research-article

Free access

Scalable linear algebra on a relational database system

Authors:

Michael Gubanov,

Dimitrije Jankov,

Christopher JermaineAuthors Info & Claims

Communications of the ACM, Volume 63, Issue 8

Pages 93 - 101

https://doi.org/10.1145/3405470

Published: 22 July 2020 Publication History

All formats PDF

Abstract

As data analytics has become an important application for modern data management systems, a new category of data management system has appeared recently: the scalable linear algebra system. We argue that a parallel or distributed database system is actually an excellent platform upon which to build such functionality. Most relational systems already have support for cost-based optimization---which is vital to scaling linear algebra computations---and it is well known how to make relational systems scalable.

We show that by making just a few changes to a parallel/distributed relational database system, such a system can become a competitive platform for scalable linear algebra. Taken together, our results should at least raise the possibility that brand new systems designed from the ground up to support scalable linear algebra are not absolutely necessary, and that such systems could instead be built on top of existing relational technology.

References

[1]

Apache spark mllib: http://spark.apache.org/docs/latest/mllib-data-types.html.

[2]

Oracle corporation: https://docs.oracle.com/cd/B1930-6_01/index.htm.

[3]

Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. Tensorflow: A system for large-scale machine learning. In Proceedings of the 12th [USENIX] Symposium on Operating Systems Design and Implementation ([OSDI] 16, 2016), 265--283.

[4]

Armbrust, M., Xin, R.S., Lian, C., Huai, Y., Liu, D., Bradley, J.K., Meng, X., Kaftan, T., Franklin, M.J., Ghodsi, A., et al. Spark sql: Relational data processing in spark. In SIGMOD (2015), ACM, 1383--1394.

Digital Library

[5]

Baumann, P., Dehmel, A., Furtado, P., Ritsch, R., Widmann, N. The multidimensional database system rasdaman. In SIGMOD Record (Volume 27, 1998), ACM, 575--577.

Digital Library

[6]

Blackford, L.S., Choi, J., Cleary, A., D'Azevedo, E., Demmel, J., Dhillon, I., Dongarra, J., Hammarling, S., Henry, G., Petitet, A., et al. ScaLAPACK Users' Guide, Volume 4. SIAM, 1997.

Digital Library

[7]

Boehm, M., Burdick, D.R., Evfimievski, A.V., Reinwald, B., Reiss, F.R., Sen, P., Tatikonda, S., Tian, Y. Systemml's optimizer: Plan generation for large-scale machine learning programs. IEEE Data Eng. Bull. 3, 37 (2014), 52--62.

[8]

Brown, P.G. Overview of SciDB: Large scale array storage, processing and analysis. In SIGMOD, 2010, 963--968.

[9]

Cai, Z., Vagena, Z., Perez, L.L., Arumugam, S., Haas, P.J., Jermaine, C. Simulation of database-valued Markov chains using SimSQL. In SIGMOD, 2013, 637--648.

[10]

Chaudhuri, S. An overview of query optimization in relational systems. In PODS (1998), ACM, 34--43.

Digital Library

[11]

Das, S., Sismanis, Y., Beyer, K.S., Gemulla, R., Haas, P.J., McPherson, J. Ricardo: integrating R and Hadoop. In SIGMOD, 2010, 987--998.

[12]

Ghoting, A., Krishnamurthy, R., Pednault, E., Reinwald, B., Sindhwani, V., Tatikonda, S., Tian, Y., Vaithyanathan, S. SystemML: Declarative machine learning on mapreduce. In ICDE, 2011, 231--242.

[13]

Goyal, P., Dollár, P., Girshick, R.B., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., He, K. Accurate, large minibatch sgd: Training imagenet in 1 hour. CoRR, 2017, abs/1706.02677.

[14]

Huang, B., Babu, S., Yang, J. Cumulon: Optimizing statistical data analysis in the cloud. In SIGMOD, 2013, 1--12.

[15]

Jankov, D., Luo, S., Yuan, B., Cai, Z., Zou, J., Jermaine, C., Gao, Z.J. Declarative recursive computation on an rdbms, or, why you should use a database for distributed machine learning. PVLDB, 2019, 12.

[16]

Lebanon, G. Metric learning for text documents. IEEE PAMI 4, 28 (2006), 497--508

[17]

Libkin, L., Machlin, R., Wong, L. A query language for multidimensional arrays: Design, implementation, and optimization techniques. In SIGMOD (1996), 228--239.

Digital Library

[18]

Qian, Z., Chen, X., Kang, N., Chen, M., Yu, Y., Moscibroda, T., Zhang, Z. Madlinq: large-scale distributed matrix computation for the cloud. In EuroSys (2012), ACM, 197--210

Digital Library

[19]

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

[20]

Zaharia, M., Chowdhury, M., Franklin M.J., Shenker, S., Stoica, I. Spark: Cluster computing with working sets. In USENIX HotCloud, 2010, 1--10.

[21]

Zhang, Y., Zhang, W., Yang, J. I/o-efficient statistical computing with riot. In ICDE, 2010, 1157--1160.

Cited By

Xu LQiu SYuan BJiang JRenggli CGan SKara KLi GLiu JWu WYe JZhang C(2024)Stochastic gradient descent without full data shuffle: with applications to in-database machine learning and deep learning systemsThe VLDB Journal10.1007/s00778-024-00845-033:5(1231-1255)Online publication date: 12-Apr-2024
https://doi.org/10.1007/s00778-024-00845-0
Olteanu DVortmeier NŽivanović Ɖ(2024)Givens rotations for QR decomposition, SVD and PCA over database joinsThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-023-00818-933:4(1013-1037)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s00778-023-00818-9
Schleich MShaikhha ASuciu D(2023)Optimizing Tensor Programs on Flexible StorageProceedings of the ACM on Management of Data10.1145/35887171:1(1-27)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.1145/3588717
Show More Cited By

Index Terms

Scalable linear algebra on a relational database system
1. Mathematics of computing
  1. Mathematical analysis
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory

Recommendations

Scalable Linear Algebra on a Relational Database System

Scalable linear algebra is important for analytics and machine learning (including deep learning). In this paper, we argue that a parallel or distributed database system is actually an excellent platform upon which to build such functionality. Most ...
Efficient and scalable xml data processing using relational database systems
An academic relational algebra database management system

This paper describes a relational algebra database management system written by two graduate students at West Virginia University. The system is designed to be used in an undergraduate database class to demonstrate queries posed in the relational ...

Comments

Information & Contributors

Information

Published In

cover image Communications of the ACM

Communications of the ACM Volume 63, Issue 8

August 2020

93 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/3411844

Editor:
Andrew A. Chien
Association for Computing Machinery, New York, NY

Issue’s Table of Contents

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 the author(s) 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].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 July 2020

Published in CACM Volume 63, Issue 8

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article
Research
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
14,914
Total Downloads

Downloads (Last 12 months)307
Downloads (Last 6 weeks)42

Reflects downloads up to 30 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Xu LQiu SYuan BJiang JRenggli CGan SKara KLi GLiu JWu WYe JZhang C(2024)Stochastic gradient descent without full data shuffle: with applications to in-database machine learning and deep learning systemsThe VLDB Journal10.1007/s00778-024-00845-033:5(1231-1255)Online publication date: 12-Apr-2024
https://doi.org/10.1007/s00778-024-00845-0
Olteanu DVortmeier NŽivanović Ɖ(2024)Givens rotations for QR decomposition, SVD and PCA over database joinsThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-023-00818-933:4(1013-1037)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s00778-023-00818-9
Schleich MShaikhha ASuciu D(2023)Optimizing Tensor Programs on Flexible StorageProceedings of the ACM on Management of Data10.1145/35887171:1(1-27)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.1145/3588717
Paganelli MSottovia PPark KInterlandi MGuerra F(2023)Pushing ML Predictions Into DBMSsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.326959235:10(10295-10308)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3269592
Xu LQiu SYuan BJiang JRenggli CGan SKara KLi GLiu JWu WYe JZhang CIves ZBonifati AEl Abbadi A(2022)In-Database Machine Learning with CorgiPile: Stochastic Gradient Descent without Full Data ShuffleProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3526150(1286-1300)Online publication date: 10-Jun-2022
https://dl.acm.org/doi/10.1145/3514221.3526150
Olteanu DVortmeier NZivanović ĐIves ZBonifati AEl Abbadi A(2022)Givens QR Decomposition over Relational DatabasesProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3526144(1948-1961)Online publication date: 10-Jun-2022
https://dl.acm.org/doi/10.1145/3514221.3526144
Park KSaur KBanda DSen RInterlandi MKaranasos KIves ZBonifati AEl Abbadi A(2022)End-to-end Optimization of Machine Learning Prediction QueriesProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3526141(587-601)Online publication date: 10-Jun-2022
https://dl.acm.org/doi/10.1145/3514221.3526141
Geerts FMuñoz TRiveros CVan den Bussche JVrgoč D(2021)Matrix Query LanguagesACM SIGMOD Record10.1145/3503780.350378250:3(6-19)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1145/3503780.3503782
Aamer HHidders JParedaens JVan den Bussche JLibkin LPichler RGuagliardo P(2021)Expressiveness within Sequence DatalogProceedings of the 40th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems10.1145/3452021.3458327(70-81)Online publication date: 20-Jun-2021
https://dl.acm.org/doi/10.1145/3452021.3458327

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Digital Edition

View this article in digital edition.

Digital Edition

Magazine Site

View this article on the magazine site (external)

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

Media

Figures

Other

Tables

View Issue’s Table of Contents