Benchmarking Query Execution Robustness
Benchmarks that focus on running queries on a well-tuned database system ignore a long-standing problem: adverse runtime conditions can cause database system performance to vary widely and unexpectedly. When the query execution engine does not exhibit resilience to these adverse conditions, addressing the resultant performance problems can contribute significantly to the total cost of ownership for a database system in over-provisioning, lost efficiency, and increased human administrative costs. For example, focused human effort may be needed to manually invoke workload management actions or fine-tune the optimization of specific queries.
We believe a benchmark is needed to measure query execution robustness, that is, how adverse or unexpected conditions impact the performance of a database system. We offer a preliminary analysis of barriers to query execution robustness and propose some metrics for quantifying the impact of those barriers. We present and analyze results from preliminary tests on four real database systems and discuss how these results could be used to increase the robustness of query processing in each case. Finally, we outline how our efforts could be expanded into a benchmark to quantify query execution robustness.
Keywordsrobust query processing robust query execution data warehouses operational business intelligence
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