Solving the 'TypeError: 'NoneType' object is not iterable' Error in PySpark ETL Module Calling

Learn how to fix the common error related to subprocess calling in PySpark ETL scripts and make your child processes work as expected.
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Troubleshooting PySpark ETL Module Execution in Python

When working on data processing and extraction, transformation, and loading (ETL) tasks using PySpark, you'll likely want to execute various ETL modules dynamically. However, if you encounter errors while trying to call these modules as child processes, you may find yourself stuck. One common issue that developers face is a TypeError: 'NoneType' object is not iterable. In this guide, we will explore the cause of this error and how to resolve it.

The Problem

While attempting to run a PySpark ETL module using Python's subprocess module, you may face this error, along with unexpected behavior when you expect the program's flow to continue smoothly. Here's a relevant snippet of the code that often leads to confusion:

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This line attempts to create a subprocess that runs the run_etl_01_job function, but ends up executing it in the current process context instead. If the function does not return anything (None), the attempt to pass this output to Popen results in a TypeError.

Understanding the Cause of the Error

The main issue arises because:

subprocess.Popen() expects a command or args list to launch as a separate process.

Passing None to Popen leads to the error since it's not an iterable command.

Suggested Solutions

1. Use the Multiprocessing Module

Instead of using subprocess, consider the multiprocessing module which is designed for this purpose. Here’s how you can modify your code:

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This approach creates separate processes that can run concurrently.

2. Handle the SparkSession Object

Although multiprocessing is a clean solution, it's essential to note that the SparkSession object may not serialize correctly during this process. You might want to explore alternatives that keep your Spark component intact.

3. Use Threading or ThreadPoolExecutor

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Using ThreadPoolExecutor, you can easily manage multiple threads without dealing with the complications of process forking and serialization.

Conclusion

The key takeaway is that when attempting to run PySpark ETL jobs from a main script, you need to ensure that you are correctly using the subprocess functionality. By leveraging multiprocessing or ThreadPoolExecutor, you can effectively run your ETL tasks while avoiding common pitfalls, such as the TypeError: 'NoneType' object is not iterable.

If you've encountered similar issues during your development, implementing these solutions should help you tide over these challenges and streamline your ETL process.

Happy coding!