CORD: Parallelizing Query Processing Across Multiple Computational Storage Devices

Query processing on large-scale scientific datasets often suffers from performance bottlenecks due to significant data transfers between storage nodes and applications in decoupled distributed storage environments. This issue is particularly pronounced in high-selectivity queries where unnecessary data is transferred between the storage plane and the compute plane. To tackle this challenge, we introduce the integration of SmartSSDs, functioning as Computational Storage Devices (CSDs), into the storage layer. By offloading simple filter-projection operations to these CSDs, we significantly reduce data transfer bottlenecks, leading to lower query latency and higher throughput. Our novel framework, CORD (parallelizing query processing across multiple Computational stORage Devices), facilitates parallel query execution across multiple CSDs while considering data locality. CORD is compatible with any decoupled storage system equipped with CSDs. Our extensive empirical evaluation demonstrates that CORD achieves up to $93 \times$ speedup for high-selectivity queries compared to traditional (compute plane) execution strategy and offers a further $1.64 \times$ speedup in cases of uneven data distribution. Additionally, we present two optimizations for batch query processing. Results from our experiments with 4 CSDs reveal substantial performance improvements provided by the optimizations embedded in CORD.