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HYDRA: A dynamic big data regenerator

Sanghi, A and Sood, R and Singh, D and Haritsa, JR and Tirthapura, S (2018) HYDRA: A dynamic big data regenerator. In: 44th International Conference on Very Large Data Bases, VLDB 2018, 27 - 31 August 2018, Rio de Janeiro, pp. 1974-1977.

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Official URL: https://doi.org/10.14778/3229863.3236238


A core requirement of database engine testing is the ability to create synthetic versions of the customer's data warehouse at the vendor site. Prior work on synthetic data regeneration suffers from critical limitations with regard to (a) scaling to large data volumes, (b) handling complex query workloads, and (c) producing data on demand. In this demo, we present HYDRA, a workload-dependent dynamic data regenerator, that materially addresses these limitations. It introduces the concept of dynamic regeneration by constructing a minuscule memory-resident database summary that can on-the-fly regenerate databases of arbitrary size during query execution. Further, since the data is generated in memory, the velocity of generation can be closely regulated. Finally, to complement dynamic regeneration, Hydra also ensures that the process of summary construction is data-scale-free.

Item Type: Conference Paper
Publication: Proceedings of the VLDB Endowment
Publisher: Association for Computing Machinery
Additional Information: The copyright for this article belongs to the Association for Computing Machinery.
Keywords: Ability testing; Big data; Data warehouses; Regenerators, Complex queries; Data regenerators; Database engine; Database summary; Dynamic regeneration; Large data volumes; Memory-resident; Query execution, Query processing
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 19 Aug 2022 05:04
Last Modified: 19 Aug 2022 05:04
URI: https://eprints.iisc.ac.in/id/eprint/75974

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