Cases18 cases

11 to 15 of 18

1. IL-0191 A label selector accidentally added to the Loki distributor Service matched only the new pods of a rollout, which took all write traffic, were OOM-killed and stalled the rollout

   Grafana Labs2022

   Symptoms

   During a rollout of the Grafana Loki distributor Deployment in one Grafana Cloud Logs environment, the write path went down for about 25 minutes. The newly started distributor pods were OOM-killed over and over, never passed their health and readiness checks, and the rollout stopped making progress.

   Service outageOOMKilledLoad imbalanceProbe failures

   Trigger

   Step 1 of moving the Loki ring off a separately deployed key-value store onto embedded memberlist added a new label, gossip_ring_member: 'true', to the distributor pods and Service, together with a small config change, and the change required a rollout of the distributors.

   Root cause

   Configuration change

   Component

   Configuration templatingService discoveryContainer memory limit

   Contributing factor

   Unreviewed defaultsIgnored early signalUnrepresentative staging

   Detection

   The first clue was kubectl get pods and the kube_deployment_status_replicas_updated metric showing that only 16 of 140 distributor pods had been updated.

   Remediation

   • scaled the distributor Deployment out horizontally so the load spread over more pods with the new label and the rollout could continue
   • reverted the migration step to end the outage
   • set service_ignored_labels:: [$._config.gossip_member_label] and passed it to k.util.serviceFor so the label is no longer copied into the Service selector

   Action items

   • Update the function that generates a Service from a Deployment or StatefulSet to handle edge cases, including this one

   Lessons

   A Service whose selector is built by copying every label of a workload changes which pods it matches as soon as a new label is added. During a rollout it then matches only the new pods, which receive all the traffic before there are enough of them. A few errors during a rollout in other environments hid the problem, because those environments gave distributors more memory and could absorb the shift. Manifest diffs kept for every pull request made it possible to see that the Service selector had changed.

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2. IL-0106 A malformed Traefik IngressRoute triggered a Traefik v2 bug once a node rollover restarted every Traefik pod, and all Kubernetes services returned 404

   DFDS2021

   Symptoms

   At 08:00 UTC on 2021-09-06 every service in DFDS's Kubernetes cluster answered with 404. Investigation started five minutes later.

   Service outageHigh error rateProbe failures

   Trigger

   The automated Kubernetes node rollover on 2021-09-06 restarted all the Traefik v2 pods, five days after a malformed IngressRoute manifest had been applied.

   Root cause

   Software defect

   Component

   Ingress controllerCloud load balancer

   Contributing factor

   Change with a delayed effectNo configuration testsExcessive blast radius

   Detection

   All services returned 404 from 08:00 UTC and root cause investigation started at 08:05. Time to detection was 5 minutes.

   Remediation

   • changed the ALB health check path, which made the targets healthy but did not stop the 404s
   • rolled back by hand to Traefik v1 as the main ingress controller
   • deleted the malformed IngressRoute by hand and confirmed the Traefik v2 bug
   • applied the changes upstream

   Action items

   • Upgrade Traefik to v2.5, where the bug is fixed, and verify that it removes the bug
   • Change the Traefik v2 health check path to something more robust
   • Set up an external liveness probe on the ingress controller
   • Consider workload segmentation to limit the blast zone
   • Look into monitoring the Traefik v2 API for rapid changes
   • Look into smoke testing during and after node rollover

   Lessons

   A malformed IngressRoute did nothing visible for five days and then took down all ingress when every Traefik v2 pod had restarted during a node rollover. Because Traefik v2 failed gracefully, the first signals pointed at the ALB health checks instead of at Traefik. On incident handling, the team concluded that appointing an incident manager could have helped, that the war room meeting should be recorded and a log book kept, that people not involved should only make suggestions in writing, and that a short first round of sharing thoughts and gut feelings is useful.

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3. IL-0176 Prometheus in a 235-node Kubernetes cluster crash-looped because over two million series exhausted host memory and the kernel OOM killer chose it first, as Burstable QoS gave it oom_score_adj 999

   LINE2020

   Symptoms

   One Monday morning the author opened the Prometheus dashboard to check a Kubernetes cluster for alerts and could not reach it. The Prometheus pod was in CrashLoopBackOff: kubectl get pods showed it 1/2 ready with 950 restarts over 3d20h on worker3.

   Container crash loopOOMKilledMonitoring outageProbe failures

   Trigger

   After start, the Prometheus time series count rose to 2,162,256 within five minutes and its memory use filled the 8GB host.

   Root cause

   Capacity exhaustion

   Component

   PrometheuskubeletLinux kernel

   Contributing factor

   No resource requests or limitsNo capacity planning

   Detection

   The author found it on a Monday morning when the Prometheus dashboard, opened to check the cluster for alerts, could not be reached.

   Remediation

   • removed most of the unnecessary settings from the Prometheus ConfigMap, after which the container stopped crashing

   Action items

   • Set appropriate resource requests and limits on the Prometheus pod
   • Reduce unneeded metrics by adjusting scrape targets or scrape interval
   • Estimate Prometheus memory needs, provide dedicated hosts with a node label such as deploy-topology: Prometheus, and pin the Prometheus pod to them with a nodeSelector

   Lessons

   Exit code 137 together with an OOM event in the kubelet log means the kernel killed the container, not kubelet. Under node memory pressure the kernel kills the process with the highest score, and kubelet derives each container's oom_score_adj from the pod's QoS class: a container without a memory request in a pod where another container has one ends up Burstable with a score near 1000, so it is killed first. In a large cluster, Prometheus memory needs to be estimated from the expected series count and requested explicitly.

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4. IL-0012 CPU limits and an unpatched kernel CFS quota bug throttled multi-threaded services into stalls, failed health checks and timeouts

   Omio2020

   Symptoms

   For about six months, applications on Omio's Google Kubernetes Engine clusters stalled at random: they got stuck, stopped answering health check requests or lost network connections, and nothing the team looked at explained why.

   CPU throttlingProbe failuresHigh error rateHigh latencyIntermittent timeoutsIntermittent connection failures

   Trigger

   Multi-threaded workloads running with CPU limits on nodes whose kernel did not yet have the fix for a known CFS quota bug that causes unnecessary throttling.

   Root cause

   Software defect

   Component

   Container CPU limitLinux kernelkubelet

   Contributing factor

   Unknown limitMonitoring blind spotUnpatched known bug

   Detection

   Months of random stalls and failed health checks led the team to read the cgroup cpu.stat throttling counters inside the pods, which showed far more throttling than the limits should allow.

   Remediation

   • removed CPU limits until the kernel with the CFS bug fix was deployed across all clusters

   Action items

   • Deploy the kernel with the CFS quota bug fix across all clusters
   • Monitor utilisation, saturation and error metrics, API latency and error rates to confirm the change behaves as expected

   Lessons

   A CPU limit is a time quota per 100 ms period, not a share of cores, so a multi-threaded container can be stopped for most of each period long before it uses the cores it was given, and a kernel bug can make that worse. Throttling is invisible in standard Kubernetes metrics and has to be read from the cgroup counters. Removing limits trades away protection against a container that takes a whole node, which is only acceptable when runtimes are tuned.

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5. IL-0034 A crashing stats agent sidecar marked every pod unready and the service mesh pulled all services out of traffic

   Airbnb2019

   Symptoms

   An operation to upgrade the stats agent in Kubernetes was announced in advance, with a warning that metrics might be unavailable to services for a few minutes. A few minutes later a reply came in saying that all of the sender's Kubernetes services seemed to be down.

   Probe failuresService outage

   Trigger

   A scheduled upgrade of the stats agent sidecar, during which the agent container crashed.

   Root cause

   Faulty deployment

   Component

   Telemetry agentService discovery

   Contributing factor

   Unverified assumptionExcessive blast radius

   Detection

   A reply to the operation announcement, a few minutes later, said that all of the sender's Kubernetes services appeared to be down.

   Action items

   • Mark logging, tracing and stats agent containers as non-critical and leave them out of the service mesh's pod readiness check

   Lessons

   Every container in a pod affects the pod's readiness, so each sidecar needs a deliberate decision about how its health should count. A sidecar that crashes is not ready even when it has no probes, and any logic that requires all containers to be ready will take the pod out of traffic.

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