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Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems

Summary: TScout collects training data for self-driving DBMSs by annotating source with hooks and generating kernel-level BPF probes. It aggregates workload, config, internal state, and hardware metrics in a PostgreSQL-compatible DBMS, with ~7% overhead, yielding better ML behavior models for OLTP/OLAP. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
hc70ae79bfc4c7822
Venue
SIGMOD
Year
2022
Pagerank
5.4258674e-05
Overall Rank
7,915 | 46.81%
DOI
10.1145/3514221.3517845
PDF
Download (CC BY 4.0)

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{butrovich_sigmod22,
        title = {{Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems}},
        author = {Butrovich, Matthew and Lim, Wan Shen and Ma, Lin and Rollinson, John and Zhang, William and Xia, Yu and Pavlo, Andrew},
        series = {{SIGMOD} '22},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3514221.3517845},
        url = {https://dl.acm.org/doi/10.1145/3514221.3517845},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

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Outgoing Citations (Sorted by Pagerank)

Showing 20 of 20 cited papers.

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

Rank Cited Paper Year Venue Pagerank
21 Efficiently Compiling Efficient Query Plans for Modern Hardware 2011 VLDB 0.00056835296
78 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00036675568
224 Self-Driving Database Management Systems 2017 CIDR 0.00024011047
228 Fast Serializable Multi-Version Concurrency Control for Main-Memory Database Systems 2015 SIGMOD 0.00023915456
235 OLTP-Bench: An Extensible Testbed for Benchmarking Relational Databases 2014 VLDB 0.00023689995
237 Serializable Isolation for Snapshot Databases 2008 SIGMOD 0.00023652724
314 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.00021276452
322 Tuning Database Configuration Parameters with iTuned 2009 VLDB 0.00021034201
437 QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning 2019 VLDB 0.00018310278
461 Query-based Workload Forecasting for Self-Driving Database Management Systems 2018 SIGMOD 0.00017841988
555 SageDB: A Learned Database System 2019 CIDR 0.0001650754
560 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.00016408613
1,516 Automatically Indexing Millions of Databases in Microsoft Azure SQL Database 2019 SIGMOD 0.00010398346
1,940 Performance and Resource Modeling in Highly-Concurrent OLTP Workloads 2013 SIGMOD 9.3298436e-05
2,837 Query Performance Prediction for Concurrent Queries using Graph Embedding 2020 VLDB 7.9495917e-05
3,272 Estimating Cardinalities with Deep Sketches 2019 SIGMOD 7.4711788e-05
3,680 openGauss: An Autonomous Database System 2021 VLDB 7.1016555e-05
3,964 MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems 2021 SIGMOD 6.889374e-05
6,300 Mainlining Databases: Supporting Fast Transactional Workloads on Universal Columnar Data File Formats 2021 VLDB 5.8174924e-05
8,238 Demonstrating UDO: A Unified Approach for Optimizing Transaction Code, Physical Design, and System Parameters via Reinforcement Learning 2021 SIGMOD 5.3696738e-05
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