Cases33 cases

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1. IL-0037 Image registry rate-limited a whole region after a crash-looping DaemonSet with imagePullPolicy Always pulled through three NAT instances

   Datadog2019

   Symptoms

   Users reported that their jobs were not starting, with some sign that image pulls were involved. Many pods across the cluster were in error states related to image pulls, while applications that were already running were not affected.

   Image pull failuresService outage

   Trigger

   A change to the permissions on a storage bucket that one application needed to read. The application could no longer start, and its DaemonSet, running on roughly a thousand nodes of the cluster, went into a crash loop with imagePullPolicy: Always, so every restart pulled the image again although it was already on the node.

   Root cause

   Capacity exhaustion

   Component

   Container registry and its credentialsNAT gatewayDatabaseAdmission webhook

   Contributing factor

   Automation amplificationSingle point of failureUnknown limitHarmful manual actionExcessive blast radius

   Detection

   Users reported that their jobs were not starting; monitoring had not caught it first.

   Remediation

   • replaced the affected NAT instances so registry traffic came from new source IPs
   • reconfigured the static IPs and firewall rules so applications could reach the Cloud SQL database again

   Action items

   • Add an admission webhook that rejects floating image tags, starting with latest
   • Apply the image tag webhook to workload objects only, not to pods, after it blocked pods created by in-cluster controllers

   Lessons

   The speakers' takeaway is to be very careful with DaemonSets in large clusters: they say they had broken everything DaemonSets depend on, including DNS, Vault and the image repository, and that it is very easy to break things when managing them by the thousand. imagePullPolicy: Always made every crash-loop restart pull the image again. Sending all registry traffic through three NAT instances put the whole region behind three source IPs, which the registry rate-limited, and changing those IPs cut the connections to a critical database reached over the internet through them. An admission webhook on pods rejects the pods that controllers in the cluster create, where no user sees the error.

   View case
2. IL-0467 Boskos crash-looped after a node drain started it from a buggy StatefulSet image, failing Kubernetes PR jobs for 9 hours

   Kubernetes project2019

   Symptoms

   Overnight into 26 March 2019 (PDT), every Prow test job that needed a GCP project from Boskos, the Kubernetes test-infra service that hands out GCP projects to test jobs, failed. The required job pull-kubernetes-e2e-gce failed on every run, so no pull request could be merged in kubernetes/kubernetes for just over nine hours, and other projects saw similar failures.

   Container crash loopService outage

   Trigger

   At 23:04 PDT the node running the only Boskos pod was drained to be replaced with a 1.13 node during the upgrade of the Prow clusters from Kubernetes 1.11 to 1.13. The replacement pod started from the StatefulSet's current spec, which named a newer Boskos image containing the bug.

   Root cause

   Faulty deployment

   Component

   CI systemNode pool

   Contributing factor

   Change with a delayed effectUnverified assumptionSingle point of failureNo alert on limitChange started before off-hours

   Detection

   Users reported the failure on GitHub at 02:50 and on Slack at 05:16 and 06:27, and an engineer saw the Slack reports on waking at 07:16. The Boskos monitoring dashboard showed the failure, but no alert caught it.

   Remediation

   • Built and deployed a new Boskos image with the bug fixed
   • Cleaned up the GCP projects that had been left open

   Action items

   • Change the Boskos configuration so that deployments actually take effect (#11956)
   • Automate Boskos deploys (#11957)
   • Improve alerting on Boskos status (#11953)

   Lessons

   The write-up lists what went poorly: a ten-hour upgrade was started shortly after 17:00 when on-call coverage ended at 18:00; deployments were not checked to confirm that something had actually rolled out; the Boskos StatefulSet was not set up to perform updates, so changing its spec left the old pod running; the December change appears never to have been tested; and Boskos was deployed by hand, unlike most of Prow, which left more room for error. Once people were available, the problem was fixed quickly.

   View case
3. IL-0064 CPU-heavy pods packed onto one Google Cloud node drove it to kernel panics, and the crash moved from node to node until the site was down

   Moonlight2019

   Symptoms

   On Friday, January 18, Moonlight's API began returning occasional errors that looked like connection problems with its Redis database. The API checks Redis on every authenticated request to validate the session. At the same time the Kubernetes monitoring reported some nodes and pods as unresponsive.

   High error rateIntermittent connection failuresNode NotReadyService outageSustained high node CPU

   Trigger

   The Kubernetes scheduler put several CPU-heavy pods on the same node, including all three replicas of the website and the server-side rendering pods.

   Root cause

   Capacity exhaustion

   Component

   Pod scheduling and placementLinux kernelDatabase

   Contributing factor

   Single point of failureUnverified assumptionAutomation amplification

   Detection

   API errors that looked like Redis connectivity problems, and Kubernetes monitoring that showed nodes and pods as unresponsive. Google Cloud Support later found the link between sustained 100% CPU and kernel panics.

   Remediation

   • added anti-affinity rules to all major deployments so their pods spread across nodes

   Action items

   • Keep working with Google Cloud support to find and fix the cause of the node kernel panics

   Lessons

   Three replicas give no fault tolerance if the scheduler places them all on one node; anti-affinity rules make the spread explicit. A node failure that comes from the workload itself moves with the workload when the scheduler reschedules it.

   View case
4. IL-0073 Intermittent Kafka connectivity overloaded Docker daemons, set off a node rescheduling cascade and poisoned the Consul gossip mesh

   Target2019sev1

   Symptoms

   An upgrade of the network subsystem under Target's datacenter OpenStack, meant to cause only a brief disruption, ran into problems and left network connectivity disrupted for several hours. Kafka, which runs on that OpenStack, became intermittently available.

   Node NotReadyLoad imbalanceHigh latencyCredentials unavailable

   Trigger

   A network subsystem upgrade in the datacenter OpenStack infrastructure disrupted connectivity for several hours and made Kafka intermittently available.

   Root cause

   Dependency failure

   Component

   Container runtimeService discoveryTelemetry agentCredential broker for pods

   Contributing factor

   Excessive blast radiusAutomation amplificationSingle point of failureUnpatched known bug

   Remediation

   • enabled gossip encryption on Consul so messages from the poisoned mesh were rejected
   • stabilized the deployment engine and redeployed the development environment with encryption, terminating instances without it
   • upgraded Consul in place to 1.2.3 and then to 1.3.0

   Action items

   • Reduce how much of an outage a single Docker daemon can cause, together with running smaller and more numerous clusters

   Lessons

   Smaller and more numerous clusters limited the damage: the smaller development clusters and production were not hit the same way as the one large cluster, and dev and prod on one cluster would have been far worse. A Docker daemon shared by many workloads is a brittle failure point. Per-workload logging and metrics sidecars were judged the right design; a shared cluster-level agent would almost certainly have made the problem worse and harder to fix.

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5. IL-0031 Kubernetes API server OOM-killed, leaving the Skipper ingress without routes; the talk points to a misconfigured CronJob manifest

   Zalando2018

   Symptoms

   Customers were affected, and Zalando's IAM service answered requests with HTTP 404. The talk shows this on slides titled "Customer impact" and "IAM returning 404", followed by a slide on the number of pods in the cluster.

   High error rateControl plane unresponsiveOOMKilled

   Trigger

   A CronJob (apiVersion batch/v2alpha1, schedule "*/15 9-19 * * Mon-Fri") whose concurrencyPolicy: Forbid and job history limits sat inside the pod template spec instead of the CronJob spec. The talk shows it right after the slides on the number of pods and the API server being OOM-killed, and the lessons add a quota on the number of pods.

   Root cause

   Capacity exhaustion

   Component

   Kubernetes API serverIngress controller

   Contributing factor

   Single point of failureExcessive blast radius

   Remediation

   • fixed the CronJob manifest: moved concurrencyPolicy: Forbid and the job history limits to the CronJob spec, added activeDeadlineSeconds: 120 and changed the schedule to "7 8-18 * * Mon-Fri"

   Action items

   • Make the ingress stay healthy during API server problems
   • Make the ingress keep the last known set of routes
   • Use a ResourceQuota on the number of pods (the example sets pods: "1500")

   Lessons

   An AWS ALB routes traffic to all hosts when all hosts report unhealthy, so the ingress should stay healthy during API server problems. An ingress controller that reads routes from the API server should keep its last known set of routes. Use a quota for the number of pods.

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