SAQO: Empowering Computational Storage Device for Efficient SQL Query Acceleration

It is efficient to accelerate SQL queries by utilizing Computational Storage Device (CSD). Since current CSDs have limited processing capacity and cannot accelerate all SQL queries, existing schemes typically offload filter operators to significantly reduce data movement to the computational storage device. However, leaving all filter operators in SQL queries to the computational storage device directly leads to poor performance. To address these problems, we propose the Self-Adaptive Query Offloading (SAQO) strategy for offloading filtering operators. The SAQO strategy dynamically chooses the best execution location for filtering operators by considering the selectivity of the operator, the computational storage device's load, and the task characteristics, which ultimately accelerates the speed of executing data query tasks. The real-world applications Spark SQL and TPC-H are used to evaluate the SAQO strategy. The SAQO strategy reduces the execution time of data query tasks by 58.7% compared to the host execution strategy and 9.4% compared to the state-of-the-art offloading strategy.