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AQUOMAN: An Analytic-Query Offloading Machine

AQUOMAN: An Analytic-Query Offloading Machine
Xu, ShuotaoBourgeat, ThomasHuang, TianhaoKim, HojunLee, SungjinArvind, Arvind
DGIST Authors
Lee, Sungjin
Issue Date
IEEE/ACM International Symposium on Microarchitecture, 386-399
Analytic workloads on terabyte data-sets are often run in the cloud, where application and storage servers are separate and connected via network. In order to saturate the storage bandwidth and to hide the long storage latency, such a solution requires an expensive server cluster with sufficient aggregate DRAM capacity and hardware threads. An alternative solution is to push the query computation into storage servers. In this paper we present an in-storage Analytics QUery Offloading MAchiNe (AQUOMAN) to offload most SQL oper- ators, including multi-way joins, to SSDs. AQUOMAN executes Table Tasks, which apply a static dataflow graph of SQL operators to relational tables to produce an output table. Table Tasks use a streaming computation model, which allows AQUOMAN to process queries with a reasonable amount of DRAM for intermediate results. AQUOMAN is a general analytic query processor, which can be integrated in the database software stack transparently. We have built a prototype of AQUOMAN in FPGAs, and using TPC-H benchmarks on 1TB data sets, shown that a single instance of 1TB AQUOMAN disk, on average, can free up 70% CPU cycles and reduce DRAM usage by 60%. One way to visualize this saving is to think that if we run queries sequentially and ignore inter-query page cache reuse, MonetDB running on a 4-core, 16GB-DRAM machine with AQUOMAN augmented SSDs performs, on average, as well as a MonetDB running on a 32-core, 128GB-DRAM machine with standard SSDs. © 2020 IEEE Computer Society. All rights reserved.
IEEE Computer Society
Related Researcher
  • Author Lee, Sungjin Data-Intensive Computing Systems Laboratory
  • Research Interests Computer System, System Software, Storage System, Non-volatile Memory, Flash-based SSD, Distributed Storage Systems
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Department of Electrical Engineering and Computer ScienceData-Intensive Computing Systems Laboratory2. Conference Papers

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