I was inspired by Revolution’s blog and step-by-step tutorial from Jeffrey Breen on the set up of a local virtual instance of Hadoop with R. However, this tutorial describes the implementation using VMware’s application. One downside to using VMware is that it’s not free. I know most of the people including me like to hear the words open-source and free, especially when it is a smooth ride. VirtualBox offers an open-source alternative and thenceforth, I chose this. Most of the trouble started after a hassle free installation of VirtualBox and creation of the cloudera’s demo VM. I came across different hurdles when it came to addition of VirtualBox Guest Additions, which is intended to spruce up the virtual machine by offering such features as a shared folder with the host OS. Although there are solutions, the resources are scattered and obscure. I did manage to clear these hurdles and went on to installing R and RStudio along with RHadoop packages. I thought it would be useful to self-taught enthusiasts like me if I lay out the steps in a comprehensive manner, since I have spent some time dealing with the quirks in the process.
Apache Hadoop is an open-source software framework that supports data-intensive distributed applications, licensed under the Apache v2 license. It supports the running of applications on large clusters of commodity hardware. The Hadoop framework transparently provides applications both reliability and data motion. Hadoop implements a computational paradigm named map/reduce, where the application is divided into many small fragments of work, each of which may be executed or re-executed on any node in the cluster. In addition, it provides a distributed file system that stores data on the compute nodes, providing very high aggregate bandwidth across the cluster. Both map/reduce and the distributed file system are designed so that node failures are automatically handled by the framework.
R and Hadoop
The most common way to link R and Hadoop is to use HDFS (potentially managed by Hive or HBase) as the long-term store for all data, and use MapReduce jobs (potentially submitted from Hive, Pig, or Oozie) to encode, enrich, and sample data sets from HDFS into R. Data analysts can then perform complex modeling exercises on a subset of prepared data in R.Revolution Analytics released RHadoop allowing integration of R and Hadoop. RHadoop is a collection of three R packages that allow users to manage and analyze data with Hadoop.
RHadoop consists of the following packages:
rmr – functions providing Hadoop MapReduce functionality in R
rhdfs – functions providing file management of the HDFS from within R
rhbase – functions providing database management for the HBase distributed database from within R
Cloudera Hadoop Demo VM
CDH is Cloudera’s 100% open source distribution of Hadoop and related projects, built specifically to meet enterprise demands. Cloudera created a set of virtual machines (VM) with everything we need to make it easy to get started with Apache Hadoop. Cloudera Hadoop’s Demo VM provides everything you need to run small jobs in a virtual environment. The packages have been implemented and tested in Cloudera’s distribution of Hadoop (CDH3) & (CDH4). and R 2.15.0. This offers a great way to get familiarized with Hadoop.
Platforms used in this tutorial:
Guest OS : Mac OS X 10.7.5 (Lion)
Virtualization software: VirtualBox 4.2.6
Cloudera Hadoop Demo VM: CDH 4.1.1
RStudio server for : 0.97.248
RHadoop packages: rmr 2.0.2
2. Download ‘Cloudera’s Hadoop Demo VM archive for CDH4
(Latest: Ver 4.1.1 runs CentOS 6.2 64 bit VM)
3. Extract ‘Cloudera’s Hadoop Demo VM’ archive
It extracts virtual machine image file: ‘cloudera-demo-vm.vmdk’
4. Copy this virtual machine image to a desired folder (eg:- folder named ‘Cloudera Hadoop’). This folder and image file has to be the permanent location of your Hadoop installation (not to be deleted!)
5. We will now create a virtual machine on VirtualBox.
Open application: ‘VirtualBox’. Click on ‘New’.
Now click ‘Start’ to initiate the virtual machine. You will several pages of output on a black screen until you finally see the desktop of the virtual machine.
Once you launch the VM, you are automatically logged in as the cloudera user.
The account details are:
The cloudera account has sudo privileges in the VM.
7. For close integration and better performance we need to install “Guest additions” in the VM.
There are some prerequisites to installation of ‘Guest additions’.
Switch to root user:
$ sudo bash
Update linux kernel:
$ yum install kernel -y
Open internet browser (Firefox) and download the following file (link below):
Click on link for file: kernel-devel-2.6.32-220.23.1.el6.x86_64.rpm
Download and save file to folder ‘Downloads’ under ‘home/cloudera’ (Either create new folder using ‘Save’ dialog box or use console: mkdir /home/cloudera/Downloads
$ yum install kernel-devel-2.6.32-220.23.1.el6.x86_64.rpm -y
$ yum install gcc -y
Link the kernel sources to a standard location using the format:
'ln -s /usr/src/kernels/[current version] /usr/src/linux'
$ ln -s /usr/src/kernels/2.6.32-220.23.1.el6.x86_64 /usr/src/linux
Installation of package 'dkms'
(It is important that you use the steps below to install 'dkms' in CentOS, which is the linux build for your Cloudera demo VM.)
Steps to install rpmforge-release package to enable rpmforge repository
$ mkdir rpm (create folder 'rpm' under 'home/cloudera' : /home/cloudera/rpm)
$ cd rpm (change to 'rpm' folder: /home/cloudera/rpm)
$ wget http://packages.sw.be/rpmforge-release/rpmforge-release-0.5.2-2.el6.rf.x86_64.rpm
Install DAG's GPG key
$ rpm --import http://apt.sw.be/RPM-GPG-KEY.dag.txt
If you get an error message like the following the key has already been imported.
error: http://apt.sw.be/RPM-GPG-KEY.dag.txt: key 1 import failed.
Verify the package you have downloaded
$ rpm -K rpmforge-release-0.5.2-2.el6.rf.*.rpmInstall the package
$ rpm -i rpmforge-release-0.5.2-2.el6.rf.*.rpm
This will add a yum repository config file and import the appropriate GPG keys.
Now install package 'dkms' as root:
$ sudo yum install dkms
Now you are ready to download and install 'VirtualBox Guest Additions'. Download 'VirtualBox Guest Additions' .iso image file ('VBoxGuestAdditions_4.2.6.iso') corresponding to your version of 'VirtualBox' installation (in this case Ver 4.2.6).(Note: The following steps are unlike what is described in most of the posts on this topic.
I faced a lot of problems in making this happen using the steps described in these posts.
I therefore, recommend this method to avoid those issues.)
Open internet browser (Firefox) and download the following file (link below). Save file to folder
'Downloads' under 'home' (already created folder)
http://download.virtualbox.org/virtualbox/4.2.6/VBoxGuestAdditions_4.2.6.isoSwitch to 'root' user:
$ sudo bash
$ mkdir /mnt/ISO
Once your folder is created go to the folder where ISO image 'VBoxGuestAdditions_4.2.6.iso is stored.
$ cd /home/cloudera/Downloads
Use command: ls to list contents of the folder.
$ mount -t iso9660 -o loop VBoxGuestAdditions_4.2.6.iso /mnt/ISO
$ cd /mnt/ISO
$ ls (lists contents of the mounted 'VBoxGuestAdditions_4.2.6.iso' image)
32Bit cert VBoxSolarisAdditions.pkg
64Bit OS2 VBoxWindowsAdditions-amd64.exe
AUTORUN.INF runasroot.sh VBoxWindowsAdditions.exe
autorun.sh VBoxLinuxAdditions.run VBoxWindowsAdditions-x86.exe
Now install 'Guest Additions' for Linux guest by running the following command.
$ sh VBoxLinuxAdditions.run
Reboot virtual machine. This completes installation of 'Guest Additions'.
(Note: Whenever you reboot, make sure there is network connection.
Check the active network icon at the top right corner.
If crossed out, click and enable network connection by clicking 'Auto eth0')
Enabling network connection at start-up of virtual machine (CentOS)I learned that the network connection is not enabled automatically at start-up.
This can be resolved by making the following changes.Run console
Switch to root user
Create/edit the following file:
This will open up this file in 'emacs' editor. (You will find it blank!)
Copy/paste the following into this file. (Hint: use Shift/Cntrl/V to paste!)DEVICE="eth0"
ONBOOT="no"Save the file.
Reboot and check if network connection is enabled at start-up (see above).8. Installation of R
First add the EPEL repository, then intall git, wget and R.
Find the latest release of the EPEL repository (http://fedoraproject.org/wiki/EPEL) and update the url accordingly.
$ sudo rpm -Uvh http://dl.fedoraproject.org/pub/epel/6/x86_64/epel-release-6-8.noarch.rpm
$ sudo yum -y install git wget R
9. Set Hadoop environment variables so R can find them too!
The following is specific for CDH4 Demo VM.
$ sudo ln -s /etc/default/hadoop-0.20-mapreduce /etc/profile.d/hadoop.sh
$ cat /etc/profile.d/hadoop.sh | sed 's/export //g' > ~/.Renviron
10. Installation of Rstudio server
$ wget http://download2.rstudio.org/rstudio-server-0.97.248-x86_64.rpm
$ sudo yum install --nogpgcheck rstudio-server-0.97.248-x86_64.rpm
11. Access Rstudio from the browser (you may use any machine in the home network)
Check IP address by running command:
Access RStudio from browser by typing the address (uses port 8787) : e.g., http://10.0.1.15:8787/
Both username and password are 'cloudera'
Password: cloudera12. Installation of RHadoop's rmr package
First install the pre-requisite packages. (Run R as root to install system-wide)
$ sudo R
R> install.packages( c('RJSONIO', 'itertools',
'digest', 'Rcpp', 'functional', 'plyr', 'stringr'),
repos='http://cran.revolutionanalytics.com')R> q() (to quit 'R' session)
Download the latest stable release of rmr (2.0.2) from github.
$ wget --no-check-certificate https://github.com/downloads/RevolutionAnalytics/RHadoop/rmr2_2.0.2.tar.gz
$ sudo R CMD INSTALL rmr2_2.0.2.tar.gz
Test that 'rmr2' loads
$ R R> library(rmr2)Loading required package: Rcpp Loading required package: RJSONIO Loading required package: digest Loading required package: functional Loading required package: stringr Loading required package: plyr R> 13. Testing with a simple example
small.ints <- to.dfs(1:1000)
out <- mapreduce(input = small.ints, map = function(k, v) keyval(v, v^2))
df <- as.data.frame(from.dfs(out))
Other examples of mapreduce function are available at: