1 / 17

Birmingham R Users Meeting - Big Data & R Data Analysis

Join our Birmingham R Users Meeting on April 23rd, 2013 hosted by Andy Pryke. Discover the world of Big Data in R, explore data mining and visualization, and delve into parallel computation with Hadoop. Learn about data size limits in R, dense vs. sparse matrices, and commercial versions of R for big data analysis. Explore RHadoop, Oracle R Connector for Hadoop, Teradata Integration Package, and more. Let's collaborate on exploring big data options in R!

claudettea
Download Presentation

Birmingham R Users Meeting - Big Data & R Data Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Birmingham R Users Meeting 23rd April 2013 Andy Pryke Andy@The-Data-Mine.co.uk / @AndyPryke Big Data,Bigger Data & Big R Data

  2. My Bias… I work in commercial data mining, data analysis and data visualisation Background in computing and artificial intelligence Use R to write programs which analyse data

  3. What is Big Data? Depends who you ask. Answers are often “too big to ….” …load into memory …store on a hard drive …fit in a standard database Plus “Fast changing” Not just relational

  4. My “Big Data” Definition “Data collections big enough to require you to change the way you store and process them.” - Andy Pryke

  5. Data Size Limits in R Standard R packages use a single thread, with data held in memory (RAM) help("Memory-limits") • Vectors limited to 2 Billion items • Memory limit of ~128Tb Servers with 1Tb+ memory are available • Also, Amazon EC2 servers up to 244Gb

  6. Overview • Problems using R with Big Data • Processing data on disk • Hadoop for parallel computation and Big Data storage / access • “In Database” analysis • What next for Birmingham R User Group?

  7. Background: R matrix class “matrix” - Built in (package base). - Stored in RAM - “Dense” - takes up memory to store zero values) Can be replaced by…..

  8. Sparse / Disk Based Matrices • Matrix – Package Matrix. Sparse. In RAM • big.matrix – Package bigmemory / bigmemoryExtras & VAM. On disk. VAM allows access from parallel R sessions • Analysis – Packages irlba, bigalgebra, biganalytics (R-Forge list)etc. More details? “Large-Scale Linear Algebra with R”, Bryan W. Lewis, Boston R Users Meetup

  9. Commercial Versions of R Revolution Analytics have specialised versions of R for parallel execution & big data I believe many if not most components are also available under Free Open Source licences, including the RHadoop set of packages Plenty more info here

  10. Background: Hadoop • Parallel data processing environment based on Google’s “MapReduce” model • “Map” – divide up data and sending it for processing to multiple nodes. • “Reduce” – Combine the results Plus: • Hadoop Distributed File System (HDFS) • HBase – Distributed database like Google’s BigTable

  11. RHadoop – Revolution Analytics Package: rmr2, rhbase, rhdfs • Example code using RMR (R Map-Reduce) • R and Hadoop – Step by Step Tutorials • Install and Demo RHadoop(Google for more of these online) • Data Hacking with RHadoop

  12. wc.map <- function(., lines) { ## split "lines" of text into a vector of individual "words" words <- unlist(strsplit(x = lines,split = " ")) keyval(words,1) ## each word occurs once } wc.reduce <- function(word, counts ) { ## Add up the counts, grouping them by word keyval(word, sum(counts)) } wordcount <- function(input, output = NULL){ mapreduce( input = input , output = output, input.format = "text", map = wc.map, reduce = wc.reduce, combine = T) } E.g. Function Output ## In, 1 ## the, 1 ## beginning, 1 ##... ## the, 2345 ## word, 987 ## beginning, 123 ##... RHadoop

  13. Other Hadoop libraries for R Other packages: hive, segue, RHIPE… segue – easy way to distribute CPU intensive work - Uses Amazon’s Elastic Map Reduce service, which costs money. - not designed for big data, but easy and fun. Example follows…

  14. # first, let's generate a 10-element list of # 999 random numbers + 1 NA: > myList <- getMyTestList() # Add up each set of 999 numbers > outputLocal <- lapply(myList, mean, na.rm=T) > outputEmr <- emrlapply(myCluster, myList, mean, na.rm=T) RUNNING - 2011-01-04 15:16:57 RUNNING - 2011-01-04 15:17:27 RUNNING - 2011-01-04 15:17:58 WAITING - 2011-01-04 15:18:29 ## Check local and cluster results match > all.equal(outputEmr, outputLocal) [1] TRUE # The key is the emrlapply() function. It works just like lapply(), # but automagically spreads its work across the specified cluster RHadoop

  15. Oracle R Connector for Hadoop • Integrates with Oracle Db, “Oracle Big Data Appliance” (sounds expensive!) & HDFS • Map-Reduce is very similar to the rmrexample • Documentation lists examples for Linear Regression, k-means, working with graphs amongst others • Introduction to Oracle R Connector for Hadoop. • Oracle also offer some in-database algorithms for R via Oracle R Enterprise (overview)

  16. Teradata Integration Package: teradataR • Teradata offer in-database analytics, accessible through R • These include k-means clustering, descriptive statistics and the ability to create and call in-database user defined functions

  17. What Next? I propose an informal “big data” Special Interest Group, where we collaborate to explore big data options within R, producing example code etc. “R” you interested?

More Related