Contender: A Resource Modeling Approach for Concurrent Query Performance Prediction

Predicting query performance under concurrency is a difficult task that has many applications in capacity planning, cloud computing, and batch scheduling. We introduce Contender, a new resource-modeling approach for predicting the concurrent query perfor-mance of analytical workloads. Contender’s unique feature is that it can generate effective predictions for both static as well as ad-hoc or dynamic workloads with low training requirements. These characteristics make Contender a practical solution for real-world deployment. Contender relies on models of hardware resource contention to predict concurrent query performance. It introduces two key met-rics, Concurrent Query Intensity (CQI) and Query Sensitivity (QS), to characterize the impact of resource contention on query interac-tions. CQI models how aggressively concurrent queries will use the shared resources. QS defines how a query’s performance changes as a function of the scarcity of resources. Contender integrates these two metrics to effectively estimate a query’s concurrent exe-cution latency using only linear time sampling of the query mixes. Contender learns from sample query executions (based on known query templates) and uses query plan characteristics to gen-erate latency estimates for previously unseen templates. Our ex-perimental results, obtained from PostgreSQL/TPC-DS, show that Contender’s predictions have an error of 19 % for known templates and 25 % for new templates, which is competitive with the state-of-the-art while requiring considerably less training time. 1