Workflow:Pola rs Polars Streaming Large Dataset Processing
| Knowledge Sources | |
|---|---|
| Domains | Data_Engineering, Big_Data, Query_Optimization |
| Last Updated | 2026-02-09 09:30 GMT |
Overview
End-to-end process for processing larger-than-RAM datasets using Polars' streaming execution engine to manage memory efficiently while maintaining query performance.
Description
This workflow covers Polars' streaming execution mode, which processes data in batches rather than loading the entire dataset into memory at once. The streaming engine uses the same lazy API as in-memory processing, so queries do not need to be rewritten. When a query is collected with engine="streaming", Polars' streaming execution engine processes data in chunks, enabling operations on datasets that exceed available RAM. The streaming engine handles scans, filters, projections, joins, group-by aggregations, and sorts in a memory-efficient manner, making it possible to process datasets of hundreds of gigabytes on a laptop.
Usage
Execute this workflow when your dataset is too large to fit in memory, or when you want to minimize memory usage for resource-constrained environments. The streaming engine is particularly effective for ETL pipelines that read from large file collections, apply filters and transformations, and write results to disk. It is also useful for any scenario where memory pressure is a concern, even if the data technically fits in RAM.
Execution Steps
Step 1: Scan Source Data Lazily
Use lazy scan functions to register data sources without loading them into memory. The streaming engine requires lazy input to process data in chunks.
Key considerations:
- Use scan_parquet, scan_csv, scan_ndjson, scan_ipc for file-based sources
- Glob patterns (e.g., "data/**/*.parquet") allow scanning many files
- Hive-partitioned datasets are supported with hive_partitioning=True
- Cloud sources (S3, GCS, Azure) are supported with appropriate storage_options
- Do not use read_* (eager) functions as they load all data into memory immediately
Step 2: Build the Query Pipeline
Construct the lazy query using the standard Polars expression API. The streaming engine supports the same operations as the in-memory engine: filter, select, with_columns, group_by, join, sort, and more.
Key considerations:
- Filter early in the pipeline to reduce the volume of data processed downstream
- Projection pushdown automatically selects only needed columns from source files
- The query optimizer applies the same optimizations as in-memory mode
- Complex operations (multiple joins, nested group_by) are supported
Step 3: Configure Streaming Execution
Select the streaming execution engine and configure any streaming-specific options. The streaming engine processes data in batches, automatically managing memory allocation and spilling to disk when necessary.
Key considerations:
- Use collect(engine="streaming") to trigger streaming execution
- The streaming engine automatically partitions data into manageable chunks
- Memory usage is bounded regardless of input data size
- Some operations may be partially streaming (processed in stages)
Step 4: Execute and Collect Results
Run the streaming query and collect results. For very large outputs, consider using sink functions that write directly to disk without materializing the full result in memory.
Key considerations:
- collect(engine="streaming") materializes the final result in memory
- For large outputs, use sink_parquet("output.parquet") to write directly to Parquet
- sink_csv("output.csv") writes streaming results directly to CSV
- sink_ipc("output.ipc") writes streaming results directly to IPC format
- Sinks avoid loading the full output DataFrame into memory
Step 5: Verify and Profile Results
Validate the output and optionally inspect the streaming query plan to understand how the engine partitioned and processed the data.
Key considerations:
- Use show_graph(plan_stage="physical", engine="streaming") to visualize the streaming plan
- Verify row counts and schema against expected output
- Monitor memory usage during execution to confirm streaming behavior
- Compare results against a small sample processed in-memory to validate correctness