Declarative Recursive Computation on an RDBMS or, Why You Should Use a Database For Distributed Machine Learning

Dimitrije Jankov; Shangyu Luo; Binhang Yuan; Zhuhua Cai; Jia Zou; Chris Jermaine; Zekai J. Gao

doi:10.14778/3317315.3317323

Declarative Recursive Computation on an RDBMS or, Why You Should Use a Database For Distributed Machine Learning

Dimitrije Jankov, Shangyu Luo, Binhang Yuan, Zhuhua Cai, Jia Zou, Chris Jermaine, Zekai J. Gao

Research output: Contribution to journal › Conference article published in journal › peer-review

27 Citations (Scopus)

Abstract

A number of popular systems, most notably Google's TensorFlow, have been implemented from the ground up to support machine learning tasks. We consider how to make a very small set of changes to a modern relational database management system (RDBMS) to make it suitable for distributed learning computations. Changes include adding better support for recursion, and optimization and execution of very large compute plans. We also show that there are key advantages to using an RDBMS as a machine learning platform. In particular, learning based on a database management system allows for trivial scaling to large data sets and especially large models, where different computational units operate on different parts of a model that may be too large to fit into RAM.

Original language	English
Pages (from-to)	822-835
Number of pages	14
Journal	Proceedings of the VLDB Endowment
Volume	12
Issue number	7
DOIs	https://doi.org/10.14778/3317315.3317323
Publication status	Published - 2018
Externally published	Yes
Event	45th International Conference on Very Large Data Bases, VLDB 2019 - Los Angeles, United States Duration: 26 Aug 2017 → 30 Aug 2017

Bibliographical note

Publisher Copyright:
© 2019, VLDB Endowment.

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10.14778/3317315.3317323

Cite this

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abstract = "A number of popular systems, most notably Google's TensorFlow, have been implemented from the ground up to support machine learning tasks. We consider how to make a very small set of changes to a modern relational database management system (RDBMS) to make it suitable for distributed learning computations. Changes include adding better support for recursion, and optimization and execution of very large compute plans. We also show that there are key advantages to using an RDBMS as a machine learning platform. In particular, learning based on a database management system allows for trivial scaling to large data sets and especially large models, where different computational units operate on different parts of a model that may be too large to fit into RAM.",

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AU - Jankov, Dimitrije

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AU - Cai, Zhuhua

AU - Zou, Jia

AU - Jermaine, Chris

AU - Gao, Zekai J.

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AB - A number of popular systems, most notably Google's TensorFlow, have been implemented from the ground up to support machine learning tasks. We consider how to make a very small set of changes to a modern relational database management system (RDBMS) to make it suitable for distributed learning computations. Changes include adding better support for recursion, and optimization and execution of very large compute plans. We also show that there are key advantages to using an RDBMS as a machine learning platform. In particular, learning based on a database management system allows for trivial scaling to large data sets and especially large models, where different computational units operate on different parts of a model that may be too large to fit into RAM.

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Declarative Recursive Computation on an RDBMS or, Why You Should Use a Database For Distributed Machine Learning

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