Top 10 Hadoop Commands [With Usages]

Plus, it now supports MySQL storage, making your Hadoop deployments more secure and flexible than ever before.

Hadoop commands like hadoop fs -ls, hadoop jar, and hadoop dfsadmin are essential for managing and interacting with Hadoop clusters. But if you’re not using them correctly, you might be missing out on key efficiencies. 

This article covers the top 10 commands with usage examples to help you sharpen your Hadoop developer skills. 

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What are the Top 10 Hadoop Commands??

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.

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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.

To efficiently manage, process, and interact with Hadoop’s ecosystem, various Hadoop commands are used. These commands allow users to handle file operations in HDFS, execute MapReduce jobs, and manage the cluster seamlessly. Whether it's storing data, retrieving files, or monitoring system performance, Hadoop commands play a crucial role in simplifying these tasks.

Also Read: Understanding Hadoop Ecosystem: Architecture, Components & Tools

Below are some convenient Hadoop Commands that allow performing various operations, such as management and HDFS clusters file processing.

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.

Use Case: Typically used when you want to create an empty file in HDFS, especially for staging or logging purposes.

Best Practice: Always check if the directory path exists before executing this command to avoid errors.

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

Troubleshooting Tip: If you’re unsure which test flag to use, remember that -e is a general existence check, while -f ensures it's specifically a file.

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.

Use Case: Useful for inspecting the contents of compressed files stored in HDFS.
Tip: If you're working with large files, using -tail instead of -text can prevent excessive data from being displayed.

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

Tip: Use -mtime to filter files by modification time or -size to filter based on file size.

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.

Tip: Use -nl to add a newline between files being merged.

Also Read: Hadoop Partitioner: Learn About Introduction, Syntax, Implementation

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

Tip: Use -q for quota information if you need to check available space.

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.

Best Practice: Ensure the destination file is large enough to accommodate the appended data.

Tip: Use -R for recursive listing to see files in subdirectories.

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.

Troubleshooting Tip: Make sure the parent directory exists; otherwise, you’ll get an error.

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.

Best Practice: Always use -R if you want to apply changes recursively to all files and subdirectories.

Also Read: Hadoop Developer Skills: Key Technical & Soft Skills to Succeed in Big Data

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Now that you’ve mastered the basic Hadoop commands, let's dive into some advanced commands that provide more control and flexibility over your Hadoop environment. These commands can help you manage, troubleshoot, and optimize your cluster more effectively. Here are four advanced commands to further expand your Hadoop skill set:

11. Hadoop Balancer Command 

hadoop balancer 

This command helps balance data across the HDFS cluster by redistributing blocks from over-utilized nodes to those with more available space. 

Tip: Regularly running the balancer ensures that your cluster remains efficient and avoids potential performance bottlenecks caused by uneven storage usage.

12. Hadoop Decommission Command 

hadoop dfsadmin -decommission <datanode> 

Use this command to safely remove a DataNode from your cluster. It ensures that the DataNode’s data is properly replicated across other nodes before it is decommissioned. 

Note: Always verify the replication status before decommissioning a DataNode to ensure data integrity.

13. Hadoop CopyToLocal Command 

hadoop fs -copyToLocal <src> <local_dest> 

This command copies files from HDFS to your local filesystem, but unlike -get, it won’t overwrite any files already present at the destination. 

Use case: Ideal when you want to copy files without risking overwriting existing local files, particularly useful for backup and migration tasks.

14. Hadoop Snapshot Command 

hadoop fs -createSnapshot <dir> <snapshot_name> 

This command allows you to create a snapshot of an HDFS directory, capturing its exact state at a specific moment. 

Application: Snapshots are invaluable for data recovery and backup strategies, enabling you to restore data without interrupting ongoing operations.

Now that you’ve learned the top Hadoop commands, it’s time to practice them in a real Hadoop environment. Set up a small cluster or use a test environment to run these commands and explore their full capabilities. Experiment with different options and file types to gain hands-on experience. 

For deeper knowledge, consider diving into Hadoop’s ecosystem tools like Hive or Pig to further enhance your big data skills.

Hadoop Command Optimization & Troubleshooting

As you work with Hadoop commands, you’ll inevitably encounter challenges that can hinder performance or cause errors. This is designed to help you troubleshoot common issues and optimize your workflow, ensuring smoother operations. 

Here are key tips for troubleshooting and optimizing your Hadoop commands.

1. Handling Missing Files or Directories

• Issue: Sometimes, commands like hadoop fs -ls or hadoop fs -cp fail if the source file or directory doesn’t exist.
• Solution: Always verify the paths before executing the commands. Use hadoop fs -mkdir to ensure directories are created when needed. A simple check like hadoop fs -test -e <path> can confirm if the file or directory exists before proceeding with the operation.

2. Resolving Data Replication Problems

• Issue: When using commands like hadoop dfsadmin -decommission, data may not replicate properly, leading to potential data loss.
• Solution: Always check the replication status using hadoop fs -stat to ensure that data replication is complete. Adjust the replication factor with hadoop fs -setrep if necessary to maintain redundancy and reliability.

3. Optimizing Performance for Large File Operations

• Issue: Commands like hadoop fs -getmerge and hadoop fs -copyToLocal can consume significant resources and slow down your system, especially with large files.
• Solution: Run hadoop balancer to evenly distribute data across the cluster and prevent over-utilized nodes. Additionally, try using the -skipTrash option for file operations to avoid unnecessary disk usage from the trash directory, speeding up your process.

4. Speeding Up File Searches in Large Directories

• Issue: When running commands like hadoop fs -find or hadoop fs -ls on directories with millions of files, performance can degrade.
• Solution: Narrow your search by using more specific filters (e.g., -name, -size, -mtime) with hadoop fs -find. For better performance, consider splitting large directories into smaller, manageable parts, or use hadoop fs -du to estimate file sizes without listing full directory contents.

5. Troubleshooting Permissions Errors

• Issue: Permissions-related issues often arise when running commands like hadoop fs -chmod or hadoop fs -put, especially when trying to access restricted files.
• Solution: Check and modify file permissions with hadoop fs -chmod to ensure that the executing user has the necessary read/write/execute access. Additionally, make sure that your user has appropriate permissions for both the source and destination directories.

6. Managing Resources for Heavy Data Tasks

• Issue: Large-scale commands, especially those transferring huge datasets or running complex queries, can lead to memory issues and resource exhaustion.
• Solution: Fine-tune resource management by adjusting memory settings (mapreduce.map.memory.mb, mapreduce.reduce.memory.mb) in your Hadoop configuration. Splitting large tasks into smaller chunks will also reduce the load on individual nodes, leading to smoother execution.

7. Reducing Errors During Data Transfer

• Issue: File transfers can sometimes fail due to network issues or size mismatches, especially with commands like hadoop fs -put and hadoop fs -get.
• Solution: Ensure a stable network connection when transferring large datasets. For efficient distributed copying, consider using distcp, a robust tool for copying large files across Hadoop clusters with minimal disruption.

Now that you’ve learned how to troubleshoot and optimize your Hadoop commands, start applying these tips to your daily workflow. Experiment with resource allocation settings, manage data replication, and handle permissions carefully. Regularly monitor your Hadoop environment to catch issues early and ensure optimal performance.

Conclusion

Handling big data requires a solid understanding of the tools at your disposal, and Hadoop commands are a crucial part of that toolkit. From creating files to managing permissions and merging data, these commands lay the foundation for effective data management. 

But, as with any complex system, it’s easy to feel lost when trying to apply them to real-life scenarios without guidance.

To help bridge this gap, upGrad’s personalized career guidance can help you explore the right learning path based on your goals. You can also visit your nearest upGrad center and start hands-on training today!  

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References: 
https://hadoop.apache.org/