{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T19:06:56Z","timestamp":1779131216246,"version":"3.51.4"},"reference-count":51,"publisher":"Association for Computing Machinery (ACM)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Manag. Data"],"published-print":{"date-parts":[[2026,5,18]]},"abstract":"<jats:p>Learned indexes show promising performance compared with traditional indexes. However, most existing learned index structures rely on fixed heuristics, which limit their robustness under dynamic workloads. In this paper, we present SELIX, a self-designing learned index that automatically generates and optimizes index structures tailored to different workloads. We introduce a unified index template that allows different nodes to adopt structural configurations, node layouts, conflict resolution policies, and search strategies for flexible and fine-grained structural composition. On top of this template, we formulate structural optimization as a learnable function that maps workload states to index configurations, enabling automated search for optimal structures in the vast design space. We train this function using a deep reinforcement learning paradigm enhanced with meta-learning, and propose a dedicated online update strategy, which together enable fast and stable structural adaptation under workload drift. Experimental results show that SELIX consistently outperforms state-of-the-art learned indexes (ALEX, LIPP, FITing-Tree, PGM, XIndex, and FINEdex) in both in-memory and on-disk settings, achieving significantly higher throughput across diverse and dynamic workloads.<\/jats:p>","DOI":"10.1145\/3802096","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T18:19:16Z","timestamp":1779128356000},"page":"1-25","source":"Crossref","is-referenced-by-count":0,"title":["On Self-Designing Learned Indexes"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0614-9461","authenticated-orcid":false,"given":"Baofu","family":"Han","sequence":"first","affiliation":[{"name":"Southeast University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9463-2045","authenticated-orcid":false,"given":"Guoyu","family":"Hu","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2150-418X","authenticated-orcid":false,"given":"Bing","family":"Li","sequence":"additional","affiliation":[{"name":"Southeast University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0914-4580","authenticated-orcid":false,"given":"Xiaokui","family":"Xiao","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4044-7742","authenticated-orcid":false,"given":"Zhanhao","family":"Zhao","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4446-1100","authenticated-orcid":false,"given":"Beng Chin","family":"Ooi","sequence":"additional","affiliation":[{"name":"Zhejiang University, Ningbo, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,5,18]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.14778\/3457390.3457393"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.14778\/3389133.3389135"},{"key":"e_1_2_1_3_1","volume-title":"The Case for Learned Index Structures. In SIGMOD Conference. ACM, 489-504","author":"Kraska Tim","year":"2018","unstructured":"Tim Kraska, Alex Beutel, Ed H. Chi, Jeffrey Dean, and Neoklis Polyzotis. 2018. The Case for Learned Index Structures. In SIGMOD Conference. ACM, 489-504."},{"key":"e_1_2_1_4_1","volume-title":"ALEX: An Updatable Adaptive Learned Index. In SIGMOD Conference. ACM, 969-984","author":"Ding Jialin","year":"2020","unstructured":"Jialin Ding, Umar Farooq Minhas, Jia Yu, Chi Wang, Jaeyoung Do, Yinan Li, Hantian Zhang, Badrish Chandramouli, Johannes Gehrke, Donald Kossmann, David B. Lomet, and Tim Kraska. 2020. ALEX: An Updatable Adaptive Learned Index. In SIGMOD Conference. ACM, 969-984."},{"key":"e_1_2_1_5_1","first-page":"4856","article-title":"A Fully On-Disk Updatable Learned Index","author":"Lan Hai","year":"2024","unstructured":"Hai Lan, Zhifeng Bao, J. Shane Culpepper, Renata Borovica-Gajic, and Yu Dong. 2024. A Fully On-Disk Updatable Learned Index. In ICDE. IEEE, 4856-4869.","journal-title":"ICDE. IEEE"},{"key":"e_1_2_1_6_1","first-page":"86","article-title":"ALT-Index","author":"Yang Yuxin","year":"2025","unstructured":"Yuxin Yang, Fang Wang, Mengya Lei, Peng Zhang, and Dan Feng. 2025. ALT-Index: A Hybrid Learned Index for Concurrent Memory Database Systems. In ICDE. IEEE, 86-98.","journal-title":"In ICDE. IEEE"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/69.755611"},{"key":"e_1_2_1_8_1","doi-asserted-by":"crossref","first-page":"1992","DOI":"10.14778\/3594512.3594528","article-title":"Learned Index","volume":"16","author":"Sun Zhaoyan","year":"2023","unstructured":"Zhaoyan Sun, Xuanhe Zhou, and Guoliang Li. 2023. Learned Index: A Comprehensive Experimental Evaluation. Proc. VLDB Endow., Vol. 16, 8 (2023), 1992-2004.","journal-title":"A Comprehensive Experimental Evaluation. Proc. VLDB Endow."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.14778\/3551793.3551848"},{"key":"e_1_2_1_10_1","volume-title":"ICML (Proceedings of Machine Learning Research","volume":"3132","author":"Ferragina Paolo","year":"2020","unstructured":"Paolo Ferragina, Fabrizio Lillo, and Giorgio Vinciguerra. 2020. Why Are Learned Indexes So Effective?. In ICML (Proceedings of Machine Learning Research, Vol. 119). PMLR, 3123-3132."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.14778\/3746405.3746415"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3654954"},{"key":"e_1_2_1_13_1","volume-title":"Proc. ACM Manag. Data","volume":"3","author":"Luo Yuanhui","year":"2025","unstructured":"Yuanhui Luo, Minhui Xie, Yiheng Tong, Shichao Jiang, and Yunpeng Chai. 2025. Understanding Robustness Issues of Updatable Learned Indexes: [Experiments & Analysis]. Proc. ACM Manag. Data, Vol. 3, 4, Article 270 (2025), 25 pages."},{"key":"e_1_2_1_14_1","volume-title":"Proc. ACM Manag. Data","volume":"3","author":"Wang Taiyi","year":"2025","unstructured":"Taiyi Wang, Liang Liang, Guang Yang, Thomas Heinis, and Eiko Yoneki. 2025. A New Paradigm in Tuning Learned Indexes: A Reinforcement Learning Enhanced Approach. Proc. ACM Manag. Data, Vol. 3, 3 (2025), 120:1-120:26."},{"key":"e_1_2_1_15_1","first-page":"11909","article-title":"Generalized Proximal Policy Optimization with Sample Reuse","author":"Queeney James","year":"2021","unstructured":"James Queeney, Yannis Paschalidis, and Christos G. Cassandras. 2021. Generalized Proximal Policy Optimization with Sample Reuse. In NeurIPS. 11909-11919.","journal-title":"NeurIPS."},{"key":"e_1_2_1_16_1","volume-title":"Sci. China Inf. Sci.","volume":"67","author":"Ooi Beng Chin","year":"2024","unstructured":"Beng Chin Ooi, Shaofeng Cai, Gang Chen, Yanyan Shen, Kian-Lee Tan, Yuncheng Wu, Xiaokui Xiao, Naili Xing, Cong Yue, Lingze Zeng, Meihui Zhang, and Zhanhao Zhao. 2024. NeurDB: an AI-powered autonomous data system. Sci. China Inf. Sci., Vol. 67, 10 (2024)."},{"key":"e_1_2_1_17_1","volume-title":"Yanyan Shen, Yuncheng Wu, and Meihui Zhang.","author":"Zhao Zhanhao","year":"2025","unstructured":"Zhanhao Zhao, Shaofeng Cai, Haotian Gao, Hexiang Pan, Siqi Xiang, Naili Xing, Gang Chen, Beng Chin Ooi, Yanyan Shen, Yuncheng Wu, and Meihui Zhang. 2025. NeurDB: On the Design and Implementation of an AI-powered Autonomous Database. In CIDR. www.cidrdb.org."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3299869.3319860"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3478289"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.14778\/3489496.3489512"},{"key":"e_1_2_1_21_1","first-page":"2212","article-title":"DILI","volume":"16","author":"Li Pengfei","year":"2023","unstructured":"Pengfei Li, Hua Lu, Rong Zhu, Bolin Ding, Long Yang, and Gang Pan. 2023. DILI: A Distribution-Driven Learned Index. Proc. VLDB Endow., Vol. 16, 9 (2023), 2212-2224.","journal-title":"A Distribution-Driven Learned Index. Proc. VLDB Endow."},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.14778\/3547305.3547322"},{"key":"e_1_2_1_23_1","volume-title":"NeurBench: Benchmarking Learned Database Components with Data and Workload Drift Modeling. arXiv preprint arXiv:2503.13822","author":"Zhao Zhanhao","year":"2025","unstructured":"Zhanhao Zhao, Haotian Gao, Naili Xing, Lingze Zeng, Meihui Zhang, Gang Chen, Manuel Rigger, and Beng Chin Ooi. 2025. NeurBench: Benchmarking Learned Database Components with Data and Workload Drift Modeling. arXiv preprint arXiv:2503.13822 (2025)."},{"key":"e_1_2_1_24_1","first-page":"562","article-title":"Generalized Search Trees for Database Systems","author":"Hellerstein Joseph M.","year":"1995","unstructured":"Joseph M. Hellerstein, Jeffrey F. Naughton, and Avi Pfeffer. 1995. Generalized Search Trees for Database Systems. In VLDB. Morgan Kaufmann, 562-573.","journal-title":"VLDB. Morgan Kaufmann"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3199671"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.14778\/3494124.3494136"},{"key":"e_1_2_1_27_1","volume-title":"Database Cracking. In Third Biennial Conference on Innovative Data Systems Research, CIDR 2007, Asilomar, CA, USA, January 7-10, 2007, Online Proceedings. www.cidrdb.org, 68-78","author":"Idreos Stratos","year":"2007","unstructured":"Stratos Idreos, Martin L. Kersten, and Stefan Manegold. 2007. Database Cracking. In Third Biennial Conference on Innovative Data Systems Research, CIDR 2007, Asilomar, CA, USA, January 7-10, 2007, Online Proceedings. www.cidrdb.org, 68-78. http:\/\/cidrdb.org\/cidr2007\/papers\/cidr07p07.pdf"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2018.00066"},{"key":"e_1_2_1_29_1","volume-title":"Playing Atari with Deep Reinforcement Learning. In NIPS Deep Learning Workshop.","author":"Mnih Volodymyr","unstructured":"Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin A. Riedmiller. 2013. Playing Atari with Deep Reinforcement Learning. In NIPS Deep Learning Workshop."},{"key":"e_1_2_1_30_1","first-page":"529","volume-title":"Nat.","volume":"518","author":"Mnih Volodymyr","year":"2015","unstructured":"Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin A. Riedmiller, Andreas Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis. 2015. Human-level control through deep reinforcement learning. Nat., Vol. 518, 7540 (2015), 529-533."},{"key":"e_1_2_1_31_1","volume-title":"Proximal Policy Optimization Algorithms. CoRR","author":"Schulman John","year":"2017","unstructured":"John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. Proximal Policy Optimization Algorithms. CoRR, Vol. abs\/1707.06347 (2017)."},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3155196"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.14778\/3342263.3342644"},{"key":"e_1_2_1_34_1","volume-title":"SIGMOD Conference. ACM, 931-944","author":"Yang Zongheng","year":"2022","unstructured":"Zongheng Yang, Wei-Lin Chiang, Sifei Luan, Gautam Mittal, Michael Luo, and Ion Stoica. 2022. Balsa: Learning a Query Optimizer Without Expert Demonstrations. In SIGMOD Conference. ACM, 931-944."},{"key":"e_1_2_1_35_1","volume-title":"An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning. In SIGMOD Conference. ACM, 415-432","author":"Zhang Ji","year":"2019","unstructured":"Ji Zhang, Yu Liu, Ke Zhou, Guoliang Li, Zhili Xiao, Bin Cheng, Jiashu Xing, Yangtao Wang, Tianheng Cheng, Li Liu, Minwei Ran, and Zekang Li. 2019. An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning. In SIGMOD Conference. ACM, 415-432."},{"key":"e_1_2_1_36_1","unstructured":"Timothy P. Lillicrap Jonathan J. Hunt Alexander Pritzel Nicolas Heess Tom Erez Yuval Tassa David Silver and Daan Wierstra. 2016. Continuous control with deep reinforcement learning. In ICLR (Poster)."},{"key":"e_1_2_1_37_1","first-page":"3104","article-title":"Sequence to Sequence Learning with Neural Networks","author":"Sutskever Ilya","year":"2014","unstructured":"Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to Sequence Learning with Neural Networks. In NIPS. 3104-3112.","journal-title":"NIPS."},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.14778\/3450980.3450984"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2505.02312"},{"key":"e_1_2_1_40_1","unstructured":"SELIX. 2025. SELIX Implementation. https:\/\/github.com\/neurdb\/selix.git"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.14778\/3407790.3407832"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.14778\/3421424.3421425"},{"key":"e_1_2_1_43_1","article-title":"Towards Optimizing Learned Index for High Performance, Memory Efficiency and NUMA Awareness","volume":"22","author":"Cui Lixiao","year":"2025","unstructured":"Lixiao Cui, Kedi Yang, Yusen Li, Gang Wang, and Xiaoguang Liu. 2025. Towards Optimizing Learned Index for High Performance, Memory Efficiency and NUMA Awareness. ACM Trans. Archit. Code Optim., Vol. 22, 3 (2025), 109:1-109:26.","journal-title":"ACM Trans. Archit. Code Optim."},{"key":"e_1_2_1_44_1","first-page":"143","article-title":"Benchmarking cloud serving systems with YCSB","author":"Cooper Brian F.","year":"2010","unstructured":"Brian F. Cooper, Adam Silberstein, Erwin Tam, Raghu Ramakrishnan, and Russell Sears. 2010. Benchmarking cloud serving systems with YCSB. In SoCC. ACM, 143-154.","journal-title":"SoCC. ACM"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589284"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3332466.3374547"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626752"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2013.6544834"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3196895"},{"key":"e_1_2_1_50_1","first-page":"28","article-title":"Benchmarking Online Index-Tuning Algorithms","volume":"34","author":"Jimenez Ivo","year":"2011","unstructured":"Ivo Jimenez, Jeff LeFevre, Neoklis Polyzotis, Huascar Sanchez, and Karl Schnaitter. 2011. Benchmarking Online Index-Tuning Algorithms. IEEE Data Eng. Bull., Vol. 34, 4 (2011), 28-35. http:\/\/sites.computer.org\/debull\/A11dec\/online.pdf","journal-title":"IEEE Data Eng. Bull."},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2009.166"}],"container-title":["Proceedings of the ACM on Management of Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3802096","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T18:29:18Z","timestamp":1779128958000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3802096"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,18]]},"references-count":51,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,5,18]]}},"alternative-id":["10.1145\/3802096"],"URL":"https:\/\/doi.org\/10.1145\/3802096","relation":{},"ISSN":["2836-6573"],"issn-type":[{"value":"2836-6573","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,18]]}}}