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Refactor Error Handling: Introducing Structured, Typed Exceptions for Clearer Server & Validation Errors

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#1,491 1 comentario 0 reacciones 0 asignados Ver en GitHub

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Nadie ha tomado este issue todavía.

Evaluación

Dificultad
4/5
Tiempo estimado
3-5 días
Aptitud para principiantes
25/100
Tipo de issue
Refactorización
Claridad
Bastante claro
Estado de actividad
Estancado
Stack tecnológico
python
Área
backend-api-design, documentation, testing-qa

Línea de trabajo

Start by reviewing the completed exception work in exceptions.py and the HTTP/XML mapping in _api_calls.py. Run the existing test suite, then update tests including test_too_long_uri to cover typed exceptions and backward compatibility. Done means the tests pass and the error-handling documentation includes retry, missing-resource, and permission examples.

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

Descripción

Problem Summary

OpenML-Python currently funnels diverse server and client-side errors into the broad exceptions OpenMLServerError and OpenMLServerException. This limits users and downstream systems from:

  1. Distinguishing retryable vs non-retryable failures
  2. Providing meaningful user-facing error messages
  3. Programmatically responding to rate limits, missing resources, or validation failures
  4. Debugging large-scale uploads where different failure modes look identical
Current Situation

All these different scenarios raise the same generic exception:

try:
    openml.datasets.get_dataset(999999)
except OpenMLServerError:
    # Could be dataset missing, rate limit, timeout, validation issue, DB issue, etc.
    pass
Proposed Solution

Introducing a hierarchical exception system with specific, typed error classes:

OpenMLServerError (existing)
├── OpenMLURITooLongError (HTTP 414)
├── OpenMLRateLimitError (HTTP 429)
├── OpenMLNotFoundError (HTTP 404)
├── OpenMLTimeoutError (HTTP 408, 504)
├── OpenMLServiceUnavailableError (HTTP 503)
├── OpenMLAuthenticationError (missing/invalid API key)
├── OpenMLNotAuthorizedError (insufficient permissions)
│
└── OpenMLServerException (existing)
    ├── OpenMLServerNoResult (existing - empty results)
    ├── OpenMLValidationError (code 163, validation failures)
    └── OpenMLDatabaseConnectionError (code 107, temporary DB issues)
Key Benefits
  1. Typed Exceptions → programmatic handling (except OpenMLRateLimitError: retry).
  2. HTTP & OpenML Code Mapping → consistent error interpretation.
  3. Rich Metadata → e.g., retry_after, resource_type, timeout_seconds.
  4. Backward Compatible → all classes still inherit existing base exceptions.

I will systematically link the changes to this thread. Hope this helps users and contributors alike.

Implementation Checklist

PR1 — Typed Exceptions (exceptions.py only)

  • Add new typed exception classes
  • Organize categories (HTTP, validation, auth, timeout, DB, etc.)
  • Keep backward compatibility

PR2 — Server Error Mapping (_api_calls.py)

  • Map HTTP status codes → typed exceptions
  • Map OpenML XML error codes → typed exceptions
  • Add message-based fallbacks
  • Add pre-check for overly long URLs

PR3 — Test Suite Updates

  • Update tests to expect typed exceptions
  • Fix test_too_long_uri with pre-request check
  • Ensure full backward compatibility

PR4 — Documentation

  • Update error-handling documentation
  • Add examples for retry, missing resources, permissions
  • Submit PR4
Lenguaje dominante
Python
Estrellas
361
Forks
296
Merge medio
2 d 20 h
PR fusionados (30 d)
2

Preparar el entorno

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  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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