Tobias Vincon
ABSTRACT
Current data-intensive systems suffer from scalability as they transfer massive amounts of data to the host DBMS to process it there. Novel near-data processing (NDP) DBMS architectures and smart storage can provably reduce the impact of raw data movement. However, transferring the result-set of an NDP operation may increase the data movement, and thus, the performance overhead. In this paper, we introduce a set of in-situ NDP result-set management techniques, such as spilling, materialization, and reuse. Our evaluation indicates a performance improvement of 1.13 × to 400 ×.
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Digital Library
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- Tobias Vincon, Lukas Weber, Arthur Bernhardt, Andreas Koch, and Ilia Petrov. 2020. nKV: Near-Data Processing with KV-Stores on Native Computational Storage. In Proc. DaMoN.Google Scholar
- Tobias Vincon, Lukas Weber, Arthur Bernhardt, Christian Riegger, Sergey Hardock, Christian Knoedler, Florian Stock, Leonardo Solis-Vasquez, Sajjad Tamimi, Andreas Koch, and Ilia Petrov. 2020. nKV in Action: Accelerating KV-Stores on Native Computational Storage with Near-Data Processing. PVLDB 12(2020).Google Scholar
- Lukas Weber, Tobias Vinçon, Christian Knödler, Leonardo Solis-Vasquez, Arthur Bernhardt, Ilia Petrov, and Andreas Koch. 2021. On the necessity of explicit cross-layer data formats in near-data processing systems. Distributed and Parallel Databases(2021).Google Scholar
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