Learning from a Spark Threading Challenge: Improving Parallel Data Processing in PySpark

As part of my continuous learning journey in data engineering and big data processing, I recently explored how parallelism and multithreading work in PySpark to improve performance in complex data pipelines.

While testing methods to process independent datasets in parallel and later merge related data, I encountered an unexpected behavior related to Spark’s internal session handling and temporary views. This gave me valuable insight into how Spark and Python threading interact and how to avoid some tricky pitfalls.

In this blog post, I’ll share what I discovered, the challenges I faced, and how I overcame them using safe concurrency patterns. The goal is to help others avoid similar issues and improve the design of their Spark applications.

The Scenario:- Imagine a pipeline where multiple independent datasets need to be processed in parallel. Each dataset goes through filtering, transformation, and staging before being merged into a target Delta table. To speed things up, I decided to use Python’s ThreadPoolExecutor to process datasets in parallel threads. Each thread would:

• Filter data relevant to a specific entity (like a business unit or region)
• Register the result as a temporary view
• Execute a merge query using that view

This seemed like a clean and scalable pattern until things didn’t go as planned.

The Challenge: Overwriting Temporary Views - Although the code worked fine during sequential tests, I noticed that in rare cases during parallel execution, incorrect data ended up in the wrong target table.

The culprit? Temporary views and shared SparkSession.

Even though I was using separate threads, all of them were operating under the same SparkSession. When two threads registered the same view name (e.g., "temp_view"), they unknowingly overwrote each other's data in the global Spark memory. As a result, one thread might run a merge using another thread’s data.

Root Cause Breakdown

Lessons Learned & Best Practices

1. Temporary Views Are Session-Scoped, Not Thread-Scoped

Even though Spark documentation says views are tied to the session, this can mislead one to think they are isolated per thread. They are not. If the same SparkSession is used, the view is shared across all threads.

Fix: Give each thread a unique view name, or avoid views altogether by passing DataFrames directly where possible.

2. Avoid Shared Variables in Multithreaded Lambda Functions

Python closures can unexpectedly reuse shared loop variables in threads.

# Problematic pattern
for user in user_list:
    pool.submit(lambda: process_data(user))        

Fix: Explicitly pass variables to the lambda function:

for user in user_list:
    pool.submit(lambda u=user: process_data(u))        

3. Keep Spark Operations Out of Threads Where Possible

Spark itself is already distributed. You can often achieve better scalability by using Spark transformations (map, foreachPartition, etc.) instead of native Python threads.

4. Centralize and Audit Data Writes

Introduce metadata tagging, audit logging, and validation checks before and after writing data to make sure everything lands in the right place.

Final Thoughts:- Sometimes, the best learning comes from unexpected bugs. This experience taught me how Spark behaves in multithreaded Python environments, and how easy it is to assume isolation where there is none.

If you're building high-concurrency pipelines in Spark, make sure to:

• Respect shared SparkSession behavior
• Avoid global temp views in multithreaded contexts
• Pass thread-local variables explicitly
• Validate and audit your outputs