将Orca优化器集成到MySQL中

A. Marathe, S. Lin, Weidong Yu, Kareem El Gebaly, P. Larson, Calvin Sun, Huawei, Calvin Sun
{"title":"将Orca优化器集成到MySQL中","authors":"A. Marathe, S. Lin, Weidong Yu, Kareem El Gebaly, P. Larson, Calvin Sun, Huawei, Calvin Sun","doi":"10.48786/edbt.2022.45","DOIUrl":null,"url":null,"abstract":"The MySQL query optimizer was designed for relatively simple, OLTP-type queries; for more complex queries its limitations quickly become apparent. Join order optimization, for example, considers only left-deep plans, and selects the join order using a greedy algorithm. Instead of continuing to patch the MySQL optimizer, why not delegate optimization of more complex queries to another more capable optimizer? This paper reports on our experience with integrating the Orca optimizer into MySQL. Orca is an extensible open-source query optimizer—originally used by Pivotal’s Greenplum DBMS—specifically designed for demanding analytical workloads. Queries submitted to MySQL are routed to Orca for optimization, and the resulting plans are returned to MySQL for execution. Metadata and statistical information needed during optimization is retrieved from MySQL’s data dictionary. Experimental results show substantial performance gains. On the TPC-DS benchmark, Orca’s plans were over 10X faster on 10 of the 99 queries, and over 100X faster on 3 queries.","PeriodicalId":88813,"journal":{"name":"Advances in database technology : proceedings. International Conference on Extending Database Technology","volume":"1 1","pages":"2:511-2:523"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Integrating the Orca Optimizer into MySQL\",\"authors\":\"A. Marathe, S. Lin, Weidong Yu, Kareem El Gebaly, P. Larson, Calvin Sun, Huawei, Calvin Sun\",\"doi\":\"10.48786/edbt.2022.45\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The MySQL query optimizer was designed for relatively simple, OLTP-type queries; for more complex queries its limitations quickly become apparent. Join order optimization, for example, considers only left-deep plans, and selects the join order using a greedy algorithm. Instead of continuing to patch the MySQL optimizer, why not delegate optimization of more complex queries to another more capable optimizer? This paper reports on our experience with integrating the Orca optimizer into MySQL. Orca is an extensible open-source query optimizer—originally used by Pivotal’s Greenplum DBMS—specifically designed for demanding analytical workloads. Queries submitted to MySQL are routed to Orca for optimization, and the resulting plans are returned to MySQL for execution. Metadata and statistical information needed during optimization is retrieved from MySQL’s data dictionary. Experimental results show substantial performance gains. On the TPC-DS benchmark, Orca’s plans were over 10X faster on 10 of the 99 queries, and over 100X faster on 3 queries.\",\"PeriodicalId\":88813,\"journal\":{\"name\":\"Advances in database technology : proceedings. International Conference on Extending Database Technology\",\"volume\":\"1 1\",\"pages\":\"2:511-2:523\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in database technology : proceedings. International Conference on Extending Database Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.48786/edbt.2022.45\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in database technology : proceedings. International Conference on Extending Database Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.48786/edbt.2022.45","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

MySQL查询优化器是为相对简单的oltp类型查询而设计的;对于更复杂的查询,它的局限性很快就会显现出来。例如,连接顺序优化只考虑左深计划,并使用贪婪算法选择连接顺序。与其继续修补MySQL优化器,为什么不将更复杂的查询优化委托给另一个更有能力的优化器呢?本文报告了我们将Orca优化器集成到MySQL中的经验。Orca是一个可扩展的开源查询优化器——最初由Pivotal的Greenplum dbms使用——专门为要求苛刻的分析工作负载而设计。提交给MySQL的查询被路由到Orca进行优化,结果计划被返回到MySQL执行。优化过程中需要的元数据和统计信息从MySQL的数据字典中检索。实验结果显示了显著的性能提升。在TPC-DS基准测试中,Orca的计划在99个查询中的10个查询中速度超过10倍,在3个查询中速度超过100倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Integrating the Orca Optimizer into MySQL
The MySQL query optimizer was designed for relatively simple, OLTP-type queries; for more complex queries its limitations quickly become apparent. Join order optimization, for example, considers only left-deep plans, and selects the join order using a greedy algorithm. Instead of continuing to patch the MySQL optimizer, why not delegate optimization of more complex queries to another more capable optimizer? This paper reports on our experience with integrating the Orca optimizer into MySQL. Orca is an extensible open-source query optimizer—originally used by Pivotal’s Greenplum DBMS—specifically designed for demanding analytical workloads. Queries submitted to MySQL are routed to Orca for optimization, and the resulting plans are returned to MySQL for execution. Metadata and statistical information needed during optimization is retrieved from MySQL’s data dictionary. Experimental results show substantial performance gains. On the TPC-DS benchmark, Orca’s plans were over 10X faster on 10 of the 99 queries, and over 100X faster on 3 queries.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Computing Generic Abstractions from Application Datasets Fair Spatial Indexing: A paradigm for Group Spatial Fairness. Data Coverage for Detecting Representation Bias in Image Datasets: A Crowdsourcing Approach Auditing for Spatial Fairness TransEdge: Supporting Efficient Read Queries Across Untrusted Edge Nodes
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1