Nimble join: A parallel star join for main memory column‐stores

Abstract

SummaryColumn‐stores perform significantly better than row‐stores on analytical workloads such as those found in data warehouses, decision support, and business intelligence applications. As mainstream data warehouses are growing into multi‐terabyte range, decision support queries should be processed in parallel to achieve adequate performance. Researchers of the column‐oriented join queries assume an unlimited reserve of main memory and focus on minimising execution time. However, some analytics require a large amount of memory to calculate intermediate results, and some interactive analytics require a fast initial response time even though queries need to process a large amount of data. Motivated by these requirements, we present a new progressive parallel star algorithm for main memory column‐stores known as “Nimble Join.” Equipped with multi‐attribute array table and a novel progressive materialisation technique, Nimble Join requires half the memory space and has two times faster initial response whilst having comparable execution time to the existing algorithm.