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We demonstrate\n            <jats:italic>AutoExecutor<\/jats:italic>\n            , a predictive system that uses machine learning models to predict query run times as a function of the number of allocated executors, that limits the maximum allowed parallelism, for Spark SQL queries running on Azure Synapse.\n          <\/jats:p>","DOI":"10.14778\/3476311.3476362","type":"journal-article","created":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T22:48:56Z","timestamp":1635461336000},"page":"2855-2858","source":"Crossref","is-referenced-by-count":11,"title":["AutoExecutor"],"prefix":"10.14778","volume":"14","author":[{"given":"Rathijit","family":"Sen","sequence":"first","affiliation":[{"name":"Microsoft"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abhishek","family":"Roy","sequence":"additional","affiliation":[{"name":"Microsoft"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alekh","family":"Jindal","sequence":"additional","affiliation":[{"name":"Microsoft"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Fang","sequence":"additional","affiliation":[{"name":"Microsoft, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeff","family":"Zheng","sequence":"additional","affiliation":[{"name":"Microsoft, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaolei","family":"Liu","sequence":"additional","affiliation":[{"name":"Microsoft, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruiping","family":"Li","sequence":"additional","affiliation":[{"name":"Microsoft"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,10,28]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Retrieved","year":"2021"},{"key":"e_1_2_1_2_1","volume-title":"Retrieved","year":"2021"},{"key":"e_1_2_1_3_1","volume-title":"Retrieved","year":"2021"},{"key":"e_1_2_1_4_1","volume-title":"Retrieved","year":"2021"},{"key":"e_1_2_1_5_1","volume-title":"Retrieved","year":"2021"},{"key":"e_1_2_1_6_1","unstructured":"2021. Amazon Athena. Retrieved July 26 2021 from https:\/\/aws.amazon.com\/athena  2021. Amazon Athena . Retrieved July 26 2021 from https:\/\/aws.amazon.com\/athena"},{"key":"e_1_2_1_7_1","volume-title":"Retrieved","year":"2021"},{"key":"e_1_2_1_8_1","unstructured":"2021. BigQuery. Retrieved July 26 2021 from https:\/\/cloud.google.com\/bigquery  2021. BigQuery . Retrieved July 26 2021 from https:\/\/cloud.google.com\/bigquery"},{"key":"e_1_2_1_9_1","volume-title":"Retrieved","author":"Lake Data","year":"2021"},{"key":"e_1_2_1_10_1","volume-title":"Retrieved","year":"2021"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.14778\/1454159.1454166"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3401071.3401656"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2009.130"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357223.3362726"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00275"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.5555\/3026877.3026887"},{"key":"e_1_2_1_17_1","unstructured":"Anish Pimpley Shuo Li Anubha Srivastava Vishal Rohra Yi Zhu Soundararajan Srinivasan Alekh Jindal Hiren Patel Shi Qiao and Rathijit Sen. 2021. Optimal Resource Allocation for Serverless Queries. (2021). arXiv:2107.08594  Anish Pimpley Shuo Li Anubha Srivastava Vishal Rohra Yi Zhu Soundararajan Srinivasan Alekh Jindal Hiren Patel Shi Qiao and Rathijit Sen. 2021. Optimal Resource Allocation for Serverless Queries. (2021). arXiv:2107.08594"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2987550.2987566"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.14778\/3476311.3476388"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.14778\/3415478.3415554"},{"key":"e_1_2_1_21_1","volume-title":"Query and Resource Optimization: Bridging the Gap. In 34th IEEE International Conference on Data Engineering. 1384--1387","author":"Viswanathan Lalitha","year":"2018"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3476311.3476362","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T11:32:21Z","timestamp":1672227141000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3476311.3476362"}},"subtitle":["predictive parallelism for spark SQL queries"],"short-title":[],"issued":{"date-parts":[[2021,7]]},"references-count":21,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2021,7]]}},"alternative-id":["10.14778\/3476311.3476362"],"URL":"https:\/\/doi.org\/10.14778\/3476311.3476362","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2021,7]]}}}