Cases14 cases

9 to 13 of 14

1. IL-0172 An AWS Tokyo data centre cooling failure took down 6 of 60 CockroachDB nodes on Kubernetes within 22 minutes, and ranges lost quorum because the Helm-deployed cluster had no availability zone locality configured

   Devsisters2021

   Symptoms

   During a team dinner, a Slack alert reported that one node of the CockroachDB cluster behind Cookie Run: Kingdom had gone down. The DevOps engineers were not worried at first, because CockroachDB is built to survive the loss of some nodes.

   Node NotReadyData lossService outage

   Trigger

   A cooling system failure in an AWS Tokyo region data centre overheated the servers in one availability zone, which held CockroachDB nodes.

   Root cause

   Cloud provider outage

   Component

   Application databaseHelm

   Contributing factor

   Unverified assumptionExcessive blast radius

   Detection

   A Slack alert reported that a database node had gone down.

   Remediation

   • copied the data outside the broken ranges into new tables with INSERT INTO ... SELECT queries that skipped the affected ranges
   • ran debug unsafe-remove-dead-replicas together with Cockroach Labs engineers, recovering one of the two lost data ranges
   • restored the last data range with the storage-layer recovery method from the previous incident

   Action items

   • Configure CockroachDB Locality so data is spread evenly across availability zones
   • Deploy with one chart and a separate values file per availability zone to set Locality
   • Replace nodes by adding Locality-configured nodes, confirming replicas in every zone, then retiring the old nodes

   Lessons

   A replication factor sized for random instance retirements does not protect against a whole availability zone failing; for a game of this scale a multi-AZ placement is required, not optional. Keeping one uniform Helm chart made zone topology hard to set, which is part of why it was left out. A repetitive chart with a values file per zone sets it reliably.

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2. IL-0094 Student pods filled the shared nodes of a JupyterHub cluster and starved the hub's core pods, freezing the hub

   2i2c2020

   Symptoms

   At 08:52 PM IST a report came in from WER that many students could not access the JupyterHub and that it was frozen; pages were stuck and nothing was usable. A rise in activity was visible by 09:02 PM, but the usual fixes, such as opening an incognito window and restarting servers, did not help.

   Service outageSustained high node CPU

   Trigger

   WER students' pods used a good chunk of their memory limit (tests had shown they almost always use just under 1G of their 2G limit, against a 512M guarantee) on nodes shared with the hub's core pods.

   Root cause

   Capacity exhaustion

   Component

   Node poolContainer memory limit

   Contributing factor

   No resource requests or limitsUnverified assumptionExcessive blast radius

   Detection

   WER reported stuck pages and students unable to reach the hub in a GitHub issue (2i2c-org/docs#27).

   Remediation

   • removed memory overcommit for the WER students
   • raised resource guarantees for all core pods, which restarted them and moved some to a new node

   Action items

   • Put user pods in a separate pool so they do not create pressure on the core pods (infrastructure#89)
   • Set limits on the support infrastructure (Prometheus, Grafana, ingress) as well (infrastructure#90)
   • Document and think about overcommit ratios for memory usage (infrastructure#91)
   • Set up better Grafana dashboards to monitor resource usage (infrastructure#92)
   • Document how people can get kubectl access to the cluster so others can look into issues too (infrastructure#87)

   Lessons

   Core pods without resource guarantees get whatever is left on a node, so when user pods on the same nodes fill the CPU and memory, the hub that serves everyone freezes. The memory overcommit ratio has to come from measured use: WER students used just under 1G against a 512M guarantee, which a 4x overcommit could not hold.

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3. 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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4. IL-0008 An unresponsive OPA validating webhook crash-looped the API server during a GKE master upgrade, and node auto-repair then recreated every node

   Jetstack2019

   Symptoms

   Jetstack was upgrading the master of a development cluster that many teams use to test their apps during the working day, a regional Google Kubernetes Engine (GKE) cluster in europe-west1. The upgrade ran through Jetstack's GKE Terraform pipeline and did not finish before the 20-minute Terraform timeout, while the GKE console still showed the cluster as upgrading.

   Upgrade stallControl plane unresponsiveNode recreation loopService outage

   Trigger

   A planned master upgrade of the shared development cluster. The same upgrade had been done on another pre-production environment earlier that day, and no API downtime was expected.

   Root cause

   Configuration change

   Component

   Admission webhookGKE control planeKubernetes API serverkubelet

   Contributing factor

   Excessive blast radiusNo health probesMonitoring blind spotAutomation amplification

   Detection

   The first sign was the master upgrade missing the 20-minute Terraform timeout; re-running the pipeline returned the kube-apiserver unhealthy error. The cause was identified with the help of Google Support.

   Remediation

   • Deleted the OPA ValidatingAdmissionWebhook resource during a window of API server access

   Action items

   • Limit the OPA webhook to the namespaces where the policy applies and development teams have access
   • Enable the webhook only for Ingress and Service resources, the only resources the policy validates
   • Add a liveness probe so OPA is restarted when it stops responding, and update the OPA documentation
   • Follow the open upstream Kubernetes issue about this behaviour

   Lessons

   With alerting on API server response times, the team might have seen CREATE and UPDATE requests slow down across the board as soon as the OPA-backed webhook was first deployed. Every workload needs probes: OPA looked so simple to deploy that the team skipped the Helm chart, which adds a livenessProbe for the admission controller containers among other adjustments to the tutorial configuration. Others had hit the same problem, and an upstream issue to improve it was still open.

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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.

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