8种经典hash算法c#实现----适用于bloom filter 的K个散列函数-优快云博客

本文探讨了在大数据统计中使用散列函数（BKDRHashFunction, APhashfunction, SDBMHashfunction等）进行数据处理的方法，并通过BloomFilter技术实现了内存优化，提升了精确率。然而，作者指出散列函数的计算耗时问题，引发对高效散列算法的进一步思考。

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using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;

namespace WindowsFormsApplication1
{
   static  class HashCode
    {

         // BKDR Hash Function
        public static int Hash1(string  str)
        {
             int seed = 131; // 31 131 1313 13131 131313 etc..
             int hash = 0;
             int count;
            char[] bitarray=str .ToCharArray ();
            count = bitarray.Length;
             while (count>0)
             {
              hash = hash * seed + (bitarray [bitarray .Length -count ]);
              count--;
               }
 
              return (hash & 0x7FFFFFFF);
        }
        //AP hash function
        public static int Hash2(string str)
    {
         int hash = 0;
        int i;
        int count;
        char[] bitarray = str.ToCharArray();
        count = bitarray.Length;
        for (i=0; i<count ; i++)
        {
          if ((i & 1) == 0)
         {
              hash ^= ((hash << 7) ^ (bitarray [i]) ^ (hash >> 3));
         }
         else
         {
            hash ^= (~((hash << 11) ^ (bitarray [i]) ^ (hash >> 5)));
         }
         count--;
         }
 
            return (hash & 0x7FFFFFFF);
        }

        //SDBM Hash function
        public static int Hash3(string str)
{
     int hash = 0;
        int i;
    int count;
    char[] bitarray = str.ToCharArray();
    count = bitarray.Length;
 
    while (count >0)
    {
        // equivalent to: hash = 65599*hash + (*str++);
        hash = (bitarray [bitarray .Length -count ]) + (hash << 6) + (hash << 16) - hash;
        count--;
    }
 
    return (hash & 0x7FFFFFFF);
}
 
// RS Hash Function
        public static int Hash4(string str)
{
     int b = 378551;
     int a = 63689;
     int hash = 0;
   
        int i;
    int count;
    char[] bitarray = str.ToCharArray();
    count = bitarray.Length;
    while (count >0)
    {
        hash = hash * a + (bitarray [bitarray .Length -count ]);
        a *= b;
        count--;
    }
 
    return (hash & 0x7FFFFFFF);
}
 
// JS Hash Function
        public static int Hash5(string str)
{
     int hash = 1315423911;
    int count;
    char[] bitarray = str.ToCharArray();
    count = bitarray.Length;
    while (count >0)
    {
        hash ^= ((hash << 5) + (bitarray [bitarray .Length -count ]) + (hash >> 2));
        count--;
    }
 
    return (hash & 0x7FFFFFFF);
}
 
// P. J. Weinberger Hash Function
        public static int Hash6(string str)
{
     int BitsInUnignedInt = ( int)(sizeof( int) * 8);
     int ThreeQuarters    = ( int)((BitsInUnignedInt  * 3) / 4);
     int OneEighth        = ( int)(BitsInUnignedInt / 8);
    int hash             = 0;
    unchecked {
     int HighBits         = ( int)(0xFFFFFFFF) << (BitsInUnignedInt - OneEighth); 
     int test             = 0;
    int count;
    char[] bitarray = str.ToCharArray();
    count = bitarray.Length;
    while (count >0)
    {
        hash = (hash << OneEighth) + (bitarray [bitarray .Length -count ]);
        if ((test = hash & HighBits) != 0)
        {
            hash = ((hash ^ (test >> ThreeQuarters)) & (~HighBits));
        }
        count--;
    }
    }
    return (hash & 0x7FFFFFFF);
}
 
// ELF Hash Function
        public static int Hash7(string str)
{
     int hash = 0;
     int x    = 0;
 int i;
    int count;
    char[] bitarray = str.ToCharArray();
    count = bitarray.Length;
    unchecked
    {
        while (count >0)
        {
            hash = (hash << 4) + (bitarray [bitarray .Length -count ]);
            if ((x = hash & (int)0xF0000000) != 0)
            {
                hash ^= (x >> 24);
                hash &= ~x;
            }
            count--;
        }
    }
    return (hash & 0x7FFFFFFF);
}
 

 
// DJB Hash Function
        public static int Hash8(string str)
{
     int hash = 5381;
     int i;
     int count;
     char[] bitarray = str.ToCharArray();
     count = bitarray.Length;
    while (count >0)
    {
        hash += (hash << 5) + (bitarray [bitarray .Length -count ]);
        count--;
    }
 
    return (hash & 0x7FFFFFFF);
}
 

    }
}

在处理大数据统计时，运用了bloom filter方法，里面使用了如上的散列函数，我只能说，内存确实降下来了。精确率确实还不错。但是，散列函数怎么会那么耗时啊。