JAPO: learning join and pushdown order for cloud-native join optimization

Graphical abstract

References

[1] Yu X, Li G, Chai C, Tang N. Reinforcement learning with tree-LSTM for join order selection. In: Proceedings of the 36th IEEE International Conference on Data Engineering (ICDE). 2020, 1297−1308

[2] Cao W, Liu Y, Cheng Z, Zheng N, Li W, Wu W, Ouyang L, Wang P, Wang Y, Kuan R, Liu Z, Zhu F, Zhang T. POLARDB meets computational storage: efficiently support analytical workloads in cloud-native relational database. In: Proceedings of the 18th USENIX Conference on File and Storage Technologies. 2020, 29−42

[3] Huang D, Liu Q, Cui Q, Fang Z, Ma X, Xu F, Shen L, Tang L, Zhou Y, Huang M, Wei W, Liu C, Zhang J, Li J, Wu X, Song L, Sun R, Yu S, Zhao L, Cameron N, Pei L, Tang X . TiDB: a raft-based HTAP database. Proceedings of the VLDB Endowment, 2020, 13( 12): 3072–3084

[4] Marcus R, Papaemmanouil O. Deep reinforcement learning for join order enumeration. In: Proceedings of the 1st International Workshop on Exploiting Artificial Intelligence Techniques for Data Management. 2018, 3

[5] Leis V, Gubichev A, Mirchev A, Boncz P, Kemper A, Neumann T . How good are query optimizers, really?. Proceedings of the VLDB Endowment, 2015, 9( 3): 204–215

Competing interests

The authors declare that they have no competing interests or financial conflicts to disclose.

RIGHTS & PERMISSIONS

2024 Higher Education Press