Cache-Based Multi-Query Optimization for Data-Intensive Scalable Computing Frameworks

• Published in: Information systems frontiers Vol. 23; no. 1; pp. 35 - 51
• Main Authors: Michiardi, Pietro, Carra, Damiano, Migliorini, Sara
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-02-2021; Springer Nature B.V
• Subjects: Business and Management; Computation; Computer networks; Control; Employment; Information systems; IT in Business; Knapsack problem; Management of Computing and Information Systems; Operations Research/Decision Theory; Optimization; Optimization techniques; Queries; Query processing; Systems Theory; Workload; Caching; Large scale computing; Optimization
• ISSN: 1387-3326; 1572-9419
• DOI: 10.1007/s10796-020-09995-2

In modern large-scale distributed systems, analytics jobs submitted by various users often share similar work, for example scanning and processing the same subset of data. Instead of optimizing jobs independently, which may result in redundant and wasteful processing, multi-query optimization techniques can be employed to save a considerable amount of cluster resources. In this work, we introduce a novel method combining in-memory cache primitives and multi-query optimization, to improve the efficiency of data-intensive, scalable computing frameworks. By careful selection and exploitation of common (sub)expressions, while satisfying memory constraints, our method transforms a batch of queries into a new, more efficient one which avoids unnecessary recomputations. To find feasible and efficient execution plans, our method uses a cost-based optimization formulation akin to the multiple-choice knapsack problem. Extensive experiments on a prototype implementation of our system show significant benefits of worksharing for both TPC-DS workloads and detailed micro-benchmarks.