Design Trade-offs for a Robust Dynamic Hybrid Hash Join (Extended Version)

TL;DR

This paper analyzes and optimizes the design trade-offs of a robust, dynamic Hybrid Hash Join algorithm, enhancing performance in database systems through extensive experiments and new partitioning strategies.

Contribution

It introduces new techniques for partition management and dynamic spilling in Hybrid Hash Join, with experimental validation in a real database system.

Findings

Optimal number of partitions improves join performance.

Partition insertion techniques enhance memory utilization.

Dynamic spilling algorithms outperform previous methods.

Abstract

The Join operator, as one of the most expensive and commonly used operators in database systems, plays a substantial role in Database Management System (DBMS) performance. Among the many different Join algorithms studied over the last decades, Hybrid Hash Join (HHJ) has proven to be one of the most efficient and widely-used join algorithms. While the performance of HHJ depends largely on accurate statistics and information about the input relations, it may not always be practical or possible for a system to have such information available. The design of HHJ depends on many details to perform well. This paper is an experimental and analytical study of the trade-offs in designing a robust and dynamic HHJ operator. We revisit the design and optimization techniques suggested by previous studies through extensive experiments, comparing them with other algorithms designed by us or used in related studies. We explore the impact of the number of partitions on the performance of HHJ and propose a lower bound and a default value for the number of partitions. We continue by designing and evaluating different partition insertion techniques to maximize memory utilization with the least CPU cost. In addition, we consider a comprehensive set of algorithms for dynamically selecting a partition to spill and compare the results against previously published studies. We then present two alternative growth policies for spilled partitions and study their effectiveness using experimental and model-based analyses. These algorithms have been implemented in the context of Apache AsterixDB and evaluated under different scenarios such as variable record sizes, different distributions of join attributes, and different storage types, including HDD, SSD, and Amazon Elastic Block Store (Amazon EBS).