Top 10 Hadoop Commands [With Usages]

In this era, with huge chunks of data, it becomes essential to deal with them. The data springing from organizations with growing customers is way larger than any traditional data management tool can store. It leaves us with the question of managing larger sets of data, which could range from gigabytes to petabytes, without using a single large computer or traditional data management tool.

This is where the Apache Hadoop framework grabs the spotlight. Before diving into Hadoop command implementation, let’s briefly comprehend the Hadoop framework and its importance.

What is Hadoop?

Hadoop is commonly used by major business organizations to solve various problems, from storing large GBs (Gigabytes) of data every day to computing operations on the data.

Traditionally defined as an open-source software framework used to store data and processing applications, Hadoop stands out quite heavily from the majority of traditional data management tools. It improves the computing power and extends the data storage limit by adding a few nodes in the framework, making it highly scalable. Besides, your data and application processes are protected against various hardware failures.

Hadoop follows a master-slave architecture to distribute and store data using MapReduce and HDFS. As depicted in the figure below, the architecture is tailored in a defined manner to perform data management operations using four primary nodes, namely Name, Data, Master, and Slave. The core components of Hadoop are built directly on top of the framework. Other components integrate directly with the segments.

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Hadoop Commands

Major features of the Hadoop framework show a coherent nature, and it becomes more user-friendly when it comes to managing big data with learning Hadoop Commands. Below are some convenient Hadoop Commands that allow performing various operations, such as management and HDFS clusters file processing. This list of commands is frequently required to achieve certain process outcomes.

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1. Hadoop Touchz

hadoop fs -touchz /directory/filename

This command allows the user to create a new file in the HDFS cluster. The “directory” in the command refers to the directory name where the user wishes to create the new file, and the “filename” signifies the name of the new file which will be created upon the completion of the command.

2. Hadoop Test Command 

hadoop fs -test -[defsz] <path>

This particular command fulfills the purpose of testing the existence of a file in the HDFS cluster. The characters from “[defsz]” in the command have to be modified as needed. Here is a brief description of these characters:

  • d -> Checks if it is a directory or not
  • e -> Checks if it is a path or not
  • f -> Checks if it is a file or not
  • s -> Checks if it is an empty path or not
  • r -> Checks the path existence and read permission
  • w -> Checks the path existence and write permission
  • z -> Checks the file size

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3. Hadoop Text Command

hadoop fs -text <src>

The text command is particularly useful to display the allocated zip file in text format. It operates by processing source files and providing its content into a plain decoded text format.

4. Hadoop Find Command

hadoop fs -find <path> … <expression>

This command is generally used for the purpose to search for files in the HDFS cluster. It scans the given expression in the command with all the files in the cluster, and displays the files that match the defined expression.

Read: Top Hadoop Tools

5. Hadoop Getmerge Command

hadoop fs -getmerge <src> <localdest>

Getmerge command allows merging one or multiple files in a designated directory on the HDFS filesystem cluster. It accumulates the files into one single file located in the local filesystem. The “src” and “localdest” represents the meaning of source-destination and local destination.

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6. Hadoop Count Command

hadoop fs -count [options] <path>

As obvious as its name, the Hadoop count command counts the number of files and bytes in a given directory. There are various options available that modify the output as per the requirement. These are as follows:

  • q -> quota shows the limit on the total number of names and usage of space
  • u -> displays only quota and usage
  • h -> gives the size of a file
  • v -> displays header

7. Hadoop AppendToFile Command

hadoop fs -appendToFile <localsrc> <dest>

It allows the user to append the content of one or many files into a single file on the specified destination file in the HDFS filesystem cluster. On execution of this command, the given source files are appended into the destination source as per the given filename in the command.

8. Hadoop ls Command

hadoop fs -ls /path

The ls command in Hadoop shows the list of files/contents in a specified directory, i.e., path. On adding “R” before /path, the output will show details of the content, such as names, size, owner, and so on for each file specified in the given directory.

9. Hadoop mkdir Command

hadoop fs -mkdir /path/directory_name

This command’s unique feature is the creation of a directory in the HDFS filesystem cluster if the directory does not exist. Besides, if the specified directory is present, then the output message will show an error signifying the directory’s existence.

10. Hadoop chmod Command

hadoop fs -chmod [-R] <mode> <path>

This command is used when there is a need to change the permissions to accessing a particular file. On giving the chmod command, the permission of the specified file is changed. However, it is important to remember that the permission will be modified when the file owner executes this command.

Also Read: Impala Hadoop Tutorial


Beginning with the important issue of data storage faced by the major organizations in today’s world, this article discussed the solution for limited data storage by introducing Hadoop and its impact on carrying out data management operations by using Hadoop commands. For beginners in Hadoop, an overview of the framework is described along with its components and architecture.

After reading this article, one can easily feel confident about their knowledge in the aspect of the Hadoop framework and its applied commands. upGrad’s Exclusive PG Certification in Big Data: upGrad offers an industry-specific 7.5 months program for PG Certification in Big Data where you will organize, analyze, and interpret Big Data with IIIT-Bangalore.

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Where is Hadoop used?

Hadoop is a Java-based framework, and it is an open-source framework. It is used for storing and processing Big Data. Hadoop is used in the security and law enforcement industry to prevent terrorist attacks, and in the detection and prevention of cyberattacks. The technology’s most important uses are in customer’s requirement understanding. Credit card companies determine their exact consumer base with the technology. Hadoop is used to develop the country, state, and cities by analysing data. It is also used in the trading field to work without human interaction. Another most common reason why the uses of Hadoop are important is that it is also used in the business processes. It has optimised the performance of the companies in many ways.

What is the future and scope of Hadoop?

With the rise of the Big Data world, there arose a need for flawless systems that can process, store, and retrieve such rising Big Data. The traditional databases are not capable enough of fastly processing vast data. Hadoop has come out like a light in the world of Big Data analytics. It has a bright future. As per the Forbes report, the Big Data market will reach heights in the coming years. There will be a need for more Hadoop developers to deal with Big data challenges. Several IT firms are adopting Hadoop technology for their research, increasing the demand for Hadoop professionals.

What are the job profiles that fall for the person having relevant skills in Hadoop?

There are various job profiles for a person with skills in Hadoop. Some of them are that of a Hadoop Administrator, who sets up a Hadoop cluster and monitors it with monitoring tools, a Hadoop Architect, who plans and designs the Big Data Hadoop architecture, a Big Data Analyst, who analyses Big Data for evaluating the company’s technical performance and a Hadoop developer, whose main task is to develop Hadoop technologies using Java and other scripting languages.

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