A PySpark job can run on a 100 GB file even if you only have 40 GB of RAM — but it depends on:
✅ Key Concepts That Make It Possible
1. Lazy Evaluation & DAG Execution
PySpark doesn’t load the entire file into memory. It builds a DAG (Directed Acyclic Graph) and processes data in partitions, not all at once.
2. Partitioning
PySpark processes data in partitions (default ~200 or 128MB each). So:
- 100 GB / 128 MB = ~800 partitions
- These are processed in batches, based on available memory.
You don’t need 100 GB RAM, because not all partitions are processed simultaneously.
3. Spill to Disk / Shuffle Memory
If memory overflows, Spark spills intermediate data to disk (slower but works). You can tune this:
spark.conf.set("spark.memory.fraction", "0.6") # 60% for execution
spark.conf.set("spark.memory.storageFraction", "0.3") # 30% for caching
4. Cluster vs. Local Mode
- If you’re in cluster mode (e.g., on YARN, Databricks, EMR), Spark distributes the load across executors.
- If you’re on a single machine (local mode) with 40 GB RAM, it’ll still work, but slower and possibly with more disk I/O.
🛠️ Tips to Ensure It Runs Smoothly
| Tip | Description |
|---|---|
Use .persist() or .cache() only when needed | Prevents memory overload |
| Repartition wisely | e.g., df = df.repartition(100) |
| Use column pruning | select("needed_col1", "col2") instead of * |
| Use Parquet/ORC over CSV | Compressed, schema-aware formats are faster |
| Use broadcast joins carefully | Avoid broadcasting large DataFrames |
| Enable spill logs | For debugging: spark.executor.extraJavaOptions=-XX:+PrintGCDetails |
⚠️ Potential Problems
- Too many shuffles → May hit memory or disk IO bottlenecks
- Large joins without partitioning → May crash
- High number of narrow vs. wide transformations → Check your DAG plan using
.explain()
✅ Conclusion:
🚀 Yes, your 100 GB file can be processed with 40 GB RAM in PySpark, if you follow good practices like partitioning, limiting caching, and avoiding skew-heavy operations.