MADlib is a free, open source library of in-database analytic methods. It provides an evolving suite of SQL-based algorithms for machine learning, data mining and statistics that run at scale within a database engine, with no need for data import/export to other tools. The goal is for MADlib to eventually serve a role for scalable database systems that is similar to the CRAN library for R: a community repository of statistical methods, this time written with scale and parallelism in mind.
In this paper we introduce the MADlib project, including the background that led to its beginnings, and the motivation for its open source nature. We provide an overview of the library’s architecture and design patterns, and provide a description of various statistical methods in that context. We include performance and speedup results of a core design pattern from one of those methods over the Greenplum parallel DBMS on a modest-sized test cluster. We then report on two initial e fforts at incorporating academic research into MADlib, which is one of the project’s goals.
MADlib is freely available at http://madlib.net, and the project is open for contributions of both new methods, and ports to additional database platforms.
Authors:
Joseph M. Hellerstein, Christoper Re, Florian Schoppmann, Daisy Zhe Wang, Eugene Fratkin, Aleks Gorajek, Kee Siong Ng, Caleb Welton, Xixuan Feng, Kun Li, Arun Kumar
Bibtex:
@article{Hellerstein:2012:MAL:2367502.2367510,
author = {Hellerstein, Joseph M. and R{'e}, Christoper and Schoppmann, Florian and Wang, Daisy Zhe and Fratkin, Eugene and Gorajek, Aleksander and Ng, Kee Siong and Welton, Caleb and Feng, Xixuan and Li, Kun and Kumar, Arun},
title = {The MADlib analytics library: or MAD skills, the SQL},
journal = {Proc. VLDB Endow.},
issue_date = {August 2012},
volume = {5},
number = {12},
month = aug,
year = {2012},
issn = {2150-8097},
pages = {1700--1711},
numpages = {12},
url = {http://dl.acm.org/citation.cfm?id=2367502.2367510},
acmid = {2367510},
publisher = {VLDB Endowment},
}
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