Experimental Django Recommender + Search Backend Best Practices

Implementation patterns for a Django backend serving mixed-results recommendations (Personalize / Databricks / microservice fan-out) and OpenSearch-backed search/feeds. 48 rules across 8 categories, ordered by execution lifecycle impact — earlier categories cascade through everything downstream.

This is the backend peer of the react-fetch-cache-patterns skill. React handles client-side waterfalls and caching; this skill handles server-side fan-out, downstream protection, OpenSearch query design, and ML-blend orchestration.

When to Apply

• Building or reviewing Django views that fan out to AWS Personalize, Databricks Model Serving, internal microservices, or any ML inference downstream
• Designing OpenSearch query endpoints (search results, infinite feeds, faceted search)
• Implementing a recommendations endpoint that blends multiple ranker outputs
• Investigating "Django backend slow when downstream is degraded" or "Personalize quota exhausted"
• Adding caching, retry, circuit breakers, or rate limiting to outbound calls
• Choosing between sync and async Django views, configuring uvicorn vs gunicorn
• Designing DRF response shapes for paginated feeds, partial results, or degraded paths

Rule Categories by Priority