Disk allocation methods for parallelizing grid files

The grid file [1] is a well known access method for multi-dimensional and spatial data. The response time needed to process path and range queries on the grid file access method can be improved significantly by distributing the data pages over multiple disks. This paper explores the disk allocation methods used to allocate the data pages of grid file among a set of disks, which can be accessed in parallel. Given N disks, a perfect allocation will speed up the processing of each query by a factor of N in this environment. The paper shows that no disk allocation is perfect for the set of all orthogonal range queries, even on uniformly distributed read-only data. We then introduce two families of allocation methods, namely the Linear allocation method and the Lattice allocation method, which are perfect for a large collection of interesting path queries (rows, columns, diagonals, anti-diagonals) and range queries (small rectangles), on an interesting set of data distributions. We address the issues in extending disk allocation methods to general data distributions with random updates. Finally, we provide experimental results on the performance of the proposed methods and other well known disk allocation methods on different query sets, data distributions and data set sizes.