DBMS | Language | Cloud Native | Github Repo | Affliated Organization |
---|---|---|---|---|
ScyllaDB | C++ | Yes | https://github.com/scylladb/scylladb | https://www.scylladb.com |
MongoDB | C++ | Yes | https://github.com/mongodb/mongo | https://www.mongodb.com |
Cassandra | Java | Yes | https://github.com/apache/cassandra | https://www.datastax.com/ |
Redis | C | Yes | https://github.com/redis/redis | https://redis.io/ |
OrientDB | Java | Yes | https://github.com/orientechnologies/orientdb | https://orientdb.org/ |
Mohamed A.S, Lyublena A, Venkatesh R, Amr E-H, Zhongxian G, etc. (2014) Orca: a modular query optimizer architecture for big data SIGMOD '14: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, June 2014, pp. 337-348, PDF
Mario A.M, Yuan S, Michael K and Saman K.H (2015) ALgorithm selection for black-box continuous optimization problems: a survey on methods and challenges Information Sciences, Vol. 317(1), pp. 224-245, PDF
Viktor L, Andrey G, Atanas M, Peter B, Alfons K and Thomas N (2015) How good are query optimizers, really? Proceedings of the VLDB Endowment, Vol. 9(3), pp. 204-215, PDF
Ryan M, Parimarjan N, Hongzi M, Chi Z, Mohammad A, Tim K, Olga P and Nesime T (2019) Neo: a learned query optimizer Proceedings of the VLDB Endowment, Vol 12(11), pp. 1705-1718 PDF
Ryan M, Parimarjan N, Hongzi M, Nesime T, Mohammad A and Time K (2021) Bao: making learned query optimization practical SIGMOD '21: Proceedings of the 2021 International Conference on Management of Data, June 2021, pp. 1275-1288, PDF, Slides
- Bao, a learned query optimizer for PostgreSQL
- Applying Bao to distributed systems
- Ten years of improvements in PostgreSQL's optimizer
- Most influential database papers
Zongheng Y, Wei-Lin C, Sifei L, Gautam M, Michael L and Ion S (2022) Balsa: learning a query optimizer without expert demonstrations SIGMOD '22: Proceedings of the 2022 International Conference on Management of Data, June 2022, pp. 931-944 PDF
Rong Z, Wei C, Bolin D, Xingguang C, Andreas P, Ziniu W and Jingren Z (2023) Lero: a learning-to-rank query optimizer Proceedings of the VLDB Endowment, Vol. 16(6), pp. 1466-1479 PDF
Xu C, Haitian C, Zibo L, Shuncheng L, Jinghong W, Kai Z, Han S and Kai Z (2023) LEON: a new framework for ML-aided query optimization Proceedings of the VLDB Endowment, Vol. 16(9), pp. 2261-2273 PDF
Tianyi C, Jun G, Hedui C and Yaofeng T (2023) Loger: a learned optimizer towards generating efficient and robust query execution plans Proceedings of the VLDB Endowment, Vol. 16(7), pp. 1777-1789 PDF
Xiang Y, Chengliang C, Guoliang L and Jiabin L (2022) Cost-based or learning-based?: a hybrid query optimizer for query plan selection Proceedings of the VLDB Endowment, Vol. 15(13), pp. 3924-3936 PDF
Christoph A, Nesime T, David C, Zhenggang X and Prithviraj P (2023) AutoSteer: learned query optimization for any SQL database Proceedings of the VLDB Endowment, Vol. 16(12), pp. 3515-3527, PDF
Rong Z, Lianggui W, Wenqing W, Di W, Jiazheng P, Yifan W, etc. (2024) PilotScope: steering databases with machine learning drivers Proceedings of the VLDB Endowment, Vol 17(5), pp. 980-993 PDF
Claude L, Pavel S and Kurt S (2023) Is your learned query optimizer behaving as you expected? a machine learning perspective PVLDB, Vol. 17, 2023-2024, PDF
Guy Lohman (2014) Is query optimization a "solved" problem? ACM Sigmod Blog PDF
Olivier C and Lihong L (2011) An empirical evaluation of thompson sampling NIPS'11: Proceedings of the 24th International Conference on Neural Information Processing Systems, Dec. 2011, pp. 2249-2257 PDF