Cases11 cases

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1. IL-0459 MediaWiki on Kubernetes terminated TLS with a wrong certificate after a deployment, and 1% of requests failed

   Wikimedia Foundation2023

   Symptoms

   On 9 August 2023, right after a MediaWiki configuration deployment at 11:41 UTC, the MediaWiki deployments on Kubernetes terminated TLS with a wrong certificate, and about 1% of Wikimedia web and API traffic, around 120 requests per second, received errors for about 30 minutes. The issue was detected by a page for failed service probes on mw-api-ext and mw-api-int.

   High error rateTLS certificate errorsControl plane lag

   Trigger

   At 09:37 a change to the global Kubernetes defaults was merged that switched supported charts to cert-manager certificates. The next MediaWiki on Kubernetes deployment, a configuration change at 11:41, picked it up.

   Root cause

   Configuration change

   Component

   cert-managerHelm

   Contributing factor

   Change with a delayed effectNo configuration tests

   Detection

   A page from the ProbeDown alerts for mw-api-ext:4447 and mw-api-int:4446 at 11:47; a KubernetesAPILatency alert had fired at 11:46.

   Remediation

   • Identified the issue and prepared a patch, https://gerrit.wikimedia.org/r/947331
   • Redeployed the MediaWiki deployments on Kubernetes

   Action items

   • Review whether deployment procedures and testing should be strengthened, for example against surprising changes on the next deployment and with canary deployments for Kubernetes
   • Look at metrics that become unavailable or unhealthy during deployments, or at reducing the impact of deployments

   Lessons

   The cause was found quickly because the person who made the breaking change was responding. The change should have been caught by CI, but the repository it was made in (hieradata) does not run the CI of the deployment charts. Only 1% of traffic going to Kubernetes kept the impact small, and the low error volume made it slower to link the errors to Kubernetes.

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2. IL-0437 Old pods from an unfinished Argo Rollouts canary could not start with a Helm-updated ConfigMap and died in a cascade

   Gunosy2023

   Symptoms

   During a traffic surge from a breaking news alert, pods of a Gunosy service on Kubernetes could not start because the containers could not read their settings from the ConfigMap. The pods that kept running took the extra load and were restarted, failed to start again, and pod after pod died.

   Container crash loop

   Trigger

   A breaking news alert increased traffic and pods were scaled out automatically while a release that removed an old database's settings from the ConfigMap had been rolled out to only one canary pod.

   Root cause

   Faulty deployment

   Component

   ConfigMaps and SecretsHelmProgressive delivery controller

   Contributing factor

   Partial rolloutAutomation amplification

   Action items

   • Configure Argo Rollouts to roll out automatically, with setWeight 1, a one-hour pause and then setWeight 100
   • Add the "helm.sh/resource-policy": keep annotation so Helm keeps past versions of the ConfigMap
   • Build and use a Helm plugin, helm-clear, that keeps only the last few ConfigMap versions by the revision number in their names, set to keep 5 in CD

   Lessons

   Gunosy holds that a review that only resolves to be careful next time would soon be followed by the same failure, because human attention cannot be relied on, so each incident gets a permanent fix built into the system. Keeping past ConfigMap versions also made it possible to roll back quickly without going through CI/CD, which a ConfigMap change had previously prevented.

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3. IL-0208 A GitLab chart bump that renamed Kubernetes Service and Deployment ports made the GCP load balancer reconfigure, and GitLab.com slowed down and returned intermittent 50x errors

   GitLab2022

   Symptoms

   At 07:53 UTC on 2022-03-31 the rate of 50x errors on GitLab.com went up, and at 07:54 PagerDuty paged the engineer on call (EOC) about the rising error rate. Users saw slow pages and intermittent 50x error pages. The incident lists the Web, API, Git and Pages services as affected.

   High error rateHigh latencyIntermittent timeouts

   Trigger

   A k8s-workloads/gitlab-com pipeline deployed merge request !1643, a GitLab chart bump that changed the port names of Kubernetes Services and Deployments (related to the Prometheus service and pod monitors), starting with the canary environment gprd-cny at 07:49 UTC.

   Root cause

   Configuration change

   Component

   HelmCloud load balancerHAProxy

   Contributing factor

   Automation amplification

   Detection

   A PagerDuty alert on the rising error rate paged the EOC at 07:54 UTC, one minute after the 50x errors went up. The engineer deploying the change noticed the errors and linked them to it.

   Remediation

   • drained the canary environment and re-enabled it after twenty minutes
   • cancelled the deployment jobs for clusters c and d before they started
   • drained gprd-us-east1-d in HAProxy and re-enabled it five minutes later
   • raised the minimum replica count above 100 (merge request !1650)
   • drained gprd-us-east1-c in HAProxy, deployed the change to it, and re-enabled it
   • reverted the minimum replica increase (merge request !1652)

   Action items

   • Implement graceful draining of a whole regional cluster
   • Create a Chatops command that puts PagerDuty into maintenance mode

   Lessons

   Renaming ports on Kubernetes Services and Deployments behind a GCP load balancer made the load balancer reconfigure, and requests were slow or timed out until the new configuration had fully applied, which took minutes. GitLab notes that alerting worked well. The follow-up work is a way to drain a whole regional cluster gracefully and a Chatops command to put PagerDuty into maintenance mode.

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4. IL-0522 An ingress-nginx chart upgrade deleted the jenkins.io Ingress and Fastly served HTTP/503 errors for hours

   Jenkins project2021

   Symptoms

   At 11:32 UTC on 18 November 2021, a user reported in the jenkinsci/jenkins Gitter channel that https://www.jenkins.io/doc/pipeline/steps/ was answering HTTP/503 errors with the message "Guru meditation" from Fastly's varnish.

   Service outageResources deleted

   Trigger

   The ingress-nginx Helm chart was upgraded from 4.0.6 to 4.0.8 at 10:42 UTC, and the new chart added an admission webhook rule that forbids Nginx configuration blocks in Ingress annotations.

   Root cause

   Breaking change in an upgrade

   Component

   Ingress controllerAdmission webhookHelmCDN and edge network

   Contributing factor

   Unverified assumptionCache-masked failureMonitoring blind spotHarmful manual action

   Detection

   A user reported in the jenkinsci/jenkins Gitter channel at 11:32 UTC that a jenkins.io page answered HTTP/503 errors, and status.jenkins.io reported the incident at 11:49 UTC.

   Remediation

   • Disabled the automated systems, rolled the ingress-nginx chart back to 4.0.6 and deleted the admission webhook by hand
   • Removed the forbidden annotations from the jenkins.io configuration
   • Upgraded the jenkins.io Helm chart to the last version known to work with ingress-nginx 4.0.8
   • Redeployed the jenkins.io Helm chart through the automated system and told Fastly to drop all cached pages

   Action items

   • Always read the changelog in depth when updating the ingress controllers, with two people pairing in real time
   • Add custom Datadog monitoring for www.origin.jenkins.io so that such an outage is detected
   • Make the monitor send the Host header www.jenkins.io so it is not redirected to Fastly
   • Check a set of pages, such as the pipeline steps page, not only the front page
   • Consider an ephemeral k3s end-to-end test before deploying to production

   Lessons

   Trusting a patch version of a Helm chart for the Ingress controller without reading its changelog in depth let a new admission rule remove the jenkins.io Ingress in production. Without monitoring on origin.jenkins.io, a user found the outage. A complete staging cluster with the same DNS setup would have caught the problem before production.

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5. IL-0197 Moving a three-node Elasticsearch StatefulSet to new persistent volumes split the cluster in two, because the Helm chart hardcoded cluster.initial_master_nodes to elasticsearch-0

   Palark2021

   Symptoms

   After the last old Elasticsearch pod, elasticsearch-0, was deleted and came back on a new, empty persistent volume, `_cat/nodes` run inside that pod listed a single node: elasticsearch-0 itself, marked as master. The same command run inside elasticsearch-1 listed the other five nodes, elasticsearch-1 to elasticsearch-5, with elasticsearch-2 as master.

   Split brain

   Trigger

   Deleting the elasticsearch-0 pod and its PVC so that it restarted on an empty volume of the new StorageClass.

   Root cause

   Operator error

   Component

   Elasticsearch or OpenSearchPersistent volumes and stateful setsHelm

   Contributing factor

   Unverified assumption

   Detection

   The engineer running the migration checked `_cat/nodes` after elasticsearch-0 restarted and saw it alone in a cluster of its own.

   Remediation

   • scaled the three deployments of workers writing to Elasticsearch down to zero
   • listed elasticsearch-0, elasticsearch-1 and elasticsearch-2 under initial_master_nodes in the elasticsearch ConfigMap and removed the cluster.initial_master_nodes environment variable from the StatefulSet
   • saved and deleted the elasticsearch discovery Service and deleted the PVC of elasticsearch-0 while the rolling update ran
   • set initial_master_nodes to elasticsearch-3, elasticsearch-4 and elasticsearch-5, created an empty PVC for elasticsearch-0 and deleted all six pods
   • recreated the discovery Service from the saved manifest, after which the six nodes formed one cluster with every index green

   Action items

   • Restore the original initial_master_nodes values in the ConfigMap and restart all pods again
   • Move all shards to nodes 0, 1 and 2 and scale the cluster down from six nodes to three
   • Commit all the manual changes to the repository

   Lessons

   A setting that only matters when a cluster first starts, such as Elasticsearch's cluster.initial_master_nodes, can sit in a Helm chart template rather than in the ConfigMap that was reviewed. When a pod named in it starts with an empty data directory, it can bootstrap a new cluster beside the existing one. The author's conclusion is that a migration plan like this one breaks on any surprise and needs a plan B ready. Before running it in a more important environment, rehearse it in a test environment configured like production, and either schedule downtime or move the load to another cluster: stop the writers, take a fresh backup, then move the data.

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