{"title":"DPconv:超快连接排序","authors":"Mihail Stoian, Andreas Kipf","doi":"arxiv-2409.08013","DOIUrl":null,"url":null,"abstract":"We revisit the join ordering problem in query optimization. The standard\nexact algorithm, DPccp, has a worst-case running time of $O(3^n)$. This is\nprohibitively expensive for large queries, which are not that uncommon anymore.\nWe develop a new algorithmic framework based on subset convolution. DPconv\nachieves a super-polynomial speedup over DPccp, breaking the $O(3^n)$\ntime-barrier for the first time. We show that the instantiation of our\nframework for the $C_\\max$ cost function is up to 30x faster than DPccp for\nlarge clique queries.","PeriodicalId":501123,"journal":{"name":"arXiv - CS - Databases","volume":"4 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DPconv: Super-Polynomially Faster Join Ordering\",\"authors\":\"Mihail Stoian, Andreas Kipf\",\"doi\":\"arxiv-2409.08013\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We revisit the join ordering problem in query optimization. The standard\\nexact algorithm, DPccp, has a worst-case running time of $O(3^n)$. This is\\nprohibitively expensive for large queries, which are not that uncommon anymore.\\nWe develop a new algorithmic framework based on subset convolution. DPconv\\nachieves a super-polynomial speedup over DPccp, breaking the $O(3^n)$\\ntime-barrier for the first time. We show that the instantiation of our\\nframework for the $C_\\\\max$ cost function is up to 30x faster than DPccp for\\nlarge clique queries.\",\"PeriodicalId\":501123,\"journal\":{\"name\":\"arXiv - CS - Databases\",\"volume\":\"4 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Databases\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.08013\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Databases","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.08013","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We revisit the join ordering problem in query optimization. The standard
exact algorithm, DPccp, has a worst-case running time of $O(3^n)$. This is
prohibitively expensive for large queries, which are not that uncommon anymore.
We develop a new algorithmic framework based on subset convolution. DPconv
achieves a super-polynomial speedup over DPccp, breaking the $O(3^n)$
time-barrier for the first time. We show that the instantiation of our
framework for the $C_\max$ cost function is up to 30x faster than DPccp for
large clique queries.