doctrine-batch-processing_skill

This skill helps evolve Symfony Doctrine models and schema safely, optimizing batch processing, integrity, and rollout discipline across migrations and tests.
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2

Bundled Files

3 weeks ago

Catalog Refreshed

2 months ago

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Readme & install

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Installation

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npx veilstart add skill makfly/superpowers-symfony --skill doctrine-batch-processing

  • reference.md5.9 KB
  • SKILL.md1.1 KB

Overview

This skill helps evolve Symfony Doctrine models and database schema safely, focusing on integrity, performance, and conservative rollout. It guides model ownership, transactional boundaries, mapping changes, and query tuning. It is designed for teams performing Doctrine batch processing and schema evolution with minimal risk.

How this skill works

The skill inspects entity mappings, ownership/ inverse relationships, and migration plans to detect risky or destructive changes. It recommends migration strategies, fetch and query tuning for hot paths, and test patterns to verify lifecycle and transactional behavior. Outputs include precise entity/migration changes, integrity decisions, performance recommendations, and rollback notes.

When to use it

  • Designing or changing entity relationships and ownership
  • Making schema or mapping changes that affect large datasets
  • Optimizing Doctrine queries and fetch strategies for hot code paths
  • Preparing migrations for progressive rollout with integrity guarantees
  • Reviewing batch processing jobs that may trigger N+1 or over-fetching

Best practices

  • Keep owning and inverse sides coherent and explicitly mapped to avoid ambiguous cascades
  • Break destructive migrations into smaller, reversible steps across releases
  • Define clear transactional boundaries for batch jobs to limit lock scope and rollback impact
  • Tune fetch modes and DQL for hot paths; prefer joins with pagination over repeated lazy loads
  • Add targeted integration tests to validate lifecycle callbacks and migration outcomes before rollout

Example use cases

  • Refactoring a many-to-many relationship into an explicit join entity without downtime
  • Converting a nullable column to non-nullable with phased default population and backfill jobs
  • Optimizing an export batch job to eliminate an N+1 by introducing a single join query and controlled pagination
  • Adding a new index and testing its impact on large batch writes before applying globally
  • Designing migration steps that allow safe rollback if integrity violations appear during background processing

FAQ

Split the change into multiple releases: add new fields or join entities first, backfill data with safe batch jobs, switch application code to use the new model, then remove old structures in a later release.

What’s the best way to detect N+1 problems before production?

Run focused integration tests and profiling on representative data sizes, inspect executed queries for repeated SELECT patterns, and use logging or tracing to measure query counts per request or job.

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