Principle:Pola rs Polars Query Plan Inspection

Overview

Query Plan Inspection is the ability to examine and visualize the logical and physical query plans before execution. This principle enables developers to understand how the query optimizer transforms their declared operations, debug performance issues, and verify that optimizations such as predicate pushdown and projection pushdown are being applied correctly.

By exposing both the unoptimized logical plan and the optimized physical plan, Polars provides transparency into the query engine's decision-making process, turning query optimization from a black box into an observable system.

Theoretical Basis

Query Plans as Directed Acyclic Graphs

A query plan is a directed acyclic graph (DAG) where each node represents an operation (scan, filter, projection, join, aggregation, sort) and edges represent data flow between operations. The plan has two representations:

Logical Plan: The direct translation of the user's code into a DAG of operations. It faithfully represents the order and structure of the user's method calls without any optimization.
Physical Plan (Optimized Plan): The result of applying optimization rules to the logical plan. The optimizer rewrites the DAG to improve execution efficiency while preserving semantic equivalence.

Optimizer Transformations

The query optimizer applies several classes of transformations when converting the logical plan to the physical plan:

Predicate Pushdown: Filter operations are moved as close to the data source as possible. A filter applied after a join may be pushed down to before the join if it depends on columns from only one side, reducing the data volume flowing through the join.
Projection Pushdown: Column selections are propagated to earlier nodes. If only 3 of 50 columns are used in the final result, the scan node reads only those 3 columns from disk.
Common Subexpression Elimination: When the same expression appears multiple times in a query, it is computed once and reused.
Slice Pushdown: When only a limited number of rows is needed (e.g., head(n)), the limit is pushed down through the plan to minimize work.
Join Reordering and Optimization: Join operations may be reordered or converted to more efficient join algorithms based on data characteristics.

Query Optimization Theory

The theoretical foundation draws from query optimization in database systems, particularly:

The Volcano/Cascades framework (Graefe, 1993, 1995) which defines optimization as a search over equivalent plan alternatives using transformation rules.
Cost-based optimization where the optimizer estimates the cost of alternative plans and selects the cheapest one.
Heuristic optimization where rules are applied in a fixed order (e.g., always push predicates down) without explicit cost estimation. Polars primarily uses heuristic optimization.

Inspection of query plans is analogous to the EXPLAIN and EXPLAIN ANALYZE statements in SQL databases (PostgreSQL, MySQL), which allow developers to examine how the database engine intends to execute a query.

Key Properties

Dual representation: Both unoptimized and optimized plans can be examined, allowing comparison of what the user wrote versus what will actually execute.
Textual and visual output: Plans can be inspected as text strings or rendered as graphical visualizations (via Graphviz).
Non-mutating: Inspecting a plan does not alter the LazyFrame or trigger execution.
Optimization transparency: Developers can verify that pushdown optimizations are applied and diagnose cases where they are not.

Applicability

This principle applies whenever:

A query is performing slower than expected and the developer needs to understand why
Verification is needed that predicates and projections are being pushed down to scan nodes
The query plan structure needs to be documented or communicated to team members
Complex queries involving multiple joins, aggregations, or subqueries need debugging

Related Pages

Metadata

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

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