Next generation flash storage will be armed with a substantial amount of computing power. In this paper, we investigate opportunities to utilize this computational capability to optimize Online Analytical Processing (OLAP) applications. We have directed our analysis at the performance of a subset of TPC-DS queries using Hadoop clusters and two database engines, SPARK-SQL and Presto. We model the expected speed-up achieved by offloading a few operations that are executed first within most SQL plans. Offloading these operations requires minimal cooperation from the database engine, and no changes to the existing plan. We show that the speed-up achieved varies significantly among queries and between engines, and that the queries benefiting the most are I/O heavy with high selectivity of the "needle in the haystack" variety. Our main contribution is estimating the speed-up anticipated from pushing the execution of a few key SQL building blocks (scan, filter, and project operations) to computational storage when using read optimized, columnar Parquet format files.

Storage disaggregation separates compute and storage to different nodes to allow for independent resource scaling and, thus, better hardware resource utilization. While disaggregation of hard-drives storage is a common practice, NVMe-SSD (i.e., PCIe-based SSD) disaggregation is considered more challenging. This is because SSDs are significantly faster than hard drives, so the latency overheads (due to both network and CPU processing) as well as the extra compute cycles needed for the offloading stack become much more pronounced. In this work, we characterize the overheads of NVMe-SSD disaggregation. We show that NVMe-over-Fabrics (NVMe-oF)—a recently released remote storage protocol specification—reduces the overheads of remote access to a bare minimum, thus greatly increasing the cost-efficiency of Flash disaggregation. Specifically, while recent work showed that SSD storage disaggregation via iSCSI degrades application-level throughput by 20%, we report on negligible performance degradation with NVMe-oF—both when using stress-tests as well as with a more-realistic KV-store workload.

Storage disaggregation separates compute and storage to different nodes in order to allow for independent resource scaling and thus, better hardware resource utilization. While disaggregation of hard-drives storage is a common practice, NVMe-SSD (i.e., PCIe-based SSD) disaggregation is considered more challenging. This is because SSDs are significantly faster than hard drives, so the latency overheads (due to both network and CPU processing) as well as the extra compute cycles needed for the offloading stack become much more pronounced.