Incident Library

IL-0287 MongoDB · 2025-10-07databaseobservability

One overloaded shard of MongoDB's internal monitoring database delayed Atlas metrics, alerts and cluster operations

MongoDB Atlas and Cloud Manager monitoring systems rely on an internal, sharded database cluster. High-traffic collections in it had not been sharded, so their load was concentrated on one shard and created a hotspot when load rose, and inefficient query patterns such as full collection scans added strain on that shard during normal operation. The postmortem dates the incident to October 6 and 7, when a rollout of an updated internal software version produced a temporary spike in resource demand, and together these overwhelmed the shard and delayed metrics ingestion and Atlas cluster operations. MongoDB applied mitigations to stabilise the system and then fixed the long-term causes; the status page marked the incident resolved at 20:03 UTC on 7 October 2025. There was no loss of data and no effect on the availability or performance of customer applications.

Severity
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Scope
Delayed or missing metrics in Atlas and Cloud Manager dashboards, Host Down alerts not firing, and delayed Atlas cluster management operations such as provisioning and resizing, intermittently over about 2 days; customer clusters and data were not affected
Users affected
MongoDB Atlas and Cloud Manager customers
Region
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Time to detect
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Time to resolve
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Symptoms

On 7 October 2025 MongoDB reported a delay in the metric ingestion pipeline of MongoDB Atlas and Cloud Manager. Metrics in Atlas dashboards arrived late or went missing, Host Down alerts did not fire, and Atlas cluster management operations such as provisioning and resizing were delayed, while customer clusters themselves stayed healthy and available.

The first investigation found high resource usage on one shard of the internal database cluster that backs the Atlas and Cloud Manager monitoring systems. Further review showed that high-traffic collections on that cluster were not sharded and sat on that single shard, that queries such as full collection scans added load to it, and that a rollout of an updated internal software version raised resource use beyond what had been expected. The postmortem describes partial impact that came and went over approximately 2 days.

Symptom Monitoring outageDatabase overloadLoad imbalance

Root cause

Root cause Capacity exhaustion

Component Database
Contributing factor Inefficient queryNo headroom
Trigger
The rollout of an updated internal software version caused a temporary spike in resource demand on the internal database cluster.

Detection

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Remediation

Action items

Lessons

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Sources

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