How to Rename Output Files in AWS Glue Scripts Using PySpark

Learn how to efficiently rename output files written to S3 by AWS Glue scripts using PySpark, including practical steps and context for better understanding.
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How to Rename Output Files in AWS Glue Scripts Using PySpark

When working with data processing in AWS Glue, many users look for ways to effectively manage their output files. A common question arises: How can you rename output files written to S3 after running an AWS Glue script using PySpark?

In this post, we'll explore the challenges of renaming files generated by AWS Glue and provide practical approaches to managing file names after they are created. This will empower you to maintain an organized and coherent data storage workflow.

Understanding AWS Glue and Its File Naming Convention

AWS Glue is a fully managed ETL (Extract, Transform, Load) service that automates the data preparation process for analytics. Since AWS Glue uses Apache Spark under the hood, it generates output files based on internal naming conventions, which often appears as random alphanumeric strings.

Why Renaming Files Is Needed

Renaming files can be crucial for:

Maintaining clarity: Meaningful file names help in identifying the content without needing to open each file.

Organizing data: Systematic file naming schemes make it easier to manage and retrieve data.

Versioning: Date or job-specific identifiers in file names can simplify tracking changes or versions of datasets.

The Challenge: Renaming Output Files in AWS Glue

Unfortunately, directly renaming output files created during AWS Glue jobs isn't supported. The Spark engine assigns filenames automatically, which means you cannot change the names during the write operation.

Current Limitations

Automatic Naming: The Spark engine controls the naming process, leading to file names that are not user-defined.

Post-processing: The renaming has to be done after the files are written to S3.

Solution: Renaming Files After the Glue Job

The good news is that while renaming files directly during the job execution isn’t possible, you can still rename them by executing a separate step after the Glue job completes.

Step-by-Step Instructions

Run Your AWS Glue Job: First, execute your Glue job as usual to generate the output files in S3.

Use an S3 Client: After the Glue job has executed successfully, use an S3 client library (such as boto3 in Python) to interact with your S3 bucket.

List Existing Files: Retrieve a list of files in the S3 bucket where your Glue job saved its output.

Rename Files: Use the copy method to duplicate the file with the new desired name, and then delete the original file:

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Note:

Be sure to adapt old_file_prefix and new_file_prefix to meet your specific naming requirements and file structure.

Conclusion

While renaming files generated by AWS Glue during the ETL process isn’t natively supported, leveraging post-processing with an S3 client allows you to manage your file names effectively. By using this approach, you can maintain clear and organized data storage for your analytical needs.

Feel free to implement this approach in your data workflows and streamline the process of managing your output files in S3!