Cases3 cases
1 to 3 of 3
IL-0110A Grafana Agent pod's high memory use and crash-looping Hangfire pods drove a Kubernetes node into system OOM and NotReady, twice in one dayDFDS2024- Symptoms
At 10:01 UTC on 2024-03-13 a Kubernetes node entered NotReady state, with CPU requests at 78% and memory requests at 82%. An alert fired in Slack at 10:03 and was picked up at 10:05. An attempt to SSH into the node at 10:13 failed because it was unresponsive; it started responding again at 10:19.
- Trigger
Pods of a dev container that used Hangfire to trigger jobs failed with OOM and crash-looped on a node where a Grafana Agent pod was already using a lot of memory.
- Root cause
- Component
- Contributing factor
- Detection
A Slack alert fired two minutes after the node went NotReady and was picked up two minutes later; the second node's NotReady at 15:03 also raised a Slack alert.
- Remediation
- marked the EC2 instance unhealthy so a new node started
- cordoned and force-drained the NotReady node and terminated its EC2 instance
- removed the new LimitRange from the Grafana Agent namespace and restarted the Grafana Agent pod
- scaled the dev container to 0 replicas, after which EC2 network traffic returned to normal
- after the second NotReady, scaled the dev container to 0 replicas again and added a new node
- emailed the team that owns the dev container, which confirmed it was working on a fix
- Action items
- Move grafana-agent to the monitoring node group
- Add alerts for when bandwidth across the cluster reaches a certain threshold
- Investigate whether memory limits can be set on the grafana-agent Helm chart
- Investigate grafana-agent memory usage
- Ask Grafana support whether grafana-agent should use a PVC
- Consider pushing node logs to Loki with limited retention
- Create a wiki page with common metrics and log queries to check when investigating issues with Kubernetes nodes
- Lessons
Dev workloads mixed with production let one crash-looping dev container take down nodes that production also used, and a monitoring agent outside its dedicated node group added to the memory pressure. Scaling the suspect container back up before its owners had fixed it brought the failure back on the replacement node. The team also noted that action items from earlier post-mortems need follow-up, and that knowledge of useful CloudWatch metrics had not been shared, which made the diagnosis harder.
- Symptoms
At 09:14 on 4 August 2023, users of the Cloud Platform reported in the #ask-cloud-platform Slack channel that Kibana was missing their application logs for long periods.
- Trigger
The newer Fluent Bit version in use had changed its chunk drop strategy; the fix that followed handled memory buffer overflow.
- Root cause
- Component
- Contributing factor
- Detection
Users reported long periods of missing logs in Kibana in the #ask-cloud-platform Slack channel at 09:14; the incident was declared at 10:09.
- Remediation
- configured Fluent Bit to handle memory buffer overflow by writing buffered logs to the file system
- made Fluent Bit flush logs in smaller chunks
- Action items
- Push notifications from the logging clusters to #lower-priority-alerts (#4704)
- Add an integration test that checks logs are being sent to the logging cluster
IL-0041fluent-bit silently stopped shipping application logs from one node to ElasticsearchPrometheusKube2020- Symptoms
On a Tuesday morning a developer reported that some production logs were missing. Only the logs from a single node were gone; the other nodes kept shipping normally through fluent-bit, the log shipper the team had run for more than three years.
- Root cause
- Component
- Contributing factor
- Detection
A developer reported missing production logs; no alert fired because fluent-bit's metrics showed no errors.
- Remediation
- added CPU to the Elasticsearch virtual machines, which cleared the write queue rejections
- rolled out Fluentd one node at a time, using node affinity to place Fluentd and anti-affinity to remove fluent-bit
- lowered Fluentd's flush_thread_count to 1 and set flush_interval to 5 seconds so the backlog of old logs did not overflow the buffer
- enabled Fluentd's Position DB so already-ingested data was skipped
- Action items
- Build monitoring for Fluentd on its Prometheus integration, including a throughput alert with separate day and night thresholds or a black-box log check
- Lessons
A log shipper can fail without an error message and with metrics that simply stop moving, so error-based alerts never fire; throughput alerts or end-to-end checks can catch it. Open source software can break in ways nobody expects, and sometimes the way out is to switch to a different shipper fast, node by node. A new shipper that starts from scratch replays every old log on the node and needs to be slowed down.
These 3 cases involve Telemetry agent and Application database. kubegap audits a Kubernetes cluster for gaps like these, read-only, from Claude Code or Codex; its checks cite public reports like the ones in this library.
By root cause2
Capacity exhaustion2 cases
- Contributing factor
- Organisation
Software defect1 case
- Contributing factor
- Organisation