Implementation:Pola rs Polars LazyFrame Collect

Overview

This implementation covers the concrete APIs for executing a lazy query plan and materializing the results into an in-memory DataFrame. The collect() method is the primary execution trigger, with options for streaming execution. The collect_all() function enables parallel execution of multiple independent queries.

APIs

• LazyFrame.collect() -> DataFrame — Execute the query plan and return a DataFrame
• LazyFrame.collect(engine="streaming") -> DataFrame — Execute the query plan in streaming mode for reduced memory usage
• pl.collect_all(queries: list[LazyFrame]) -> list[DataFrame] — Execute multiple independent query plans in parallel
• LazyFrame.head(n) -> LazyFrame — Limit the query to the first n rows (enables slice pushdown)

Source Reference

• File: docs/source/src/python/user-guide/lazy/execution.py (Lines 1-40)
• Repository: Pola_rs_Polars

I/O Contract

Direction	Type	Description
Input	LazyFrame	A LazyFrame containing a complete query plan ready for execution
Input (collect_all)	list[LazyFrame]	A list of independent LazyFrames to execute in parallel
Output (collect)	DataFrame	An in-memory DataFrame containing the materialized query results
Output (collect_all)	list[DataFrame]	A list of DataFrames corresponding to the input LazyFrames

Key Parameters

Parameter	Type	Default	Description
engine	str	"cpu"	Execution engine: "cpu" (default in-memory) or "streaming" (batch-based streaming)
n (for head)	int	5	Number of rows to limit the result to; enables slice pushdown optimization

Example Code

Standard Collection

import polars as pl

q = (
    pl.scan_csv("data.csv")
    .filter(pl.col("a") > 5)
    .select(pl.col("a"), pl.col("b"))
)

# Execute and materialize
df = q.collect()
print(df)

Streaming Collection

import polars as pl

q = (
    pl.scan_parquet("large_dataset.parquet")
    .filter(pl.col("status") == "active")
    .group_by("region")
    .agg(pl.col("revenue").sum())
)

# Streaming execution for large datasets
df = q.collect(engine="streaming")

Parallel Collection of Multiple Queries

import polars as pl

q1 = pl.scan_csv("sales.csv").filter(pl.col("year") == 2025)
q2 = pl.scan_csv("inventory.csv").group_by("product").agg(pl.col("quantity").sum())
q3 = pl.scan_csv("customers.csv").select(pl.col("id"), pl.col("name"))

# Execute all three queries in parallel
results = pl.collect_all([q1, q2, q3])

sales_df = results[0]
inventory_df = results[1]
customers_df = results[2]

Head with Slice Pushdown

import polars as pl

q = (
    pl.scan_csv("data.csv")
    .sort("score", descending=True)
    .head(10)
)

# Only the top 10 rows are materialized
df = q.collect()

Import

import polars as pl

Behavior Notes

• Optimization runs automatically: When collect() is called, the query optimizer runs before execution. There is no need to manually trigger optimization.
• collect() is terminal: After collection, the result is an eager DataFrame. To continue with lazy operations, call .lazy() on the resulting DataFrame.
• Streaming mode limitations: Not all operations are supported in streaming mode. Operations that require full materialization (e.g., certain join types, global sorts) may fall back to in-memory execution or raise an error.
• collect_all() enables parallelism: Independent queries passed to collect_all() may execute concurrently, utilizing multiple CPU cores. This is more efficient than collecting each query sequentially.
• head() enables slice pushdown: When head(n) is used, the optimizer can push the row limit down through the plan, potentially stopping file reads early.
• Memory considerations: For datasets that do not fit in memory, use engine="streaming" to process data in batches. The streaming engine maintains bounded memory usage at the cost of some throughput.

Related Pages

• Principle:Pola_rs_Polars_Lazy_Query_Collection
• Implementation:Pola_rs_Polars_Scan_LazyFrame_Creation
• Implementation:Pola_rs_Polars_LazyFrame_Expression_Chaining
• Implementation:Pola_rs_Polars_DataFrame_Write_and_Convert
• Environment:Pola_rs_Polars_Python_Runtime_Environment
• Environment:Pola_rs_Polars_GPU_Execution_Environment
• Heuristic:Pola_rs_Polars_Lazy_Over_Eager_Preference
• Heuristic:Pola_rs_Polars_Collect_All_For_Diverging_Queries
• Heuristic:Pola_rs_Polars_GPU_Aggregation_Join_Speedup
• Heuristic:Pola_rs_Polars_Use_Spawn_Not_Fork_Multiprocessing

Metadata

Field	Value
Source Repository	Pola_rs_Polars
Source File	docs/source/src/python/user-guide/lazy/execution.py:L1-40
Domain	Data Engineering, Query Execution, Materialization
Last Updated	2026-02-09 10:00 GMT