Electrical and Computer Engineering
21st International Conference on Extending Database Technology (EDBT)
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Journal or Book Title
Proceedings of the 21st International Conference on Extending Database Technology (EDBT)
March 26-29, 2018
A core requirement of database engine testing is the ability to create synthetic versions of the customer’s data warehouse at the vendor site. A rich body of work exists on synthetic database regeneration, but suffers critical limitations with regard to: (a) maintaining statistical fidelity to the client’s query processing, and/or (b) scaling to large data volumes. In this paper, we present HYDRA, a workload-dependent database regenerator that leverages a declarative approach to data regeneration to assure volumetric similarity, a crucial aspect of statistical fidelity, and materially improves on the prior art by adding scale, dynamism and functionality. Specifically, Hydra uses an optimized linear programming (LP) formulation based on a novel regionpartitioning approach. This spatial strategy drastically reduces the LP complexity, enabling it to handle query workloads on which contemporary techniques fail. Second, Hydra incorporates deterministic post-LP processing algorithms that provide high efficiency and improved accuracy. Third, Hydra introduces the concept of dynamic regeneration by constructing a minuscule database summary that can on-the-fly regenerate databases of arbitrary size during query execution, while obeying volumetric specifications derived from the query workload. A detailed experimental evaluation on standard OLAP benchmarks demonstrates that Hydra can efficiently and dynamically regenerate large warehouses that accurately mimic the desired statistical characteristics.
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Sanghi, Anupam; Sood, Raghav; Haritsa, Jayant; and Tirthapura, Srikanta, "Scalable and Dynamic Regeneration of Big Data Volumes" (2018). Electrical and Computer Engineering Conference Papers, Posters and Presentations. 52.