research-article

Workload merging potential in SAP Hybris

Authors:

Robin Rehrmann,

Martin Keppner,

Wolfgang Lehner,

Carsten Binnig,

Arne SchwarzAuthors Info & Claims

DBTest '20: Proceedings of the workshop on Testing Database Systems

Article No.: 7, Pages 1 - 6

https://doi.org/10.1145/3395032.3395326

Published: 19 June 2020 Publication History

Abstract

OLTP DBMSs in enterprise scenarios are often facing the challenge to deal with workload peaks resulting from events such as Cyber Monday or Black Friday. The traditional solution to prevent running out of resources and thus coping with such workload peaks is to use a significant over-provisioning of the underlying infrastructure. Another direction to cope with such peak scenarios is to apply resource sharing. In a recent work, we showed that merging read statements in OLTP scenarios offers the opportunity to maintain low latency for systems under heavy load without over-provisioning.

In this paper, we analyze a real enterprise OLTP workload --- SAP Hybris --- with respect to statements types, complexity, and hot-spot statements to find potential candidates for workload sharing in OLTP. We additionally share work of the Hybris workload in our system OLTPShare and report on savings with respect to CPU consumption. Another interesting effect we show is that with OLTPShare, we can increase the SAP Hybris throughput by 20%.

References

[1]

State of online retail performance. Technical report, Akamai Technologies, Inc., Spring 2017.

[2]

D. An. Find out how you stack up to new industry benchmarks for mobile page speed. Technical report, Google Global Product Lead, Mobile Web, February 2017.

[3]

M. Armstrong. Chart: Unstoppable Amazon | Statista. https://www.statista.com/chart/11785/unstoppable-amazon/, July 2018. [Online; accessed 10-January-2020].

[4]

G. Candea, N. Polyzotis, and R. Vingralek. Predictable performance and high query concurrency for data analytics. PVLDB, 20(2):227--248, 2011.

[5]

L. Chen, Y. Lin, J. Wang, H. Huang, D. Chen, and Y. Wu. Query grouping-based multi-query optimization framework for interactive sql query engines on hadoop. Concurrency and Computation: Practice and Experience, 30:e4676, 08 2018.

[6]

B. F. Cooper, A. Silberstein, E. Tam, R. Ramakrishnan, and R. Sears. Benchmarking cloud serving systems with YCSB. In Proceedings of the 1st ACM Symposium on Cloud Computing, SoCC '10, pages 143--154, New York, NY, USA, 2010. ACM.

Digital Library

[7]

D. J. DeWitt, R. H. Katz, F. Olken, L. D. Shapiro, M. R. Stonebraker, and D. A. Wood. Implementation techniques for main memory database systems. In Proc. ACM SIGMOD Int. Conf. Manag. Dat., SIGMOD '84, pages 1--8, New York, NY, USA, 1984. ACM.

Digital Library

[8]

M. R. Emily Wilson and T. Kejser. Analyzing I/O Characteristics and Sizing Storage Systems for SQL Server Database Applications. Technical report, Microsoft, 2010.

[9]

J. M. Faleiro and D. J. Abadi. Rethinking serializable multiversion concurrency control. PVLDB, 8(11):1190--1201, 2015.

Digital Library

[10]

Q. Ge, P. Peng, Z. Xu, L. Zou, and Z. Qin. FMQO: A federated RDF system supporting multi-query optimization. In Web and Big Data - Third International Joint Conference, APWeb-WAIM 2019, Chengdu, China, August 1-3, 2019, Proceedings, Part II, pages 397--401, 2019.

[11]

G. Giannikis, G. Alonso, and D. Kossmann. Shareddb: Killing one thousand queries with one stone. PVLDB, 5(6):526--537, 2012.

Digital Library

[12]

G. Huang, X. Cheng, J. Wang, Y. Wang, D. He, T. Zhang, F. Li, S. Wang, W. Cao, and Q. Li. X-engine: An optimized storage engine for large-scale e-commerce transaction processing. In Proceedings of the 2019 International Conference on Management of Data, SIGMOD '19, pages 651--665, New York, NY, USA, 2019. ACM.

Digital Library

[13]

J. Krueger, C. Kim, M. Grund, N. Satish, D. Schwalb, J. Chhugani, H. Plattner, P. Dubey, and A. Zeier. Fast updates on read-optimized databases using multi-core cpus. PVLDB, 5(1):61--72, 2011.

Digital Library

[14]

J. Leeka and K. Rajan. Incorporating super-operators in big-data query optimizers. Proc. VLDB Endow., 13(3):348--361, Nov. 2019.

Digital Library

[15]

D. Makreshanski, G. Giannikis, G. Alonso, and D. Kossmann. Mqjoin: Efficient shared execution of main-memory joins. PVLDB, 9(6):480--491, 2016.

Digital Library

[16]

D. Makreshanski, J. Giceva, C. Barthels, and G. Alonso. Batchdb: Efficient isolated execution of hybrid oltp+olap workloads for interactive applications. In Proceedings of the 2017 ACM International Conference on Management of Data, SIGMOD '17, pages 37--50, New York, NY, USA, 2017. ACM.

Digital Library

[17]

R. Marroquin, I. Müller, D. Makreshanski, and G. Alonso. Pay one, get hundreds for free: Reducing cloud costs through shared query execution. In SoCC, 2018.

Digital Library

[18]

K. May. Airline system look-to-book ratios soar, expected to go 10x higher. https://www.phocuswire.com/Airline-system-look-to-book-ratios-soar-expected-to-go-10x-higher, December 2015. ]Online; accessed 15-March-2019].

[19]

N. May, A. Böhm, and W. Lehner. SAP HANA -- The Evolution of an In-Memory DBMS from Pure OLAP Processing Towards Mixed Workloads. In B. Mitschang, D. Nicklas, F. Leymann, H. Schöning, M. Herschel, J. Teubner, T. Härder, O. Kopp, and M. Wieland, editors, Datenbanksysteme für Business, Technologie und Web (BTW 2017), pages 545--546. Gesellschaft für Informatik, Bonn, 2017.

[20]

S. Naga. Open Source Performance Testing Using Apache JMeter. Technical report, Cognizant Technology Solutions, 2009.

[21]

H. H. Ohad Rodeh and D. Chambliss. Visualizing Block IO Workloads. Technical report, IBM Research Division, October 2013.

[22]

I. Psaroudakis, M. Athanassoulis, M. Olma, and A. Ailamaki. Reactive and proactive sharing across concurrent analytical queries. In Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, SIGMOD '14, pages 889--892, New York, NY, USA, 2014. ACM.

Digital Library

[23]

R. Rehrmann, C. Binnig, A. Böhm, K. Kim, W. Lehner, and A. Rizk. OLTPshare: The Case for Sharing in OLTP Workloads. Proc. VLDB Endow., 11(12):1769--1780, Aug. 2018.

Digital Library

[24]

N. Roussopoulos. View indexing in relational databases. ACM Trans. Database Syst., 7(2):258--290, June 1982.

Digital Library

[25]

M. Samet. Consolidating Oracle® OLTP Workloads with XtremIO. Technical Report Part Number H13828-1 (Rev. 02), EMC Corporation, December 2014.

[26]

SAP Hybris. SAP Hybris Commerce, Architecture and Technology. Technical report, SAP Hybris, 2016.

[27]

T. K. Sellis. Multiple-query optimization. ACM Trans. Database Syst., 13(1):23--52, Mar. 1988.

Digital Library

[28]

Z. Shang, X. Liang, D. Tang, C. Ding, A. J. Elmore, S. Krishnan, and M. J. Franklin. Crocodiledb: Efficient database execution through intelligent deferment. In CIDR 2020, 10th Conference on Innovative Data Systems Research, Amsterdam, The Netherlands, January 12-15, 2020, Online Proceedings. www.cidrdb.org, 2020.

[29]

M. M. Simo Neuvonen, Antoni Wolski and V. Raatikka. Telecommunication application transaction processing (TATP) benchmark description. Technical report, IBM Software Group Information Management, March 2009.

[30]

T. Willhalm, N. Popovici, Y. Boshmaf, H. Plattner, A. Zeier, and J. Schaffner. Simdscan: Ultra fast in-memory table scan using on-chip vector processing units. PVLDB, 2(1):385--394, 2009.

Digital Library

[31]

D. V. A. Zeyuan Shang and A. Pavlo. Carnegie Mellon Database Application Catalog (CMDBAC). http://cmdbac.cs.cmu.edu, 2018. [Online; accessed 01-March-2018].

Cited By

Yanev MHarvey P(2022)Herding the FLOQ: Flow Optimised Queueing2022 IFIP Networking Conference (IFIP Networking)10.23919/IFIPNetworking55013.2022.9829812(1-9)Online publication date: 13-Jun-2022
https://doi.org/10.23919/IFIPNetworking55013.2022.9829812
Lu KZhao NWan JFei CZhao WDeng T(2022)TridentKV: A Read-Optimized LSM-Tree Based KV Store via Adaptive Indexing and Space-Efficient PartitioningIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.311859933:8(1953-1966)Online publication date: 1-Aug-2022
https://doi.org/10.1109/TPDS.2021.3118599
Rehrmann RBinnig CBöhm AKim KLehner W(2021)Sharing opportunities for OLTP workloads in different isolation levelsProceedings of the VLDB Endowment10.14778/3401960.340196713:10(1696-1708)Online publication date: 10-Mar-2021
https://dl.acm.org/doi/10.14778/3401960.3401967

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cover image ACM Conferences

DBTest '20: Proceedings of the workshop on Testing Database Systems

June 2020

42 pages

ISBN:9781450380010

DOI:10.1145/3395032

Copyright © 2020 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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SIGMOD: ACM Special Interest Group on Management of Data

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 June 2020

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SIGMOD/PODS '20: International Conference on Management of Data

June 19, 2020

Oregon, Portland

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DBTest '20 Paper Acceptance Rate 7 of 10 submissions, 70%;

Overall Acceptance Rate 31 of 56 submissions, 55%

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Cited By

Yanev MHarvey P(2022)Herding the FLOQ: Flow Optimised Queueing2022 IFIP Networking Conference (IFIP Networking)10.23919/IFIPNetworking55013.2022.9829812(1-9)Online publication date: 13-Jun-2022
https://doi.org/10.23919/IFIPNetworking55013.2022.9829812
Lu KZhao NWan JFei CZhao WDeng T(2022)TridentKV: A Read-Optimized LSM-Tree Based KV Store via Adaptive Indexing and Space-Efficient PartitioningIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.311859933:8(1953-1966)Online publication date: 1-Aug-2022
https://doi.org/10.1109/TPDS.2021.3118599
Rehrmann RBinnig CBöhm AKim KLehner W(2021)Sharing opportunities for OLTP workloads in different isolation levelsProceedings of the VLDB Endowment10.14778/3401960.340196713:10(1696-1708)Online publication date: 10-Mar-2021
https://dl.acm.org/doi/10.14778/3401960.3401967

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