Principle:Pola rs Polars Lazy Data Scanning

Overview

Lazy Data Scanning is a deferred execution pattern in Polars where data sources are referenced but not loaded into memory until the query plan is explicitly executed. Instead of immediately reading rows from a file, a scan operation creates a query plan node that records the data source location and schema, deferring all I/O until collection time.

This principle is foundational to the Polars lazy evaluation model. By registering a data source in the query plan DAG without materializing any rows, the query optimizer gains the opportunity to push down predicates and projections to the scan level, dramatically reducing the volume of data that must actually be read from disk or network storage.

Theoretical Basis

Deferred Execution Model

The lazy scanning principle is rooted in the deferred execution model commonly found in database query engines and functional programming languages. In this model, operations are not performed at the point of invocation. Instead, they are recorded as nodes in a directed acyclic graph (DAG) representing the full computation. Execution only occurs when an explicit trigger (such as collect()) is invoked.

This separation of plan construction from plan execution enables a class of optimizations that are impossible in eager (immediate) evaluation:

Predicate Pushdown: Filter conditions defined later in the pipeline can be pushed down to the scan node, so that rows not matching the predicate are never read from the source file. For columnar formats like Parquet, this can leverage row group statistics to skip entire chunks of data.
Projection Pushdown: Column selections defined downstream are propagated to the scan node, so that only the required columns are read. This is particularly effective with columnar file formats where columns are stored independently.
Slice Pushdown: When only a limited number of rows is needed (e.g., head(n)), the scan node can stop reading after the required number of rows.

Relational Database Foundations

The academic basis for lazy data scanning derives from query optimization in relational database systems. The foundational work on predicate pushdown and projection pushdown originates from the System R optimizer (Selinger et al., 1979) and the Volcano/Cascades optimization framework (Graefe, 1995). These systems demonstrated that reordering and pushing operations closer to data sources yields orders-of-magnitude improvements in query performance.

Polars applies these same principles to file-based data processing, treating CSV, Parquet, NDJSON, and IPC files as analogous to database tables. The scan node in the query plan is the logical equivalent of a table scan operator in a relational query plan.

Scan as a Logical Operator

In the Polars query plan DAG, a scan node is a leaf operator with no inputs and a single output. It encodes:

The source path or URI of the data file
The schema (column names and data types), either inferred or explicitly provided
Any pushed-down predicates that filter rows at read time
Any pushed-down projections that limit which columns are read

The optimizer transforms the initial logical plan by propagating downstream operations into the scan node wherever possible, producing an optimized physical plan that minimizes I/O.

Key Properties

Zero upfront I/O: No data is read when a scan operation is called. The LazyFrame object is a lightweight reference to a query plan.
Schema inference is minimal: For formats with embedded schemas (Parquet, IPC), schema discovery requires reading only file metadata. For schema-less formats (CSV, NDJSON), a small sample may be read.
Composability: Scan nodes integrate seamlessly with downstream expression, filter, join, and aggregation nodes in the query plan.
Format agnosticism: The same lazy pattern applies regardless of whether the source is CSV, Parquet, NDJSON, or IPC, abstracting storage format details behind a uniform LazyFrame interface.

Applicability

This principle applies whenever:

Data resides in files on disk or accessible via URL
The full dataset is larger than needed for the final result
Query performance benefits from minimizing I/O through predicate and projection pushdown
Memory constraints require avoiding full dataset materialization

Related Pages

Metadata

Field	Value
Source Repository	Pola_rs_Polars
Domain	Data Engineering, Query Optimization, Lazy Evaluation
Last Updated	2026-02-09 10:00 GMT

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