This paper studies the effectiveness of pushing parts of DBMS analytics queries into the Simple Storage Service (S3) engine of Amazon Web Services (AWS), using a recently released capability called S3 Select. We show that some DBMS primitives (filter, projection, aggregation) can always be cost-effectively moved into S3. Other more complex operations (join, top-K, group-by) require reimplementation to take advantage of S3 Select and are often candidates for pushdown. We demonstrate these capabilities through experimentation using a new DBMS that we developed, PushdownDB. Experimentation with a collection of queries including TPC-H queries shows that PushdownDB is on average 30% cheaper and 6.7X faster than a baseline that does not use S3 Select.
@article{arxiv.2002.05837,
title = {PushdownDB: Accelerating a DBMS using S3 Computation},
author = {Xiangyao Yu and Matt Youill and Matthew Woicik and Abdurrahman Ghanem and Marco Serafini and Ashraf Aboulnaga and Michael Stonebraker},
journal= {arXiv preprint arXiv:2002.05837},
year = {2020}
}