Cheetah: Accelerating Database Queries with Switch Pruning

Summary: Offloads part of query processing to programmable switches via data pruning to drop entries guaranteed not to affect results. Cheetah implements pruning algorithms on Barefoot Tofino and Spark, delivering 40–200% faster query completion than Spark across diverse workloads. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: h0de0ea845945531e
Venue: SIGMOD
Year: 2020
Pagerank: 5.9338314e-05
Overall Rank: 5,958 | 59.95%
DOI: 10.1145/3318464.3389698

Incoming Non-self Citations Over Time

Authors

1. Muhammad Tirmazi (Harvard University)
2. Ran Ben Basat (Harvard University)
3. Jiaqi Gao (Harvard University)
4. Minlan Yu (Harvard University)

BibTeX Citation

@inproceedings{tirmazi_sigmod20,
        title = {{Cheetah: Accelerating Database Queries with Switch Pruning}},
        author = {Tirmazi, Muhammad and Basat, Ran Ben and Gao, Jiaqi and Yu, Minlan},
        series = {{SIGMOD} '20},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3318464.3389698},
        url = {https://dl.acm.org/doi/10.1145/3318464.3389698},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 6 of 6 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
4,173	In-Network Support for Transaction Triaging	2021	VLDB	6.7593239e-05
7,939	GraphINC: Graph Pattern Mining at Network Speed	2023	SIGMOD	5.4223464e-05
8,157	P4DB - The Case for In-Network OLTP	2022	SIGMOD	5.3880191e-05
9,687	DPDPU: Data Processing with DPUs	2025	CIDR	5.1408809e-05
10,584	P4KVS: A Role-Replica Separation Offloading Method to Achieve In-Network Consistency for KV Stores Based on P4 Switches	2026	SIGMOD	4.9793485e-05
11,676	Templating Shuffles	2023	CIDR	4.9793485e-05

Outgoing Citations (Sorted by Pagerank)

Showing 14 of 14 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
23	Spark SQL: Relational Data Processing in Spark	2015	SIGMOD	0.00055406774
43	A Comparison of Approaches to Large-Scale Data Analysis	2009	SIGMOD	0.00045546775
1,139	Query Processing on Smart SSDs: Opportunities and Challenges	2013	SIGMOD	0.00011860667
1,454	Fast Computation of Database Operations using Graphics Processors	2004	SIGMOD	0.00010601431
1,778	Ibex—An Intelligent Storage Engine with Support for Advanced SQL Off-loading	2014	VLDB	9.6619986e-05
2,230	Data Warehousing and Analytics Infrastructure at Facebook	2010	SIGMOD	8.799877e-05
2,319	Hardware Acceleration for Spatial Selections and Joins	2003	SIGMOD	8.6460188e-05
2,347	Filter Before You Parse: Faster Analytics on Raw Data with Sparser	2018	VLDB	8.6012176e-05
2,464	Output-optimal Parallel Algorithms for Similarity Joins	2017	PODS	8.4260608e-05
2,576	Secure Database-as-a-Service with Cipherbase	2013	SIGMOD	8.2748237e-05
2,815	GPL: A GPU-based Pipelined Query Processing Engine	2016	SIGMOD	7.9777435e-05
4,931	FPGA-based Data Partitioning	2017	SIGMOD	6.348544e-05
4,996	The Case for Network-Accelerated Query Processing	2019	CIDR	6.3226807e-05
7,282	Lowering the Latency of Data Processing Pipelines Through FPGA based Hardware Acceleration	2020	VLDB	5.5666052e-05

Semantically Similar Papers

#	Overall Rank	Paper	Year	Venue
1	12,764	Slicing Long-Running Queries	2010	VLDB
2	1,028	High-Speed Query Processing over High-Speed Networks	2016	VLDB
3	13,119	Adaptive Distributed Query Processing	2003	VLDB
4	12,482	A Partitioning Framework for Aggressive Data Skipping	2014	VLDB
5	1,036	Fine-grained Partitioning for Aggressive Data Skipping	2014	SIGMOD
6	9,621	Parallelizing Query Optimization on Shared-Nothing Architectures	2016	VLDB
7	8,269	Accelerate Distributed Joins with Predicate Transfer	2025	SIGMOD
8	8,535	New Query Optimization Techniques in the Spark Engine of Azure Synapse	2022	VLDB
9	4,996	The Case for Network-Accelerated Query Processing	2019	CIDR
10	1,837	Cheetah: A High Performance, Custom Data Warehouse on Top of MapReduce	2010	VLDB