Characterizing Resource Sensitivity of Database Workloads

The performance of real world database workloads is heavily influenced by the resources available to run the workload. Therefore, understanding the performance impact of changes in resource allocations on a workload is key to achieving predictable performance. In this work, we perform an in-depth study of the sensitivity of several database workloads, running on Microsoft SQL Server on Linux, to resources such as cores, caches, main memory, and non-volatile storage. We consider transactional, analytical, and hybrid workloads that model real-world systems, and use recommended configurations such as storage layouts and index organizations at different scale factors. Our study lays out the wide spectrum of resource sensitivities, and leads to several findings and insights that are highly valuable to computer architects, cloud DBaaS (Database-as-a-Service) providers, database researchers, and practitioners. For instance, our results indicate that throughput improves more with more cores than with more cache beyond a critical cache size; depending upon the compute vs. I/O activity of a workload, hyper-threading may be detrimental in some cases. We discuss our extensive experimental results and present insights based on a comprehensive analysis of query plans and various query execution statistics.