FlexpushdownDB: rethinking computation pushdown for cloud OLAP DBMSs
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Author(s)
Yang, Yifei
Yu, Xiangyao
Serafini, Marco
Aboulnaga, Ashraf
Stonebraker, Michael
Date Issued
July 10, 2024
Journal
The VLDB Journal
Publisher
Springer Berlin Heidelberg
Citation
Yang, Y., Yu, X., Serafini, M. et al. FlexpushdownDB: rethinking computation pushdown for cloud OLAP DBMSs. The VLDB Journal 33, 1643–1670 (2024).
Version
Author's final manuscript
Abstract
Modern cloud-native OLAP databases adopt a storage-disaggregation architecture that separates the management of computation and storage. A major bottleneck in such an architecture is the network connecting the computation and storage layers. Computation pushdown is a promising solution to tackle this issue, which offloads some computation tasks to the storage layer to reduce network traffic. This paper presents FlexPushdownDB (FPDB), where we revisit the design of computation pushdown in a storage-disaggregation architecture, and then introduce several optimizations to further accelerate query processing. First, FPDB supports hybrid query execution, which combines local computation on cached data and computation pushdown to cloud storage at a fine granularity. Within the cache, FPDB uses a novel Weighted-LFU cache replacement policy that takes into account the cost of pushdown computation. Second, we design adaptive pushdown as a new mechanism to avoid throttling the storage-layer computation during pushdown, which pushes the request back to the computation layer at runtime if the storage-layer computational resource is insufficient. Finally, we derive a general principle to identify pushdown-amenable computational tasks, by summarizing common patterns of pushdown capabilities in existing systems, and further propose two new pushdown operators, namely, selection bitmap and distributed data shuffle. Evaluation on SSB and TPC-H shows each optimization can improve the performance by 2.2 × , 1.9 × , and 3 × respectively.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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DOI of Published Version
https://doi.org/10.1007/s00778-024-00867-8
