Production Implementation: Orchestration, Caching, and Observability

PIPELINE ORCHESTRATION
The request coordinator fans out to retrievers in parallel, waits with 30ms timeout, merges candidates, applies filters, then passes to ranking. Each retriever runs independently. The coordinator collects whatever arrives before timeout and proceeds with partial candidates rather than failing. This degradation strategy keeps the system available even when individual retrievers are slow.
CACHING STRATEGIES
User embeddings cache for 5 to 15 minutes since preferences change slowly. Item embeddings cache for hours. Retrieval results for popular queries cache for 1 to 5 minutes. Ranking features are tricky: static features (item age, category) cache well, personalized features cannot. Typical systems achieve 60 to 80% cache hit rate on retrieval, reducing average latency by 40%.
OBSERVABILITY AND DEBUGGING
Log at each stage: candidate count, score distribution (min, max, median), latency, sample item IDs. Build debug mode storing full candidate lists for sampled requests. When recommendations look wrong, retrieve the trace to see what retrieval returned, what ranking scored, where it broke.
✅ Best Practice: Propagate a request ID through all stages. Include it in metrics and traces so you can correlate retriever latency, candidate counts, and ranking scores for any request.
A/B TESTING PIPELINE CHANGES
Changes to retrieval affect candidate diversity, affecting ranking behavior, affecting engagement. A new retriever improving recall 5% might surface candidates with lower ranking scores. Always measure end to end metrics (clicks, conversions), not just stage metrics. Run experiments at least one week to capture weekly patterns.

💡 Key Takeaways

✓Fan out to retrievers in parallel with 30ms timeout - proceed with partial results rather than fail

✓Cache user embeddings for 5-15 mins, item embeddings for hours, retrieval results for 1-5 mins

✓Typical systems achieve 60-80% cache hit rate on retrieval, reducing latency by 40%

✓Log candidate count, score distribution, latency, and sample IDs at each stage for debugging

✓Test pipeline changes end-to-end for at least one week to capture engagement effects

📌 Interview Tips

1Describe the orchestration flow: coordinator → parallel retrievers (30ms timeout) → merge → filter → rank

2Explain cache strategy: embeddings cache well, personalized features cannot be cached

3Discuss debugging: trace request ID through all stages to find where recommendations went wrong

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