Follow-up · depth 2
What metrics prove Aggregation Pipeline is healthy in prod?
What metrics prove Aggregation Pipeline is healthy in prod?
Answers use simple, clear English.
Audio N/AQuick interview answer
Prove Aggregation Pipeline is healthy with latency (p50/p95/p99), error/saturation rates, and queue/event-loop lag — not just CPU. Context: Use Aggregation Pipeline as the core idea. Example shape: Monthly revenue by category: $match date range, $unwind items, $group by category sum price..
Detailed answer
Production health signals for Aggregation Pipeline: • Latency: p50/p95/p99 of the critical path that uses Aggregation Pipeline. • Errors: failure rate, timeouts, and retry storms. • Saturation: queue depth, event-loop delay, thread/pool utilization. • Business SLIs: request success and user-visible freshness where relevant. Trade-off lens: Pros: Powerful analytics in database; reduces app-side processing. Cons: $lookup expensive on large collections without indexes; memory limits on sort/group. Explain thresholds + alerts, then how you triage. Parent context: Use Aggregation Pipeline as the core idea. Example shape: Monthly revenue by category: $match date range, $unwind items, $group by category sum price..
Full explanation
Good answers name measurable signals and what “bad” looks like for Aggregation Pipeline. Stages: $match → $group → $sort → $lookup (join) → $project. Pipeline runs server-side; push filters early in $match for index use.
Follow-up questions
Only answered follow-ups are shown — click to open with full answers
Parent context — Aggregation Pipeline
Use Aggregation Pipeline as the core idea. Example shape: Monthly revenue by category: $match date range, $unwind items, $group by category sum price..
View full parent question →