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

摘要

Column-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.