Process Advisors

*Subject to Terms and Condition

Hadoop file system provides you a privilege as it stores the data in multiple copies. Also, it’s a cost-effective solution for any business to store their data efficiently. HDFS Operations acts as the key to open the vaults in which you store the data to be available from remote locations.

Following are the topics that’ll be covered in this HDFS tutorial:

Starting HDFS

Format the configured HDFS file system and then open the namenode (HDFS server) and execute the following HDFS command.

$ hadoop namenode -format

Start the distributed file system and follow the command listed below to start the namenode as well as the data nodes in cluster.


Watch this Big Data & Hadoop Full Course – Learn Hadoop In 12 Hours tutorial!

Read & Write Operations in HDFS

You can execute almost all operations on Hadoop Distributed File Systems that can be executed on the local file system. You can execute various reading, writing operations such as creating a directory, providing permissions, copying files, updating files, deleting, etc. You can add access rights and browse the file system to get the cluster information like the number of dead nodes, live nodes, spaces used, etc.

HDFS Operations to Read the file

To read any file from the HDFS, you have to interact with the NameNode as it stores the metadata about the DataNodes. The user gets a token from the NameNode and that specifies the address where the data is stored. 

You can put a read request to NameNode for a particular block location through distributed file systems. The NameNode will then check your privilege to access the DataNode and allows you to read the address block if the access is valid. 

$ hadoop fs -cat <file>

HDFS Operations to write in file

Similar to the read operation, the HDFS Write operation is used to write the file on a particular address through the NameNode. This NameNode provides the slave address where the client/user can write or add data. After writing on the block location, the slave replicates that block and copies to another slave location using the factor 3 replication. The salve is then reverted back to the client for authentication. 

The process for accessing a NameNode is pretty similar to that of a reading operation. Below is the HDFS write commence:

bin/hdfs dfs -ls  <path>

Take the Big Data Training and learn the key technologies from subject matter experts.

Listing Files in HDFS

Finding the list of files in a directory and the status of a file using ‘ls’ command in the terminal. Syntax of ls can be passed to a directory or a filename as an argument which are displayed as follows:

$ $HADOOP_HOME/bin/hadoop fs -ls <args>

Inserting Data into HDFS

Below mentioned steps are followed to insert the required file in the Hadoop file system.

Step1: Create an input directory

$ $HADOOP_HOME/bin/hadoop fs -mkdir /user/input

Step2: Use the Hadoop HDFS put Command transfer and store the data file from the local systems to the HDFS using the following commands in the terminal.

$ $HADOOP_HOME/bin/hadoop fs -put /home/intellipaat.txt /user/input

Step3: Verify the file using Hadoop HDFS ls Command

$ $HADOOP_HOME/bin/hadoop fs -ls /user/input

Retrieving Data from HDFS

For instance, if you have a file in HDFS called Intellipaat. Then retrieve the required file from the Hadoop file system by carrying out:

Step1: View the data using the HDFS cat command.

$ $HADOOP_HOME/bin/hadoop fs -cat /user/output/intellipaat

Step2: Gets the file from HDFS to the local file system using get command as shown below

$ $HADOOP_HOME/bin/hadoop fs -get /user/output/ /home/hadoop_tp/

Shutting Down the HDFS

Shut down the HDFS files by following the below HDFS command


Multi-Node Cluster

Installing Java

Syntax of java version command

$ java -version

Following output is presented.

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)

Get 100% Hike!

Master Most in Demand Skills Now !

Creating User Account

System user account is used on both master and slave systems for the Hadoop installation.

# useradd hadoop
# passwd hadoop

Mapping the nodes

Hosts files should be edited in /etc/ folder on each and every nodes and IP address of each system followed by their host names must be specified mandatorily.

# vi /etc/hosts

Enter the following lines in the /etc/hosts file. hadoop-master hadoop-slave-1 hadoop-slave-2

Configuring Key Based Login

Ssh should be set up in each node so they can easily converse with one another without any prompt for a password.

# su hadoop
$ ssh-keygen -t rsa
$ ssh-copy-id -i ~/.ssh/ [email protected]
$ ssh-copy-id -i ~/.ssh/ [email protected]
$ ssh-copy-id -i ~/.ssh/ [email protected]
$ chmod 0600 ~/.ssh/authorized_keys
$ exit

Installation of Hadoop

Hadoop should be downloaded in the master server using the following procedure.

# mkdir /opt/hadoop
# cd /opt/hadoop/
# wget
# tar -xzf hadoop-1.2.0.tar.gz
# mv hadoop-1.2.0 hadoop
# chown -R hadoop /opt/hadoop
# cd /opt/hadoop/hadoop/
Prepare yourself for the industry by going through this HDFS Interview Questions And Answers!

Configuring Hadoop

Hadoop server must be configured in core-site.xml and should be edited wherever required.

hdfs-site.xml file should be editted.

mapred-site.xml file should be edited as per the requirement example is being shown.


JAVA_HOME, HADOOP_CONF_DIR, and HADOOP_OPTS should be edited as follows:

export JAVA_HOME=/opt/jdk1.7.0_17
export HADOOP_CONF_DIR=/opt/hadoop/hadoop/conf

Installing Hadoop on Slave Servers

Hadoop should be installed on all the slave servers

# su hadoop
$ cd /opt/hadoop
$ scp -r hadoop hadoop-slave-1:/opt/hadoop
$ scp -r hadoop hadoop-slave-2:/opt/hadoop

Career Transition

Configuring Hadoop on Master Server

Master server configuration

# su hadoop
$ cd /opt/hadoop/hadoop
Master Node Configuration
$ vi etc/hadoop/masters

Slave Node Configuration

$ vi etc/hadoop/slaves

Name Node format 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/
STARTUP_MSG: args = [-format]
STARTUP_MSG: version = 1.2.0
STARTUP_MSG: build = -r 1479473; compiled by 'hortonfo' on Monday 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/

Hadoop Services

Starting Hadoop services on the Hadoop-Master procedure explains its setup.

$ cd $HADOOP_HOME/sbin

Addition of a New DataNode in the Hadoop Cluster is as follows:


Add new nodes to an existing Hadoop cluster with some suitable network configuration. Consider the following network configuration for new node Configuration:

IP address :
netmask :
hostname :

Adding a User and SSH Access

Add a user working under “hadoop” domain and the user must have the access added and password of Hadoop user can be set to anything one wants.

useradd hadoop
passwd hadoop

To be executed on master

mkdir -p $HOME/.ssh
chmod 700 $HOME/.ssh
ssh-keygen -t rsa -P '' -f $HOME/.ssh/id_rsa
cat $HOME/.ssh/ >> $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/ [email protected]:/home/hadoop/

Execution done on slaves

su hadoop ssh -X [email protected]

Content of public key must be copied into file “$HOME/.ssh/authorized_keys” and then the permission for the same must be changed as per the requirement.

cd $HOME
mkdir -p $HOME/.ssh
chmod 700 $HOME/.ssh
cat >>$HOME/.ssh/authorized_keys
chmod 644 $HOME/.ssh/authorized_keys

ssh login must be changed from the master machine. It is possible that the ssh to the new node without a password from the master must be verified.

ssh [email protected] or [email protected]

Setting  Hostname for New Node

Hostname is setup in the file directory  /etc/sysconfig/network
On new slave3 machine

Machine must be restarted again or hostname command should be run under new machine with the corresponding hostname to make changes effectively.

On slave3 node machine:

/etc/hosts must be updated on all machines of the cluster slave3

ping the machine with hostnames to check whether it is resolving to IP address.


Start the DataNode on New Node

Datanode daemon should be started manually using $HADOOP_HOME/bin/ script. Master (NameNode) should correspondingly join the cluster after automatically contacted. New node should be added to the configuration/slaves file in the master server. New node will be identified by script-based commands.

Login to new node

su hadoop or ssh -X [email protected]

HDFS is started on a newly added slave node

./bin/ start datanode

 jps command output must be checked on a new node.

$ jps
7141 DataNode
10312 Jps

Removing a DataNode

Node can be removed from a cluster while it is running, without any worries of data loss. A decommissioning feature is made available by HDFS which ensures that removing a node is performed securely.

Step 1

Login to master machine so that the user can check Hadoop is being installed.

$ su hadoop

Step 2

Before starting the cluster an exclude file must be configured where a key named dfs.hosts.exclude should be added to our$HADOOP_HOME/etc/hadoop/hdfs-site.xmlfile.

NameNode’s local file system contains a list of machines which are not permitted to connect to HDFS receives full path by this key and the value associated with it as follows.

<name>dfs.hosts.exclude</name><value>/home/hadoop/hadoop-1.2.1/hdfs_exclude.txt</value><description>>DFS exclude</description>

Step 3

Hosts with respect to decommission are determined.

File reorganization by the hdfs_exclude.txt for each and every machine to be decommissioned which will results in preventing them from connecting to the NameNode.

Step 4

Force configuration reloads.

“$HADOOP_HOME/bin/hadoop dfsadmin -refreshNodes” should be run
$ $HADOOP_HOME/bin/hadoop dfsadmin -refreshNodes

NameNode will be forced made to re-read its configuration, as this is inclusive for the newly updated ‘excludes’ file. Nodes will be decommissioned over a period of time intervals, and allowing time for each node’s blocks to be replicated onto machines which are scheduled to be active.jps command output should be checked on Once the work is done DataNode process will shutdown automatically.

Step 5

Shutdown nodes.

The decommissioned hardware can be carefully shut down for maintenance purpose after the decommission process has been finished.

$ $HADOOP_HOME/bin/hadoop dfsadmin -report

Step 6

Excludes are edited again and once the machines have been decommissioned, they are removed from the ‘excludes’ file. “$HADOOP_HOME/bin/hadoop dfsadmin -refreshNodes” will read the excludes file back into the NameNode.

Data Nodes will rejoin the cluster after the maintenance has been completed, or if additional capacity is needed in the cluster again is being informed.

To run/shutdown tasktracker

$ $HADOOP_HOME/bin/ stop tasktracker
$ $HADOOP_HOME/bin/ start tasktracker

Certification in Bigdata Analytics

Add a new node with the following steps

1) Take a new system which gives access to create a new username and password

2) Install the SSH and with master node setup ssh connections

3) Add sshpublic_rsa id key having an authorized keys file

4) Add the new data node hostname, IP address and other informative details in /etc/hosts slaves file192.168.1.102 slave3

5) Start the DataNode on the New Node

6) Login to the new node command like suhadoop or Ssh -X [email protected]

7) Start HDFS of newly added in the slave node by using the following command ./bin/ start data node

8) Check the output of jps command on a new node.

Advantages of learning HDFS Operations

Below are the major advantages of learning HDFS operations:

  • Highly Scalable, you can expand the big data programs based on the user experience and rise in demand. 
  • HDFS Operations are easy to understand and require less coding. 
  • Hadoop provides a cost-effective storage solution for organizations of all sizes. Here, the clients only have to pay for the resources based on the time period they’re utilizing them. 
  • The distributed file system is a cluster of cloud servers from different locations working in a synchronized manner. This makes data processing much faster and efficiently processes huge datasets within seconds.
  • HDFS is a new technology that many companies are adapting to, so learning it cloud provides a big leap in your career. 
  • When the data is sent to the node, its automatically replicated to other nodes of the cluster. This means you have multiple copies of the data. You’ll not lose your data in the event of failure. 

Check out our Hadoop Community if you have any questions. 


Hadoop Distributed File System is a highly scalable, flexible, fault-tolerant, and reliable system that stores the data across multiple nodes on different servers. It follows a master-slave architecture, where the NameNode acts as a master, and the DataNode as the slave. HDFS Operations are used to access these NameNodes and interact with the data. The files are broken down into blocks where the client can store the data, read, write, and perform various operations by completing the authentication process.

Course Schedule

Name Date Details
Big Data Course 10 Jun 2023(Sat-Sun) Weekend Batch
View Details
Big Data Course 17 Jun 2023(Sat-Sun) Weekend Batch
View Details
Big Data Course 24 Jun 2023(Sat-Sun) Weekend Batch
View Details

Leave a Reply

Your email address will not be published. Required fields are marked *