Cases69 cases

39 to 43 of 69

1. IL-0016 Prometheus on the live cluster ran out of memory and was OOMKilled, and the monitoring node it ran on went NotReady

   UK Ministry of Justice2023

   Symptoms

   At 14:05 on 25 July 2023, a high-priority PagerDuty alert from Pingdom reported that the "Prometheus - live healthcheck" was DOWN. Prometheus on the live cluster was unavailable, and the Cloud Platform lost its monitoring for a while.

   Monitoring outageOOMKilledNode NotReady

   Trigger

   The growing number of namespaces and Prometheus rules raised Prometheus' memory needs beyond what its node instance size could hold.

   Root cause

   Capacity exhaustion

   Component

   PrometheusNode poolContainer memory limit

   Contributing factor

   No alert on limit

   Detection

   A Pingdom health check on Prometheus failed and raised a high-priority PagerDuty alert.

   Remediation

   • drained the monitoring node, moving Prometheus to another monitoring node
   • changed the node instance size to r6i.4xlarge
   • raised the Prometheus memory limit to 100Gi
   • set the Prometheus container limits to 12 CPU cores and 110Gi of memory

   Action items

   • Add an alert on node memory usage and on a pod using up most of a node's memory (issue #4538)

   View case
2. IL-0028 Temporal History service latency rose because its pods were CPU-throttled while CPU usage read below 60 percent

   Temporal2023

   Symptoms

   Request latency from the Temporal Server History service, the service that starts workflow executions and records their progress, was higher and less steady than it should be on a self-hosted Kubernetes deployment. The CPU dashboard showed the history pods using less than 60 percent of their CPU, yet the same pods were being throttled.

   CPU throttlingHigh latency

   Trigger

   Normal load on History pods that had a CPU limit of 2 cores on 8-core nodes, with the Go runtime's GOMAXPROCS left at its default of one per node core.

   Root cause

   Capacity exhaustion

   Component

   Container CPU limitLanguage runtime settingsPrometheus

   Contributing factor

   Unreviewed defaultsMonitoring blind spot

   Detection

   A Prometheus query for CPU throttling showed the history pods throttled although their CPU usage read under 60 percent.

   Remediation

   • set the GOMAXPROCS environment variable to 2 to match the 2-core CPU limit

   Action items

   • Set GOMAXPROCS automatically from the Kubernetes CPU limit, from Temporal release 1.21.0

   Lessons

   A CPU usage graph averaged over 15 seconds or more can sit far below 100 percent while a container is being throttled, so throttling needs its own metric. A Go program in a container should have GOMAXPROCS matched to its CPU limit, or to its CPU request when it has no limit. The author recommends not using CPU limits at all.

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3. IL-0195 The kube-apiservers of a cluster with more than 200 nodes ran out of memory and crashed one after another under mass LIST requests from Cilium agents

   Palark2023

   Symptoms

   A tech support engineer called Palark to say that a customer's cluster, production environment included, had practically stopped working. On the control plane nodes the kube-apiserver processes had used up all the memory and were crashing and restarting over and over, so the Kubernetes API was unreachable and completely non-functional.

   Control plane unresponsiveOOMKilled

   Trigger

   Cilium agent pods on more than 200 nodes sent LIST requests to the Kubernetes API en masse at the same time.

   Root cause

   Capacity exhaustion

   Component

   Kubernetes API serverCilium

   Contributing factor

   Unreviewed defaults

   Detection

   A tech support engineer phoned the team to report that the customer's cluster was practically non-operational.

   Remediation

   • added CPU and memory to the control plane nodes in two steps, from 8 CPUs and 16 GB of RAM to 16 CPUs and 64 GB, until the API stabilised
   • restarted the Cilium agents in a test window agreed with the customer to reproduce the memory growth
   • deployed a FlowSchema and a PriorityLevelConfiguration named cilium-pods that limit and queue LIST requests from the Cilium service accounts to cilium.io resources
   • reduced the control plane nodes back to their original resources

   Action items

   • Make an API Priority and Fairness configuration a standard part of Palark's Kubernetes platform

   Lessons

   In a large cluster, many agents sending LIST requests at once can exhaust kube-apiserver memory, and with several API servers the crash moves from one to the next. APF can hold back such a client: a FlowSchema matches its requests and a PriorityLevelConfiguration gives them a small concurrency share and a queue, so the requests run more slowly instead of all at once. With APF enabled, the total concurrency it divides is the sum of --max-requests-inflight (default 400) and --max-mutating-requests-inflight (default 200), shared among priority levels in proportion to their assured concurrency shares, so raising one level's share lowers every other level's limit. Long-running requests such as logs and exec, and WATCH requests, are not subject to APF. The response headers X-Kubernetes-PF-FlowSchema-UID and X-Kubernetes-PF-PriorityLevel-UID show where a request landed, and the metrics Apiserver_flowcontrol_rejected_requests_total, Apiserver_current_inqueue_requests and Apiserver_flowcontrol_request_execution_seconds, with the debug endpoints /debug/api_priority_and_fairness/dump_priority_levels and /debug/api_priority_and_fairness/dump_queues, show how the queues behave.

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4. IL-0165 Some LKE users in Frankfurt could not reach their kube-apiservers with kubectl after backend services failed under memory constraints

   Linode2023

   Symptoms

   At 14:11 UTC on May 25, 2023, Linode learned that part of its LKE users in the Frankfurt data center could not reach their kube-apiservers through kubectl. The status page opened at 14:27 UTC with "We are investigating reports of connectivity issues with the LKE service within the Frankfurt Datacenter".

   Control plane unresponsive

   Root cause

   Capacity exhaustion

   Component

   Kubernetes API serverContainer memory limit

   Contributing factor

   –

   Detection

   Linode was made aware of the problem at 14:11 UTC; the status page refers to reports of connectivity issues.

   Remediation

   • increased memory limits

   Action items

   • Increase resilience, redundancy and monitoring for the LKE components affected by the incident

   View case
5. IL-0194 After a kube-apiserver restart, pod LIST requests from every pod of a Vector DaemonSet nearly tripled etcd memory and restarted all master nodes

   Palark2023

   Symptoms

   One evening, right after an ordinary restart of kube-apiserver in a customer's production cluster, etcd memory consumption rose almost threefold. The sudden growth led to a cascading restart of all the master nodes.

   Control plane unresponsiveDatabase overload

   Trigger

   An ordinary restart of kube-apiserver, after which every pod of the log collector DaemonSet sent its list requests at the same time.

   Root cause

   Capacity exhaustion

   Component

   etcdKubernetes API serverTelemetry agent

   Contributing factor

   Unknown limitUnreviewed defaults

   Detection

   Memory graphs showed etcd memory on the master nodes almost tripling after a kube-apiserver restart; the audit logs then pointed to one DaemonSet's list requests.

   Remediation

   • created a PriorityLevelConfiguration named limit-list-custom that queues requests (assuredConcurrencyShares 5, 16 queues, handSize 4, queueLengthLimit 50)
   • created a FlowSchema that routes list and get requests on pods from the log collector's service account to that priority level

   Action items

   • Submit a pull request adding resource version support to the Kubernetes Rust library
   • Open a pull request to the Vector log collector to address the startup list requests
   • Apply the same flow-control approach to a similar problem found with the Cilium CNI

   Lessons

   A field selector reduces what kube-apiserver returns, not what it reads from etcd: listing the pods of one node still reads every pod in the cluster. A DaemonSet whose pods all list at startup multiplies that by the node count each time kube-apiserver restarts. Where exact freshness is not needed, resourceVersion=0 lets kube-apiserver answer from its cache; where the client cannot set it, API Priority and Fairness can queue the requests so they reach etcd gradually.

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