research-article

Choosing a cloud DBMS: architectures and tradeoffs

Authors:

Matthew Perron,

Michael Stonebraker,

Marco Serafini,

Ashraf Aboulnaga,

Tim KraskaAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 12, Issue 12

Pages 2170 - 2182

https://doi.org/10.14778/3352063.3352133

Published: 01 August 2019 Publication History

Abstract

As analytic (OLAP) applications move to the cloud, DBMSs have shifted from employing a pure shared-nothing design with locally attached storage to a hybrid design that combines the use of shared-storage (e.g., AWS S3) with the use of shared-nothing query execution mechanisms. This paper sheds light on the resulting tradeoffs, which have not been properly identified in previous work. To this end, it evaluates the TPC-H benchmark across a variety of DBMS offerings running in a cloud environment (AWS) on fast 10Gb+ networks, specifically database-as-a-service offerings (Redshift, Athena), query engines (Presto, Hive), and a traditional cloud agnostic OLAP database (Vertica). While these comparisons cannot be apples-to-apples in all cases due to cloud configuration restrictions, we nonetheless identify patterns and design choices that are advantageous. These include prioritizing low-cost object stores like S3 for data storage, using system agnostic yet still performant columnar formats like ORC that allow easy switching to other systems for different workloads, and making features that benefit subsequent runs like query precompilation and caching remote data to faster storage optional rather than required because they disadvantage ad hoc queries.

References

[1]

O. Agmon Ben-Yehuda, M. Ben-Yehuda, A. Schuster, and D. Tsafrir. Deconstructing amazon ec2 spot instance pricing. ACM Trans. Econ. Comput., 1(3):16:1--16:20, 2013.

Digital Library

[2]

AWS. Redshift documentation: Factors affecting query performance, 2012. https://docs.aws.amazon.com/redshift/latest/dg/c-query-performance.html, Last accessed 2018-06-15.

[3]

AWS. Cluster configuration guidelines and best practices, 2019. https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-plan-instances-guidelines.html, Last accessed 2019-02-01.

[4]

D. Bermbach, J. Kuhlenkamp, A. Dey, A. Ramachandran, A. Fekete, and S. Tai. Benchfoundry: A benchmarking framework for cloud storage services. In Proc. Int. Conf. on Service-Oriented Computing (ICSOC), pages 314--330, 2017.

Digital Library

[5]

C. Binnig, D. Kossmann, T. Kraska, and S. Loesing. How is the weather tomorrow?: Towards a benchmark for the cloud. In Proc. Second Int. Workshop on Testing Database Systems, DBTest '09, pages 9:1--9:6, 2009.

Digital Library

[6]

B. F. Cooper, A. Silberstein, E. Tam, R. Ramakrishnan, and R. Sears. Benchmarking cloud serving systems with YCSB. In Proc. 1st ACM Sym. on Cloud Computing, SoCC '10, pages 143--154, 2010.

Digital Library

[7]

B. Dageville, T. Cruanes, M. Zukowski, V. Antonov, A. Avanes, J. Bock, J. Claybaugh, D. Engovatov, M. Hentschel, J. Huang, A. W. Lee, A. Motivala, A. Q. Munir, S. Pelley, P. Povinec, G. Rahn, S. Triantafyllis, and P. Unterbrunner. The snowflake elastic data warehouse. In Proc. 2016 Int. Conf. on Management of Data, SIGMOD '16, pages 215--226, 2016.

Digital Library

[8]

Databricks. Benchmarking Big Data SQL Platforms in the Cloud: TPC-DS benchmarks demonstrate Databricks Runtime 3.0's superior performance, 2017. https://databricks.com/blog/2017/07/12/benchmarking-big-data-sql-platforms-in-the-cloud.html, Last accessed 2018-07-15.

[9]

M. Dayarathna and T. Suzumura. Graph database benchmarking on cloud environments with XGDBench. Automated Software Eng., 21(4):509--533, 2014.

Digital Library

[10]

A. Gupta, D. Agarwal, D. Tan, J. Kulesza, R. Pathak, S. Stefani, and V. Srinivasan. Amazon Redshift and the case for simpler data warehouses. In Proc. of the 2015 Int. Conf. Management of Data, SIGMOD '15, pages 1917--1923, 2015.

Digital Library

[11]

M. Han, K. Daudjee, K. Ammar, M. T. Özsu, X. Wang, and T. Jin. An experimental comparison of Pregel-like graph processing systems. PVLDB, 7(12):1047--1058, 2014.

Digital Library

[12]

Hortonworks. Apache Tez: Overview, 2018. https://hortonworks.com/apache/tez/, Last accessed 2018-08-01.

[13]

D. Kossmann, T. Kraska, and S. Loesing. An evaluation of alternative architectures for transaction processing in the cloud. In Proc. of the 2010 Int. Conf. Management of Data, SIGMOD '10, pages 579--590, 2010.

Digital Library

[14]

A. Lamb, M. Fuller, R. Varadarajan, N. Tran, B. Vandiver, L. Doshi, and C. Bear. The Vertica analytic database: C-store 7 years later. PVLDB, 5(12):1790--1801, 2012.

Digital Library

[15]

A. Lenk, M. Menzel, J. Lipsky, S. Tai, and P. Offermann. What are you paying for? performance benchmarking for infrastructure-as-a-service offerings. In Proc. 2011 IEEE 4th Int. Conf. on Cloud Computing, CLOUD '11, pages 484--491, 2011.

Digital Library

[16]

T. Neumann. Efficiently compiling efficient query plans for modern hardware. PVLDB, 4(9):539--550, 2011.

Digital Library

[17]

N. Nix. CIA tech official calls Amazon cloud project 'transformational'. Bloomberg, June 2018. https://www.bloomberg.com/news/articles/2018-06-20/cia-tech-official-calls-amazon-cloud-project-transformational, Last accessed 2018-10-01.

[18]

J. Schad, J. Dittrich, and J.-A. Quiané-Ruiz. Runtime measurements in the cloud: Observing, analyzing, and reducing variance. PVLDB, 3(1-2):460--471, 2010.

Digital Library

[19]

R. Sethi, M. Traverso, D. Sundstrom, D. Phillips, W. Xie, Y. Sun, N. Yigitbasi, H. Jin, E. Hwang, N. Shingte, and C. Berner. Presto: SQL on everything. In IEEE 35th Int. Conf. on Data Eng. (ICDE), pages 1802--1813, 2019.

[20]

A. Shiu. Why we chose Redshift, 2015. https://amplitude.com/blog/2015/03/27/why-we-chose-redshift, Last accessed 2018-11-05.

[21]

M. Stonebraker, A. Pavlo, R. Taft, and M. L. Brodie. Enterprise database applications and the cloud: A difficult road ahead. In 2014 IEEE Int. Conf. Cloud Eng.}, pages 1--6, 2014.

Digital Library

[22]

D. Sundstrom. Even faster: Data at the speed of Presto ORC, 2015. https://code.fb.com/core-data/even-faster-data-at-the-speed-of-presto-orc/, Last accessed 2018-04-15.

[23]

A. Thusoo, J. S. Sarma, N. Jain, Z. Shao, P. Chakka, S. Anthony, H. Liu, P. Wyckoff, and R. Murthy. Hive: A warehousing solution over a map-reduce framework. PVLDB, 2(2):1626--1629, 2009.

Digital Library

[24]

B. Vandiver, S. Prasad, P. Rana, E. Zik, A. Saeidi, P. Parimal, S. Pantela, and J. Dave. Eon mode: Bringing the Vertica columnar database to the cloud. In Proc. 2018 Int. Conf. Management of Data, SIGMOD '18, pages 797--809, 2018.

Digital Library

[25]

B. Varghese, O. Akgun, I. Miguel, L. Thai, and A. Barker. Cloud benchmarking for performance. In IEEE 6th Int. Conf. Cloud Computing Technology and Science, pages 535--540, 2014.

Digital Library

[26]

B. Varghese, L. T. Subba, L. Thai, and A. Barker. Container-based cloud virtual machine benchmarking. In IEEE Int. Conf. on Cloud Eng. (IC2E), pages 192--201, 2016.

[27]

A. Verbitski, A. Gupta, D. Saha, M. Brahmadesam, K. Gupta, R. Mittal, S. Krishnamurthy, S. Maurice, T. Kharatishvili, and X. Bao. Amazon Aurora: Design considerations for high throughput cloud-native relational databases. In Proc. ACM Int. Conf. on Management of Data, SIGMOD '17, pages 1041--1052, 2017.

Digital Library

[28]

Vertica. Configuring storage (documentation). https://www.vertica.com/docs/9.1.x/HTML/index.htm#Authoring/UsingVerticaOnAWS/ConfiguringStorage.htm, Last accessed 2019-01-13.

Cited By

van Renen AHorn DPfeil PVaidya KDong WNarayanaswamy MLiu ZSaxena GKipf AKraska T(2024)Why TPC is Not Enough: An Analysis of the Amazon Redshift FleetProceedings of the VLDB Endowment10.14778/3681954.368203117:11(3694-3706)Online publication date: 30-Aug-2024
https://doi.org/10.14778/3681954.3682031
Srivastava TFernandez R(2024)Saving Money for Analytical Workloads in the CloudProceedings of the VLDB Endowment10.14778/3681954.368201817:11(3524-3537)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.14778/3681954.3682018
Baumstark AParadies MSattler KKläbe SBaumann S(2024)So Far and yet so Near - Accelerating Distributed Joins with CXLProceedings of the 20th International Workshop on Data Management on New Hardware10.1145/3662010.3663449(1-9)Online publication date: 10-Jun-2024
https://dl.acm.org/doi/10.1145/3662010.3663449
Show More Cited By

Choosing a cloud DBMS: architectures and tradeoffs
1. Information systems
  1. Data management systems

Recommendations

Cloud service engineering
ICSE '10: Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 2

Building on compute and storage virtualization, Cloud Computing provides scalable, network-centric, abstracted IT infrastructure, platforms, and applications as on-demand services that are billed by consumption. Cloud Service Engineering is the ...
Cloud Storage as the Infrastructure of Cloud Computing
ICICCI '10: Proceedings of the 2010 International Conference on Intelligent Computing and Cognitive Informatics

As an emerging technology and business paradigm, Cloud Computing has taken commercial computing by storm. Cloud computing platforms provide easy access to a company’s high-performance computing and storage infrastructure through web services. With cloud ...
Orchestrating DBMS Benchmarking in the Cloud with Kubernetes
Performance Evaluation and Benchmarking
Abstract
Containerization has become a common practise in software provisioning. Kubernetes (K8s) is useful in deploying containers in clusters, in managing their lifecycle, in scheduling and resource allocation. The benchmarking process requires the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 12, Issue 12

August 2019

547 pages

ISSN:2150-8097

Editors:
Lei Chen,
Fatma Özcan

Issue’s Table of Contents

Publisher

VLDB Endowment

Publication History

Published: 01 August 2019

Published in PVLDB Volume 12, Issue 12

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

16
Total Citations
View Citations
694
Total Downloads

Downloads (Last 12 months)106
Downloads (Last 6 weeks)6

Reflects downloads up to 30 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

van Renen AHorn DPfeil PVaidya KDong WNarayanaswamy MLiu ZSaxena GKipf AKraska T(2024)Why TPC is Not Enough: An Analysis of the Amazon Redshift FleetProceedings of the VLDB Endowment10.14778/3681954.368203117:11(3694-3706)Online publication date: 30-Aug-2024
https://doi.org/10.14778/3681954.3682031
Srivastava TFernandez R(2024)Saving Money for Analytical Workloads in the CloudProceedings of the VLDB Endowment10.14778/3681954.368201817:11(3524-3537)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.14778/3681954.3682018
Baumstark AParadies MSattler KKläbe SBaumann S(2024)So Far and yet so Near - Accelerating Distributed Joins with CXLProceedings of the 20th International Workshop on Data Management on New Hardware10.1145/3662010.3663449(1-9)Online publication date: 10-Jun-2024
https://dl.acm.org/doi/10.1145/3662010.3663449
Bang TDouglas CCrooks NHellerstein J(2024)SkyPIE: A Fast & Accurate Oracle for Object PlacementProceedings of the ACM on Management of Data10.1145/36393102:1(1-27)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639310
Nathan VSingh VLiu ZRahman MKipf AHorn DPagano DSaxena GNarayanaswamy BKraska TBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)Intelligent Scaling in Amazon RedshiftCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3653394(269-279)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3626246.3653394
Yang YYu XSerafini MAboulnaga AStonebraker M(2024)FlexpushdownDB: rethinking computation pushdown for cloud OLAP DBMSsThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-024-00867-833:5(1643-1670)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s00778-024-00867-8
Paulos GZhang TZhang YZhu GKarimov JJacobsen HOnut IShirani POnut IOnut IBranco P(2023)Efficient Data Transfer in Shared-storage Cloud Data Processing Systems with OPTICSProceedings of the 33rd Annual International Conference on Computer Science and Software Engineering10.5555/3615924.3623630(230-234)Online publication date: 11-Sep-2023
https://dl.acm.org/doi/10.5555/3615924.3623630
Durner DLeis VNeumann T(2023)Exploiting Cloud Object Storage for High-Performance AnalyticsProceedings of the VLDB Endowment10.14778/3611479.361148616:11(2769-2782)Online publication date: 24-Aug-2023
https://dl.acm.org/doi/10.14778/3611479.3611486
van Renen ALeis V(2023)Cloud Analytics BenchmarkProceedings of the VLDB Endowment10.14778/3583140.358315616:6(1413-1425)Online publication date: 1-Feb-2023
https://dl.acm.org/doi/10.14778/3583140.3583156
Wu TGu RLi YMa HChen YZhu YYu XXu THuang Y(2023)Raven: Benchmarking Monetary Expense and Query Efficiency of OLAP Engines on the CloudDatabase Systems for Advanced Applications10.1007/978-3-031-30678-5_45(593-605)Online publication date: 17-Apr-2023
https://dl.acm.org/doi/10.1007/978-3-031-30678-5_45
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents