# PushdownDB: Accelerating a DBMS using S3 Computation

arXiv: https://arxiv.org/abs/2002.05837 · HTML: https://mlsanity.com/paper/2002.05837/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2020.

## Built on by (most cited first, 1 of 1)

- [Accelerating Transfer Learning with Near-Data Computation on Cloud Object Stores](https://mlsanity.com/paper/2210.08650/) (2022)

## Abstract (arXiv)

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.