ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning (Extended Version) - Siddiqui, Tarique; Jo, Saehan; Wu, Wentao; Wang, Chi; Narasayya, Vivek; Chaudhuri, Surajit (ACM SIGMOD 2022, 2022)
Today’s database systems include index advisors that recommend an appropriate set of indexes for an input workload. Since index tuning on large and complex workloads can be resource-intensive and time-consuming, workload compression techniques have been proposed to improve the scalability of index tuning. Workload compression techniques aim to efficiently identify a small subset of queries in the workload to tune such that the indexes recommended when tuning the compressed workload give similar performance improvements as when tuning the input workload. In this paper, we propose ISUM, a new workload compression algorithm that is based on two key ideas: a low-overhead technique for estimating the improvement in performance of the input workload when a subset of queries is selected for index tuning, and a novel method for concisely representing information across queries in the workload that improves scalability by avoiding pairwise comparisons between queries when choosing the set of queries to tune. Our evaluation over industry benchmarks and real-world customer workloads shows that ISUM results in a 1.4× of median and 2× of maximum performance improvements for the input workload when compared to prior techniques over similar compressed workload sizes.