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[Enhancement]: [Stanford V02] Integrate distribution-aware filter cases into VectorDBBench

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#882 0 comentarios 0 reacciones 0 asignados Ver en GitHub

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Evaluación

Dificultad
4/5
Tiempo estimado
3-5 días
Aptitud para principiantes
45/100
Tipo de issue
Nueva funcionalidad
Claridad
Bastante claro
Estado de actividad
Activo
Stack tecnológico
python
Área
data, databases

Línea de trabajo

The issue references VectorDBBench and depends on issue #881. Start by examining the existing benchmark adapters and fixtures from V01. Look for where filter configurations are loaded and how recall/latency reporting currently works. The goal is to integrate distribution-aware filter cases, ensuring unsupported backend predicates are skipped explicitly. Run the existing benchmarks to understand the current flow before modifying case configuration and filter mappings.

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

Descripción

medium stanford
Is there an existing issue for this?
  • I have searched the existing issues.

No directly matching open issue was identified in the catalog duplicate check. Recheck the target branch and in-flight PRs with a maintainer before claiming this task.

Project

Catalog ID: V02
Difficulty: medium
Estimated incremental effort: 8–12 hours

Run comparable filtered-search benchmarks using the fixtures and ground truth from V01.

Implementation scope

Add case configuration/loading and supported filter mappings; connect recall/latency reporting with distribution and actual-selectivity metadata.

Acceptance criteria

Cases run reproducibly; recall uses filtered ground truth, and unsupported backend predicates are explicitly skipped rather than silently changed.

Dependencies
Boundaries and preparation

Requires V01. Reuse benchmark adapters; do not redesign every backend or mix incomparable filter semantics.

The estimate assumes familiarity with the implementation language, a working build/test environment, and maintainer-approved interfaces and fixtures. It includes implementation, focused validation and first review revisions; it excludes initial environment setup, dependency implementation and CI/review waiting. If those prerequisites are missing, split or re-estimate the task. Performance improvements must be measured against a reproducible baseline, including regressions; no speedup is guaranteed by this issue.

Reference and delivery
  • Reference: https://github.com/zilliztech/VectorDBBench
  • Deliver a runnable component/feature or example, focused correctness validation, and usage notes. A bounded component is not a claim that all production paths are complete.
  • Coordinate with maintainers on the current target branch and related refactors before starting. For Milvus feature work, link the corresponding design in https://github.com/milvus-io/milvus-design-docs from the implementation PR; do not treat a pending design as already approved.
Lenguaje dominante
Python
Estrellas
1.2k
Forks
448
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Primeros pasos

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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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