Trustworthy garbage collection: complete reference discovery (#1469) + race-safe deletion (#1445)

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Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
35/100
Tipo de issue
Nueva funcionalidad
Claridad
Bastante claro
Estado de actividad
Activo
Stack tecnológico
python

Línea de trabajo

Empieza leyendo las issues #1469 y #1445 para entender la secuencia de implementación requerida y los requisitos de referenced_paths y de eliminación en dos fases. Después revisa los objetivos de documentación indicados, especialmente reference/specs/garbage-collection.md, how-to/garbage-collection.md y reference/specs/codec-api.md. Se considera terminado cuando la implementación aborda tanto el descubrimiento como la seguridad frente a condiciones de carrera, y la documentación cubre el contrato de codec, el flujo de trabajo, la configuración, la concurrencia y la semántica de restauración.

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

Descripción

enhancement

Umbrella tracking the invariant: garbage collection must never delete an object that is still referenced. Today it can, via two independent failure modes that look similar but have different root causes:

  1. Incomplete discovery — #1469: dj.gc.scan hardcodes the built-in codec names (hash/blob/attach, object/npy), so files referenced by a custom codec are never enumerated. The store scan then reports live files as orphans and collect() deletes them. (Row delete also fails to remove custom-codec files.)
  2. Stale discovery / TOCTOU race — #1445: the scan is correct, but an insert during the scan→delete window has its file deleted out from under it.

They are complementary layers of one goal, not duplicates — and must be fixed in order:

  • #1469 — complete reference discovery (correctness, FIRST). Codec-owned referenced_paths hook; scan and delete-cleanup become codec-driven instead of name-hardcoded. Closes active data loss for real pipelines (aeon_mecha).
  • #1445 — race-safe deletion (hardening, SECOND). Two-phase quarantine → grace → purge with re-check-before-delete; _trash/ prefix for state.
Why the sequence matters

#1445's purge() re-check reruns the scan. On a codec-blind scan (pre-#1469) it would still classify a live custom-codec file as an orphan and purge it after the grace window — so the grace window can't save you from a scan that never sees the reference. Complete discovery (#1469) is the precondition for safe deletion (#1445).

Documentation (datajoint-docs) — to land with the implementation

Add

  • reference/specs/garbage-collection.md (new normative spec — none exists today). The orphan-determination model, the referenced_paths codec contract, the two-phase quarantine/grace/purge state machine, config keys (gc.grace_seconds), re-check/concurrency semantics, backend atomic-move requirements, and restore. (#1445 explicitly asks for a written spec.)

Update

  • how-to/garbage-collection.md ("Clean Up Object Storage") — document the two-phase workflow (quarantine / purge / restore, grace_seconds); note custom-codec external files are now handled; revise the "single-pass, best-effort" admonition added in #189 once two-phase lands.
  • reference/specs/codec-api.md — document referenced_paths as part of the Codec contract (required for any codec that owns external artifacts).
  • how-to/create-custom-codec.md, explanation/custom-codecs.md, how-to/use-plugin-codecs.md — author guidance: if your codec writes external files, implement referenced_paths so delete + GC see them (otherwise files leak or, worse, get misclassified as orphans and deleted).
  • reference/specs/provenance.md — the GC concurrency wording references single-pass semantics; align once two-phase ships (minor).
  • Optionally a short explainer (e.g. in explanation/object-storage-overview.md) on how DataJoint tracks external references — codecs own their paths; delete and GC consult them.
Lenguaje dominante
Python
Estrellas
197
Forks
98
Merge medio
6 d 7 h
PR fusionados (30 d)
1

Guía de contribución

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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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