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What Is Apache Hadoop?


  The Apache™ Hadoop® project develops open-source software for reliable, scalable, distributed computing.

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.

The project includes these modules:

  • Hadoop Common: The common utilities that support the other Hadoop modules.
  • Hadoop Distributed File System (HDFS™): A distributed file system that provides high-throughput access to application data.
  • Hadoop YARN: A framework for job scheduling and cluster resource management.
  • Hadoop MapReduce: A YARN-based system for parallel processing of large data sets.

Other Hadoop-related projects at Apache include:

  • Ambari™: A web-based tool for provisioning, managing, and monitoring Apache Hadoop clusters which includes support for Hadoop HDFS, Hadoop MapReduce, Hive, HCatalog, HBase, ZooKeeper, Oozie, Pig and Sqoop. Ambari also provides a dashboard for viewing cluster health such as heatmaps and ability to view MapReduce, Pig and Hive applications visually alongwith features to diagnose their performance characteristics in a user-friendly manner.
  • Avro™: A data serialization system
  • Cassandra™: A scalable multi-master database with no single points of failure.

  • Chukwa™: A data collection system for managing large distributed systems.
  • HBase™: A scalable, distributed database that supports structured data storage for large tables.

  • Hive™: A data warehouse infrastructure that provides data summarization and ad hoc querying.

  • Mahout™: A Scalable machine learning and data mining library.

  • Pig™: A high-level data-flow language and execution framework for parallel computation.

  • Spark™: A fast and general compute engine for Hadoop data. Spark provides a simple and expressive programming model that supports a wide range of applications, including ETL, machine learning, stream processing, and graph computation.

  • Tez™: A generalized data-flow programming framework, built on Hadoop YARN, which provides a powerful and flexible engine to execute an arbitrary DAG of tasks to process data for both batch and interactive use-cases. Tez is being adopted by Hive™, Pig™ and other frameworks in the Hadoop ecosystem, and also by other commercial software (e.g. ETL tools), to replace Hadoop™ MapReduce as the underlying execution engine.

  • ZooKeeper™: A high-performance coordination service for distributed applications.

How to install hadoop

 Apache Hadoop

Following are the steps for installing Hadoop. I have just listed the steps with very brief explanation at some places. This is more or less like some reference notes for installation. I made a note of this when I was installing Hadoop on my system for the very first time.

Please let me know if you need any specific details.

Installing HDFS (Hadoop Distributed File System)
OS : Linux Mint (Ubuntu)

Installing Sun Java on Linux (Mint/Ubuntu)

sudo add-apt-repository ppa:webupd8team/java sudo apt-get update sudo apt-get install oracle-java7-installer sudo update-java-alternatives -s java-7-oracle

Create hadoop user

Install SSH Server if not already present. This i

$ sudo apt-get install openssh-server 
$ su - hduser
 $ ssh-keygen -t rsa -P "" 
$ cat $HOME/.ssh/ >> $HOME/.ssh/authorized_keys

Disable IPV6

$sudo gedit /etc/sysctl.conf

This command will open sysctl.conf in text editor, you can copy the following lines at the end of the file:

#disable ipv6 net.ipv6.conf.all.disable_ipv6 = 1 net.ipv6.conf.default.disable_ipv6 = 1 net.ipv6.conf.lo.disable_ipv6 = 1
$sudo sysctl -p

To make sure that IPV6 is disabled, you can run the following command:

$cat /proc/sys/net/ipv6/conf/all/disable_ipv6

Installing Hadoop

Download hadoop from Apache Downloads.

$wget $ cd /home/hduser $ tar xzf hadoop-0.22.2.tar.gz $ mv hadoop-0.22.2 hadoop

Edit .bashrc

# Set Hadoop-related environment variables export HADOOP_HOME=/home/hduser/hadoop # Set JAVA_HOME (we will also configure JAVA_HOME directly for Hadoop later on) export JAVA_HOME=/usr/lib/jvm/java-6-sun # Add Hadoop bin/ directory to PATH export PATH=$PATH:$HADOOP_HOME/bin


We need only to update the JAVA_HOME variable in this file. Simply you will open this file using a text editor using the following command:

$vi /home/hduser/hadoop/conf/

Add/update the following

export JAVA_HOME=/usr/lib/jvm/java-6-sun

Temp directory for hadoop

$mkdir /home/hduser/tmp
$vi /home/hduser/hadoop/conf/core-site.xml

Then add the following configurations between <configuration> .. </configuration> xml elements:

<!— In: conf/core-site.xml —> <property> <name>hadoop.tmp.dir</name> <value>/home/hduser/tmp</value> <description>A base for other temporary directories.</description> </property>  <property> <name></name> <value>hdfs://localhost:54310</value> <description>The name of the default file system. A URI whose scheme and authority determine the FileSystem implementation. The uri’s scheme determines the config property (fs.SCHEME.impl) naming the FileSystem implementation class. The uri’s authority is used to determine the host, port, etc. for a filesystem.</description> </property>

We will open the hadoop/conf/mapred-site.xml using a text editor and add the following configuration values (like core-site.xml)

<!— In: conf/mapred-site.xml —> <property> <name>mapred.job.tracker</name> <value>localhost:54311</value> <description>The host and port that the MapReduce job tracker runs at. If “local”, then jobs are run in-process as a single map and reduce task. </description> </property>


Open hadoop/conf/hdfs-site.xml using a text editor and add the following configurations:

<!— In: conf/hdfs-site.xml —> <property> <name>dfs.replication</name> <value>1</value> <description>Default block replication. The actual number of replications can be specified when the file is created. The default is used if replic
</description> </property>

Formatting NameNode

You should format the NameNode in your HDFS. You should not do this step when the system is running. It is usually done once at first time of your installation.

Run the following command

$/home/hduser/hadoop/bin/hadoop namenode -format

Starting Hadoop Cluster

From hadoop/bin

./ ./

Stopping Hadoop Cluster

From hadoop/bin

./ ./

To check for processes running use:



$ps -eaf | grep java

Tasks running should be as follows:

NameNode DataNode SecondaryNameNode JobTracker TaskTracker

Example Application to test success of hadoop:

From hadoop/bin

$hadoop jar ../hadoop-mapred-examples-0.22.0.jar pi 3 10

The should complete successfully with several details and output value of pi.




I am a software developer who loves to learn and build new things. To share my learning I blog here and have also built Hadoop Screencasts (, that hosts screencasts on Apache Hadoop and its components.