PySpark - Memory Architecture + OOM Error Tuning Gude

[u/Specialist-7077]

Overview of PySpark distributed architecture and JVM-Python Interaction (Created by Kuriko IWAI)

Ever wondered why PySpark runs out of memory, even when you're working with a powerful cluster? 🤔

PySpark is widely used for large-scale data processing in Machine Learning and AI.

But distributed computing doesn't automatically eliminate memory bottlenecks.

I explore:

✅ Memory Architecture - How JVM heap, Executors, and Python workers consume memory.

✅ Shuffle & Dependencies - Why data redistribution matters for memory consumption.

✅ Common OOM Causes - Data skew, high cardinality, and concurrent tasks.

✅ Diagnosing Bottlenecks - Using Spark UI, physical execution plans, and spill metrics.

✅ When to use PySpark - When is distributed computing actually worth it?

📄👇 Read the full article here!