Cases6 cases

3 to 6 of 6

1. IL-0110 A Grafana Agent pod's high memory use and crash-looping Hangfire pods drove a Kubernetes node into system OOM and NotReady, twice in one day

   DFDS2024

   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.

   Node NotReadyContainer crash loopOOMKilledMonitoring outage

   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

   Capacity exhaustion

   Component

   Telemetry agentContainer memory limitApplication database

   Contributing factor

   Excessive blast radiusNo resource requests or limitsHarmful manual actionMonitoring blind spot

   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.

   View case
2. IL-0202 A burst of queries and database writes exhausted connection pools and memory on Honeycomb's ingest hosts, and Kubernetes crash-loop back-off kept failed hosts offline so ingest capacity stayed too low to recover

   Honeycomb2023sev1

   Symptoms

   Standard alerting paged Honeycomb's on-call engineers about the performance and stability of event ingestion, and the automated load-shedding mechanisms tripped and sent further notifications. The status page described a higher than normal error rate and response time on the ingestion service.

   High error rateHigh latencyContainer crash loopService outageDatabase overload

   Trigger

   An abnormally large number of queries arrived from many separate connections within a few minutes.

   Root cause

   Capacity exhaustion

   Component

   Application databaseIn-memory cache

   Contributing factor

   Retry stormAutomation amplification

   Detection

   Standard alerting notified the on-call engineers about ingestion performance and stability, and the automated load-shedding mechanisms added notifications when they tripped. The first status post, about alerts on a higher than normal error rate and response time, went up at 18:28 PST (02:28 UTC), 13 minutes after the 02:15 UTC start.

   Remediation

   • Scaled the ingest fleet up manually and aggressively
   • Cut off most ingest for a few minutes so the ingest hosts could come back with enough capacity
   • Temporarily lowered the rate limits for the most aggressive traffic sources until their traffic subsided

   Action items

   • Keep the ingest fleet scaled up to prevent a repeat while longer-term fixes are worked on
   • Decide on the projects that will address these failure paths

   Lessons

   Load shedding and auto-scaling were in place, yet a burst that arrived faster than the cache could spread its writes still took the ingest hosts down. During recovery, Kubernetes' crash loop back-off worked against the operators: the hosts that had failed stayed offline while the fleet needed every host. Clients that retry whole OTel batches after a partial drop can multiply traffic many times over, so per-source rate limits were what finally stabilised ingest.

   View case
3. IL-0093 Ingress controller pods crash-looped one after another because the leader pod had exhausted its shared memory

   UK Ministry of Justice2020

   Symptoms

   At 10:43 on 14 August 2020 two of the six ingress controller pods in the cluster were in a crash loop. Restarting those pods did not fix them.

   Container crash loop

   Root cause

   Capacity exhaustion

   Component

   Ingress controller

   Contributing factor

   Harmful manual actionSingle point of failure

   Detection

   Two of the six ingress controller pods were seen crash-looping at 10:43; the incident was declared at 11:01.

   Remediation

   • restarted the leader ingress controller pod so the other replicas could connect and get the latest nginx configuration

   View case
4. IL-0176 Prometheus in a 235-node Kubernetes cluster crash-looped because over two million series exhausted host memory and the kernel OOM killer chose it first, as Burstable QoS gave it oom_score_adj 999

   LINE2020

   Symptoms

   One Monday morning the author opened the Prometheus dashboard to check a Kubernetes cluster for alerts and could not reach it. The Prometheus pod was in CrashLoopBackOff: kubectl get pods showed it 1/2 ready with 950 restarts over 3d20h on worker3.

   Container crash loopOOMKilledMonitoring outageProbe failures

   Trigger

   After start, the Prometheus time series count rose to 2,162,256 within five minutes and its memory use filled the 8GB host.

   Root cause

   Capacity exhaustion

   Component

   PrometheuskubeletLinux kernel

   Contributing factor

   No resource requests or limitsNo capacity planning

   Detection

   The author found it on a Monday morning when the Prometheus dashboard, opened to check the cluster for alerts, could not be reached.

   Remediation

   • removed most of the unnecessary settings from the Prometheus ConfigMap, after which the container stopped crashing

   Action items

   • Set appropriate resource requests and limits on the Prometheus pod
   • Reduce unneeded metrics by adjusting scrape targets or scrape interval
   • Estimate Prometheus memory needs, provide dedicated hosts with a node label such as deploy-topology: Prometheus, and pin the Prometheus pod to them with a nodeSelector

   Lessons

   Exit code 137 together with an OOM event in the kubelet log means the kernel killed the container, not kubelet. Under node memory pressure the kernel kills the process with the highest score, and kubelet derives each container's oom_score_adj from the pod's QoS class: a container without a memory request in a pod where another container has one ends up Burstable with a score near 1000, so it is killed first. In a large cluster, Prometheus memory needs to be estimated from the expected series count and requested explicitly.

   View case

By root cause1