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: 5975
Venue: SIGMOD
Year: 2020
Pagerank: 6.0604381e-05
Overall Rank: 5,871 | 59.73%
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
4,626	In-Network Support for Transaction Triaging	2021	VLDB	6.5985783e-05
7,998	P4DB - The Case for In-Network OLTP	2022	SIGMOD	5.5093728e-05
8,179	GraphINC: Graph Pattern Mining at Network Speed	2023	SIGMOD	5.472762e-05
9,511	DPDPU: Data Processing with DPUs	2025	CIDR	5.25736e-05
10,388	P4KVS: A Role-Replica Separation Offloading Method to Achieve In-Network Consistency for KV Stores Based on P4 Switches	2026	SIGMOD	5.093636e-05
11,360	Templating Shuffles	2023	CIDR	5.093636e-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
24	Spark SQL: Relational Data Processing in Spark	2015	SIGMOD	0.00054865648
44	A Comparison of Approaches to Large-Scale Data Analysis	2009	SIGMOD	0.00046055057
1,130	Query Processing on Smart SSDs: Opportunities and Challenges	2013	SIGMOD	0.00012052236
1,452	Fast Computation of Database Operations using Graphics Processors	2004	SIGMOD	0.00010745803
1,773	Ibex—An Intelligent Storage Engine with Support for Advanced SQL Off-loading	2014	VLDB	9.7895875e-05
2,191	Data Warehousing and Analytics Infrastructure at Facebook	2010	SIGMOD	8.9780154e-05
2,283	Hardware Acceleration for Spatial Selections and Joins	2003	SIGMOD	8.8099424e-05
2,414	Filter Before You Parse: Faster Analytics on Raw Data with Sparser	2018	VLDB	8.6078841e-05
2,462	Output-optimal Parallel Algorithms for Similarity Joins	2017	PODS	8.5487602e-05
2,537	Secure Database-as-a-Service with Cipherbase	2013	SIGMOD	8.4549061e-05
2,848	GPL: A GPU-based Pipelined Query Processing Engine	2016	SIGMOD	8.0538815e-05
4,840	FPGA-based Data Partitioning	2017	SIGMOD	6.483442e-05
4,879	The Case for Network-Accelerated Query Processing	2019	CIDR	6.4669093e-05
7,170	Lowering the Latency of Data Processing Pipelines Through FPGA based Hardware Acceleration	2020	VLDB	5.6840081e-05

Semantically Similar Papers

#	Overall Rank	Paper	Year	Venue
1	12,473	Slicing Long-Running Queries	2010	VLDB
2	1,076	High-Speed Query Processing over High-Speed Networks	2016	VLDB
3	12,829	Adaptive Distributed Query Processing	2003	VLDB
4	12,191	A Partitioning Framework for Aggressive Data Skipping	2014	VLDB
5	9,446	Parallelizing Query Optimization on Shared-Nothing Architectures	2016	VLDB
6	1,044	Fine-grained Partitioning for Aggressive Data Skipping	2014	SIGMOD
7	8,721	Accelerate Distributed Joins with Predicate Transfer	2025	SIGMOD
8	8,439	New Query Optimization Techniques in the Spark Engine of Azure Synapse	2022	VLDB
9	4,879	The Case for Network-Accelerated Query Processing	2019	CIDR
10	1,791	Cheetah: A High Performance, Custom Data Warehouse on Top of MapReduce	2010	VLDB