Principle:Pola rs Polars Streaming Engine Configuration
| Knowledge Sources | |
|---|---|
| Domains | Data Engineering, Streaming |
| Last Updated | 2026-02-09 10:00 GMT |
Overview
Selecting the streaming execution engine that processes data in batches, enabling larger-than-RAM dataset processing with bounded memory usage.
Description
Polars supports multiple execution engines for materializing lazy query plans. The default engine loads all data into memory and processes it at once. The streaming engine, activated by passing engine="streaming" to the terminal collect() call, processes the query plan in chunks rather than materializing all data simultaneously.
The streaming engine configuration principle works as follows:
- Engine selection at collect time -- The choice of execution engine is deferred to the very last step. The entire query plan is built identically regardless of which engine will run it. Only the
collect(engine="streaming")call determines that batch-based processing will be used. - Batch-based execution -- The streaming engine partitions source data into batches (sized to fit in available memory), processes each batch through the entire pipeline, and merges partial results. For operations like
filterandselect, each batch is processed independently. For aggregations likegroup_by, partial aggregates are computed per batch and merged at the end. - Automatic fallback -- Operations that cannot be streamed (e.g., operations requiring full materialization of intermediate results) automatically fall back to the in-memory engine for that specific pipeline stage. This ensures correctness while maximizing the streaming benefit.
- Bounded memory usage -- By processing data in fixed-size batches, the streaming engine provides predictable memory consumption proportional to batch size rather than dataset size. This is the key enabler for out-of-core processing.
The streaming engine is particularly effective for pipelines dominated by scan-filter-aggregate patterns, where each batch can be fully processed without reference to other batches.
Usage
Use streaming engine configuration whenever you need to:
- Process datasets that exceed available RAM.
- Achieve predictable, bounded memory usage during query execution.
- Run production pipelines on machines with limited memory.
- Process large cloud-hosted datasets without downloading them entirely first.
Theoretical Basis
The streaming engine draws on the iterator model (also called the Volcano model) from query execution in relational databases.
In the iterator model, each operator in the query plan implements a next() interface that produces one tuple (or one batch) at a time:
while batch = source.next():
batch = filter.process(batch)
batch = project.process(batch)
accumulator.merge(aggregate.process(batch))
result = accumulator.finalize()
Batch processing extends the iterator model by operating on groups of rows rather than individual tuples, amortizing per-tuple overhead and enabling vectorized computation within each batch.
Pipeline breakers are operations that require all input data before producing any output (e.g., global sort, certain types of joins). These operations break the streaming pipeline and force buffering:
Streamable operations: filter, select, with_columns, group_by (partial)
Pipeline breakers: sort (global), join (hash build phase)
When a pipeline breaker is encountered, the streaming engine either buffers data for that stage or falls back to the in-memory engine for that portion of the plan, while still streaming the rest.
| Concept | Relevance |
|---|---|
| Iterator / Volcano model | Each operator processes data incrementally via a next() interface |
| Batch processing | Groups of rows are processed together for vectorized efficiency |
| Pipeline breakers | Operations requiring full materialization force buffering or fallback |
| Bounded memory | Memory usage is proportional to batch size, not dataset size |