research-article

Efficient transaction processing in SAP HANA database: the end of a column store myth

Authors:

Wolfgang Lehner,

Christof BornhövdAuthors Info & Claims

SIGMOD '12: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data

Pages 731 - 742

https://doi.org/10.1145/2213836.2213946

Published: 20 May 2012 Publication History

Abstract

The SAP HANA database is the core of SAP's new data management platform. The overall goal of the SAP HANA database is to provide a generic but powerful system for different query scenarios, both transactional and analytical, on the same data representation within a highly scalable execution environment. Within this paper, we highlight the main features that differentiate the SAP HANA database from classical relational database engines. Therefore, we outline the general architecture and design criteria of the SAP HANA in a first step. In a second step, we challenge the common belief that column store data structures are only superior in analytical workloads and not well suited for transactional workloads. We outline the concept of record life cycle management to use different storage formats for the different stages of a record. We not only discuss the general concept but also dive into some of the details of how to efficiently propagate records through their life cycle and moving database entries from write-optimized to read-optimized storage formats. In summary, the paper aims at illustrating how the SAP HANA database is able to efficiently work in analytical as well as transactional workload environments.

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Cited By

Qian SGoel AGavrilovska ATerry D(2024)Massively parallel multi-versioned transaction processingProceedings of the 18th USENIX Conference on Operating Systems Design and Implementation10.5555/3691938.3691979(765-781)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.5555/3691938.3691979
Ahn MWillhalm TMay NLee DDesai SBooss DKim JSingh NRitter DRebholz O(2024)An Examination of CXL Memory Use Cases for In-Memory Database Management Systems Using SAP HANAProceedings of the VLDB Endowment10.14778/3685800.368580917:12(3827-3840)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.14778/3685800.3685809
Yu GWu ZKossmann FLi TMarkakis MNgom AMadden SKraska T(2024)Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRADProceedings of the VLDB Endowment10.14778/3681954.368202617:11(3629-3643)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.14778/3681954.3682026
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Index Terms

Efficient transaction processing in SAP HANA database: the end of a column store myth
1. Information systems
  1. Data management systems
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Database query processing and optimization (theory)

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cover image ACM Conferences

SIGMOD '12: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data

May 2012

886 pages

ISBN:9781450312479

DOI:10.1145/2213836

General Chairs:
K. Selçuk Candan
Arizona State University
,
Yi Chen
Arizona State University
,
Richard Snodgrass
University of Arizona
,
Program Chair:
Luis Gravano
Columbia University
,
Publications Chair:
Ariel Fuxman
Microsoft Research

Copyright © 2012 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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Published: 20 May 2012

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SIGMOD/PODS '12: International Conference on Management of Data

May 20 - 24, 2012

Arizona, Scottsdale, USA

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SIGMOD '12 Paper Acceptance Rate 48 of 289 submissions, 17%;

Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

Qian SGoel AGavrilovska ATerry D(2024)Massively parallel multi-versioned transaction processingProceedings of the 18th USENIX Conference on Operating Systems Design and Implementation10.5555/3691938.3691979(765-781)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.5555/3691938.3691979
Ahn MWillhalm TMay NLee DDesai SBooss DKim JSingh NRitter DRebholz O(2024)An Examination of CXL Memory Use Cases for In-Memory Database Management Systems Using SAP HANAProceedings of the VLDB Endowment10.14778/3685800.368580917:12(3827-3840)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.14778/3685800.3685809
Yu GWu ZKossmann FLi TMarkakis MNgom AMadden SKraska T(2024)Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRADProceedings of the VLDB Endowment10.14778/3681954.368202617:11(3629-3643)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.14778/3681954.3682026
Schmidt TDurner DLeis VNeumann T(2024)Two Birds With One Stone: Designing a Hybrid Cloud Storage Engine for HTAPProceedings of the VLDB Endowment10.14778/3681954.368200117:11(3290-3303)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.14778/3681954.3682001
Halfpap SSchlosser RSerra ESpezzano F(2024)Fragment Allocations for Partially Replicated Databases Considering Data Modifications and Changing WorkloadsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679767(758-767)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679767
Zhang CLi GZhang JZhang XFeng J(2024)HTAP Databases: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.338969336:11(6410-6429)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2024.3389693
Song HZhou WCui HPeng XLi F(2024)A survey on hybrid transactional and analytical processingThe VLDB Journal10.1007/s00778-024-00858-933:5(1485-1515)Online publication date: 4-Jun-2024
https://doi.org/10.1007/s00778-024-00858-9
Magalhaes ABrayner AMonteiro J(2024)MM-DIRECTThe VLDB Journal10.1007/s00778-024-00846-z33:3(859-882)Online publication date: 27-Mar-2024
https://doi.org/10.1007/s00778-024-00846-z
Lin W(2024)A Design of Hybrid Transactional and Analytical Processing Database for Energy Efficient Big Data QueriesGreen, Pervasive, and Cloud Computing10.1007/978-981-99-9893-7_10(128-138)Online publication date: 23-Jan-2024
https://doi.org/10.1007/978-981-99-9893-7_10
Kraska TLi TMadden SMarkakis MNgom AWu ZYu G(2023)Check Out the Big Brain on BRAD: Simplifying Cloud Data Processing with Learned Automated Data MeshesProceedings of the VLDB Endowment10.14778/3611479.361152616:11(3293-3301)Online publication date: 24-Aug-2023
https://dl.acm.org/doi/10.14778/3611479.3611526
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