What metrics prove Aggregation Pipeline is healthy in prod?

Answers use simple, clear English.

Quick 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

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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..

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