Result-Set Management for NDP Operations on Smart Storage
Tobias Vinçon, Christian Knoedler, Arthur Bernhardt, Leonardo Solis-Vasquez, Lukas Weber, Andreas Koch, Ilia Petrov · Data Management on New Hardware · 2022
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 ×.