How to Exclude Files Based on Name When Using AWS Glue with PySpark

Learn how to effectively filter out unwanted files in AWS Glue when reading data from S3 buckets, especially when dealing with non-parquet files using PySpark.
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Addressing the Challenge of Excluding Files in PySpark with AWS Glue

When working with AWS Glue to create DataFrames from data stored in S3 buckets, encountering errors due to non-standard file formats can be frustrating. This is particularly true when your pipeline tries to read files that do not conform to the expected format, as is the case with a non-parquet file causing an error during the read operation. If you've found yourself dealing with an error such as:

[[See Video to Reveal this Text or Code Snippet]]

You may be wondering: Is there a way to exclude certain files based on their name from being read? The good news is that there are effective strategies to tackle this issue!

Solution Overview

To successfully exclude unwanted files such as non-parquet files, you can consider switching to create_dynamic_frame.from_catalog() instead of the traditional create_data_frame.from_catalog(). Below, we will discuss the steps required to implement this solution and get your data pipeline running smoothly again.

Step 1: Use create_dynamic_frame.from_catalog()

Switching to a dynamic frame allows you greater flexibility when working with various file types. Here’s how to do it:

Updated Syntax:

[[See Video to Reveal this Text or Code Snippet]]

This change typically resolves the error you are facing and successfully reads the parquet files.

Step 2: Leverage Additional Options for Exclusions

If you want to exclude certain file types explicitly, you have the option to utilize the additional_options parameter. For instance:

Exclusion Example:

[[See Video to Reveal this Text or Code Snippet]]

This code snippet instructs the Glue job to exclude any files with specific extensions, such as JSON or parquet, as designated in the brackets.

Step 3: Understand the Limitations of create_data_frame

It's important to note that if you still wish to use create_data_frame, there are inherent limitations that you should be aware of:

The create_data_frame method does not support partition filtering in the same manner as dynamic frames. This means that you may still encounter errors while processing mixed file types in the same directory.

Additional Considerations

When working with AWS Glue and S3, always ensure to verify the file types in your bucket. Mixing different file formats can not only lead to errors but also complicate your data processing workflow. Here are a few tips to avoid issues in the future:

Organize your S3 buckets to separate types of files.

Regularly audit your bucket content to ensure consistency in file formats.

Use naming conventions that easily identify non-data files.

Conclusion

By switching to create_dynamic_frame.from_catalog() and leveraging the additional_options for exclusions, you can effectively resolve errors caused by unwanted file types in AWS Glue. Adapting your approach not only fixes immediate issues but also enhances your overall data handling process in PySpark.

Whatever your data processing needs may be, understanding how to manage file types with AWS Glue can streamline your work and improve efficiency. Start applying these tips today, and take one step closer to a robust data integration strategy with PySpark!

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