Modeling Analytics for Computational Storage
Veronica Lagrange Moutinho Dos Reis, Harry Li, Anahita Shayesteh · 2020
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.