How I processed ONE billion rows in PySpark without crashing (and You Can Too!)

Ever tried running a PySpark job on 1 billion rows, only to watch it crash and burn?

Yep, been there. My job kept failing, and I was 🤏 this close to throwing my laptop out the window.

Turns out, the culprits were:

• Memory leaks 🤯
• Slow I/O operations 🐌
• Joins so inefficient they made me question life choices 🤦‍♂️

But after some trial and error (and a few cups of coffee ☕), I cracked the code!

Here’s how I made PySpark run like a dream:

1. Ditch CSV, embrace Parquet

Using CSV for big data is like driving a bicycle on a highway — slow, painful, and not built for scale.

• CSVs take 5X more space and load at snail speed.
• Parquet? 10X faster reads and way better compression.
• Always enable snappy compression:

df.write.parquet("output_path", compression="snappy")

💡 Swapping CSV with Parquet was an instant speed boost!

2. Partition Like a Pro