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(1) Limited compatibility with Spark SQL: these approaches rely on physical operator enumeration, while Spark SQL doesn't support it; (2) Unreliable cost estimation: they often select execution plans with poor performance due to inaccurate cost estimation; (3) Time-consuming plan enumeration: they take much time to generate a large number of candidate execution plans in Spark SQL. To overcome these issues, in this paper, we propose LEAP, the first learned query optimizer tailored for Spark SQL, which can be integrated seamlessly into Spark SQL and solves the compatibility issue. Also, to avoid the unreliable cost value estimation, LEAP selects execution plans with an estimation-free method, which directly performs comparisons between the plans. Furthermore, LEAP employs an efficient progressive plan enumeration algorithm with pruning techniques to find better plans with fewer enumerations. Extensive experiments on three public benchmarks show the effectiveness of LEAP. It reduces the end-to-end execution time of the native optimizer by up to 54% and other learned methods by up to 94%.<\/jats:p>","DOI":"10.14778\/3712221.3712234","type":"journal-article","created":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T18:03:04Z","timestamp":1744048984000},"page":"675-687","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["LEAP: A Low-Cost Spark SQL Query Optimizer using Pairwise Comparison"],"prefix":"10.14778","volume":"18","author":[{"given":"Junhao","family":"Ye","sequence":"first","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahui","family":"Li","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuren","family":"Mao","sequence":"additional","affiliation":[{"name":"Zhejiang University &amp; Zhejiang Key Laboratory of Big Data Intelligent Computing"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunjun","family":"Gao","sequence":"additional","affiliation":[{"name":"Zhejiang University &amp; Zhejiang Key Laboratory of Big Data Intelligent Computing"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianyi","family":"Li","sequence":"additional","affiliation":[{"name":"Aalborg University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,4,7]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2019. https:\/\/github.com\/greatji\/Learning-based-cost-estimator"},{"key":"e_1_2_1_2_1","unstructured":"2023. https:\/\/github.com\/Blondig\/Lero-on-Spark"},{"key":"e_1_2_1_3_1","unstructured":"2023. https:\/\/github.com\/apache\/doris"},{"key":"e_1_2_1_4_1","unstructured":"2024. https:\/\/github.com\/HuashiSCNU0303\/LEAP\/tree\/main\/full_version"},{"key":"e_1_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Michael Armbrust Reynold S Xin Cheng Lian Yin Huai Davies Liu Joseph K Bradley Xiangrui Meng Tomer Kaftan Michael J Franklin Ali Ghodsi et al. 2015. 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