
                elements[j++]=tab[i];
            }
        }
        return elements;
    }

  
     public int size() {//当前存了多少对键值
        return distinct;
    }

    
    public boolean isEmpty() {
        return distinct == 0;
    }

     // 如果元素放得太满，就必须进行rehash(再哈希)。再哈希使空间增大，并将原有的对象重新导入新的LongHashMap中，
   //而原始的LongHashMap被删除。loadfactor(装载因子)决定何时要对LongHashMap进行再哈希。

    protected void rehash(int newCapacity) {//用新的容量重新构建LongHashMap
        int oldCapacity = table.length;//原来的容量
        long oldTable[] = table;//原来的键
        Object oldValues[] = values;//原来的值
        byte oldState[] = state;

        long newTable[] = new long[newCapacity];
        Object newValues[] = new Object[newCapacity];
        byte newState[] = new byte[newCapacity];
        
         //(int) (newCapacity * minLoadFactor);
        this.lowWaterMark  = chooseLowWaterMark(newCapacity,this.minLoadFactor);
        this.highWaterMark = chooseHighWaterMark(newCapacity,this.maxLoadFactor);

        this.table = newTable;
        this.values = newValues;
        this.state = newState;
        this.freeEntries = newCapacity-this.distinct; // 当前的剩余空间

        for (int i = oldCapacity ; i-- > 0 ;) {
            if (oldState[i]==FULL) {
                long element = oldTable[i];
                int index = indexOfInsertion(element);
                newTable[index]=element;
                newValues[index]=oldValues[i];
                newState[index]=FULL;
            }
        }
    }

   

  
    public Object[] values() {
        Object[] elements = new Object[distinct];

        Object[] val = values;
        byte[] stat = state;

        int j=0;
        for (int i = stat.length ; i-- > 0 ;) {
            if (stat[i]==FULL) {
                elements[j++]=val[i];
            }
        }
        return elements;
    }

   

    
    private int chooseHighWaterMark(int capacity, double maxLoad) {
          return Math.min(capacity-2, (int) (capacity * maxLoad));
    }

   
    protected int chooseLowWaterMark(int capacity, double minLoad) {
        return (int) (capacity * minLoad);
    }

    
    protected int chooseMeanCapacity(int size, double minLoad, double maxLoad) {
        return nextPrime(Math.max(size+1, (int) ((2*size / (minLoad+maxLoad)))));
    }

 
    protected int chooseShrinkCapacity(int size, double minLoad, double maxLoad) {
        return nextPrime(Math.max(size+1, (int) ((4*size / (minLoad+3*maxLoad)))));
    }

  
    protected int nextPrime(int desiredCapacity) {//对指定的容量,在素数表中进行对半查找,返回一个素数容量
        int i = java.util.Arrays.binarySearch(primeCapacities, desiredCapacity);
        if(desiredCapacity==100) System.out.println("i="+i);
        if (i<0) {
             i = -i -1; 
        }
        return primeCapacities[i];
    }

   private void printState(){
      for(int i=0;i<state.length;i++)
        System.out.print(state[i]+"  ");
      System.out.println();
   }
    
    public static final int LARGEST_PRIME = Integer.MAX_VALUE; //最大的素数.

   
    private static final int[] primeCapacities = {//容量素数表
        LARGEST_PRIME,

        //chunk #1
        5,11,23,47,97,197,397,797,1597,3203,6421,12853,25717,51437,102877,205759,
        411527,823117,1646237,3292489,6584983,13169977,26339969,52679969,105359939,
        210719881,421439783,842879579,1685759167,

        //chunk #2
        433,877,1759,3527,7057,14143,28289,56591,113189,226379,452759,905551,1811107,
        3622219,7244441,14488931,28977863,57955739,115911563,231823147,463646329,927292699,
        1854585413,

        //chunk #3
        953,1907,3821,7643,15287,30577,61169,122347,244703,489407,978821,1957651,3915341,
        7830701,15661423,31322867,62645741,125291483,250582987,501165979,1002331963,
        2004663929,

        //chunk #4
        1039,2081,4177,8363,16729,33461,66923,133853,267713,535481,1070981,2141977,4283963,
        8567929,17135863,34271747,68543509,137087021,274174111,548348231,1096696463,

        //chunk #5
        31,67,137,277,557,1117,2237,4481,8963,17929,35863,71741,143483,286973,573953,
        1147921,2295859,4591721,9183457,18366923,36733847,73467739,146935499,293871013,
        587742049,1175484103,

        //chunk #6
        599,1201,2411,4831,9677,19373,38747,77509,155027,310081,620171,1240361,2480729,
        4961459,9922933,19845871,39691759,79383533,158767069,317534141,635068283,1270136683,

        //chunk #7
        311,631,1277,2557,5119,10243,20507,41017,82037,164089,328213,656429,1312867,
        2625761,5251529,10503061,21006137,42012281,84024581,168049163,336098327,672196673,
        1344393353,

        //chunk #8
        3,7,17,37,79,163,331,673,1361,2729,5471,10949,21911,43853,87719,175447,350899,
        701819,1403641,2807303,5614657,11229331,22458671,44917381,89834777,179669557,
        359339171,718678369,1437356741,

        //chunk #9
        43,89,179,359,719,1439,2879,5779,11579,23159,46327,92657,185323,370661,741337,
        1482707,2965421,5930887,11861791,23723597,47447201,94894427,189788857,379577741,
        759155483,1518310967,

        //chunk #10
        379,761,1523,3049,6101,12203,24407,48817,97649,195311,390647,781301,1562611,
        3125257,6250537,12501169,25002389,50004791,100009607,200019221,400038451,800076929,
        1600153859
    };

    static { 
        java.util.Arrays.sort(primeCapacities);
    }

    //测试一下
     public static void main(String args[]){
      LongHashMap lh=new LongHashMap(5);//初始容量为5
      System.out.println("size="+lh.size());
      for(int i=0;i<3;i++)//先放三个
        lh.put(i, Integer.valueOf(i));
      System.out.println("size="+lh.size());
      lh.removeKey(1);//删除二个
       lh.removeKey(2);
      lh.put(123,"ok");//添加一个

      //看看状态
      lh.printState();
      lh.put(1234,"oo");//再放一个
      //看看状态 
      lh.printState();

      //取出来
       System.out.println(lh.get(0));
      System.out.println(lh.get(123));
      System.out.println(lh.get(1234));

    }
}

运行：

C:\ex>java   LongHashMap
size=0
size=3
1  2  2  1  0
1  0  0  0  1  0  0  0  0  0  1  0  0  0  0  0  0
0
ok
oo

源码：