R can be used for large scale data analysis on Hadoop through tools like Rhadoop, R + Hadoop Streaming, and Rhipe. The document demonstrates using Rhadoop to analyze a large mortality dataset on Hadoop. It shows how to install Rhadoop, run a word count example, and analyze causes of death from the dataset using MapReduce jobs with R scripts. While Rhadoop allows scaling R to big data on Hadoop, its development is ongoing so backward compatibility must be considered. Other options like Pig with R may provide better integration than Rhadoop alone.

1. A-Tsai (Chung-Tsai Su)
SPN
2013/10/1
Introduction of R on Hadoop

2. Agenda
• When Should You Use R?
• When Should You Consider Hadoop?
• How to use R on Hadoop?
– Rhadoop
– R + Hadoop Streaming
– Rhipe
• Demo
• Conclusions

3. When Should You Use R?
(Page 16)

4. http://3.bp.blogspot.com/-SbrlR5E0tks/UGCxeL_f5YI/AAAAAAAAL3M/lroU3yF-3_0/s1600/BigDataLandscape.png

5. https://developers.google.com/appengine/docs/python/images/mapreduce_mapshuffle.png

6. When should you consider Hadoop?
(Page 576)

7. (Page 576)

8. (Page 576)
RHadoop

9. RHadoop

10. Packages of RHadoop
http://revolution-computing.typepad.com/.a/6a010534b1db25970b0154359c29bf970c-800wi

11. RHadoop

12. Installation (in textbook)devtools
> library(devtools)
> install_url("https://github.com/downloads/RevolutionAnalytics/RHadoop/
rmr_1.3.tar.gz")
Installing rmr_1.3.tar.gz from https://github.com/downloads/
RevolutionAnalytics/RHadoop/rmr_1.3.tar.gz
Installing rmr
Installing dependencies for rmr:
...
> # make sure to set HADOOP_HOME to the location of your HADOOP installation,
> # HADOOP_CONF to the location of your hadoop config files, and make sure
> # that the hadoop bin diretory is on your path
> Sys.setenv(HADOOP_HOME="/Users/jadler/src/hadoop-0.20.2-cdh3u4")
> Sys.setenv(HADOOP_CONF=paste(Sys.getenv("HADOOP_HOME"),
+ "/conf", sep=""))
> Sys.setenv(PATH=paste(Sys.getenv("PATH"), ":", Sys.getenv("HADOOP_HOME"),
+ "/bin", sep=""))
> install_url("https://github.com/downloads/RevolutionAnalytics/RHadoop/
rhdfs_1.0.4.tar.gz")
Installing rhdfs_1.0.4.tar.gz from https://github.com/downloads/
RevolutionAnalytics/RHadoop/rhdfs_1.0.4.tar.gz
Installing rhdfs
...
> install_url("https://github.com/downloads/RevolutionAnalytics/
RHadoop/rhbase_1.0.4.tar.gz")(Refer to page 581)

13. Installation
http://blog.fens.me/rhadoop-rhadoop/
• Download Rhadoop package from
https://github.com/RevolutionAnalytics/RHadoop/wiki
• $ R CMD javareonf
• $ R
– Install rJava, reshape2, Rcpp, iterators, itertools, digest,
RJSONIO, functional, and bitops.
• >q()
• $ R CMD INSTALL rhdfs_1.0.6.tar.gz
• $ R CMD INSTALL rmr2_2.2.2.tar.gz
• Check whether successful installation
– > library(rhdfs)
– > hdfs.init()
– > hdfs.ls(“/user”)

14. First Example: WordCount

15. Hadoop Portal

16. An example RHadoop application
• Mortality Public Use File Documentation
– The dataset contains a record of every death in the United States,
including the cause of death and demographic information about the
deceased. (in 2009, the mortality data file was 1.1GB and contained
2,441,219 records)
$ wget ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/DVS/mortality/mort2009us.zip
$ unzip mort2009us.zip
$ hadoop fs -mkdir mort09
$ hadoop fs -copyFromLocal VS09MORT.DUSMCPUB mort09
$ hadoop fs -ls mort09
Found 1 items
-rw-r--r-- 3 jadler supergroup 1196197310 2012-08-02 16:31
/user/jadler/mort09/VS09MORT.DUSMCPUB

17. /home/spndc/src/Rhadoop/mort09.R (1/3)read.fwf read.fwf
.X
mort.schema <- c(
.X0=19, ResidentStatus=1, .X1=40, Education1989=2, Education2003=1,
EducationFlag=1,MonthOfDeath=2,.X2=2,Sex=1,AgeDetail=4, AgeSubstitution=1,
AgeRecode52=2,AgeRecode27=2,AgeRecode12=2,AgeRecodeInfant22=2,
PlaceOfDeath=1,MaritalStatus=1,DayOfWeekofDeath=1,.X3=16,
CurrentDataYear=4, InjuryAtWork=1, MannerOfDeath=1, MethodOfDisposition=1,
Autopsy=1,.X4=34,ActivityCode=1,PlaceOfInjury=1,ICDCode=4,
CauseRecode358=3,.X5=1,CauseRecode113=3,CauseRecode130=3,
CauseRecode39=2,.X6=1,Conditions=281,.X8=1,Race=2,BridgeRaceFlag=1,
RaceImputationFlag=1,RaceRecode3=1,RaceRecode5=1,.X9=33,
HispanicOrigin=3,.X10=1,HispanicOriginRecode=1)
> # according to the documentation, each line is 488 characters long
> sum(mort.schema)
[1] 488

18. /home/spndc/src/Rhadoop/mort09.R (2/3)

19. /home/spndc/src/Rhadoop/
mort09.R (3/3)

20. /home/spndc/src/Rhadoop/mort09_1.R (1/4)

21. /home/spndc/src/Rhadoop/mort09_1.R (2/4)

22. /home/spndc/src/Rhadoop/mort09_1.R (3/4)

23. /home/spndc/src/Rhadoop/mort09_1.R (4/4)

24. R + Hadoop Streaming

25. Hadoop Streaming
http://biomedicaloptics.spiedigitallibrary.org/data/Journals/BIOMEDO/23543/125003_1_2.png

26. #! /usr/bin/env Rscript
mort.schema <- ...
unpack.line <- ...
age.decode <- ...
con <- file("stdin", open="r")
while(length(line <- readLines(con, n=1)) > 0) {
parsed <- unpack.line(line,mort.schema)
write(paste(parsed[["CauseRecode39"]],
age.decode(parsed[["AgeDetail"]]),
sep="t"),
stdout())
}
close(con)
/home/spndc/src/Rhadoop/map.R

27. /home/spndc/src/Rhadoop/reduce.R
#! /usr/bin/env Rscript
cause.decode <- ...
con <- file("stdin", open="r")
current.key <- NA
cumulative.age <- 0
count <- 0
print.results <- function(k, n, d) {
write(paste(cause.decode(k),n/d,sep="t"),stdout())
}
while(length(line <- readLines(con, n=1)) > 0) {
parsed <- strsplit(line,"t")
key <- parsed[[1]][1]
value <- type.convert(parsed[[1]][2], as.is=TRUE)
if (is.na(current.key)) {
current.key <- key
} else if (current.key != key) {
print.results(current.key, cumulative.age, count)
current.key <- key
cumulative.age <- 0
count <- 0
}
if (!is.na(value)) {
cumulative.age <- cumulative.age + value
count <- count + 1
}
}
close(con)
print.results(current.key, cumulative.age, count)

28. /home/spndc/src/Rhadoop/streaming.sh
#!/bin/sh
/usr/java/hadoop-1.2.0/bin/hadoop
jar /usr/java/hadoop-1.2.0/contrib/streaming/hadoop-streaming-1.2.0.jar
-input mort09
-output averagebycondition
-mapper map.R
-reducer reduce.R
-file map.R
-file reduce.R

29. Output
[spndc@localhost hadoop-1.2.0]$ bin/hadoop fs -text averagebycondition/part-00000
Tuberculosis 60.5
Malignant neoplasms of cervix uteri, corpus uteri and ovary 68.0631578947368
Malignant neoplasm of prostate 78.0705882352941
Malignant neoplasms of urinary tract 72.5656565656566
Non-Hodgkin's lymphoma 69.56
Leukemia 72.8674698795181
Other malignant neoplasms 66.8361581920904
Diabetes mellitus 68.2723404255319
Alzheimer's disease 85.419795221843
Hypertensive heart disease with or without renal disease 68.0833333333333
Ischemic heart diseases 72.1750619322874
Other diseases of heart 74.925
Essential 70.468085106383
Cerebrovascular diseases 76.0950639853748
Atherosclerosis 80.12
…
Malignant neoplasm of breast 67.3815789473684

30. RHipe

31. RHIPE
http://www.datadr.org/index.html

32. Installation

33. API
RHIPE v0.65.3

34. Example
RHIPE v0.65.3

35. Recommendation System

36. Live Demo

37. Conclusions
• Rhadoop is a good way to scale out, but it might be not the
best way.
• Rhadoop is still under fast developing cycle, so you might
be aware of the backward compatible issue.
• So far, SPN has no plan to adopt Rhadoop for data
analysis.
• One of R fans suggests that using Pig with R will be better
than using Rhadoop directly.

38. Reference
• Rhadoop Wiki
– https://github.com/RevolutionAnalytics/RHadoop/wiki
• Rhipe
– http://www.datadr.org/
• Rhadoop實踐系列文章：
– http://blog.fens.me/series-rhadoop/
• 阿貝好威的實驗室
– http://lab.howie.tw/2013/01/Big-Data-Analytic-Weka-vs-Mahout-vs-
R.html
• R and Hadoop 整合初體驗
– http://michaelhsu.tw/2013/05/01/r-and-hadoop-%E5%88%9D
%E9%AB%94%E9%A9%97/

39. Thank You

40. Backup