DBenVis: A Visual Analytics System for Comparing DBMS Performance via Benchmark Programs

TL;DR

DBenVis is an interactive visual analytics system designed to compare DBMS performance using benchmark programs, supporting OLTP and OLAP workloads with innovative visualization techniques for better insights.

Contribution

The paper introduces DBenVis, a novel system that unifies and visualizes benchmark results across diverse DBMSs, including a new visualization idiom for query plans.

Findings

Effective visualization aids performance analysis.

System supports both OLTP and OLAP benchmarks.

Case studies confirm usability and effectiveness.

Abstract

Database benchmarking is an essential method for evaluating and comparing the performance characteristics of a database management system (DBMS). It helps researchers and developers to evaluate the efficacy of their optimizations or newly developed DBMS solutions. Also, companies can benefit by analyzing the performance of DBMS under specific workloads and leveraging the result to select the most suitable system for their needs. The proper interpretation of raw benchmark results requires effective visualization, which helps users gain meaningful insights. However, visualization of the results requires prior knowledge, and existing approaches often involve time-consuming manual tasks. This is due to the absence of a unified visual analytics system for benchmark results across diverse DBMSs. To address these challenges, we present DBenVis, an interactive visual analytics system that provides efficient and versatile benchmark results visualization. DBenVis is designed to support both online transaction processing (OLTP) and online analytic processing (OLAP) benchmarks. DBenVis provides an interactive comparison view, which enables users to perform an in-depth analysis of performance characteristics across various metrics among different DBMSs. Notably, we devise an interactive visual encoding idiom for the OLAP benchmark to represent a query execution plan as a tree. In the process of building a system, we propose novel techniques for parsing meaningful data from raw benchmark results and converting the query plan to a D3 hierarchical format. Through case studies conducted with domain experts, we demonstrate the efficacy and usability of DBenVis.