Optimizing One-time and Continuous Subgraph Queries using Worst-case Optimal Joins

Amine Mhedhbi, C. Kankanamge, S. Salihoglu
{"title":"Optimizing One-time and Continuous Subgraph Queries using Worst-case Optimal Joins","authors":"Amine Mhedhbi, C. Kankanamge, S. Salihoglu","doi":"10.1145/3446980","DOIUrl":null,"url":null,"abstract":"We study the problem of optimizing one-time and continuous subgraph queries using the new worst-case optimal join plans. Worst-case optimal plans evaluate queries by matching one query vertex at a time using multiway intersections. The core problem in optimizing worst-case optimal plans is to pick an ordering of the query vertices to match. We make two main contributions: 1. A cost-based dynamic programming optimizer for one-time queries that (i) picks efficient query vertex orderings for worst-case optimal plans and (ii) generates hybrid plans that mix traditional binary joins with worst-case optimal style multiway intersections. In addition to our optimizer, we describe an adaptive technique that changes the query vertex orderings of the worst-case optimal subplans during query execution for more efficient query evaluation. The plan space of our one-time optimizer contains plans that are not in the plan spaces based on tree decompositions from prior work. 2. A cost-based greedy optimizer for continuous queries that builds on the delta subgraph query framework. Given a set of continuous queries, our optimizer decomposes these queries into multiple delta subgraph queries, picks a plan for each delta query, and generates a single combined plan that evaluates all of the queries. Our combined plans share computations across operators of the plans for the delta queries if the operators perform the same intersections. To increase the amount of computation shared, we describe an additional optimization that shares partial intersections across operators. Our optimizers use a new cost metric for worst-case optimal plans called intersection-cost. When generating hybrid plans, our dynamic programming optimizer for one-time queries combines intersection-cost with the cost of binary joins. We demonstrate the effectiveness of our plans, adaptive technique, and partial intersection sharing optimization through extensive experiments. Our optimizers are integrated into GraphflowDB.","PeriodicalId":6983,"journal":{"name":"ACM Transactions on Database Systems (TODS)","volume":"11 1","pages":"1 - 45"},"PeriodicalIF":0.0000,"publicationDate":"2021-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Database Systems (TODS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3446980","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 18

Abstract

We study the problem of optimizing one-time and continuous subgraph queries using the new worst-case optimal join plans. Worst-case optimal plans evaluate queries by matching one query vertex at a time using multiway intersections. The core problem in optimizing worst-case optimal plans is to pick an ordering of the query vertices to match. We make two main contributions: 1. A cost-based dynamic programming optimizer for one-time queries that (i) picks efficient query vertex orderings for worst-case optimal plans and (ii) generates hybrid plans that mix traditional binary joins with worst-case optimal style multiway intersections. In addition to our optimizer, we describe an adaptive technique that changes the query vertex orderings of the worst-case optimal subplans during query execution for more efficient query evaluation. The plan space of our one-time optimizer contains plans that are not in the plan spaces based on tree decompositions from prior work. 2. A cost-based greedy optimizer for continuous queries that builds on the delta subgraph query framework. Given a set of continuous queries, our optimizer decomposes these queries into multiple delta subgraph queries, picks a plan for each delta query, and generates a single combined plan that evaluates all of the queries. Our combined plans share computations across operators of the plans for the delta queries if the operators perform the same intersections. To increase the amount of computation shared, we describe an additional optimization that shares partial intersections across operators. Our optimizers use a new cost metric for worst-case optimal plans called intersection-cost. When generating hybrid plans, our dynamic programming optimizer for one-time queries combines intersection-cost with the cost of binary joins. We demonstrate the effectiveness of our plans, adaptive technique, and partial intersection sharing optimization through extensive experiments. Our optimizers are integrated into GraphflowDB.
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
使用最坏情况最优连接优化一次性和连续子图查询
我们研究了使用新的最坏情况最优连接计划优化一次性和连续子图查询的问题。最坏情况最优计划通过使用多路交叉点一次匹配一个查询顶点来评估查询。优化最坏情况最优计划的核心问题是选择匹配的查询顶点的顺序。我们做出了两个主要贡献:1。用于一次性查询的基于成本的动态规划优化器,它(i)为最坏情况最优计划选择有效的查询顶点排序,(ii)生成混合计划,将传统的二进制连接与最坏情况最优风格的多路交叉口混合在一起。除了我们的优化器之外,我们还描述了一种自适应技术,该技术在查询执行期间更改最坏情况最优子计划的查询顶点顺序,以提高查询评估的效率。我们的一次性优化器的计划空间包含了基于先前工作的树分解而不在计划空间中的计划。2. 基于增量子图查询框架的连续查询的基于成本的贪心优化器。给定一组连续查询,我们的优化器将这些查询分解为多个增量子图查询,为每个增量查询选择一个计划,并生成一个单独的组合计划来计算所有查询。如果操作符执行相同的交集,那么我们的组合计划将在增量查询的计划的操作符之间共享计算。为了增加共享的计算量,我们描述了一个额外的优化,该优化在多个操作符之间共享部分交集。我们的优化器使用一种新的最坏情况最优计划的成本度量,称为交叉口成本。在生成混合计划时,我们针对一次性查询的动态规划优化器将交集成本与二元连接成本结合起来。我们通过大量的实验证明了我们的方案、自适应技术和部分交叉口共享优化的有效性。我们的优化器集成到GraphflowDB中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
On Finding Rank Regret Representatives Answering (Unions of) Conjunctive Queries using Random Access and Random-Order Enumeration Persistent Summaries Influence Maximization Revisited: Efficient Sampling with Bound Tightened The Space-Efficient Core of Vadalog
×
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