A systematic approach to benchmarking SQL processing engines on AWS

This article presents a systematic framework for benchmarking SQL processing engines on AWS using Apache JMeter, helping organizations select the right analytics solution for large-scale data processing.

• AWS offers diverse SQL solutions: Athena (serverless), Redshift (data warehouse), EMR (managed open source), EKS (self-managed), and Marketplace partners
• Evaluation challenges include creating production-like environments, developing realistic test datasets, and replicating real-world query patterns
• Petabyte-scale testing requires careful resource management, data distribution analysis, concurrency handling, and comprehensive metrics collection
• Traditional benchmarks (TPC-DS, TPC-H) have limitations; workload-specific testing reveals performance characteristics better
• Apache JMeter provides extensible architecture, concurrent workload simulation, built-in metrics, and CI/CD integration for SQL testing
• Testing methodology includes query pattern selection, data volume variations, progressive concurrency testing, and weighted query distribution
• Sequential testing establishes baselines; concurrent testing simulates real-world multi-user scenarios with varying query weights
• JMeter setup requires EC2 instance, Java 17+ LTS, JDBC drivers for target SQL engines, and appropriate security configuration
• Post-test analysis compares performance across engines, factors in costs, and refines test plans based on findings

This framework reduces proof-of-concept cycles from months to weeks and enables data-driven SQL engine selection decisions aligned with specific workload requirements.