# Cheetah: Accelerating Database Queries with Switch Pruning

arXiv: https://arxiv.org/abs/2004.05076 · HTML: https://mlsanity.com/paper/2004.05076/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2020.

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

- [Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs](https://mlsanity.com/paper/2605.05607/) (2026)

## Abstract (arXiv)

Modern database systems are growing increasingly distributed and struggle to reduce query completion time with a large volume of data. In this paper, we leverage programmable switches in the network to partially offload query computation to the switch. While switches provide high performance, they have resource and programming constraints that make implementing diverse queries difficult. To fit in these constraints, we introduce the concept of data pruning -- filtering out entries that are guaranteed not to affect output. The database system then runs the same query but on the pruned data, which significantly reduces processing time. We propose pruning algorithms for a variety of queries. We implement our system, Cheetah, on a Barefoot Tofino switch and Spark. Our evaluation on multiple workloads shows 40 - 200% improvement in the query completion time compared to Spark.