This blog explains the setup of the Hadoop Multi-Node cluster on a distributed environment.
As the whole cluster cannot be demonstrated, we are explaining the Hadoop cluster environment using three systems (one master and two slaves); given below are their IP addresses.
• Hadoop Master: 192.168.1.15 (hadoop-master)
• Hadoop Slave: 192.168.1.16 (hadoop-slave-1)
• Hadoop Slave: 192.168.1.17 (hadoop-slave-2)
Follow the steps given below to have Hadoop Multi-Node cluster setup.

Installing Java

Java is the main prerequisite for Hadoop. First of all, you should verify the existence of java in your system using “java -version”. The syntax of java version command is given below.
$ java -version
If everything works fine it will give you the following output.
java version “1.7.0_71”
Java(TM) SE Runtime Environment (build 1.7.0_71-b13)
Java HotSpot(TM) Client VM (build 25.0-b02, mixed mode)
If java is not installed in your system, then follow the given steps for installing java.
Step 1
Download java (JDK <latest version> – X64.tar.gz) by visiting the following link www.oracle.com
Then jdk-7u71-linux-x64.tar.gz will be downloaded into your system.
Step 2
Generally you will find the downloaded java file in Downloads folder. Verify it and extract the jdk-7u71-linux-x64.gz file using the following commands.
$ cd Downloads/
$ ls
jdk-7u71-Linux-x64.gz

$ tar zxf jdk-7u71-Linux-x64.gz
$ ls
jdk1.7.0_71 jdk-7u71-Linux-x64.gz
Step 3
To make java available to all the users, you have to move it to the location “/usr/local/”. Open the root, and type the following commands.
$ su
password:
# mv jdk1.7.0_71 /usr/local/
# exit
Step 4
For setting up PATH and JAVA_HOME variables, add the following commands to ~/.bashrc file.
export JAVA_HOME=/usr/local/jdk1.7.0_71
export PATH=PATH:$JAVA_HOME/bin
Now verify the java -version command from the terminal as explained above. Follow the above process and install java in all your cluster nodes.

Creating User Account

Create a system user account on both master and slave systems to use the Hadoop installation.
# useradd hadoop
# passwd hadoop

Mapping the nodes

You have to edit hosts file in /etc/ folder on all nodes, specify the IP address of each system followed by their host names.
# vi /etc/hosts
enter the following lines in the /etc/hosts file.

192.168.1.109 hadoop-master
192.168.1.145 hadoop-slave-1
192.168.56.1 hadoop-slave-2

Configuring Key Based Login

Setup ssh in every node such that they can communicate with one another without any prompt for password.
# su hadoop
$ ssh-keygen -t rsa
$ ssh-copy-id -i ~/.ssh/id_rsa.pub tecklearn@hadoop-master
$ ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop_tp1@hadoop-slave-1
$ ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop_tp2@hadoop-slave-2
$ chmod 0600 ~/.ssh/authorized_keys
$ exit

Installing Hadoop

In the Master server, download and install Hadoop using the following commands.
# mkdir /opt/hadoop
# cd /opt/hadoop/
# wget http://apache.mesi.com.ar/hadoop/common/hadoop-1.2.1/hadoop-1.2.0.tar.gz
# tar -xzf hadoop-1.2.0.tar.gz
# mv hadoop-1.2.0 hadoop
# chown -R hadoop /opt/hadoop
# cd /opt/hadoop/hadoop/

Configuring Hadoop

You have to configure Hadoop server by making the following changes as given below.
core-site.xml
Open the core-site.xml file and edit it as shown below.
<configuration>
<property>
<name>fs.default.name</name>
<value>hdfs://hadoop-master:9000/</value>
</property>
<property>
<name>dfs.permissions</name>
<value>false</value>
</property>
</configuration>
hdfs-site.xml
Open the hdfs-site.xml file and edit it as shown below.
<configuration>
<property>
<name>dfs.data.dir</name>
<value>/opt/hadoop/hadoop/dfs/name/data</value>
<final>true</final>
</property>

<property>
<name>dfs.name.dir</name>
<value>/opt/hadoop/hadoop/dfs/name</value>
<final>true</final>
</property>

<property>
<name>dfs.replication</name>
<value>1</value>
</property>
</configuration>
mapred-site.xml
Open the mapred-site.xml file and edit it as shown below.
<configuration>
<property>
<name>mapred.job.tracker</name>
<value>hadoop-master:9001</value>
</property>
</configuration>
hadoop-env.sh
Open the hadoop-env.sh file and edit JAVA_HOME, HADOOP_CONF_DIR, and HADOOP_OPTS as shown below.
Note − Set the JAVA_HOME as per your system configuration.
export JAVA_HOME=/opt/jdk1.7.0_17
export HADOOP_OPTS=-Djava.net.preferIPv4Stack=true
export HADOOP_CONF_DIR=/opt/hadoop/hadoop/conf

Installing Hadoop on Slave Servers

Install Hadoop on all the slave servers by following the given commands.
# su hadoop
$ cd /opt/hadoop
$ scp -r hadoop hadoop-slave-1:/opt/hadoop
$ scp -r hadoop hadoop-slave-2:/opt/hadoop

Configuring Hadoop on Master Server

Open the master server and configure it by following the given commands.
# su hadoop
$ cd /opt/hadoop/hadoop
Configuring Master Node
$ vi etc/hadoop/masters

hadoop-master
Configuring Slave Node
$ vi etc/hadoop/slaves

hadoop-slave-1
hadoop-slave-2
Format Name Node on Hadoop Master
# su hadoop
$ cd /opt/hadoop/hadoop
$ bin/hadoop namenode –format
11/10/14 10:58:07 INFO namenode.NameNode: STARTUP_MSG:
/************************************************************
STARTUP_MSG: Starting NameNode
STARTUP_MSG: host = hadoop-master/192.168.1.109
STARTUP_MSG: args = [-format]
STARTUP_MSG: version = 1.2.0
STARTUP_MSG: build = https://svn.apache.org/repos/asf/hadoop/common/branches/branch-1.2 -r 1479473;
compiled by ‘hortonfo’ on Mon May 6 06:59:37 UTC 2013
STARTUP_MSG: java = 1.7.0_71

************************************************************/
11/10/14 10:58:08 INFO util.GSet: Computing capacity for map BlocksMap
editlog=/opt/hadoop/hadoop/dfs/name/current/edits
………………………………………………….
………………………………………………….
………………………………………………….
11/10/14 10:58:08 INFO common.Storage: Storage directory
/opt/hadoop/hadoop/dfs/name has been successfully formatted.
11/10/14 10:58:08 INFO namenode.NameNode:
SHUTDOWN_MSG:
/************************************************************
SHUTDOWN_MSG: Shutting down NameNode at hadoop-master/192.168.1.15
************************************************************/

Starting Hadoop Services

The following command is to start all the Hadoop services on the Hadoop-Master.
$ cd $HADOOP_HOME/sbin
$ start-all.sh

Adding a New DataNode in the Hadoop Cluster

Given below are the steps to be followed for adding new nodes to a Hadoop cluster.
Networking
Add new nodes to an existing Hadoop cluster with some appropriate network configuration. Assume the following network configuration.
For New node Configuration −
IP address : 192.168.1.103
netmask : 255.255.255.0
hostname : slave3.in

Adding User and SSH Access

Add a User
On a new node, add “hadoop” user and set password of Hadoop user to “hadoop123″ or anything you want by using the following commands.
useradd hadoop
passwd hadoop

Setup Password less connectivity from master to new slave.

Execute the following on the master
mkdir -p $HOME/.ssh
chmod 700 $HOME/.ssh
ssh-keygen -t rsa -P ” -f $HOME/.ssh/id_rsa
cat $HOME/.ssh/id_rsa.pub >> $HOME/.ssh/authorized_keys
chmod 644 $HOME/.ssh/authorized_keys
Copy the public key to new slave node in hadoop user $HOME directory
scp $HOME/.ssh/id_rsa.pub hadoop@192.168.1.103:/home/hadoop/
Execute the following on the slaves
Login to hadoop. If not, login to hadoop user.
su hadoop ssh -X hadoop@192.168.1.103
Copy the content of public key into file “$HOME/.ssh/authorized_keys” and then change the permission for the same by executing the following commands.
cd $HOME
mkdir -p $HOME/.ssh
chmod 700 $HOME/.ssh
cat id_rsa.pub >>$HOME/.ssh/authorized_keys
chmod 644 $HOME/.ssh/authorized_keys
Check ssh login from the master machine. Now check if you can ssh to the new node without a password from the master.
ssh hadoop@192.168.1.103 or hadoop@slave3

Set Hostname of New Node

You can set hostname in file /etc/sysconfig/network
On new slave3 machine

NETWORKING = yes
HOSTNAME = slave3.in
To make the changes effective, either restart the machine or run hostname command to a new machine with the respective hostname (restart is a good option).
On slave3 node machine −
hostname slave3.in
Update /etc/hosts on all machines of the cluster with the following lines −
192.168.1.102 slave3.in slave3
Now try to ping the machine with hostnames to check whether it is resolving to IP or not.
On new node machine −
ping master.in

Start the DataNode on New Node

Start the datanode daemon manually using $HADOOP_HOME/bin/hadoop-daemon.sh script. It will automatically contact the master (NameNode) and join the cluster. We should also add the new node to the conf/slaves file in the master server. The script-based commands will recognize the new node.
Login to new node
su hadoop or ssh -X hadoop@192.168.1.103
Start HDFS on a newly added slave node by using the following command
./bin/hadoop-daemon.sh start datanode
Check the output of jps command on a new node. It looks as follows.
$ jps
7141 DataNode
10312 Jps

Removing a DataNode from the Hadoop Cluster

We can remove a node from a cluster on the fly, while it is running, without any data loss. HDFS provides a decommissioning feature, which ensures that removing a node is performed safely. To use it, follow the steps as given below −
Step 1 − Login to master
Login to master machine user where Hadoop is installed.
$ su hadoop
Step 2 − Change cluster configuration
An exclude file must be configured before starting the cluster. Add a key named dfs.hosts.exclude to our $HADOOP_HOME/etc/hadoop/hdfs-site.xml file. The value associated with this key provides the full path to a file on the NameNode’s local file system which contains a list of machines which are not permitted to connect to HDFS.
For example, add these lines to etc/hadoop/hdfs-site.xml file.
<property>
<name>dfs.hosts.exclude</name>
<value>/home/hadoop/hadoop-1.2.1/hdfs_exclude.txt</value>
<description>DFS exclude</description>
</property>
Step 3 − Determine hosts to decommission
Each machine to be decommissioned should be added to the file identified by the hdfs_exclude.txt, one domain name per line. This will prevent them from connecting to the NameNode. Content of the “/home/hadoop/hadoop-1.2.1/hdfs_exclude.txt” file is shown below, if you want to remove DataNode2.
slave2.in
Step 4 − Force configuration reload
Run the command “$HADOOP_HOME/bin/hadoop dfsadmin -refreshNodes” without the quotes.
$ $HADOOP_HOME/bin/hadoop dfsadmin -refreshNodes
This will force the NameNode to re-read its configuration, including the newly updated ‘excludes’ file. It will decommission the nodes over a period of time, allowing time for each node’s blocks to be replicated onto machines which are scheduled to remain active.
On slave2.in, check the jps command output. After some time, you will see the DataNode process is shutdown automatically.
Step 5 − Shutdown nodes
After the decommission process has been completed, the decommissioned hardware can be safely shut down for maintenance. Run the report command to dfsadmin to check the status of decommission. The following command will describe the status of the decommission node and the connected nodes to the cluster.
$ $HADOOP_HOME/bin/hadoop dfsadmin -report
Step 6 − Edit excludes file again
Once the machines have been decommissioned, they can be removed from the ‘excludes’ file. Running “$HADOOP_HOME/bin/hadoop dfsadmin -refreshNodes” again will read the excludes file back into the NameNode; allowing the DataNodes to rejoin the cluster after the maintenance has been completed, or additional capacity is needed in the cluster again, etc.
Special Note − If the above process is followed and the tasktracker process is still running on the node, it needs to be shut down. One way is to disconnect the machine as we did in the above steps. The Master will recognize the process automatically and will declare as dead. There is no need to follow the same process for removing the tasktracker because it is NOT much crucial as compared to the DataNode. DataNode contains the data that you want to remove safely without any loss of data.
The tasktracker can be run/shutdown on the fly by the following command at any point of time.
$ $HADOOP_HOME/bin/hadoop-daemon.sh stop tasktracker
$HADOOP_HOME/bin/hadoop-daemon.sh start task tracker.

So, this brings us to the end of blog. This Tecklearn ‘How to set up Hadoop Multi-Node Cluster on a distributed environment’ helps you with commonly asked questions if you are looking out for a job in Big Data and Hadoop Domain.
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Introduction
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• Why Hadoop?
• Core Hadoop Components
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HDFS
• HDFS Features
• Writing and Reading Files
• NameNode Memory Considerations
• Overview of HDFS Security
• Using the Namenode Web UI
• Using the Hadoop File Shell
Getting Data into HDFS
• Ingesting Data from External Sources with Flume
• Ingesting Data from Relational Databases with Sqoop
• REST Interfaces
• Best Practices for Importing Data
YARN and MapReduce
• What Is MapReduce?
• Basic MapReduce Concepts
• YARN Cluster Architecture
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• Using the YARN Web UI
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Planning Your Hadoop Cluster
• General Planning Considerations
• Choosing the Right Hardware
• Network Considerations
• Configuring Nodes
• Planning for Cluster Management
Hadoop Installation and Initial Configuration
• Deployment Types
• Installing Hadoop
• Specifying the Hadoop Configuration
• Performing Initial HDFS Configuration
• Performing Initial YARN and MapReduce Configuration
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Installing and Configuring Hive, Impala, and Pig
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Hadoop Clients
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Cloudera Manager
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• Cloudera Manager Topology
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• Installing Hadoop Using Cloudera Manager
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Advanced Cluster Configuration
• Advanced Configuration Parameters
• Configuring Hadoop Ports
• Explicitly Including and Excluding Hosts
• Configuring HDFS for Rack Awareness
• Configuring HDFS High Availability
Hadoop Security
• Why Hadoop Security Is Important
• Hadoop’s Security System Concepts
• What Kerberos Is and How it Works
• Securing a Hadoop Cluster with Kerberos
Managing and Scheduling Jobs
• Managing Running Jobs
• Scheduling Hadoop Jobs
• Configuring the Fair Scheduler
• Impala Query Scheduling
Cluster Maintenance
• Checking HDFS Status
• Copying Data Between Clusters
• Adding and Removing Cluster Nodes
• Rebalancing the Cluster
• Cluster Upgrading
Cluster Monitoring and Troubleshooting
• General System Monitoring
• Monitoring Hadoop Clusters
• Common Troubleshooting Hadoop Clusters
• Common Misconfigurations
Introduction to Pig
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Basic Data Analysis with Pig
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Processing Complex Data with Pig
• Storage Formats
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Multi-Dataset Operations with Pig
• Techniques for Combining Data Sets
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• Set Operations
• Splitting Data Sets
Pig Troubleshooting and Optimization
• Troubleshooting Pig
• Logging
• Using Hadoop’s Web UI
• Data Sampling and Debugging
• Performance Overview
• Understanding the Execution Plan
• Tips for Improving the Performance of Your Pig Jobs
Introduction to Hive and Impala
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• What Is Impala?
• Schema and Data Storage
• Comparing Hive to Traditional Databases
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Querying with Hive and Impala
• Databases and Tables
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• Data Types
• Differences Between Hive and Impala Query Syntax
• Using Hue to Execute Queries
• Using the Impala Shell
Data Management
• Data Storage
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Data Storage and Performance
• Partitioning Tables
• Choosing a File Format
• Managing Metadata
• Controlling Access to Data
Relational Data Analysis with Hive and Impala
• Joining Datasets
• Common Built-In Functions
• Aggregation and Windowing
Working with Impala
• How Impala Executes Queries
• Extending Impala with User-Defined Functions
• Improving Impala Performance
Analyzing Text and Complex Data with Hive
• Complex Values in Hive
• Using Regular Expressions in Hive
• Sentiment Analysis and N-Grams
• Conclusion
Hive Optimization
• Understanding Query Performance
• Controlling Job Execution Plan
• Bucketing
• Indexing Data
Extending Hive
• SerDes
• Data Transformation with Custom Scripts
• User-Defined Functions
• Parameterized Queries
Importing Relational Data with Apache Sqoop
• Sqoop Overview
• Basic Imports and Exports
• Limiting Results
• Improving Sqoop’s Performance
• Sqoop 2
Introduction to Impala and Hive
• Introduction to Impala and Hive
• Why Use Impala and Hive?
• Comparing Hive to Traditional Databases
• Hive Use Cases
Modelling and Managing Data with Impala and Hive
• Data Storage Overview
• Creating Databases and Tables
• Loading Data into Tables
• HCatalog
• Impala Metadata Caching
Data Formats
• Selecting a File Format
• Hadoop Tool Support for File Formats
• Avro Schemas
• Using Avro with Hive and Sqoop
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• Compression
Data Partitioning
• Partitioning Overview
• Partitioning in Impala and Hive
Capturing Data with Apache Flume
• What is Apache Flume?
• Basic Flume Architecture
• Flume Sources
• Flume Sinks
• Flume Channels
• Flume Configuration
Spark Basics
• What is Apache Spark?
• Using the Spark Shell
• RDDs (Resilient Distributed Datasets)
• Functional Programming in Spark
Working with RDDs in Spark
• A Closer Look at RDDs
• Key-Value Pair RDDs
• MapReduce
• Other Pair RDD Operations
Writing and Deploying Spark Applications
• Spark Applications vs. Spark Shell
• Creating the SparkContext
• Building a Spark Application (Scala and Java)
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• The Spark Application Web UI
• Configuring Spark Properties
• Logging
Parallel Programming with Spark
• Review: Spark on a Cluster
• RDD Partitions
• Partitioning of File-based RDDs
• HDFS and Data Locality
• Executing Parallel Operations
• Stages and Tasks
Spark Caching and Persistence
• RDD Lineage
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Common Patterns in Spark Data Processing
• Common Spark Use Cases
• Iterative Algorithms in Spark
• Graph Processing and Analysis
• Machine Learning
• Example: k-means
Preview: Spark SQL
• Spark SQL and the SQL Context
• Creating DataFrames
• Transforming and Querying DataFrames
• Saving DataFrames
• Comparing Spark SQL with Impala
Hadoop Testing
• Hadoop Application Testing
• Roles and Responsibilities of Hadoop Testing Professional
• Framework MRUnit for Testing of MapReduce Programs
• Unit Testing
• Test Execution
• Test Plan Strategy and Writing Test Cases for Testing Hadoop Application
Big Data Testing
• BigData Testing
• Unit Testing
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• Non-Functional Testing
• Golden Data Set
System Testing
• Building and Set up
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• Solary Server
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Security Testing
• Security Testing
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• Hadoop Cluster
• Security-Authorization RBA
• IBM Project
Automation Testing
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