Question

I’ve a program that does Block Nested loop join (link text). Basically what it does is, it reads contents from a file (say 10GB file) into buffer1 (say 400MB), puts it into a hash table. Now read contents of the second file (say 10GB file) into buffer 2 (say 100MB) and see if the elements in buffer2 are present in the hash. Outputting the result doesn’t matter. I’m just concerned with efficiency of the program for now. In this program, I need to read 8 bytes at a time from both files so I use long long int.
The problem is my program is very inefficient. How can I make it efficient ?

// I compile using g++ -o hash hash.c -std=c++0x

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <unistd.h>
#include <sys/time.h>
#include <stdint.h>
#include <math.h>
#include <limits.h>
#include <iostream>
#include <algorithm>
#include <vector>
#include <unordered_map>
using namespace std;

typedef std::unordered_map<unsigned long long int, unsigned long long int> Mymap; 
int main()
{

uint64_t block_size1 = (400*1024*1024)/sizeof(long long int);  //block size of Table A - division operator used to make the block size 1 mb - refer line 26,27 malloc statements.
uint64_t block_size2 = (100*1024*1024)/sizeof(long long int);   //block size of table B

int i=0,j=0, k=0;
uint64_t x,z,l=0;
unsigned long long int *buffer1 = (unsigned long long int *)malloc(block_size1 * sizeof(long long int));
unsigned long long int *buffer2 = (unsigned long long int *)malloc(block_size2 * sizeof(long long int));

Mymap c1 ;                                                          // Hash table
//Mymap::iterator it;

FILE *file1 = fopen64("10G1.bin","rb");  // Input is a binary file of 10 GB
FILE *file2 = fopen64("10G2.bin","rb");

printf("size of buffer1 : %llu \n", block_size1 * sizeof(long long int));
printf("size of buffer2 : %llu \n", block_size2 * sizeof(long long int));


while(!feof(file1))
        {
        k++;
        printf("Iterations completed : %d \n",k);
        fread(buffer1, sizeof(long long int), block_size1, file1);                          // Reading the contents into the memory block from first file

        for ( x=0;x< block_size1;x++)
            c1.insert(Mymap::value_type(buffer1[x], x));                                    // inserting values into the hash table

//      std::cout << "The size of the hash table is" << c1.size() * sizeof(Mymap::value_type) << "\n" << endl;

/*      // display contents of the hash table 
            for (Mymap::const_iterator it = c1.begin();it != c1.end(); ++it) 
            std::cout << " [" << it->first << ", " << it->second << "]"; 
            std::cout << std::endl; 
*/

                while(!feof(file2))
                {   
                    i++;                                                                    // Counting the number of iterations    
//                  printf("%d\n",i);

                    fread(buffer2, sizeof(long long int), block_size2, file2);              // Reading the contents into the memory block from second file

                    for ( z=0;z< block_size2;z++)
                        c1.find(buffer2[z]);                                                // finding the element in hash table

//                      if((c1.find(buffer2[z]) != c1.end()) == true)                       //To check the correctness of the code
//                          l++;
//                  printf("The number of elements equal are : %llu\n",l);                  // If input files have exactly same contents "l" should print out the block_size2
//                  l=0;                    
                }
                rewind(file2);
                c1.clear();                                         //clear the contents of the hash table
    }

    free(buffer1);
    free(buffer2);  
    fclose(file1);
    fclose(file2);
}

Update :

Is it possible to directly read a chunk (say 400 MB) from a file and directly put it into hash table using C++ stream readers? I think that can further reduce the overhead.

Answer 1

The running time for your program is (l₁ x bs₁ x l₂ x bs₂) (where l₁ is the number of lines in the first file, and bs₁ is the block size for the first buffer, and l₂ is the number of lines in the second file, and bs₂ is the block size for the second buffer) since you have four nested loops. Since your block sizes are constant, you can say that your order is O(n x 400 x m x 400) or O(1600mn), or in the worst case O(1600n²) which essentially ends up being O(n²).

You can have an O(n) algorithm if you do something like this (pseudocode follows):

map = new Map();
duplicate = new List();
unique = new List();

for each line in file1
   map.put(line, true)
end for

for each line in file2
   if(map.get(line))
       duplicate.add(line)
   else
       unique.add(line)
   fi
end for

Now duplicate will contain a list of duplicate items and unique will contain a list of unique items.

In your original algorithm, you needlessly traverse the second file for every line in the first file. So you actually end up losing the benefit of the hash (which gives you O(1) lookup time). The trade-off in this case, of course, is that you have to store the entire 10GB in memory which probably is not that helpful. Usually in cases like these the trade-off is between run-time and memory.

There is probably a better way to do this. I need to think about it some more. If not, I’m pretty sure someone will come up with a better idea :).

UPDATE

You can probably reduce memory-usage if you can find a good way to hash the line (that you read in from the first file) so that you get a unique value (i.e., a 1-to-1 mapping between the line and the hash value). Essentially you would do something like this:

for each line in file1
   map.put(hash(line), true)
end for

for each line in file2
   if(map.get(hash(line)))
       duplicate.add(line)
   else
       unique.add(line)
   fi
end for

Here the hash function is the one that performs the hashing. This way you don’t have to store all the lines in memory. You only have to store their hashed values. This might help you a little bit. Even still, in the worse case (where you are either comparing two files that are identical, or entirely different) you can still end up with 10Gb in memory for either duplicate or unique list. You can get around with it with the loss of some information if you simply store a count of unique or duplicate items instead of the items themselves.