{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T09:54:25Z","timestamp":1773482065055,"version":"3.50.1"},"reference-count":47,"publisher":"Association for Computing Machinery (ACM)","issue":"4","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:p>SkinnerMT is an adaptive query processing engine, specialized for multi-core platforms. SkinnerMT features different strategies for parallel processing that allow users to trade between average run time and performance robustness.<\/jats:p><jats:p>First, SkinnerMT supports execution strategies that execute multiple query plans in parallel, thereby reducing the risk to find near-optimal plans late and improving robustness. Second, SkinnerMT supports data-parallel processing strategies. Its parallel multi-way join algorithm is sensitive to the assignment from tuples to threads. Here, SkinnerMT uses a cost-based optimization strategy, based on runtime feedback. Finally, SkinnerMT supports hybrid processing methods, mixing parallel search with data-parallel processing.<\/jats:p><jats:p>The experiments show that parallel search increases robustness while parallel processing increases average-case performance. The hybrid approach combines advantages from both. Compared to traditional database systems, SkinnerMT is preferable for benchmarks where query optimization is hard. Compared to prior adaptive processing baselines, SkinnerMT exploits parallelism better.<\/jats:p>","DOI":"10.14778\/3574245.3574272","type":"journal-article","created":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T23:14:12Z","timestamp":1677021252000},"page":"905-917","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["SkinnerMT"],"prefix":"10.14778","volume":"16","author":[{"given":"Ziyun","family":"Wei","sequence":"first","affiliation":[{"name":"Cornell University, Ithaca, NY, USA"}]},{"given":"Immanuel","family":"Trummer","sequence":"additional","affiliation":[{"name":"Cornell University, Ithaca, NY, USA"}]}],"member":"320","published-online":{"date-parts":[[2023,2,21]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"1064","article-title":"Massively parallel sort-merge joins in main memory multi-core database systems","volume":"5","author":"Albutiu MC","year":"2012","unstructured":"MC Albutiu , Alfons Kemper , and T Neumann . 2012 . Massively parallel sort-merge joins in main memory multi-core database systems . VLDB 5 , 10 (2012), 1064 -- 1075 . http:\/\/dl.acm.org\/citation.cfm?id=2336678 MC Albutiu, Alfons Kemper, and T Neumann. 2012. Massively parallel sort-merge joins in main memory multi-core database systems. VLDB 5, 10 (2012), 1064--1075. http:\/\/dl.acm.org\/citation.cfm?id=2336678","journal-title":"VLDB"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/342009.335420"},{"key":"e_1_2_1_3_1","unstructured":"Shivnath Babu. 2005. Adaptive query processing in the looking glass. In CIDR. 238 -- 249. http:\/\/citeseerx.ist.psu.edu\/viewdoc\/summary?doi=10.1.1.98.3279 Shivnath Babu. 2005. Adaptive query processing in the looking glass. In CIDR. 238 -- 249. http:\/\/citeseerx.ist.psu.edu\/viewdoc\/summary?doi=10.1.1.98.3279"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1066157.1066171"},{"key":"e_1_2_1_5_1","doi-asserted-by":"crossref","unstructured":"K. Balakrishnan. 2019. Exponential distribution: theory methods and applications. K. Balakrishnan. 2019. Exponential distribution: theory methods and applications.","DOI":"10.1201\/9780203756348"},{"key":"e_1_2_1_6_1","unstructured":"Markus Bibinger. 2013. Notes on the sum and maximum of independent exponentially distributed random variables with different scale parameters. (2013) 1--9. arXiv:1307.3945 http:\/\/arxiv.org\/abs\/1307.3945 Markus Bibinger. 2013. Notes on the sum and maximum of independent exponentially distributed random variables with different scale parameters. (2013) 1--9. arXiv:1307.3945 http:\/\/arxiv.org\/abs\/1307.3945"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2008.160"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-72401-0_8"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/1409360.1409380"},{"key":"e_1_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Sophie Cluet and Guido Moerkotte. 1995. On the complexity of generating optimal left-deep processing trees with cross products. In ICDT. 54--67. http:\/\/link.springer.com\/chapter\/10.1007\/3-540-58907-4_6 Sophie Cluet and Guido Moerkotte. 1995. On the complexity of generating optimal left-deep processing trees with cross products. In ICDT. 54--67. http:\/\/link.springer.com\/chapter\/10.1007\/3-540-58907-4_6","DOI":"10.1007\/3-540-58907-4_6"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1561\/1900000001"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/69.50905"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.14778\/1687627.1687675"},{"key":"e_1_2_1_14_1","first-page":"204","article-title":"How good are query optimizers, really","volume":"9","author":"Gubichev Andrey","year":"2015","unstructured":"Andrey Gubichev , Peter Boncz , Alfons Kemper , and Thomas Neumann . 2015 . How good are query optimizers, really ? PVLDB 9 , 3 (2015), 204 -- 215 . Andrey Gubichev, Peter Boncz, Alfons Kemper, and Thomas Neumann. 2015. How good are query optimizers, really? PVLDB 9, 3 (2015), 204--215.","journal-title":"PVLDB"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICAC.2008.12"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.14778\/1453856.1453882"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/1559845.1559853"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3389704"},{"key":"e_1_2_1_19_1","volume-title":"Adaptive cardinality estimation. arXiv preprint arXiv:1711.08330","author":"Ivanov Oleg","year":"2017","unstructured":"Oleg Ivanov and Sergey Bartunov . 2017. Adaptive cardinality estimation. arXiv preprint arXiv:1711.08330 ( 2017 ). Oleg Ivanov and Sergey Bartunov. 2017. Adaptive cardinality estimation. arXiv preprint arXiv:1711.08330 (2017)."},{"key":"e_1_2_1_20_1","volume-title":"International Conference on Machine Learning. PMLR, 1238--1246","author":"Karnin Zohar","year":"2013","unstructured":"Zohar Karnin , Tomer Koren , and Oren Somekh . 2013 . Almost optimal exploration in multi-armed bandits . In International Conference on Machine Learning. PMLR, 1238--1246 . Zohar Karnin, Tomer Koren, and Oren Somekh. 2013. Almost optimal exploration in multi-armed bandits. In International Conference on Machine Learning. PMLR, 1238--1246."},{"key":"e_1_2_1_21_1","unstructured":"Andreas Kipf Thomas Kipf Bernhard Radke Viktor Leis Peter Boncz and Alfons Kemper. 2018. Learned cardinalities: estimating correlated joins with deep learning. In CIDR. 1--8. arXiv:1809.00677 http:\/\/arxiv.org\/abs\/1809.00677 Andreas Kipf Thomas Kipf Bernhard Radke Viktor Leis Peter Boncz and Alfons Kemper. 2018. Learned cardinalities: estimating correlated joins with deep learning. In CIDR. 1--8. arXiv:1809.00677 http:\/\/arxiv.org\/abs\/1809.00677"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/11871842_29"},{"key":"e_1_2_1_23_1","unstructured":"Sanjay Krishnan Zongheng Yang Ken Goldberg Joseph Hellerstein and Ion Stoica. 2020. Learning to optimize join queries with deep reinforcement learning. In aiDM. 1--6. arXiv:1808.03196 http:\/\/arxiv.org\/abs\/1808.03196 Sanjay Krishnan Zongheng Yang Ken Goldberg Joseph Hellerstein and Ion Stoica. 2020. Learning to optimize join queries with deep reinforcement learning. In aiDM. 1--6. arXiv:1808.03196 http:\/\/arxiv.org\/abs\/1808.03196"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/2882903.2915235"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3542700.3542703"},{"key":"e_1_2_1_26_1","doi-asserted-by":"crossref","unstructured":"Ryan Marcus and Olga Papaemmanouil. 2018. Deep reinforcement learning for join order enumeration. In aiDM. 3. arXiv:arXiv:1803.00055v2 Ryan Marcus and Olga Papaemmanouil. 2018. Deep reinforcement learning for join order enumeration. In aiDM. 3. arXiv:arXiv:1803.00055v2","DOI":"10.1145\/3211954.3211957"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3542700.3542702"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.14778\/3425879.3425882"},{"key":"e_1_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Hung Q Ngo and Christopher R\u00e9. 2014. Beyond Worst-case Analysis for Joins with Minesweeper. In PODS. 234--245. Hung Q Ngo and Christopher R\u00e9. 2014. Beyond Worst-case Analysis for Joins with Minesweeper. In PODS. 234--245.","DOI":"10.1145\/2594538.2594547"},{"key":"e_1_2_1_30_1","unstructured":"OpenJDK. 2022. JEP 318: Epsilon: A no-op garbage collector (Experimental). https:\/\/openjdk.java.net\/jeps\/318 Accessed: 2022-08-30. OpenJDK. 2022. JEP 318: Epsilon: A no-op garbage collector (Experimental). https:\/\/openjdk.java.net\/jeps\/318 Accessed: 2022-08-30."},{"key":"e_1_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Jennifer Ortiz Magdalena Balazinska Johannes Gehrke and S. Sathiya Keerthi. 2018. Learning State Representations for Query Optimization with Deep Reinforcement Learning. In DEEM. arXiv:1803.08604 http:\/\/arxiv.org\/abs\/1803.08604 Jennifer Ortiz Magdalena Balazinska Johannes Gehrke and S. Sathiya Keerthi. 2018. Learning State Representations for Query Optimization with Deep Reinforcement Learning. In DEEM. arXiv:1803.08604 http:\/\/arxiv.org\/abs\/1803.08604","DOI":"10.1145\/3209889.3209890"},{"key":"e_1_2_1_32_1","unstructured":"PostgreSQL. 2022. Group The PostgreSQL Global Development. https:\/\/www.postgresql.org. Accessed: 2022-08-30. PostgreSQL. 2022. Group The PostgreSQL Global Development. https:\/\/www.postgresql.org. Accessed: 2022-08-30."},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2003.1260805"},{"key":"e_1_2_1_34_1","doi-asserted-by":"crossref","unstructured":"PG G Selinger MM M Astrahan D D Chamberlin R A Lorie and T G Price. 1979. Access path selection in a relational database management system. In SIGMOD. 23--34. http:\/\/dl.acm.org\/citation.cfm?id=582095.582099 PG G Selinger MM M Astrahan D D Chamberlin R A Lorie and T G Price. 1979. Access path selection in a relational database management system. In SIGMOD. 23--34. http:\/\/dl.acm.org\/citation.cfm?id=582095.582099","DOI":"10.1145\/582095.582099"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.14778\/3025111.3025125"},{"key":"e_1_2_1_36_1","unstructured":"Michael Stillger Guy M Lohman Volker Markl and Mokhtar Kandil. 2001. LEO - DB2's LEarning Optimizer. In PVLDB. 19--28. Michael Stillger Guy M Lohman Volker Markl and Mokhtar Kandil. 2001. LEO - DB2's LEarning Optimizer. In PVLDB. 19--28."},{"key":"e_1_2_1_37_1","unstructured":"TPC. 2013. TPC-H Benchmark. http:\/\/www.tpc.org\/tpch\/ TPC. 2013. TPC-H Benchmark. http:\/\/www.tpc.org\/tpch\/"},{"key":"e_1_2_1_38_1","doi-asserted-by":"crossref","unstructured":"Immanuel Trummer and Christoph Koch. 2016. Parallelizing query optimization on shared-nothing architectures. In VLDB. 660--671. Immanuel Trummer and Christoph Koch. 2016. Parallelizing query optimization on shared-nothing architectures. In VLDB. 660--671.","DOI":"10.14778\/2947618.2947622"},{"key":"e_1_2_1_39_1","doi-asserted-by":"crossref","unstructured":"Immanuel Trummer Junxiong Wang Deepak Maram Samuel Moseley Saehan Jo and Joseph Antonakakis. 2019. SkinnerDB: regret-bounded query evaluation via reinforcement learning. In SIGMOD. 1039--1050. Immanuel Trummer Junxiong Wang Deepak Maram Samuel Moseley Saehan Jo and Joseph Antonakakis. 2019. SkinnerDB: regret-bounded query evaluation via reinforcement learning. In SIGMOD. 1039--1050.","DOI":"10.1145\/3299869.3300088"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.5441\/002\/icdt.2014.13"},{"key":"e_1_2_1_42_1","doi-asserted-by":"crossref","unstructured":"Stratis D Viglas Jeffrey F Naughton and Josef Burger. 2003. Maximizing the output rate of multi-way join queries over streaming information sources. In PVLDB. 285--296. http:\/\/dl.acm.org\/citation.cfm?id=1315451.1315477 Stratis D Viglas Jeffrey F Naughton and Josef Burger. 2003. Maximizing the output rate of multi-way join queries over streaming information sources. In PVLDB. 285--296. http:\/\/dl.acm.org\/citation.cfm?id=1315451.1315477","DOI":"10.1016\/B978-012722442-8\/50033-1"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/1559845.1559938"},{"key":"e_1_2_1_44_1","doi-asserted-by":"crossref","unstructured":"Ziyun Wei and Immanuel Trummer. 2022. SkinnerMT: Parallelizing for Efficiency and Robustness in Adaptive Query Processing on Multicore Platforms. Technical Report. https:\/\/github.com\/cornelldbgroup\/skinnerdb\/blob\/skinnermt\/skinnermt.pdf Ziyun Wei and Immanuel Trummer. 2022. SkinnerMT: Parallelizing for Efficiency and Robustness in Adaptive Query Processing on Multicore Platforms. Technical Report. https:\/\/github.com\/cornelldbgroup\/skinnerdb\/blob\/skinnermt\/skinnermt.pdf","DOI":"10.14778\/3574245.3574272"},{"key":"e_1_2_1_45_1","doi-asserted-by":"crossref","unstructured":"Lucas Woltmann Claudio Hartmann Maik Thiele and Dirk Habich. 2019. Cardinality estimation with local deep learning models. In aiDM. 1--8. Lucas Woltmann Claudio Hartmann Maik Thiele and Dirk Habich. 2019. Cardinality estimation with local deep learning models. In aiDM. 1--8.","DOI":"10.1145\/3329859.3329875"},{"key":"e_1_2_1_46_1","unstructured":"Wentao Wu Jefrey F. Naughton and Harneet Singh. 2016. Sampling-based query re-optimization. In SIGMOD. 1721--1736. arXiv:1601.05748 http:\/\/arxiv.org\/abs\/1601.05748 Wentao Wu Jefrey F. Naughton and Harneet Singh. 2016. Sampling-based query re-optimization. In SIGMOD. 1721--1736. arXiv:1601.05748 http:\/\/arxiv.org\/abs\/1601.05748"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.14778\/2733085.2733092"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.4304\/jcp.6.10.2004-2012"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3574245.3574272","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T21:00:54Z","timestamp":1728939654000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3574245.3574272"}},"subtitle":["Parallelizing for Efficiency and Robustness in Adaptive Query Processing on Multicore Platforms"],"short-title":[],"issued":{"date-parts":[[2022,12]]},"references-count":47,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["10.14778\/3574245.3574272"],"URL":"https:\/\/doi.org\/10.14778\/3574245.3574272","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2022,12]]},"assertion":[{"value":"2023-02-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}