Cases2 cases

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1. IL-0184 JupyterHub's KubeSpawner could not create containers and kubectl was refused because the API server and other internal certificates of a kubeadm cluster had expired

   dbhang2021

   Symptoms

   KubeSpawner, which starts JupyterHub user containers on Kubernetes, failed to create containers with an error the author had never seen, saying an SSL certificate had expired.

   Control plane unresponsiveDeploys failing

   Root cause

   Certificate or credential expiry

   Component

   Kubernetes API serverkubelet

   Contributing factor

   Unknown limitNo alert on limit

   Detection

   KubeSpawner failed to create a container and logged an SSL certificate verification error.

   Remediation

   • checked certificate expiry with kubeadm alpha certs check-expiration
   • moved the old certificates and /etc/kubernetes/*.conf files to a backup directory
   • regenerated all certificates with kubeadm init phase certs all
   • regenerated the kubeconfig files with kubeadm init phase kubeconfig all
   • copied /etc/kubernetes/admin.conf to ~/.kube/config and restarted kubelet
   • restarted the kube-system containers with docker restart so they picked up the new credentials

   Lessons

   kubeadm clusters run on certificates that expire, and when the API server certificate expires kubectl cannot be used to fix it, so renewal has to happen on the control plane node with kubeadm. After renewal, the kubeconfig files and the running control plane containers must also pick up the new certificates, or kubectl returns Unauthorized.

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2. IL-0035 Worker nodes went NotReady after a new ElastAlert rule without memory limits used up node memory

   NU.nl2019

   Symptoms

   The NU.nl development team got reports that several services on one of its Kubernetes clusters behaved erratically: occasional error pages, slow responses and timeouts. The affected services were mostly internal tools and dashboards.

   Node NotReadyHigh error rateHigh latencyIntermittent timeoutsMonitoring outageOOMKilled

   Trigger

   A new ElastAlert rule, deployed shortly before from the command line, that fired if the Cloudfront-* Elasticsearch indexes stopped receiving documents; those indexes take a few million requests per hour, and the rule made ElastAlert's memory grow without bound.

   Root cause

   Configuration change

   Component

   Container memory limitkubeletPrometheus

   Contributing factor

   No resource requests or limitsUnreviewed defaultsNo alert on limitMonitoring blind spotExcessive blast radius

   Detection

   Users reported error pages, slow responses and timeouts from several services; no alert fired.

   Remediation

   • terminated the NotReady nodes one by one so the auto-scaling group replaced them (the new nodes also went NotReady, but Prometheus and Grafana landed on a stable node)
   • found the ElastAlert pods' memory growth on the Grafana pod memory dashboard
   • fixed the ElastAlert problem that caused the memory growth

   Action items

   • Define alerts on resource usage such as CPU, memory and disk space
   • Keep researching an alerting strategy that combines Elasticsearch, Prometheus Alertmanager and New Relic
   • Consider exporting metrics out of the cluster and running Grafana outside it, on EC2 or a separate Kubernetes cluster
   • Either visualise the important metrics in the ELK stack or make Prometheus and Grafana more available, and improve the metrics strategy
   • Set resource limits for ElastAlert through the Helm values
   • Configure namespaces with default memory requests and limits
   • Isolate customer-facing workloads from build, logging and monitoring workloads (a dedicated node group, or later a separate tools cluster)
   • Deploy everything through automation such as Jenkins pipelines, and consider a GitOps approach
   • Deploy through a pipeline that includes a smoke test step
   • Make Kubernetes and cloud infrastructure work part of the team's sprints, picked up by all team members while pairing with more experienced members, and hold workshops that go deep into specific topics

   Lessons

   Fixing the root cause covers only a small part of what an incident can teach; the chain of smaller failures around it is where most of the learning is. A pod without a memory limit can take whole nodes down, so limits need defaults at the namespace level. Monitoring that runs inside the cluster it watches goes blind with it. Changes installed by hand from the command line leave the rest of the team without a record of what changed.

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