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Benchmarking bigglm

By Joseph Rickert In a recent blog post, David Smith reported on a talk that Steve Yun and I gave at STRATA in NYC about building and benchmarking Poisson GLM models on various platforms. The results presented showed that the rxGlm function from Revolution Analytics’ RevoScaleR package running on a five node cluster outperformed a Map Reduce/ Hadoop implementation as well as an implementation of legacy software running on a large server. An alert R user posted the following comment on the blog: As a poisson regression was used, it would be nice to also see as a benchmark the computational speed when using the biglm package in open source R? Just import your csv in sqlite and run biglm to obtain your poisson regression. Biglm also loads in data in R in chunks in order to update the model so that looks more similar to the RevoScaleR setup then just running plain glm in R. This seemed like a reasonable, simple enough experiment. So we tried it. The benchmark results presented at STRATA were done on a 145 million record file, but as a first step, I thought that I would try it on a 14 million record subset that I already had loaded on my PC, a quad core Dell, with i7 processors and 8GB of RAM.  It took almost an hour to build the SQLite data base: # make a SQLite database out of the csv file library(sqldf) sqldf("attach AdataT2SQL as new") file <- file.path(getwd(),"adatat2.csv") read.csv.sql(file, sql = "create table main.AT2_10Pct as select * from file">

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More Stories By David Smith

David Smith is Vice President of Marketing and Community at Revolution Analytics. He has a long history with the R and statistics communities. After graduating with a degree in Statistics from the University of Adelaide, South Australia, he spent four years researching statistical methodology at Lancaster University in the United Kingdom, where he also developed a number of packages for the S-PLUS statistical modeling environment. He continued his association with S-PLUS at Insightful (now TIBCO Spotfire) overseeing the product management of S-PLUS and other statistical and data mining products.<

David smith is the co-author (with Bill Venables) of the popular tutorial manual, An Introduction to R, and one of the originating developers of the ESS: Emacs Speaks Statistics project. Today, he leads marketing for REvolution R, supports R communities worldwide, and is responsible for the Revolutions blog. Prior to joining Revolution Analytics, he served as vice president of product management at Zynchros, Inc. Follow him on twitter at @RevoDavid

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