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We have built openGauss, an enterprise-grade open-source database system. openGauss has fulfilled its design goal of high performance, high availability, high security, and high intelligence. For high performance, it leverages NUMA (non-uniform memory access)-aware data access among multiple cores to enable efficient concurrent transaction processing, and symmetric multi-processing to make use of parallel processing resources adaptively. Moreover, memory-optimized tables (MOTs) are designed to put everything in memory. For high availability, a three-tier pooling architecture that shares storage among the master and standby instances is proposed to achieve availability at 99.99%, containing both a distributed memory service (DMS) and a distributed storage service (DSS). For high security, it is a fully encrypted database with safe storage features, efficient complex querying, and tamper-proof. For high intelligence, an AI-based optimizer in the kernel and a self-driving platform named DBMind are demonstrated to achieve better performance and greater user-friendliness. openGauss has served over 150 enterprises and institutions since its release in 2020. We share the lessons we learned from its development and operation, and our customers.
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Zhou X, Li G, Wu J, Liu J, Sun Z, Zhang X. A learned query rewrite system. Proceedings of the VLDB Endowment, 2023, 16(12): 4110–4113. DOI: 10.14778/3611540.3611633.
Yu X, Chai C, Li G, Liu J. Cost-based or learning-based?: A hybrid query optimizer for query plan selection. Proceedings of the VLDB Endowment, 2022, 15(13): 3924–3936. DOI: 10.14778/3565838.3565846.
Dutt A, Wang C, Nazi A, Kandula S, Narasayya V, Chaudhuri S. Selectivity estimation for range predicates using lightweight models. Proceedings of the VLDB Endowment, 2019, 12(9): 1044–1057. DOI: 10.14778/3329772.3329780.
Sun J, Zhang J, Sun Z, Li G, Tang N. Learned cardinality estimation: A design space exploration and a comparative evaluation. Proceedings of the VLDB Endowment, 2021, 15(1): 85–97. DOI: 10.14778/3485450.3485459.
Sun J, Li G. An end-to-end learning-based cost estimator. Proceedings of the VLDB Endowment, 2019, 13(3): 307–319. DOI: 10.14778/3368289.3368296.