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Big Data and Analytics By @MElRefaey | @BigDataExpo #BigData

Hadoop is a framework that simplifies the processing of data sets distributed across clusters of servers

This post is the first in a series of blog posts that will explore and exploit the Big Data and analytics tools. I will walk through easy steps to start working with such tools like Apache Hadoop, Pig, Mahout and solve some problems related to analytics and learning in the large scale by exploiting such tools, and shed the light on some of the challenges we face while working with these tools.

1. Apache Hadoop
1.1 Overview

Hadoop is a framework that simplifies the processing of data sets distributed across clusters of servers. Two of the main components of Hadoop are HDFS and MapReduce.HDFS is the file system that is used by Hadoop to store all the data. This file system spans across all the nodes that are being used by Hadoop. These nodes could be on a single server or they can be spread across a large number of servers.In this section, we will go through the instruction of how to get the Hadoop up and running with the configurations needed to make it useful for other components/frameworks that integrate or depends on Hadoop (e.g. Hive, Pig, HBase etc.).

Note: The installation will be a Pseudo distribution.

1.2 Tools and Versions
I've used the following tools and versions throughout this installation:

  • Ubuntu 14.04 LTS
  • Java 1.7.0_65 (java-7-openjdk-amd64)
  • Hadoop 2.5.1

1.3    Installation and Configurations

1. Install Java using the following command:

apt-get update apt-get install default-jdk

2. Create Security Keys using the following commands:

ssh-keygen -t rsa -P ' ' cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys

3. Download Hadoop tar file using:

wget http://www.webhostingreviewjam.com/mirror/apache/hadoop/common/hadoop-2.5.1/hadoop-2.5.1.tar.gz

4. Extract the tar file using:

tar -xzvf hadoop-2.5.1.tar.gz

5. Move the extracted files into a location you can easily recognize, and easily change the version used without much modifications using:

mv hadoop-2.5.1/ /usr/local/hadoop

6. Configure the following environment variables in the bashrc file (to make sure every time they are set with the machine sartup):

#HADOOP VARIABLES START export JAVA_HOME=/usr/lib/jvm/java-7-openjdk-amd64 export HADOOP_INSTALL=/usr/local/hadoop export PATH=$PATH:$HADOOP_INSTALL/bin export PATH=$PATH:$HADOOP_INSTALL/sbin export HADOOP_MAPRED_HOME=$HADOOP_INSTALL export HADOOP_COMMON_HOME=$HADOOP_INSTALL export HADOOP_HDFS_HOME=$HADOOP_INSTALL export YARN_HOME=$HADOOP_INSTALL export HADOOP_COMMON_LIB_NATIVE_DIR=$HADOOP_INSTALL/lib/native export HADOOP_OPTS="-Djava.library.path=$HADOOP_INSTALL/lib" #HADOOP VARIABLES END

7. Source the bashrc file after changes, for the system to recognize the changes using the following command:

source ~/.bashrc

8. Edit the Hadoop-env.sh using vim:

vim /usr/local/hadoop/etc/hadoop/hadoop-env.sh

The hadoop-env.sh file should look like this:


That will make the value of the JAVA_HOME always available to Hadoop whenever it starts.

9-      Edit the core-site.xml file using vim as well:

vim /usr/local/hadoop/etc/hadoop/core-site.xml
The file will look like:

10-   Edit the YARN file yarn-site.xml as follows:

vim /usr/local/hadoop/etc/hadoop/yarn-site.xml
The file will look like:

11. Create and edit the mapred-site.xml file:

vim /usr/local/hadoop/etc/hadoop/mapred-site.xml
The file will contains the following property, that specify which framework will be used for MapReduce:


12. Edit the hdfs-site.xml file, in order to specify the directories that will be used as datanode and namenode on that server.

vim /usr/local/hadoop/etc/hadoop/hdfs-site.xml
Create the two directories:  mkdir -p /usr/local/hadoop_store/hdfs/namenode                mkdir -p /usr/local/hadoop_store/hdfs/datanode
after editing the file, it will contains the following  properties:


13.Forma t the new Hadoop file system using the following command:

hdfs namenode -format
Note: This operation needs to be done once before we start using Hadoop. If it is executed again after Hadoop has been used, it'll destroy all the data on the Hadoop filesystem.

14. Now, all configurations are done, we can start using Hadoop, we should first run the following shell scripts:

start-dfs.sh                     start-yarn.sh
And to make sure everything is okay, and the right process is running, run the command jps and see the following:



15-   We can run MapReduce examples that exist in Hadoop bundle, but we need to run the following:

We should create the HDFS directories required to execute MapReduce jobs:
hdfs dfs -mkdir /user hdfs dfs -mkdir /user/mohamed
and copy the input files to be processed into the distributed filesystem:
hdfs dfs -put {here is the path to the files to be copied} input

16-   We can check the web console for the resource manager, HDFS nodes and running jobs as shown in the following screens:







Issues and problems:

I've experienced some issues related to: Ø  Formatting the HDFS, and I resolved it by changing permissions and ownership of the user who can format the namenode and datanode. Ø  Problem connecting to the resource manager, with the following error: ipc.Client: Retrying connect to server:

0.0.0.0/0.0.0.0:8032. Already tried 0 time(s); maxRetries=45
INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032 And I resolved it by: adding a few properties to yarn-site.xml :



We reached to the end of our first post on big data and analytics, hope you enjoyed reading it and experiminting with Hadoop installation and configuration. next post will be about Apache Pig.

Read the original blog entry...

More Stories By Mohamed El-Refaey

Work as head of research and development at EDC (Egypt Development Center) a member of NTG. previously worked for Qlayer, Acquired by (Sun Microsystems), when my passion about cloud computing domain started. with more than 10 years of experience in software design and development in e-commerce, BPM, EAI, Web 2.0, Banking applications, financial market, Java and J2EE. HIPAA, SOX, BPEL and SOA, and late two year focusing on virtualization technology and cloud computing in studies, technical papers and researches, and international group participation and events. I've been awarded in recognition of innovation and thought leadership while working as IT Specialist at EDS (an HP Company). Also a member of the Cloud Computing Interoperability Forum (CCIF) and member of the UCI (Unified Cloud Interface) open source project, in which he contributed with the project architecture.

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