650GB of Data (Delta Lake on S3). Polars vs. DuckDB vs. Daft vs. Spark
9 days ago
- #cluster-fatigue
- #data-processing
- #single-node-rebellion
- The article discusses 'cluster fatigue' in data processing, highlighting the high costs and complexity of using distributed systems like Spark for datasets that can now be handled by single-node frameworks.
- Three single-node frameworks—DuckDB, Polars, and Daft (D.P.D.)—are tested against a 650GB Delta Lake dataset on S3 to evaluate their performance on commodity hardware (32GB RAM, 16 CPU EC2 instance).
- DuckDB successfully processes the 650GB dataset in 16 minutes without any tuning, showcasing its capability to handle large datasets efficiently.
- Polars, using its Lazy API, completes the task in 12 minutes, outperforming DuckDB slightly but lacks support for Deletion Vectors in Delta Lake, a significant limitation.
- Daft takes 50 minutes to process the same dataset, indicating it might require optimization or better configuration for such large-scale tasks.
- PySpark, tested on a similar single-node setup, takes over an hour, reinforcing the argument that single-node frameworks can be viable alternatives for many workloads.
- The experiment demonstrates that single-node frameworks can efficiently process large datasets, integrate with Lake House architectures, and offer reasonable runtimes on affordable hardware.
- The article advocates for reconsidering the necessity of distributed computing for many datasets, suggesting that single-node solutions can provide simpler, cost-effective alternatives.