Modeling the complex interactions that arise when query workloads share computing resources and data is challenging albeit critical for a number of tasks such as Quality of Service (QoS) management in the emerging cloud-based database platforms, effective resource allocation for time-sensitive processing tasks, and user-experience management for interactive systems. In our work, we develop practical models for query performance prediction (QPP) for heterogeneous, concurrent query workloads in analytical databases.<br/> <br/> Specifically, we propose and evaluate several learning-based solutions for QPP. We first address QPP for static workloads that originate from well-known query classes. Then, we propose a more general solution for dynamic, ad hoc workloads. Finally, we address the issue of generalizing QPP for different hardware platforms such as those available from cloud-service providers.<br/> <br/> Our solutions use a combination of isolated and concurrent query execution samples, as well as new query workload features and metrics that can capture how different query classes behave for various levels of resource availability and contention. We implemented our solutions on top of PostgreSQL and evaluated them experimentally by quantifying their effectiveness for analytical data and workloads, represented by the established benchmark suites TPC-H and TPC-DS. The results show that learning-based QPP can be both feasible and effective for many static and dynamic workload scenarios.
Duggan, Jennie M.,
"Query Performance Prediction for Analytical Workloads"
(2013).
Computer Science Theses and Dissertations.
Brown Digital Repository. Brown University Library.
https://doi.org/10.7301/Z0TH8K16
