apache / apache/datafusion-python

Expose SessionContext.add_optimizer_rule for Python-defined logical optimizer rules

Abierto
#1,574 0 comentarios 0 reacciones 0 asignados Ver en GitHub
Lenguaje dominante
Python
Estrellas
604
Forks
174
Merge medio
1 d 7 h
PR fusionados (30 d)
4

Descripción

## Background

`SessionContext::add_optimizer_rule` registers an `Arc` that runs during logical plan optimization. The Python bindings only expose `remove_optimizer_rule`, never the additive side. PR #1557 ultimately landed `add_physical_optimizer_rule` for the physical pipeline via FFI but did not address the logical pipeline.

## Upstream signature

```rust
pub fn add_optimizer_rule(&self, optimizer_rule: Arc)
```

## User value

Lets users add domain-specific logical rewrites (predicate normalization, redundant join elimination, scan pruning informed by external metadata) without forking DataFusion. Complements the existing `remove_optimizer_rule` to round out the surface.

## Why deferred

Blocked upstream. As documented in PR #1557, DataFusion does not currently expose an FFI bridge for the logical `OptimizerRule` / `AnalyzerRule` traits, and there are no Python constructors for `LogicalPlan` node variants -- a pure-Python rule could observe plans but not transform them. The PR #1557 commit history shows an initial attempt at a Python-defined logical rule that was abandoned for this reason. This is filed for tracking; it should be revisited once upstream lands an `FFI_OptimizerRule` (mirroring `FFI_PhysicalOptimizerRule`).

Guía de contribución

No hay ninguna guía de contribución indexada para este repositorio

Línea de trabajo

Start by reviewing the existing Python binding for SessionContext.remove_optimizer_rule and the add_physical_optimizer_rule work in PR #1557. Revisit this issue once upstream provides FFI_OptimizerRule and Python LogicalPlan constructors; done means Python-defined logical rules can be registered through add_optimizer_rule and exercised through the binding.

Escrito por el modelo de indexación a partir del texto del issue.

Evaluación

Stack tecnológico
python, rust
Área
data-engineering
Tipo de issue
Nueva funcionalidad
Dificultad
5/5
Tiempo estimado
Más de una semana
Estado de actividad
Tranquilo
Claridad
Bien especificado
Aptitud para principiantes
25/100

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