OPTIMIZATION OF LOCAL PARALLEL INDEX (LPI) IN PARALLEL/DISTRIBUTED DATABASE SYSTEMS

Abstract

The widespread growth of data has created many problems for businesses, such as delay
requests; in this paper, we propose several methods of partitioning an index B*Tree in multi-processor
machines in parallel/distributed database systems and collaboration between processors when executing
multi-queries. When optimizing, indexing automatically comes to mind; we distinguish two types of
indexing: B*Tree and Bitmap. Since the advent of multicore computers (multi processors) parallelism
becomes an indispensable part of optimization. Our work will focus on partitioning each table on three parts
following indexing key partitioning; each processor will host a partition of the index, and the first processor
that will finish will immediately take another partition of the index pending according to the priority. The
parallelism will reduce the CPU cost then reduces execution time; collaboration between processors will
further reduce these costs.