Evaluating Proposed Loaddata Command Enhancements

Your proposal to add performance optimization flags to Django's loaddata command is interesting and addresses a real performance concern. Based on the Django documentation on database optimization, particularly regarding bulk operations, here's an analysis of your proposal:

Alignment with Django's Optimization Principles

Django's documentation explicitly recommends using bulk methods to reduce SQL statements, which aligns with your proposal. The bulk_create() method is specifically highlighted as a way to improve performance by reducing database queries.

Considerations for Your Proposed Flags

1. --force-insert Flag

This approach could indeed provide performance benefits by avoiding unnecessary UPDATE attempts. The 50% improvement you've observed is significant and makes sense conceptually.

2. --bulk_create Flag

Your reported 1000-fold speed improvement with bulk operations is consistent with Django's optimization guidelines. However, as you correctly noted, there are important caveats to consider:

• The model's save() method won't be called
• pre_save and post_save signals won't be sent
• It doesn't work with child models in multi-table inheritance
• It doesn't work with many-to-many relationships
• Primary key retrieval has database-specific limitations
• There are considerations around conflict handling

Implementation Approach

Your implementation as a subclass of the Command class is a clean approach. The fact that you've achieved significant improvements with minimal code changes (~20 lines) is promising.

Recommendation

This functionality would likely be valuable to many Django users who need to load large fixtures efficiently. The optional nature of these flags respects that these optimizations come with trade-offs that users should explicitly opt into.

To move forward with getting this included in Django:

1. Consider submitting a feature proposal to Django's development team through their official channels
2. Include your proof-of-concept implementation and performance benchmarks
3. Clearly document the caveats and limitations in the implementation
4. Emphasize that these are opt-in features for users who understand the trade-offs

The proposal aligns well with Django's performance optimization philosophy while respecting backward compatibility by making these features optional.