Heuristic:Pola rs Polars Streaming For Large Datasets
| Knowledge Sources | |
|---|---|
| Domains | Optimization, Memory_Management |
| Last Updated | 2026-02-09 10:00 GMT |
Overview
Use `collect(engine="streaming")` for larger-than-memory datasets to process data in batches, reducing peak memory usage while also improving performance over the in-memory engine.
Description
Polars' default `collect()` processes all data as one batch, requiring all data to fit in memory at peak usage. The streaming engine processes data incrementally in batches, enabling queries on datasets that exceed available RAM. Some operations are inherently non-streaming (e.g., full sorts, certain joins) and will fall back to the in-memory engine transparently. The streaming engine is also more performant than the in-memory engine for many operations, making it beneficial even when data fits in memory.
Usage
Use this heuristic when processing datasets that approach or exceed available memory, or when you want improved performance on large datasets. Particularly useful with scan operations (`pl.scan_parquet`, `pl.scan_csv`) which allow the streaming engine to control how much data is read at a time.
The Insight (Rule of Thumb)
- Action: Replace `.collect()` with `.collect(engine="streaming")` in lazy query pipelines.
- Value: Processes data in batches, reducing peak memory from O(dataset_size) to O(batch_size). Also provides better performance than the in-memory engine for many operations.
- Trade-off: Some operations are not streaming-compatible and will fall back to in-memory execution transparently. Inspect the physical plan with `show_graph()` to identify memory-intensive nodes.
Reasoning
The Polars documentation states: "Instead of processing all the data at once, Polars can execute the query in batches allowing you to process datasets that do not fit in memory. Besides memory pressure, the streaming engine also is more performant than Polars' in-memory engine." The streaming engine is the recommended approach for production workloads on large datasets. The physical plan graph visualization includes a legend showing memory intensity of each operation, which helps debug memory or performance issues.