When two of the most powerful innovations in modern analytics come together, the result is revolutionary. This session will provide an overview of R, the Open Source programming language used by more than 2 million users that was specifically developed for statistical analysis and data visualization. It will discuss the ways that R and Hadoop have been integrated and look at use case that provides real-world experience. Finally it will provide suggestions of how enterprises can take advantage of both of these industry-leading technologies.

1. Revolution Analytics
Leveraging R in Hadoop
Environments
November 9, 2011
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2. In Today’s Presentation:
About Revolution Analytics
Why R and Hadoop?
The Packages (rhdfs, rhbase, rmr)
Examples

3. Most advanced statistical
analysis software available
The professor who invented analytic software for
Half the cost of the experts now wants to take it to the masses
commercial alternatives
2M+ Users
Power
 3,000+ Applications
Finance
Statistics
Life Sciences
Predictive Manufacturing
Analytics Productivity
Retail
Data Mining Telecom Enterprise
Visualization
Social Media Readiness
Government

4. What’s the Difference Between R and
Revolution R Enterprise?
Revolution R is 100% R and More®
Multi-Threaded Web-Based Web Services Big Data Parallel
Math Libraries GUI API Analysis Tools
Technical IDE / Developer
Support GUI
4,000+ Community Build
Packages R Engine Assurance
Language Libraries
For more information contact: info@revolutionanalytics.com
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5. Let’s Talk about R and Hadoop
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6. Why R and Hadoop?
Hadoop - a scalable infrastructure for
processing massive amounts of data
Storage – HDFS, HBASE
Distributed Computing - MapReduce
R - a statistical programming language
Need for more than counts and averages
Analyze all of the data
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7. Motivation for this project
Make it easy for the R programmer to
interact with the Hadoop data stores and
write MapReduce programs
Run R on a massively distributed system
without having to understand the underlying
infrastructure
Statisticians stay focused on the analysis
Open source
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8. R and Hadoop – The R Packages
Capabilities delivered as individual
HBASE R packages
HDFS
rhdfs - R and HDFS
R
Thrift rhbase - R and HBASE
Map or
Reduce
rmr - R and MapReduce
Task rhbase
rhdfs
Node
Downloads available from
R Client Github
Job
Tracker rmr
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9. rhdfs
Manipulate HDFS directly from R
Mimic as much of the HDFS Java API as
possible
Examples:
Read a HDFS text file into a data frame.
Serialize/Deserialize a model to HDFS
Write an HDFS file to local storage
rhdfs/pkg/inst/unitTests
rhdfs/pkg/inst/examples
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10. rhdfs Functions
File Manipulations - hdfs.copy, hdfs.move, hdfs.rename,
hdfs.delete, hdfs.rm, hdfs.del, hdfs.chown, hdfs.put,
hdfs.get
File Read/Write - hdfs.file, hdfs.write, hdfs.close, hdfs.flush,
hdfs.read, hdfs.seek, hdfs.tell, hdfs.line.reader,
hdfs.read.text.file
Directory - hdfs.dircreate, hdfs.mkdir
Utility - hdfs.ls, hdfs.list.files, hdfs.file.info, hdfs.exists
Initialization – hdfs.init, hdfs.defaults
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11. rhbase
Manipulate HBASE tables and their content
Uses Thrift C++ API as the mechanism to
communicate to HBASE
Examples
Create a data frame from a collection of rows
and columns in an HBASE table
Update an HBASE table with values from a data
frame
rhbase/pkg/inst/unitTests
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12. rhbase Functions
Table Manipulation – hb.new.table, hb.delete.table,
hb.describe.table, hb.set.table.mode, hb.regions.table
Row Read/Write - hb.insert, hb.get, hb.delete,
hb.insert.data.frame, hb.get.data.frame, hb.scan
Utility - hb.list.tables
Initialization - hb.defaults, hb.init
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13. Writing MapReduce programs in R
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14. rmr - For R Programmers
• A way to access big data sets
• A simple way to write parallel programs –
everyone will have to
• Very R-like, building on the functional
characteristics of R
• Just a library
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15. rmr – For MapReduce Developers
• Much simpler than writing Java
• Not as simple as Hive, Pig at what they do,
but more general
• Great for prototyping, can transition to
production -- optimize instead of rewriting!
Lower risk, always executable.
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16. rmr mapreduce Function
mapreduce (input, output, map, reduce, …)
input – input folder
output – output folder
map – R function used as map
reduce – R function used as reduce
… - other advanced parameters
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17. Some Simple Things
Example showing sampling and counting
map = function(k, v) if (hash(k) %% 10 == 0) keyval(k, v)
reduce = function(k, vv) keyval(k, length(vv))
mapreduce(input, output, map, reduce)

18. More Simple Things
HIVE
INSERT OVERWRITE TABLE pv_gender_sum
SELECT pv_users.gender, count (DISTINCT pv_users.userid)
FROM pv_users
GROUP BY pv_users.gender;
rmr
mapreduce(input =
mapreduce(input = "pv_users",
map = function(k, v) keyval(v['userid'], v['gender']),
reduce = function(k, vv) keyval(k, vv[[1]]),
output = "pv_gender_sum",
map = function(k,v) keyval(v, 1)
reduce = function(k, vv) keyval(k, sum(unlist(vv)))
Takeaways
A language like HIVE makes a class of problems easy to solve, but it is not a general tool
The cost of doing the same operation in rmr is modest and it provides a broader set of capabilities
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19. Complex Things
k-means Clustering
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22. k-means - Implementation
Well known design (MacQueen, 1967)
Comparison of the k-means in MapReduce
Pig
From Hortonworks
Requires coding in 3 languages (Python-Pig-Java)
100 lines of code
rmr
20 lines of only R code
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23. k-means - Highlights
map = function(k,v)
keyval(which.min(distances(centers,v)),v)
reduce = function(k,vv)
keyval(NULL, col.average(vv))
centers = from.dfs(
mapreduce("data-points", map, reduce))
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24. k-means - Optimizations
Slow Fast Notes
for(i in 1:100) a=b+c light use of R interpreter, use
a[i] = b[i] + c[i] fast vector primitives, C if
necessary
[ 1, 2, 3, 4, 5] [[ 1, 2, 3, 4, 5],[6, 7, 8, 9, use beefier records, say 1k
10],[11, 12, 13, 14, 15]... points per record
distance(center, point) norm(center - P) compute all distances with
fast matrix operations
combiner = FALSE combiner = TRUE reduce often and early, use
combiner
keyval(k, mean(…)) keyval(k, replace means with (sum,
c(total, count)) count) pairs to enable early
reduction
https://github.com/RevolutionAnalytics/RHadoop/wiki/Fast-k-means
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25. Final thoughts
R and Hadoop together offer innovation and
flexibility needed to meet analytics
challenges of big data
We need contributors to this project!
Developers
Documentation
Use cases
General Feedback
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26. Resources
RHadoop Open source project:
https://github.com/RevolutionAnalytics/RHa
doop/wiki
Revolution R Enterprise: bit.ly/Enterprise-R
Cloudera CDH:
http://www.cloudera.com/hadoop/
Email: rhadoop@revolutionanalytics.com
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27. Thank you.
The leading commercial provider of software and support for the popular
open source R statistics language.
www.revolutionanalytics.com 650.330.0553 Twitter: @RevolutionR
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