Cases51 cases

41 to 45 of 51

1. IL-0033 Uneven load across kube-dns pods and a DNS packets-per-second limit pushed service latency to 20 seconds

   Airbnb2019

   Symptoms

   On May 10 the latency graphs of several services showed a few spikes of up to about one second, then latency climbed to five seconds and then to 20 seconds, and it stayed there for a long time. The same pattern appeared on many services, not just one.

   High latencyIntermittent timeoutsLoad imbalanceDNS resolution failures

   Trigger

   Several services sent a high volume of external DNS queries, all of which went through kube-dns.

   Root cause

   Capacity exhaustion

   Component

   kube-dnskube-proxyClient-side DNS resolver and cache

   Contributing factor

   Unreviewed defaults

   Detection

   Latency graphs of several services showed the rise; engineers then looked at load per kube-dns pod.

   Lessons

   By default a pod discovers DNS through the kube-dns cluster IP and uses the ClusterFirst policy, so even external lookups go through kube-dns. If a pod does not need cluster DNS, set its dnsPolicy to Default or None. Check the DNS policy early and often. iptables picks backends at random and is not a load balancer, so pods behind a cluster IP, kube-dns included, can carry very different loads.

   View case
2. IL-0026 Kubernetes nodes hit SystemOOM because fluentd-sumologic log shipper pods had no memory limits

   Blue Matador2019

   Symptoms

   On a Saturday at 5:12 pm, Blue Matador's own monitoring raised a SystemOOM event for one node in the production Kubernetes cluster. Four minutes later a second warning reported a low EBS Burst Balance on that node's root volume. CloudWatch data showed the burst balance had actually bottomed out at 5:02 pm, before the memory event; the warning arrived late because EBS metrics lag 10 to 15 minutes.

   OOMKilled

   Trigger

   Memory use of the fluentd-sumologic log shipper pods grew until the node ran out of memory; log volume in Sumo Logic had been steady, so a burst of logs was not the cause.

   Root cause

   Capacity exhaustion

   Component

   Telemetry agentContainer memory limit

   Contributing factor

   No resource requests or limitsIgnored early signal

   Detection

   Blue Matador's own monitoring product alerted on the SystemOOM event, followed minutes later by a low EBS Burst Balance warning for the same node.

   Remediation

   • restarted all fluentd-sumologic pods so they started with low memory
   • set RUBY_GC_HEAP_OLDOBJECT_LIMIT_FACTOR to 0.9 on the fluentd-sumologic pods
   • added memory requests of 128Mi and limits of 1024Mi, and CPU requests of 100m and limits of 250m, to the fluentd-sumologic pods

   Action items

   • Apply default resource limits and resource quotas so that every pod runs with requests and limits

   Lessons

   When checking for restarted pods, look in every namespace, not only the application one. Set resource requests and limits on every pod, third-party ones in particular, so that one workload cannot exhaust a node. Investigate side signals such as the disk I/O spike while the logs still exist.

   View case
3. IL-0002 CI builder pods could not get AWS IAM credentials because kubelet fell minutes behind its API rate limit

   Zalando2019

   Symptoms

   Builds on Zalando's in-house Continuous Delivery Platform (CDP), which runs CI/CD on Kubernetes, timed out and failed for almost an hour on 11 June 2019. The Kubernetes builder pods could not get AWS IAM credentials.

   High error rateCredentials unavailableControl plane lag

   Trigger

   An unlucky rescaling of the CDP Kubernetes cluster left only one node available for builder pods. Builder pods were created and deleted on that node so quickly that kubelet fell behind.

   Root cause

   Capacity exhaustion

   Component

   kubeletCredential broker for podsKubernetes API serverCI system

   Contributing factor

   Unreviewed defaultsSingle point of failure

   Remediation

   • Scaled the CDP cluster up by hand so builder pods spread over more than one node

   Action items

   • Raise the kubelet QPS limit from the default 5 to 50 in the global cluster configuration

   View case
4. IL-0076 Kubernetes masters went down after Dex filled etcd with AuthRequest objects and deleting its namespace started an endless list-and-timeout loop

   Fullstaq2019

   Symptoms

   Dex, the OIDC proxy that handled Kubernetes authentication, stored its data in the Kubernetes API (storage type kubernetes, inCluster: true) and had built up a huge number of AuthRequest objects. Listing all AuthRequests timed out, and without the list they could not be deleted.

   Control plane unresponsiveMonitoring outage

   Trigger

   The dex namespace was deleted so that Kubernetes would clean up the accumulated AuthRequest objects itself.

   Root cause

   Capacity exhaustion

   Component

   Kubernetes API serveretcdCluster authentication provider

   Contributing factor

   Unreviewed defaultsHarmful manual actionRetry stormLimited debug accessIgnored early signal

   Detection

   The speaker says he had got hints of the problem earlier. While the masters were down, plenty of alerts arrived in Slack, but monitoring could not be reached because authentication and port-forwarding no longer worked.

   Remediation

   • removed the kubernetes finalizer from the dex namespace by calling the API directly (kill-kube-ns)
   • deployed Dex to a new namespace with sane defaults

   Lessons

   Dex with Kubernetes as its storage keeps every AuthRequest as an API object, and with a long TTL they pile up until the API cannot list them. Deleting a namespace that holds that many objects makes Kubernetes list them in a loop and can overload the masters. Monitoring that is reached through cluster authentication is lost when authentication is what broke.

   View case
5. 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 gatewayApplication databaseAdmission 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

By root cause1