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Modern cloud-native OLAP databases adopt a storage-disaggregation architecture that separates the management of compu- tation 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 pro- cessing. 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 mecha- nism 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.
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SkyhookDM: Data Processing in Ceph with Programmable Storage
With ever larger data sets and cloud-based storage systems, it becomes increasingly more attractive to move computation to data, a common principle in big data systems. Historically, data management systems have pushed computation nearest to the data in order to reduce data moving through query execution pipelines. Computational storage approaches address the problem of both data reduction nearest the source as well as offloading some processing to the storage layer.
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- PAR ID:
- 10182302
- Date Published:
- Journal Name:
- USENIX login;
- Volume:
- 45
- Issue:
- 2
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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