An idea that may be useful in speed thing up is illustrated below. You probably are already aware of this method but here it is anyway. The method can clearly be improved but I will provide a simple example to illustrate the idea.
Assumption: This example assumes that a single index can be used to access the same record contents for all columns.
1) A homogeneous index for a column is created by listing the first byte in each record.
2) An index is created for all columns of interest.
3) The column indexes are combined left to right in the order to be sorted followed by the row index value. At this point you have a homogeneous index for the columns of interest.
4) Sort the index using conventional methods.
5) The composite index now defines a set of ordered partitions on the data set. The individual partitions must be sorted on the complete record values but the scope of the sort has been reduced (which may be helpful on very large data sets). The partitions can also be used to speed up ordered record selection.
Tom K.
Jean-Claude,
<...snip...>
To give an example - to sort on col 2, 4 reverse, and then 3 could be done using something like this:
m2 = sortmap(col[2])
m3 = sortmap(col[3])
m4 = sortmap(col[4])
result = view.remap(m3.remap(reverse(m4)).remap(m2))
(with partial use, i.e. when fetching only a slice of the result, all sorts of neat tricks can be added, leading to behavior which I think resembles what you are describing above)
Except that the above "permutation stacking" is not exactly right... alas.
Are you looking for ideas on how to precondition or extend the column definition to improve the above calculation, or are you looking for effient methods of performing the above calculation without assuming any preconditioning or attributes on the col's?
I'm not familiar with the method you used to perform the column wise store. I'm I correct in assuming that the values in a row are determined by equivelent offsets in the column stores?
Tom K.
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