Real Production Scenarios

Introduction

Real production scenarios are messier than tutorial examples. This lesson presents three incident scripts drawn from common Atlas support cases — your job is to walk through each, identify the cause, and write the remediation.

Understanding the topic

Scenarios in this lesson:

• S1 — Latency spike at 09:00 daily: marketing cron runs an unindexed analytics query.
• S2 — Replication lag climbs after a deploy: a new write-heavy collection has no shard key, all writes hit one shard.
• S3 — Cluster suddenly read-only: disk hit 95%; auto-scale was disabled to save cost.

Syntax reference

Standard investigation flow:

1. Atlas → Metrics    →  identify which metric crossed (CPU? IO? oplog window? connections?)
2. Atlas → Profiler   →  capture top slow queries in the window
3. db.currentOp()     →  who's running long?     killOp if pathological
4. explain()          →  why is the suspect query slow?
5. Hypothesize fix    →  index | rewrite | scale | shard | cap concurrency
6. Stage + load test  →  prove the fix on staging before prod
7. Roll out + monitor →  watch for 24h, postmortem

Best practices

• Always check Atlas Profiler first — it captures slow queries with no setup.
• Match the metric to the cause: oplog window drop ⇒ slow secondary; connection spike ⇒ pool too big.
• Cap concurrency upstream (queue, rate limit) before scaling MongoDB blindly.