{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T06:11:40Z","timestamp":1775283100068,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":31,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,6,9]],"date-time":"2021-06-09T00:00:00Z","timestamp":1623196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,6,9]]},"DOI":"10.1145\/3448016.3457568","type":"proceedings-article","created":{"date-parts":[[2021,6,18]],"date-time":"2021-06-18T17:22:30Z","timestamp":1624036950000},"page":"2557-2569","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":31,"title":["Steering Query Optimizers: A Practical Take on Big Data Workloads"],"prefix":"10.1145","author":[{"given":"Parimarjan","family":"Negi","sequence":"first","affiliation":[{"name":"Massachusetts Institute of Technology, Cambridge, MA, USA"}]},{"given":"Matteo","family":"Interlandi","sequence":"additional","affiliation":[{"name":"Microsoft, Redmond, WA, USA"}]},{"given":"Ryan","family":"Marcus","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology &amp; Intel Labs, Cambridge, MA, USA"}]},{"given":"Mohammad","family":"Alizadeh","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology, Cambridge, MA, USA"}]},{"given":"Tim","family":"Kraska","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology, Cambridge, MA, USA"}]},{"given":"Marc","family":"Friedman","sequence":"additional","affiliation":[{"name":"Microsoft, Redmond, MA, USA"}]},{"given":"Alekh","family":"Jindal","sequence":"additional","affiliation":[{"name":"Microsoft, Redmond, WA, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,6,18]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/2228298.2228327"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3190662"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.14778\/1454159.1454166"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2882903.2903741"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.14778\/3407790.3407820"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.14778\/3329772.3329780"},{"key":"e_1_3_2_2_7_1","first-page":"19","article-title":"The cascades framework for query optimization","volume":"18","author":"Graefe Goetz","year":"1995","unstructured":"Goetz Graefe . 1995 . The cascades framework for query optimization . IEEE Data Eng. Bull. , Vol. 18 , 3 (1995), 19 -- 29 . Goetz Graefe. 1995. The cascades framework for query optimization. IEEE Data Eng. Bull., Vol. 18, 3 (1995), 19--29.","journal-title":"IEEE Data Eng. Bull."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.14778\/1920841.1921027"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357223.3362726"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3190656"},{"key":"e_1_3_2_2_11_1","volume-title":"Learned cardinalities: Estimating correlated joins with deep learning. arXiv preprint arXiv:1809.00677","author":"Kipf Andreas","year":"2018","unstructured":"Andreas Kipf , Thomas Kipf , Bernhard Radke , Viktor Leis , Peter Boncz , and Alfons Kemper . 2018. Learned cardinalities: Estimating correlated joins with deep learning. arXiv preprint arXiv:1809.00677 ( 2018 ). Andreas Kipf, Thomas Kipf, Bernhard Radke, Viktor Leis, Peter Boncz, and Alfons Kemper. 2018. Learned cardinalities: Estimating correlated joins with deep learning. arXiv preprint arXiv:1809.00677 (2018)."},{"key":"e_1_3_2_2_12_1","volume-title":"Learning to optimize join queries with deep reinforcement learning. arXiv preprint arXiv:1808.03196","author":"Krishnan Sanjay","year":"2018","unstructured":"Sanjay Krishnan , Zongheng Yang , Ken Goldberg , Joseph Hellerstein , and Ion Stoica . 2018. Learning to optimize join queries with deep reinforcement learning. arXiv preprint arXiv:1808.03196 ( 2018 ). Sanjay Krishnan, Zongheng Yang, Ken Goldberg, Joseph Hellerstein, and Ion Stoica. 2018. Learning to optimize join queries with deep reinforcement learning. arXiv preprint arXiv:1808.03196 (2018)."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.14778\/2850583.2850594"},{"key":"e_1_3_2_2_14_1","volume-title":"Bao: Learning to Steer Query Optimizers. arXiv preprint arXiv:2004.03814","author":"Marcus Ryan","year":"2020","unstructured":"Ryan Marcus , Parimarjan Negi , Hongzi Mao , Nesime Tatbul , Mohammad Alizadeh , and Tim Kraska . 2020 . Bao: Learning to Steer Query Optimizers. arXiv preprint arXiv:2004.03814 (2020). Ryan Marcus, Parimarjan Negi, Hongzi Mao, Nesime Tatbul, Mohammad Alizadeh, and Tim Kraska. 2020. Bao: Learning to Steer Query Optimizers. arXiv preprint arXiv:2004.03814 (2020)."},{"key":"e_1_3_2_2_15_1","volume-title":"Neo: A learned query optimizer. arXiv preprint arXiv:1904.03711","author":"Marcus Ryan","year":"2019","unstructured":"Ryan Marcus , Parimarjan Negi , Hongzi Mao , Chi Zhang , Mohammad Alizadeh , Tim Kraska , Olga Papaemmanouil , and Nesime Tatbul . 2019 . Neo: A learned query optimizer. arXiv preprint arXiv:1904.03711 (2019). %balance Ryan Marcus, Parimarjan Negi, Hongzi Mao, Chi Zhang, Mohammad Alizadeh, Tim Kraska, Olga Papaemmanouil, and Nesime Tatbul. 2019. Neo: A learned query optimizer. arXiv preprint arXiv:1904.03711 (2019). %balance"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3211954.3211957"},{"key":"e_1_3_2_2_17_1","volume-title":"https:\/\/www.microsoft.com\/en-us\/sql-server\/ Retrieved","author":"Corp. Microsoft. 2020. SQL Server.","year":"2020","unstructured":"Corp. Microsoft. 2020. SQL Server. https:\/\/www.microsoft.com\/en-us\/sql-server\/ Retrieved November 23, 2020 from Corp. Microsoft. 2020. SQL Server. https:\/\/www.microsoft.com\/en-us\/sql-server\/ Retrieved November 23, 2020 from"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDEW49219.2020.00034"},{"key":"e_1_3_2_2_19_1","volume-title":"An Empirical Analysis of Deep Learning for Cardinality Estimation. arXiv preprint arXiv:1905.06425","author":"Ortiz Jennifer","year":"2019","unstructured":"Jennifer Ortiz , Magdalena Balazinska , Johannes Gehrke , and S Sathiya Keerthi . 2019. An Empirical Analysis of Deep Learning for Cardinality Estimation. arXiv preprint arXiv:1905.06425 ( 2019 ). Jennifer Ortiz, Magdalena Balazinska, Johannes Gehrke, and S Sathiya Keerthi. 2019. An Empirical Analysis of Deep Learning for Cardinality Estimation. arXiv preprint arXiv:1905.06425 (2019)."},{"key":"e_1_3_2_2_20_1","unstructured":"Adam Paszke Sam Gross Soumith Chintala Gregory Chanan Edward Yang Zachary DeVito Zeming Lin Alban Desmaison Luca Antiga and Adam Lerer. 2017. Automatic differentiation in pytorch. (2017).  Adam Paszke Sam Gross Soumith Chintala Gregory Chanan Edward Yang Zachary DeVito Zeming Lin Alban Desmaison Luca Antiga and Adam Lerer. 2017. Automatic differentiation in pytorch. (2017)."},{"key":"e_1_3_2_2_21_1","unstructured":"Dipanjan (DJ) Sarkar. 2019. Categorical Data. https:\/\/towardsdatascience.com\/understanding-feature-engineering-part-2-categorical-data-f54324193e63  Dipanjan (DJ) Sarkar. 2019. Categorical Data. https:\/\/towardsdatascience.com\/understanding-feature-engineering-part-2-categorical-data-f54324193e63"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.14778\/3415478.3415554"},{"key":"e_1_3_2_2_23_1","unstructured":"Jeff Shute Radek Vingralek Bart Samwel Ben Handy Chad Whipkey Eric Rollins Mircea Oancea Kyle Littlefield David Menestrina Stephan Ellner etal 2013. F1: A distributed SQL database that scales. (2013).  Jeff Shute Radek Vingralek Bart Samwel Ben Handy Chad Whipkey Eric Rollins Mircea Oancea Kyle Littlefield David Menestrina Stephan Ellner et al. 2013. F1: A distributed SQL database that scales. (2013)."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3380584"},{"key":"e_1_3_2_2_25_1","volume-title":"International Workshop on Business Intelligence for the Real-Time Enterprise. Springer, 89--96","author":"Waas Florian M","year":"2008","unstructured":"Florian M Waas . 2008 . Beyond conventional data warehousing-massively parallel data processing with greenplum database . In International Workshop on Business Intelligence for the Real-Time Enterprise. Springer, 89--96 . Florian M Waas. 2008. Beyond conventional data warehousing-massively parallel data processing with greenplum database. In International Workshop on Business Intelligence for the Real-Time Enterprise. Springer, 89--96."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.14778\/3236187.3236191"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3329859.3329875"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.14778\/3291264.3291267"},{"key":"e_1_3_2_2_29_1","volume-title":"NeuroCard: one cardinality estimator for all tables. arXiv preprint arXiv:2006.08109","author":"Yang Zongheng","year":"2020","unstructured":"Zongheng Yang , Amog Kamsetty , Sifei Luan , Eric Liang , Yan Duan , Xi Chen , and Ion Stoica . 2020. NeuroCard: one cardinality estimator for all tables. arXiv preprint arXiv:2006.08109 ( 2020 ). Zongheng Yang, Amog Kamsetty, Sifei Luan, Eric Liang, Yan Duan, Xi Chen, and Ion Stoica. 2020. NeuroCard: one cardinality estimator for all tables. arXiv preprint arXiv:2006.08109 (2020)."},{"key":"e_1_3_2_2_30_1","volume-title":"Deep unsupervised cardinality estimation. arXiv preprint arXiv:1905.04278","author":"Yang Zongheng","year":"2019","unstructured":"Zongheng Yang , Eric Liang , Amog Kamsetty , Chenggang Wu , Yan Duan , Xi Chen , Pieter Abbeel , Joseph M Hellerstein , Sanjay Krishnan , and Ion Stoica . 2019. Deep unsupervised cardinality estimation. arXiv preprint arXiv:1905.04278 ( 2019 ). Zongheng Yang, Eric Liang, Amog Kamsetty, Chenggang Wu, Yan Duan, Xi Chen, Pieter Abbeel, Joseph M Hellerstein, Sanjay Krishnan, and Ion Stoica. 2019. Deep unsupervised cardinality estimation. arXiv preprint arXiv:1905.04278 (2019)."},{"key":"e_1_3_2_2_31_1","first-page":"10","article-title":"Spark: Cluster computing with working sets","volume":"10","author":"Zaharia Matei","year":"2010","unstructured":"Matei Zaharia , Mosharaf Chowdhury , Michael J Franklin , Scott Shenker , Ion Stoica , 2010 . Spark: Cluster computing with working sets . HotCloud , Vol. 10 , 10 -- 10 (2010), 95. Matei Zaharia, Mosharaf Chowdhury, Michael J Franklin, Scott Shenker, Ion Stoica, et al. 2010. Spark: Cluster computing with working sets. HotCloud, Vol. 10, 10--10 (2010), 95.","journal-title":"HotCloud"}],"event":{"name":"SIGMOD\/PODS '21: International Conference on Management of Data","location":"Virtual Event China","acronym":"SIGMOD\/PODS '21","sponsor":["SIGMOD ACM Special Interest Group on Management of Data"]},"container-title":["Proceedings of the 2021 International Conference on Management of Data"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3448016.3457568","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3448016.3457568","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:25:04Z","timestamp":1750195504000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3448016.3457568"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,9]]},"references-count":31,"alternative-id":["10.1145\/3448016.3457568","10.1145\/3448016"],"URL":"https:\/\/doi.org\/10.1145\/3448016.3457568","relation":{},"subject":[],"published":{"date-parts":[[2021,6,9]]},"assertion":[{"value":"2021-06-18","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}