Cases11 cases

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1. IL-0010 A templating change that dropped its {{ }} delimiters made Argo CD delete all 478 Skyscanner services in every region

   Skyscanner2021

   Symptoms

   On 25 August 2021, skyscanner.net was unavailable and the Skyscanner mobile apps could not work, for travellers and business partners everywhere at the same time. The full global outage lasted four and a half hours.

   Service outageResources deleted

   Trigger

   A peer-reviewed change to a rarely changed root-level file of the cells templating configuration, intended as a no-op, that left out the {{ }} delimiters; it was submitted around 15:30 GMT, merged to master and rolled out automatically to every region at once.

   Root cause

   Configuration change

   Component

   Argo CDConfiguration templating

   Contributing factor

   Missed in reviewNo staged rolloutNo configuration testsUntested backupAutomation amplificationExcessive blast radius

   Remediation

   • restored the correct configuration for each cluster and let Argo CD reconcile the cluster back to it
   • restored services through the backup and restoration process
   • brought one region back first with the critical services and served all traffic from it from around 20:30 GMT
   • restored and verified the remaining regions and non-critical services over Thursday and Friday

   Action items

   • Avoid global configuration deploys, including rare change paths like this one
   • Treat templated configuration as code and add testing and linting
   • Plan and wargame more drastic failure scenarios in runbooks
   • Verify backup and restore processes, including access to the backups and the loss of a region
   • Refactor runbooks so they are clear and, where appropriate, idempotent for a stressed engineer in the early morning
   • Reconsider how much configuration is templated across regions, weighing configuration drift against automated global rollout

   Lessons

   Global configuration changes had been prevented in many cases, but not in this rarely used path. Configuration that gains templating and logic has become code and needs tests and at least linting. The failure scenarios and runbooks were not pessimistic enough, a backup only counts once it has been restored, and runbooks need care so they work for a stressed engineer at night. Automation that templates configuration across regions trades drift for the risk of rolling one mistake out everywhere. An experienced incident commander who kept the response calm and blameless made a large difference.

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2. IL-0008 An unresponsive OPA validating webhook crash-looped the API server during a GKE master upgrade, and node auto-repair then recreated every node

   Jetstack2019

   Symptoms

   Jetstack was upgrading the master of a development cluster that many teams use to test their apps during the working day, a regional Google Kubernetes Engine (GKE) cluster in europe-west1. The upgrade ran through Jetstack's GKE Terraform pipeline and did not finish before the 20-minute Terraform timeout, while the GKE console still showed the cluster as upgrading.

   Upgrade stallControl plane unresponsiveNode recreation loopService outage

   Trigger

   A planned master upgrade of the shared development cluster. The same upgrade had been done on another pre-production environment earlier that day, and no API downtime was expected.

   Root cause

   Configuration change

   Component

   Admission webhookGKE control planeKubernetes API serverkubelet

   Contributing factor

   Excessive blast radiusNo health probesMonitoring blind spotAutomation amplification

   Detection

   The first sign was the master upgrade missing the 20-minute Terraform timeout; re-running the pipeline returned the kube-apiserver unhealthy error. The cause was identified with the help of Google Support.

   Remediation

   • Deleted the OPA ValidatingAdmissionWebhook resource during a window of API server access

   Action items

   • Limit the OPA webhook to the namespaces where the policy applies and development teams have access
   • Enable the webhook only for Ingress and Service resources, the only resources the policy validates
   • Add a liveness probe so OPA is restarted when it stops responding, and update the OPA documentation
   • Follow the open upstream Kubernetes issue about this behaviour

   Lessons

   With alerting on API server response times, the team might have seen CREATE and UPDATE requests slow down across the board as soon as the OPA-backed webhook was first deployed. Every workload needs probes: OPA looked so simple to deploy that the team skipped the Helm chart, which adds a livenessProbe for the admission controller containers among other adjustments to the tutorial configuration. Others had hit the same problem, and an upstream issue to improve it was still open.

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3. IL-0052 A cryptocurrency miner ran on every node of the development and staging clusters through a public, unauthenticated Weave Scope dashboard

   JW Player2019

   Symptoms

   Datadog alerted the DevOps team to a high normalised load average, first on the staging cluster (Day 0, 21:06 EST) and the next day on the development cluster (Day 1, 16:53 EST). The load was first put down to a legitimate service that kept scaling up and down in both environments, and because of the volume of identical alerts they were muted until things settled.

   Sustained high node CPUUnauthorised access or a foreign workload

   Trigger

   An automated crawler found the Weave Scope dashboard on a public load balancer that required no authentication. Weave Scope can open a shell in running containers, and its scope-agent containers ran privileged, as root and with the host's root filesystem mounted, so a command started there could run on the host.

   Root cause

   Configuration change

   Component

   Cluster dashboardCloud load balancer

   Contributing factor

   Ignored early signalUnreviewed defaultsExcessive blast radiusMonitoring blind spot

   Detection

   Datadog alerts on high normalised load average, muted at first as noise from a legitimate service; on Day 3 an engineer ran top on a node and found the gcc process at 100% CPU.

   Remediation

   • stopped Weave Scope and removed its deployment from all Kubernetes clusters
   • checked access logs for unauthorised access to the instances
   • confirmed the miner's outbound connections went only to a mining pool
   • isolated one affected node for later investigation
   • rotated out every node in each cluster so all affected hosts were destroyed and rebuilt

   Action items

   • Audit all Kubernetes and deployment YAML files and security groups for services that are unintentionally public or misconfigured
   • Replace load-average alerts with more meaningful monitors and tune them to signal a real anomaly
   • Research tools such as falcon or sysdig for behavioural monitoring and anomaly and intrusion detection
   • Evaluate Istio and Linkerd for observing and controlling end-to-end network traffic
   • Analyse the cost of scanning Docker images and containers for known vulnerabilities
   • Set aside time in architecture discussions to revisit older choices such as running containers as root
   • Share knowledge of large projects, such as Kubernetes version migrations, across the whole team

   Lessons

   Load average was not a useful signal for spotting a problem in the clusters, and muted noisy alerts nearly hid the miner. A load balancer that Kubernetes creates belongs to Kubernetes: a manual security group edit in the AWS console is reverted, so internal exposure must be declared in the Kubernetes YAML. A dashboard that can open shells, in front of privileged agents that mount the host's root filesystem, gives whoever reaches it root on every node.

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4. 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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