article

Orthogonal optimization of subqueries and aggregation

Authors:

César Galindo-Legaria,

Milind JoshiAuthors Info & Claims

ACM SIGMOD Record, Volume 30, Issue 2

Pages 571 - 581

https://doi.org/10.1145/376284.375748

Published: 01 May 2001 Publication History

Abstract

There is considerable overlap between strategies proposed for subquery evaluation, and those for grouping and aggregation. In this paper we show how a number of small, independent primitives generate a rich set of efficient execution strategies —covering standard proposals for subquery evaluation suggested in earlier literature. These small primitives fall into two main, orthogonal areas: Correlation removal, and efficient processing of outerjoins and GroupBy. An optimization approach based on these pieces provides syntax-independence of query processing with respect to subqueries, i. e. equivalent queries written with or without subquery produce the same efficient plan.

We describe techniques implemented in Microsoft SQL Server (releases 7.0 and 8.0) for queries containing sub-queries and/or aggregations, based on a number of orthogonal optimizations. We concentrate separately on removing correlated subqueries, also called “query flattening,” and on efficient execution of queries with aggregations. The end result is a modular, flexible implementation, which produces very efficient execution plans. To demonstrate the validity of our approach, we present results for some queries from the TPC-H benchmark. From all published TPC-H results in the 300GB scale, at the time of writing (November 2000), SQL Server has the fastest results on those queries, even on a fraction of the processors used by other systems.

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Cited By

Lee YKim KLee WHan W(2024)In-depth Analysis of Continuous Subgraph Matching in a Common Delta Query Compilation FrameworkProceedings of the ACM on Management of Data10.1145/36549502:3(1-27)Online publication date: 30-May-2024
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Information

Published In

cover image ACM SIGMOD Record

ACM SIGMOD Record Volume 30, Issue 2

June 2001

625 pages

ISSN:0163-5808

DOI:10.1145/376284

Editors:
Timos Sellis
National Technical Univ. of Athens
,
Sharad Mehrotra
Univ. of California at Irvine

Issue’s Table of Contents

SIGMOD '01: Proceedings of the 2001 ACM SIGMOD international conference on Management of data
May 2001
630 pages
ISBN:1581133324
DOI:10.1145/375663
Editors:
Timos Sellis,
Sharad Mehrotra

Copyright © 2001 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 01 May 2001

Published in SIGMOD Volume 30, Issue 2

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Cited By

Lee YKim KLee WHan W(2024)In-depth Analysis of Continuous Subgraph Matching in a Common Delta Query Compilation FrameworkProceedings of the ACM on Management of Data10.1145/36549502:3(1-27)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654950
Zhang CFarouk T(2024)Sharing Queries with Nonequivalent User-defined Aggregate FunctionsACM Transactions on Database Systems10.1145/364913349:2(1-46)Online publication date: 10-Apr-2024
https://dl.acm.org/doi/10.1145/3649133
Simon EAmann BLiu RGançarski S(2023)Controlling the Correctness of Aggregation Operations During Sessions of Interactive Analytic QueriesJournal of Data and Information Quality10.1145/357581215:2(1-41)Online publication date: 23-Jun-2023
https://dl.acm.org/doi/10.1145/3575812
Fent PBirler ANeumann T(2022)Practical planning and execution of groupjoin and nested aggregatesThe VLDB Journal10.1007/s00778-022-00765-x32:6(1165-1190)Online publication date: 22-Oct-2022
https://doi.org/10.1007/s00778-022-00765-x
Fent PNeumann T(2021)A practical approach to groupjoin and nested aggregatesProceedings of the VLDB Endowment10.14778/3476249.347628814:11(2383-2396)Online publication date: 27-Oct-2021
https://dl.acm.org/doi/10.14778/3476249.3476288
Ko SLee THong KLee WSeo ISeo JHan WLi GLi ZIdreos SSrivastava D(2021)iTurboGraphProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3457243(977-990)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3457243
Floratos SXiao MWang HGuo CYuan YLee RZhang X(2021)NestGPU: Nested Query Processing on GPU2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00092(1008-1019)Online publication date: Apr-2021
https://doi.org/10.1109/ICDE51399.2021.00092
Ramachandra KPark K(2019)BlackMagicProceedings of the VLDB Endowment10.14778/3352063.335207212:12(1810-1813)Online publication date: 1-Aug-2019
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Wu YAlawini ADeutch DMilo TDavidson S(2019)ProvCiteProceedings of the VLDB Endowment10.14778/3317315.331731712:7(738-751)Online publication date: 1-Mar-2019
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Ramachandra KPark KEmani KHalverson AGalindo-Legaria CCunningham C(2018)FroidProceedings of the VLDB Endowment10.1145/3164135.316414011:4(432-444)Online publication date: 5-Oct-2018
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