{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T01:17:32Z","timestamp":1778807852351,"version":"3.51.4"},"reference-count":13,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:p>\n            Query optimizers rely heavily on selectivity estimates to choose efficient execution plans, but inaccuracies in these estimates often result in poor query performance. We introduce\n            <jats:bold>Hint-QPT<\/jats:bold>\n            (\n            <jats:bold>Hint<\/jats:bold>\n            s for Robust\n            <jats:bold>Q<\/jats:bold>\n            uery\n            <jats:bold>P<\/jats:bold>\n            erformance\n            <jats:bold>T<\/jats:bold>\n            uning), an interactive tool designed to help users diagnose and improve query performance. Hint-QPT proactively recommends robust plans that are resilient to uncertainty in selectivity estimates, identifies sensitive subqueries for which selectivity estimation errors greatly affect plan quality, and provides intuitive interfaces for targeted selectivity adjustments. Users can either choose the recommended robust plans for execution, or acquire additional statistics on the identified sensitive subqueries to tune query performance. Moreover, Hint-QPT visualizes the alternative execution plans and their costs under uncertainty, helping users to better understand their robustness.\n          <\/jats:p>","DOI":"10.14778\/3750601.3750663","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T13:37:51Z","timestamp":1758029871000},"page":"5327-5330","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Hint-QPT: Hints for Robust Query Performance Tuning"],"prefix":"10.14778","volume":"18","author":[{"given":"Haibo","family":"Xiu","sequence":"first","affiliation":[{"name":"Duke University, Durham, NC, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[{"name":"Duke University, Durham, NC, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qianyu","family":"Yang","sequence":"additional","affiliation":[{"name":"Duke University, Durham, NC, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weihang","family":"Guo","sequence":"additional","affiliation":[{"name":"Duke University, Durham, NC, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxi","family":"Liu","sequence":"additional","affiliation":[{"name":"Duke University, Durham, NC, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pankaj K.","family":"Agarwal","sequence":"additional","affiliation":[{"name":"Duke University, Durham, NC, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sudeepa","family":"Roy","sequence":"additional","affiliation":[{"name":"Duke University, Durham, NC, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Yang","sequence":"additional","affiliation":[{"name":"Duke University, Durham, NC, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,9,16]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2411.14788"},{"key":"e_1_2_1_2_1","unstructured":"DALIBO. 2016. A VueJS component to show a graphical vizualization of a PostgreSQL execution plan. https:\/\/github.com\/dalibo\/pev2."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1561\/1900000077"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1561\/1900000089"},{"key":"e_1_2_1_5_1","volume-title":"Proceedings of the 31st international conference on Very large data bases. VLDB Endowment. 1228\u20131239","author":"Haritsa Naveen Reddy","year":"2005","unstructured":"Naveen Reddy Jayant R Haritsa. 2005. Analyzing plan diagrams of database query optimizers. In Proceedings of the 31st international conference on Very large data bases. VLDB Endowment. 1228\u20131239."},{"key":"e_1_2_1_6_1","volume-title":"RobOpt: A Tool for Robust Workload Optimization Based on Uncertainty-Aware Machine Learning. In Companion of the 2024 International Conference on Management of Data. 468\u2013471","author":"Kamali Amin","year":"2024","unstructured":"Amin Kamali, Verena Kantere, Calisto Zuzarte, and Vincent Corvinelli. 2024. RobOpt: A Tool for Robust Workload Optimization Based on Uncertainty-Aware Machine Learning. In Companion of the 2024 International Conference on Management of Data. 468\u2013471."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.14778\/2850583.2850594"},{"key":"e_1_2_1_8_1","volume-title":"SQLVis: Visual Query Representations for Supporting SQL Learners. In 2021 IEEE Symposium on Visual Languages and Human-Centric Computing (VL\/HCC). IEEE.","author":"Miedema Daphne","year":"2021","unstructured":"Daphne Miedema and George Fletcher. 2021. SQLVis: Visual Query Representations for Supporting SQL Learners. In 2021 IEEE Symposium on Visual Languages and Human-Centric Computing (VL\/HCC). IEEE."},{"key":"e_1_2_1_9_1","unstructured":"Satoshi Nagayasu. 2023. pg_hint_plan. https:\/\/github.com\/ossc-db\/pg_hint_plan."},{"key":"e_1_2_1_10_1","unstructured":"Google Spanner. 2025. Tune a query using the query plan visualizer. https:\/\/cloud.google.com\/spanner\/docs\/tune-query-with-visualizer."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.14778\/3554821.3554854"},{"key":"e_1_2_1_12_1","volume-title":"https:\/\/github.com\/Hap-Hugh\/PG16","author":"Xiu Haibo","unstructured":"Haibo Xiu. 2024. Modified PostgreSQL 16.2. https:\/\/github.com\/Hap-Hugh\/PG16."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.14778\/3704965.3704971"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3750601.3750663","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T13:37:53Z","timestamp":1758029873000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3750601.3750663"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8]]},"references-count":13,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["10.14778\/3750601.3750663"],"URL":"https:\/\/doi.org\/10.14778\/3750601.3750663","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2025,8]]},"assertion":[{"value":"2025-09-16","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}