Essential Hadoop Commands for Beginners with Examples

Introduction

Hadoop is a powerful open-source framework that revolutionized the way we handle large datasets, enabling efficient storage and processing of massive amounts of data. Built to handle the challenges of big data, Hadoop is widely used in industries for tasks like data analysis, machine learning, and more.

In this blog, we’ll explore essential Hadoop commands that every beginner should know. Each command will be explained in detail and accompanied by practical examples to make learning easy and effective.

Basic Hadoop Commands

1. Check Hadoop Version

Use this command to verify the installed version of Hadoop:

hadoop version

Output:

2. Get General Help

To see a list of available Hadoop commands:

hadoop help

Output :

Displays all the general and subcommands available in Hadoop.

HDFS (Hadoop Distributed File System) Commands

HDFS is the backbone of Hadoop's storage system. Below are essential HDFS commands with examples.

1. Creating Directories

hdfs dfs -mkdir /user/hadoop
hdfs dfs -mkdir /user/hadoop/input

Output :

Creates a directory named input under /user/hadoop.

2. Listing Files and Directories

hdfs dfs -ls /user/hadoop

Output :

It shows the list of files and directories under the hadoop directories available.

3. Copying Files from Local to HDFS

Before copying a file to HDFS, make sure the file exists in your local directory. You can create a sample file if it doesn't exist:

Step 1: Create a Sample File

mkdir ~/data
sudo echo "Hello, this is a sample file for Hadoop commands." > ~/data/sample.txt

Output :

created a data directory and text file with text.

Step 2: Verify the File Exists

cat ~/data/sample.txt

Output :

Display text from sample.txt file.

Step 3: Copy the File to HDFS

hdfs dfs -put ~/data/sample.txt /user/hadoop/input

This command uploads sample.txt from the local directory to /user/hadoop/input in HDFS.

Output :

Uploads the file sample.txt from the local machine to HDFS.

4. Viewing File Contents

hdfs dfs -cat /user/hadoop/input/sample.txt

Output :

Displays the content of sample.txt.

5. Copying Files from HDFS to Local

mkdir ~/output 
hdfs dfs -get /user/hadoop/input/sample.txt ~/output/

Output :

Downloads the file sample.txt to the local output directory.

Viewing File Contents

cat ~/output/sample.txt

Output :

6. Removing Files and Directories

hdfs dfs -rm /user/hadoop/input/sample.txt

Output :

Deletes the specified file.

7. Checking File Replication

In previous command we remove the sample.txt file, so first we again move sample text file from local to hdfs

hdfs dfs -put ~/data/sample.txt /user/hadoop/input
hdfs dfs -stat %r /user/hadoop/input/sample.txt

Output :

Displays the replication factor of the file.

8. Disk Space Usage

hdfs dfs -du -h /user/hadoop

Output :

File Operations Commands

1. Moving Files

Before moving the files you need to make sure the processed directory should be available in hadoop directory otherwise it should be raised the error.

hdfs dfs -mv /user/hadoop/input/sample.txt /user/hadoop/processed/

Output :

Moves the file sample.txt to the processed directory.

2. Renaming Files

hdfs dfs -mv /user/hadoop/processed/sample.txt /user/hadoop/processed/data.txt

Output :

Renames the file to data.txt.

3. Changing File Permissions

hdfs dfs -chmod 644 /user/hadoop/processed/data.txt

Output :

Sets read-write permissions for the owner and read-only for others.

4. Changing Ownership

hdfs dfs -chown user:group /user/hadoop/processed/data.txt

Output :

Administrative Commands

1. Starting Hadoop Services

start-dfs.sh
start-yarn.sh

Output :

2. Checking Service Status

jps

Output :

Common Errors and Troubleshooting

1. Permission Denied:

   • Solution: Use the hdfs dfs -chmod command to modify permissions.

2. Directory Not Found:

   • Solution: Ensure the path exists before running commands.

3. Insufficient Replication:

   • Solution: Increase the replication factor using the hdfs dfs -setrep command.

Mastering these Hadoop commands is the first step to effectively managing big data projects. Hadoop's robust ecosystem empowers you to work with vast datasets seamlessly, and proficiency with these commands will make your journey smoother.

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