Principle:Pola rs Polars Streaming Plan Profiling

Knowledge Sources	Polars Polars Docs
Domains	Data Engineering, Streaming
Last Updated	2026-02-09 10:00 GMT

Overview

Visualizing the physical streaming query plan to understand which operations run in streaming mode, identify pipeline breakers, and assess memory intensity.

Description

Building a streaming query is only half the challenge. Understanding how the streaming engine will execute it is equally important. The streaming plan profiling principle provides tools for inspecting the physical execution graph with streaming-specific annotations, enabling practitioners to diagnose performance bottlenecks and verify that their queries are actually running in streaming mode.

The streaming plan profiling principle works as follows:

1. Physical plan visualization -- The show_graph method renders the physical execution plan as a visual graph (PNG or SVG). When configured with engine="streaming" and plan_stage="physical", it shows the streaming-specific physical plan rather than the logical plan.
2. Memory intensity annotations -- Operations in the plan graph are color-coded or annotated by memory intensity. Lightweight streaming operations (filter, project) are visually distinguished from memory-intensive operations (hash join build phase, global sort) that may require buffering.
3. Pipeline breaker identification -- The visualization highlights operations that break the streaming pipeline -- those requiring full materialization of intermediate results before producing output. Common pipeline breakers include global sorts and certain join strategies. Identifying these helps practitioners restructure queries to maximize streaming throughput.
4. Text-based plan inspection -- The explain method provides a text representation of the optimized query plan, showing which optimizations (predicate pushdown, projection pushdown) have been applied. This is useful for quick inspection without generating a graphical output.

Together, these tools enable an iterative optimization workflow: build a query, inspect its streaming plan, identify bottlenecks, restructure the query, and re-inspect.

Usage

Use streaming plan profiling whenever you need to:

• Verify that a query will actually execute in streaming mode.
• Identify pipeline breakers that force buffering or fallback to in-memory execution.
• Diagnose unexpected memory usage in a streaming pipeline.
• Understand which optimizer transformations were applied to a query.
• Document the execution strategy of a production pipeline.

Theoretical Basis

Streaming plan profiling draws on query plan visualization and execution profiling from database systems engineering.

Query plan visualization renders the execution DAG as a human-readable graph. Each node represents a physical operator, and edges represent data flow:

PhysicalPlan:
  Aggregate(group_by=[species], agg=[mean(sepal_width)])
    <- Filter(sepal_length > 5)
      <- CsvScan("iris.csv", projection=[sepal_length, sepal_width, species])

In the streaming context, operators are classified by their pipeline execution model:

Pipelineable operators (process one batch at a time):
  - Filter, Select, WithColumns, Projection
  - Partial aggregation (per-batch accumulation)

Pipeline breakers (require full input before producing output):
  - Global Sort (must see all data to determine order)
  - Hash Join build phase (must build full hash table)
  - Final aggregation merge (must combine all partial results)

The physical plan graph annotates each operator with its execution characteristics, enabling practitioners to reason about memory usage and throughput:

Memory usage of streaming plan:
  M_total = max(M_batch + sum(M_breaker_i))
  where M_batch is memory per batch (bounded)
  and M_breaker_i is memory for each pipeline breaker (may grow with data)

Concept	Relevance
Query plan visualization	Renders execution DAG as a human-readable graph for inspection
Execution profiling	Annotates operators with memory intensity and execution characteristics
Pipeline execution models	Classifies operators as pipelineable or pipeline breakers
Optimizer verification	Shows which pushdown optimizations were applied to the plan

Related Pages

Implemented By

• Implementation:Pola_rs_Polars_Streaming_Show_Graph