Guests Alexander Korotkov, Andres Freund, and Nikolay discuss the importance of benchmarking in databases. They cover topics like conducting experiments, system limits, query optimization, and realistic workloads. They also mention specific tools like PG bench and JMeter, as well as case studies and references to books on system performance.
Benchmarking is a crucial tool to explore system limits, make data-driven decisions, and compare different situations.
Crafting a workload using synthetic and real-world approaches helps bridge the gap between purely synthetic workloads and production workloads.
Deep dives
The Importance of Benchmarking
Benchmarking is a crucial tool in the database engineering field, allowing professionals to explore the limits of their systems, make data-driven decisions, and compare different situations. It serves as a stress test to push systems to their limits and identify areas for improvement. Additionally, benchmarking helps in regression testing, ensuring that system performance remains consistent or improves over time. Moreover, it aids in making critical decisions during urgent situations, such as choosing between different platform options. While benchmarking can also be used for marketing purposes, other goals like stress testing, decision-making, and regression testing are regarded as more important.
Collecting Realistic Workloads
Creating a proper workload for benchmarking is crucial. Synthetic workloads, like the ones generated by popular tools like pgbench, provide a basic understanding of system performance but may not accurately reflect real-world scenarios. To get closer to reality, mirroring production workloads is recommended, although challenges such as log collection and observability need to be considered. Crafting a workload using a combination of synthetic approaches and data extraction techniques helps bridge the gap between purely synthetic workloads and real production workloads. Microbenchmarks, such as running FIO tests and using sysbench, can also provide valuable insights into specific system components like disk I/O and CPU performance.
Artifacts Collection and Analysis
When conducting benchmarks, it is essential to collect and store all relevant artifacts for analysis. This includes monitoring dashboards, logs, system views, and other performance-related data. Automating the process of collecting these artifacts ensures accurate and reliable results. Analyzing the collected artifacts helps identify bottlenecks, understand system behavior, and make informed decisions. An understanding of system performance and database internals is key to interpreting the results of benchmarking accurately.
Key Considerations and Resources for Benchmarking
Benchmarking requires careful consideration and expertise. Resources like Brendan Gregg's book on system performance serve as valuable references for understanding benchmarking concepts and best practices. Case studies and comparisons between database management systems, such as those done by Mark Callahan, provide insightful information for those conducting their own benchmarks. Open-source projects like Hydra and tools like Clickhouse can be leveraged to compare different systems and reproduce benchmarking scenarios. Additionally, it is important to consider the limitations and risks associated with benchmarking, such as log collection challenges and the potential impact on production systems.