Optimizing join index based join processing: a graph partitioning approach

Abstract

The cost of join computation, which uses a join index in a sequential system with limited buffer space, depends primarily on the page access sequence used to fetch the pages of the base relations. We introduce a graph partitioning model that will minimize the length of the page access sequence thus minimizing the redundant I/O, given a fixed buffer. Experiments with Sequoia 2000 data sets show that the graph partitioning method outperforms the existing methods based on sorting and online clustering, particularly for a small number of buffers and high join selectivity.