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Parquet checkpointing adds null dictionary entries and rejects mixed JSON arrays

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  • ¿Algo no está bien?
#1,045 0 comentarios 0 reacciones 1 asignado Ver en GitHub

Los mantenedores suelen responder en 1 día

@nabinchha ya está trabajando en esto.

Desde el 19/10/2026.

  • #1062 de @nabinchha — fusionado

Evaluación

Dificultad
4/5
Tiempo estimado
3-5 días
Aptitud para principiantes
48/100
Tipo de issue
Error
Claridad
Bastante claro
Estado de actividad
Activo
Stack tecnológico
pandas, python
Área
data

Línea de trabajo

Start by running the attached repro.py scripts with and without --encode, then inspect the Parquet checkpoint writer and its read path. The issue reports failures for dictionaries and mixed JSON arrays in custom-column and structured-column outputs; identify where checkpoint serialization handles these values. Done means both cases round-trip with absent keys distinct from explicit nulls and mixed arrays stored successfully.

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

Descripción

bug needs-discussion
Priority Level

Medium (Annoying but has workaround)

Describe the bug

Data Designer 0.9.3 can alter dictionaries during Parquet storage and fail to store schema-valid mixed arrays.

Arrow inference treats dictionaries as structs, merging their keys and adding null values for absent entries:

Input:
{"fields": {"alpha": 1}}
{"fields": {"beta": 2}}

Restored:
{"fields": {"alpha": 1, "beta": null}}
{"fields": {"alpha": null, "beta": 2}}

This changes the data’s meaning by making absent keys indistinguishable from explicit null values.

Mixed arrays such as [1, "two", true, null] fail checkpoint writing with ArrowInvalid:

Could not convert 'two' with type str: tried to convert to int64

These problems affect custom-column outputs. They also occur with schema-declared structured outputs after isolating the separate parser bug in #1043.

Steps/Code to reproduce bug

The attached scripts use deterministic inputs and require no credentials or model calls.

Reproduce through custom columns, then verify the JSON-text workaround:

uv run --script repro.py
uv run --script repro.py --encode

Reproduce through LLMStructuredColumnConfig:

uv run --script repro_structured.py --case mixed-array
uv run --script repro_structured.py --case typed-map --respect-schema
uv run --script repro_structured.py --case json-map --respect-schema

repro_structured.py mocks model generation while retaining DD’s actual parsing, structured-column generation, and storage. It tests both Pydantic and JSON Schema declarations.

--respect-schema diagnostically disables the parser’s additionalProperties rewrite to isolate storage behavior. It does not change the storage writer. The mixed-array case reproduces without this diagnostic.

Tested with:

data-designer==0.9.3
pydantic==2.14.1
pandas==2.3.3
pyarrow==24.0.0

repro_structured.py
repro.py

Expected behavior

Supported dictionary and flexible JSON values should retain their contents through checkpoint writes and reads. Absent dictionary keys must remain distinguishable from explicit nulls, and schema-valid mixed arrays should be stored successfully

Agent Diagnostic / Prior Investigation

Bug was encountered when making changes to a DD pipeline. Issue content and reproduction scripts are AI generated and human reviewed.

Fixed records round-trip correctly. Explicitly encoding whole custom-column payloads as JSON text preserves dictionaries and mixed arrays. Disabling the separate parser rewrite does not resolve the storage failures.

Additional context

Split from #1043, which tracks valid dictionary entries being removed during parsing.

Checklist
  • I reproduced this issue or provided a minimal example
  • I searched the docs/issues myself, or had my agent do so
  • If I used an agent, I included its diagnostics above
Lenguaje dominante
Python
Estrellas
2.4k
Forks
241
Merge medio
1 d 1 h
PR fusionados (30 d)
86

Preparar el entorno

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
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  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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