Microsoft R server for distributed computing โดย กฤษฏิ์ คำตื้อ Technical Evangelist Microsoft (Thailand) Limited ในงาน THE FIRST NIDA BUSINESS ANALYTICS AND DATA SCIENCES CONTEST/CONFERENCE จัดโดย คณะสถิติประยุกต์และ DATA SCIENCES THAILAND

1. Microsoft R Server on Spark
Purpose:
This lab will demonstrate how to use Microsoft R Server on a Spark cluster. It will start by
outlining the steps to spin up the cluster in Azure, how to install RStudio with R Server, and an
example of how to use ScaleR to analyze data in a Spark cluster.
Pre-requisites
1. Be sure to have your Azure subscription enabled.
2. You will need to have a Secure Shell (SSH) client installed to remotely connect to the
HDInsight cluster and run commands directly on the cluster. This is needed since the
cluster will be using a Linux OS. The recommended client is PuTTY. Use the following link
to download and install PuTTY: PuTTY Download
a. Optionally, you can create an SSH key to connect to your cluster. The following
steps will assume that you are using a password. The following links include more
information on how to create and use SSH keys with HDInsight:
Use SSH with Linux-based Hadoop on HDInsight from Windows
Use SSH with Linux-based Hadoop on HDInsight from Linux, Unix, or OS X
Creating the R Server on Spark Cluster
1. In the Azure portal, select New > Data + Analytics > HDInsight

2. 2. Enter a name in the Cluster Name field and select the appropriate Azure
subscription in the Subscription field.
3. Click Select Cluster Type. On the Cluster Type blade, select the following
options:
a. Cluster Type: R Server on Spark
b. Cluster Tier: Premium
Click Select to save the cluster type configuration.
4. Click Credentials to create the cluster login username and password and the SSH
username and password. This is also where you can upload a key instead of using
a username/password for SSH authentication.
5. Click the Data Source field. Create a new storage account and a default container
for the cluster to use.
6. Click the Pricing field. Here you will be able to specify the number of Worker
nodes, the size of the Worker nodes, the size of the Head nodes and the R server

3. node size (this is the edge node that you will connect to using SSH to run your R
code). For demo purposes, you can leave the default settings in place.
7. Optionally, you can select External Metastores for Hive and Oozie in the Optional
Configuration field if you have SQL Databases created to store Hive/Oozie job
metadata. For this demo, this option will remain blank.
8. Either create a new Resource group or select an existing on in the Resource
Group field.
9. Click Create to create the cluster.
Installing RStudio with R Server on HDInsight
The following steps assume that you have downloaded and installed PuTTY. Please refer
to the Prerequisites section at the top of this document for the link to download PuTTY.
1. Identify the edge node of the cluster. To find the name of the edge node, select
the recently created HDInsight cluster in the HDInsight Clusters blade. From
there, select Settings > Applications > R Server for HDInsight. The SSH
Endpoint is the name of the edge node for the cluster.
2. SSH into the edge node. Use the following steps to connect to the edge node:

4. a. To connect to the edge node, open PuTTY. The following is a screenshot of
PuTTY when it is opened up:
b. In the Category pane, select Session. Enter the SSH address of the
HDInsight server in the Host Name (or IP address) text box. This address
could be either the address of the head node or the address of the edge
node. Use the address of the edge node to connect to the edge node and
configure RStudio. Click Open to connect to the cluster.

5. c. Log in with the SSH credentials that were created when the cluster was
created.
3. Once connected, become a root user on the cluster. Use the following command
in the SSH session:
sudo su -
4. Download the custom script to install RStudio. Use the following command in the
SSH session
wget http://mrsactionscripts.blob.core.windows.net/rstudio-server-community-
v01/InstallRStudio.sh
5. Change the permissions on the custom script file and run the script. Use the
following commands:
chmod 755 InstallRStudio.sh
./InstallRStudio.sh

6. 6. Create an SSH tunnel to the cluster by mapping localhost:8787 on the HDInsight
Cluster to the client machine. This can be done through PuTTY.
a. Open PuTTY, and enter your connection information.
b. In the Category pane, expand Connection, expand SSH, and select
Tunnels.
c. Enter 8787 as the Source port and localhost:8787 as the Destination.
Click Add and then click Open to open an SSH connection.
d. When prompted, log in to the server with your SSH credentials. This will
establish an SSH session and enable the tunnel.
7. Open a web browser and enter the following URL based on the port entered for
the tunnel:
http://localhost:8787/
8. You will be prompted to enter the SSH username and password to connect to the
cluster.

7. 9. The following command will download a test script that executes R based Spark
jobs on the cluster. Run this command from the PuTTY session:
wget http://mrsactionscripts.blob.core.windows.net/rstudio-server-community-
v01/testhdi_spark.r
10. In RStudio, you will see the test script that was just downloaded in the lower right
pane. Double click the file to open it and click Run to run the code.
Use a compute context and simple statistics with ScaleR
A compute context allows you to control whether computation will be performed locally
on the edge node, or whether it will be distributed across the nodes in the HDInsight
cluster.
1. From the R console, use the following to load example data into the default
storage for HDInsight.
# Set the HDFS (WASB) location of example data
bigDataDirRoot <- "/example/data"
# create a local folder for storaging data temporarily
source <- "/tmp/AirOnTimeCSV2012"
dir.create(source)
# Download data to the tmp folder
remoteDir <- "http://packages.revolutionanalytics.com/datasets/AirOnTimeCSV2012"
download.file(file.path(remoteDir, "airOT201201.csv"), file.path(source,
"airOT201201.csv"))
download.file(file.path(remoteDir, "airOT201202.csv"), file.path(source,
"airOT201202.csv"))
download.file(file.path(remoteDir, "airOT201203.csv"), file.path(source,
"airOT201203.csv"))
download.file(file.path(remoteDir, "airOT201204.csv"), file.path(source,
"airOT201204.csv"))
download.file(file.path(remoteDir, "airOT201205.csv"), file.path(source,
"airOT201205.csv"))
download.file(file.path(remoteDir, "airOT201206.csv"), file.path(source,
"airOT201206.csv"))
download.file(file.path(remoteDir, "airOT201207.csv"), file.path(source,
"airOT201207.csv"))
download.file(file.path(remoteDir, "airOT201208.csv"), file.path(source,
"airOT201208.csv"))
download.file(file.path(remoteDir, "airOT201209.csv"), file.path(source,
"airOT201209.csv"))
download.file(file.path(remoteDir, "airOT201210.csv"), file.path(source,
"airOT201210.csv"))

8. download.file(file.path(remoteDir, "airOT201211.csv"), file.path(source,
"airOT201211.csv"))
download.file(file.path(remoteDir, "airOT201212.csv"), file.path(source,
"airOT201212.csv"))
# Set directory in bigDataDirRoot to load the data into
inputDir <- file.path(bigDataDirRoot,"AirOnTimeCSV2012")
# Make the directory
rxHadoopMakeDir(inputDir)
# Copy the data from source to input
rxHadoopCopyFromLocal(source, bigDataDirRoot)
2. Next, let's create some data info and define two data sources so that we can work
with the data.
# Define the HDFS (WASB) file system
hdfsFS <- RxHdfsFileSystem()
# Create info list for the airline data
airlineColInfo <- list(
DAY_OF_WEEK = list(type = "factor"),
ORIGIN = list(type = "factor"),
DEST = list(type = "factor"),
DEP_TIME = list(type = "integer"),
ARR_DEL15 = list(type = "logical"))
# get all the column names
varNames <- names(airlineColInfo)
# Define the text data source in hdfs
airOnTimeData <- RxTextData(inputDir, colInfo = airlineColInfo, varsToKeep =
varNames, fileSystem = hdfsFS)
# Define the text data source in local system
airOnTimeDataLocal <- RxTextData(source, colInfo = airlineColInfo, varsToKeep =
varNames)
# formula to use
formula = "ARR_DEL15 ~ ORIGIN + DAY_OF_WEEK + DEP_TIME + DEST"
3. Let's run a logistic regression over the data using the local compute context.
# Set a local compute context
rxSetComputeContext("local")
# Run a logistic regression
system.time(
modelLocal <- rxLogit(formula, data = airOnTimeDataLocal)
)
# Display a summary
summary(modelLocal)

9. 4. Next, let's run the same logistic regression using the Spark context. The Spark
context will distribute the processing over all the worker nodes in the HDInsight
cluster.
# Define the Spark compute context
mySparkCluster <- RxSpark()
# Set the compute context
rxSetComputeContext(mySparkCluster)
# Run a logistic regression
system.time(
modelSpark <- rxLogit(formula, data = airOnTimeData)
)
# Display a summary
summary(modelSpark)
ScaleR Example with Linear Regression and Plots
This example will show different compute contexts, how to do linear regression in
RevoScaleR and how to do some simple plots. It utilized airline delay data for airports
across the United States.
#copy local file to HDFS
rxHadoopMakeDir("/share")
rxHadoopCopyFromLocal(system.file("SampleData/AirlineDemoSmall.csv",package="RevoScaleR"), "/share")
myNameNode <- "default"
myPort <- 0
# Location of the data
bigDataDirRoot <- "/share"
# define HDFS file system
hdfsFS <- RxHdfsFileSystem(hostName=myNameNode, port=myPort)
# specify the input file in HDFS to analyze
inputFile <-file.path(bigDataDirRoot,"AirlineDemoSmall.csv")
# create Factors for days of the week
colInfo <- list(DayOfWeek = list(type = "factor",
levels = c("Monday","Tuesday","Wednesday",
"Thursday","Friday","Saturday","Sunday")))
# define the data source
airDS <- RxTextData(file = inputFile, missingValueString = "M",
colInfo = colInfo, fileSystem = hdfsFS)
# First test the "local" compute context
rxSetComputeContext("local")
# Run a linear regression
system.time(

10. model <- rxLinMod(ArrDelay~CRSDepTime+DayOfWeek, data = airDS)
)
# display a summary of model
summary(model)
# define MapReduce compute context
myHadoopMRCluster <- RxHadoopMR(consoleOutput=TRUE,
nameNode=myNameNode,
port=myPort,
hadoopSwitches="-libjars /etc/hadoop/conf")
# set compute context
rxSetComputeContext(myHadoopMRCluster)
# Run a linear regression
system.time(
model1 <- rxLinMod(ArrDelay~CRSDepTime+DayOfWeek, data = airDS)
)
# display a summary of model
summary(model1)
rxLinePlot(ArrDelay~DayOfWeek, data= airDS)
# define Spark compute context
mySparkCluster <- RxSpark(consoleOutput=TRUE)
# set compute context
rxSetComputeContext(mySparkCluster)
# Run a linear regression
system.time(
model2 <- rxLinMod(ArrDelay~CRSDepTime+DayOfWeek, data = airDS)
)
# display a summary of model
summary(model2)
# Run 4 tasks via rxExec
rxExec( function() {Sys.info()["nodename"]}, timesToRun = 4 )
Wrap Up
This lab was meant to demonstrate how to use Microsoft R Server on a Spark cluster. For
more information, refer to the references listed in the References section.
References
1. https://azure.microsoft.com/en-us/documentation/articles/hdinsight-hadoop-r-
server-get-started/

11. Microsoft R server for distributed computing
The First NIDA Business Analytics and Data Sciences Contest/Conference
วันที่ 1-2 กันยายน 2559 ณ อาคารนวมินทราธิราช สถาบันบัณฑิตพัฒนบริหารศาสตร์
-แนะนํา Microsoft R Server
-Distributed Computing มีวิธีการอย่างไร และมีประโยชน์อย่างไร
-แนะนําวิธีการ Configuration สําหรับ Distributed Computing
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กฤษฏิ์ คําตื้อ,
Technical Evangelist,
Microsoft (Thailand)
-Distributed computing กับ Big Data
-Analytics บน R server
-สาธิตและสอนในลักษณะ workshop
Computer Lab 2 ชั้น 10 อาคารสยามบรมราชกุมารี
1 กันยายน 2559 เวลา 9.00-12.30

12. 2. https://azure.microsoft.com/en-us/documentation/articles/hdinsight-hadoop-r-
server-install-r-studio/
3. https://azure.microsoft.com/en-us/documentation/articles/hdinsight-hadoop-
linux-use-ssh-windows/#connect-to-a-linux-based-hdinsight-cluster