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What is the difference between lazy and eager execution?
Eager operations run as you write them and return materialized results, such as a DataFrame. Lazy operations build a query plan that runs when you request its result, usually with .collect(). A LazyFrame is therefore a description of work to do, not the resulting data.
For example, an eager file workflow reads the data before applying later transformations:
import polars as pl
df = pl.read_csv("events.csv")
result = df.filter(pl.col("status") == "active").select("user_id")A lazy scan keeps the file input in the plan until collection:
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import polars as pl
result = (
pl.scan_csv("events.csv")
.filter(pl.col("status") == "active")
.select("user_id")
.collect()
)The key distinction is when execution happens. The lazy form gives Polars the opportunity to optimize across the scan, filter, and selection together. See the Polars Lazy API guide.
Why use lazy execution for a pipeline?
A lazy query gives Polars a view of the whole operation chain before it runs. Its optimizer can reorder or simplify work, including filtering earlier, reading only required columns, pushing down slices, eliminating common subplans, choosing join order, coercing types, and estimating cardinality. These optimizations can reduce unnecessary reading or intermediate work, depending on the query and source.
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For file-backed workflows, begin with a lazy scan_* function when practical. A scan keeps the source in the plan, which lets eligible filters and column selections be pushed into the reader. A read_* function loads the input eagerly first, so subsequent operations cannot optimize what was already materialized. Polars documents lazy usage for file scans and existing DataFrames and details its query optimizations.
If data is already in a DataFrame, you can still switch to lazy planning from that point: call .lazy(), compose subsequent operations, then call .collect(). This does not undo the fact that the DataFrame was already read into memory.
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When is eager execution the better choice?
Eager execution is often more convenient for short, interactive work when you need to see each result before deciding what to do next. It makes intermediate DataFrames directly available for inspection and avoids planning a query that may change repeatedly as you explore.
The Polars user guide says the lazy API should generally be preferred “unless you are either interested in the intermediate results or are doing exploratory work and don’t know yet what your query is going to look like.” Treat that as a practical default, not a promise that lazy execution wins on every small operation. The guide’s lazy API overview explains the distinction.
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How to choose for common situations
| Situation | Start with | Reason |
|---|---|---|
| Several transformations over CSV, Parquet, IPC, or JSON files | Lazy scan, transformations, then .collect() |
Polars can plan the pipeline end to end and push eligible filtering or column selection toward the scan. |
| Interactive exploration where each intermediate result matters | Eager operations | Each step produces a result you can inspect immediately. |
| Data is already in a DataFrame, but you want subsequent operations planned together | Convert with .lazy(), compose, then collect |
Lazy optimization can apply to work after conversion. |
| Input may exceed available memory | Try lazy execution with the streaming engine, then inspect the plan | Eligible operations may run in batches, but some work may require in-memory execution or trigger fallback. |
| One expensive plan branches into multiple outputs | Consider pl.collect_all |
Combined execution can enable common-subplan elimination across diverging queries. |
What does .collect() do, and does Polars cache a LazyFrame?
.collect() is the boundary that executes a lazy plan and returns a materialized result. Merely constructing a LazyFrame does not run it, and the LazyFrame itself is not a result guaranteed to be cached.
If separate downstream queries use the same LazyFrame and each is collected independently, Polars does not guarantee that shared work is computed only once. For diverging queries, pl.collect_all can combine their execution and allow common-subplan elimination. The query execution guide covers collection, reuse, and combined execution.
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Does lazy execution mean streaming or lower memory use?
No. Lazy execution means deferred planning and execution; it does not mean every operation streams. You can request the streaming engine with .collect(engine="streaming"). Eligible queries can process data in batches, which may reduce memory pressure, but some operations are inherently non-streaming or unsupported by that engine and can fall back to in-memory execution.
Use streaming as an execution option to test for a suitable workload, not as a guarantee that the entire query stays out of memory. Polars describes its streaming engine and the relevant limits in its query execution documentation.
How can you check what Polars will optimize?
For performance-sensitive work, inspect the plan instead of inferring execution order from the order in which you wrote operations. Use .explain() to view the query plan and check whether expected pushdowns or other optimizations appear. Plan visualization can provide another view of the operations. The query plan guide explains optimized and non-optimized plan inspection.
query = (
pl.scan_csv("events.csv")
.filter(pl.col("status") == "active")
.select("user_id")
)
print(query.explain())
result = query.collect()Polars documentation evolves, so check the documentation for the version you use when relying on version-sensitive engine behavior or APIs.
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