DBIx::SpatialKeys - Perl module for the management of spatial keys.
use DBIx::SpatialKey; $key = new DBIx::SpatialKey 'binary_morton', 99, 99; $index = $key->index(45, 60); $upperleft = $key->index(40, 50); $lowerright = $key->index(50, 70); # now $upperleft lt $index lt $lowerright
Ever had the problem of managing multi-dimensional (spatial) data but your database only had one-dimensional indices (b-tree etc.)? Queries like
select data from table where latitude > 40 and latitude < 50 and longitude> 50 and longitude< 70;
are quite inefficient, unless longitude and latitude are part of the same spatial index (e.g. a r-tree).
Spatial keys are a method to map multi-dimensional data into single-dimensional indices that most databases support. This module only support one very specialised mapping method which is very fast but only efficient on integer data. (If you need other indexing methods, like two-dimensional Hilbert indices just ask). It works like this:
First, generate a key object that is used to map data into the index. Every argument specifies a dimension, and its value is the higest value allowed for that dimension (i.e. "num-1"). To index rgb556 data you would use the following key:
$key = new DBIx::SpatialKey 'binary_morton', 31, 31, 63;
If you want to generate the key for the colour <10,11,40> you call the index function:
$idx = $key->index(10,11,40);
The returned string will take just as much bits as is necessary to represent one key value (in this example, 5+5+6 bits, i.e. two bytes), and could in theory be used to recover the data again (but I haven't written such function yet).
If you want to search for a colour that is similar to this one in the database you would use two keys, one for the lower bound and one for the upper bound:
$lo = $key->index( 9,10,39); $hi = $key->index(11,12,41); possible_keys = select ... where idx >= $lo and idx <= $hi;
This would return all similar colours (and some additional ones, so you still have to test each one seperately!). If you had millions of colours in your database this would be very efficient, as it saves on the number of database operations (just a single range op) and can cut down the number of disk accesses considerably.
In the future I plan to add other keys (like normal morton keys). If you know how to generalize hilbert curves to the n-dimensional case please contact me, as I'm too dumb to know.
Also, a range function that would return more than one range (to give more precise searches) will be added in the future.
Create new key object of type 'binary_morton'. The trailing arguments give the maximum value for each dimension.
Return a key (a binary string) containing the packed dimensions.
Unpack the key into its components again.
Marc Lehmann <firstname.lastname@example.org>.