Cases11 cases

10 to 11 of 11

1. 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.

   View case
2. IL-0503 mybinder.org launches failed after a GKE node ran out of memory and its nginx-ingress proxy pod crash-looped

   Binder Project2017

   Symptoms

   On 30 November 2017 at 16:59 PST, mybinder.org operators noticed that Grafana looked wrong and that launching a new server failed with an error that a temporary user could not be created. Pods that were already running kept running, but no new ones were being created.

   Service outageContainer crash loopProbe failuresOOMKilled

   Trigger

   A build container, which ran without a memory limit, most likely used up all the memory on the node that ran the proxy pod.

   Root cause

   Capacity exhaustion

   Component

   Ingress controllerContainer memory limitJupyterHub

   Contributing factor

   No resource requests or limitsHarmful manual actionNo alert on limit

   Detection

   An operator happened to look at Grafana at 16:59 PST, saw that it looked wrong, and confirmed the error by launching a new server.

   Remediation

   • Cordoned the node that had run out of memory
   • Uncordoned the node once the proxy was working

   Action items

   • Limit the memory available to build pods
   • Change the JupyterHub configuration so user pods are not cleaned up when the jupyterhub pod restarts
   • Nominate one person to drive the response, possibly in a shared terminal, so only one person changes the cluster
   • Alert when something is wrong with the cluster, for example by counting 4xx responses on the jupyterhub pod

   View case

By root cause4