Computer-science analysis of sorting algorithms show that, on average, no generic sorting algorithm can scale faster than O(nlogn) (see Orders of Magnitude). However, many applications don’t require a “general” sort. You often have additional information that can help you to improve the speed of a particular sort.

For example, if you have 1000 items to sort, and each item can be given a unique ordering value that corresponds to a unique integer between 1 and 1000, the best sort is simply to slot the items directly into their correct locations in an array. No comparisons between the items are necessary.

Of course, typically you can’t map your elements so neatly. But if you can map items to integer keys that are more or less evenly distributed, you can still take advantage of improved sorting characteristics. Bear in mind that an array of partially sorted items can be sorted faster than a typical unsorted array.

When you can guess the approximate final position of the items in the collection to be sorted, you can use this knowledge to improve sorting speed. You should specifically look out for sorts where:

The distribution of the keys is fairly critical. A regular distribution allows them to be mapped straightforwardly into array indexes. An uneven distribution is difficult to map. But if you have an uneven distribution and can specify a mapping that allows you to flatten out the keys in some way, it may still be possible to apply this methodology. For example, if you know that your keys will have a normal (bell-curve) distribution, you can apply an inverse bell-curve function to the keys to flatten them out to array indexes.

For this technique to work, the mapped keys do not need to be unique. Several keys or groups of keys can map to the same value or values. Indeed, it is quite difficult to make the index mapping unique. You need to be aware of this and handle the resulting collisions. Normally, you can map clusters of keys into subsections of the sorted array. These subsections are probably not internally sorted, but they may be correctly sorted against each other (i.e., all elements in subsection 1 are ordered below all elements in subsection 2, all elements in subsection 2 are ordered below all elements in subsection 3, etc.). This way, the problem has been modified to sort multiple smaller subsections (which is faster than sorting the whole array), and hence the array is sorted more quickly.

Note that Object.hashCode( ) provides a mechanism for generating an integer for any object. However, the resulting hash code is not guaranteed to be evenly distributed or even unique, nor is it at all guaranteed to be consistent across different VMs or even over multiple runs of one VM. Consequently, the hash code is of little use for any kind of mapping.

Karl-Dietrich Neubert^[66] gives a detailed implementation of this approach, where the algorithm provides O(n) sorting behavior and also minimizes the extra memory needed to manage the sort.

I include here a Java implementation of Neubert’s algorithm with comments in the code. I have applied the implementation to an array of objects that have an integer field to specify the sort order, but of course the algorithm can be easily generalized to other cases where object ordering can be mapped to numbers. For a more detailed discussion of the algorithm, refer to Neubert’s article. The implementation given here performs sorting significantly faster than either the sort in java.util.Arrays (in Java 2) or a handcrafted quicksort (the most optimized final version with no casts from the first section of this chapter); see Table 9-3. Note also that this sort is O(n), and thus increases linearly in time, whereas the other sorts are O(nlogn) and so have a superlinear speedup.

Note that the sort at the end of the Neubert algorithm is an insertion sort running over the entire array. Insertion sorts provide better performance than quicksorts for partially ordered arrays. This final insertion sort ensures that keys incorrectly classified by the group distribution end up in the right location:

public interface FlashSortable{
  public int sortOrder( );
}

public static void flashsort(FlashSortable[] arr)
{
  //Number of groups into which the elements are classified
  //Neubert suggests 0.2 to 0.5 times the number of elements in the array.
  int num_groups = (int) (0.4 * arr.length);

  //Count the number of elements in each group
  int[] groups = new int[num_groups];
    
  flashsort(arr, num_groups, groups);
}

public static void flashsort(FlashSortable[] arr, int num_groups, int[] groups)
{
  //First get the minimum and maximum values
  int min = arr[0].sortOrder( );
  int max_idx = 0;
  int i;
  for (i = arr.length-1; i > 0; i--)
  {
    if (arr[i].sortOrder( ) < min)
      min = arr[i].sortOrder( );
    if (arr[i].sortOrder( ) > arr[max_idx].sortOrder( ))
      max_idx = i;
  }
  //If they are the same, all elements are identical
  //so the array is already sorted.
  if (min == arr[max_idx].sortOrder( ))
    return;
  
  //Count the number of elements in each group.
  //Take care to handle possible integer overflow by
  //casting to larger datatypes where this might occur.
  double scaling_constant = (num_groups - 1) /
         ( ((double) arr[max_idx].sortOrder( )) - min);
  int group;
  for (i = arr.length-1; i >= 0; i--)
  {
    group = (int) (scaling_constant * (((long) arr[i].sortOrder( )) - min));
    groups[group]++;
  }
  
  //Set the groups to point to the indexes in the array
  //that are the last index for each group.
  groups[0]--;
  for (i = 1; i < groups.length; i++)
  {
    groups[i] += groups[i-1];
  }
  
  //Put the biggest element at index 0 so that the swapping
  //algorithm below starts on the largest element & max group.
  FlashSortable old_value = arr[max_idx];
  arr[max_idx] = arr[0];
  arr[0] = old_value;
  
  //start with element at index 0
  int idx = 0;
  //and the maximum group
  group = num_groups - 1;

  //Start moving elements into their groups.
  //We need to make 'arr.length' moves at most,
  //but if we have one move left in the outer loop
  //then the remaining element is already in the right place,
  //so we need test for only 'arr.length-1' moves.
  int number_of_moves_left = arr.length - 1;
  
  FlashSortable new_value;
  while(number_of_moves_left > 0)
  {
    //When the first group fills up, we start scanning
    //for elements left in the wrong groups, and move them.
    //Note that we scan through the whole object array only once.
    while(idx > groups[group])
    {
      idx++;
      group = (int) (scaling_constant * (((long) arr[idx].sortOrder( )) - min));
    }
    
    new_value = arr[idx];
    //We run this loop until the first group fills up.
    //Then we run the previous scan loop to get back into this loop.
    while( idx != (groups[group]+1) )
    {
      group = (int) (scaling_constant * (((long) new_value.sortOrder( )) - min));
      old_value = arr[groups[group]];
      arr[groups[group]] = new_value;
      new_value = old_value;
      groups[group]--; //decrement the pointer to the next index
      number_of_moves_left--;
    }
  }
  
  //Now we have our partially ordered array,
  //we do an insertion sort to order the remainder.
  for (i = arr.length - 3; i >= 0; i--)
  {
    if (arr[i+1].sortOrder( ) < arr[i].sortOrder( ))
    {
      old_value = arr[i];
      idx = i;
      while(arr[idx+1].sortOrder( ) < old_value.sortOrder( ))
      {
        arr[idx] = arr[idx+1];
        idx++;
      }
      arr[idx] = old_value;
    }
  }
}