  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 efforts 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.
