Cases35 cases

31 to 35 of 35

1. 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 kernelApplication database

   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.

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2. IL-0075 Kubernetes API servers pegged on CPU when the cluster grew to about 290 nodes, traced to Datadog agents polling the API

   Civis Analytics2019

   Symptoms

   After Civis released its new script infrastructure on Kubernetes internally in September 2018, developers reported that kubectl commands were slow, and users intermittently could not start notebooks and services.

   Control plane lagControl plane unresponsiveHigh latencyHigh error rateDNS resolution failures

   Trigger

   Migrating clients' Python, R and container scripts from the in-house Bocce system to Kubernetes (the project called Croquet) pushed the cluster to about 290 EC2 instances under a large script load.

   Root cause

   Capacity exhaustion

   Component

   Kubernetes API serverTelemetry agentetcd

   Contributing factor

   Unknown limitUnverified assumptionMonitoring blind spotNo capacity planning

   Detection

   Developer reports of slow kubectl commands and user reports of notebooks and services failing to start, then high CPU on the master nodes.

   Remediation

   • moved internal users back to the old Bocce infrastructure
   • resized the three master nodes from m4.large to m4.4xlarge
   • changed the Datadog setup so per-node agents no longer polled the API server for service metadata
   • raised max-requests-inflight for non-mutating requests from 400 to 1200
   • grew the etcd EBS volumes from 20 to 100 GB (100 to 300 IOPS)
   • switched client scripts from the ClusterFirst DNS policy to Default

   Action items

   • Upgrade etcd from 2.2.1 to 3.x once Kops supports a safe migration
   • Solve the in-cluster DNS timeouts so client scripts can talk to other workloads in the cluster

   Lessons

   Kubernetes can reach thousands of nodes only after tuning, and third-party DaemonSets are a common cause of control plane load: an agent on every node that polls or watches the API server multiplies with the cluster size. Datadog, kube2iam and the fluentd metadata filter all showed this. A small configuration change often fixes it, but finding it took load testing and the API server access logs.

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3. IL-0050 Pods on Kubernetes failed to resolve an external Redis hostname because of ndots:5, the musl resolver in Alpine images, and Netty's DNS resolver

   Toyota Connected2018

   Symptoms

   Services that used the team's Redis-dependent library crashed on start-up, and their teams reported it to the ops members. The Spring Boot logs showed the applications failing to resolve the Redis hostname during bean initialisation.

   DNS resolution failuresIntermittent connection failures

   Root cause

   Software defect

   Component

   Client-side DNS resolver and cache

   Contributing factor

   Unreviewed defaultsUnverified assumption

   Detection

   Service teams reported start-up crashes to the ops members of the team; later the resolution failures were seen in Kibana.

   Remediation

   • set ndots to 4 with a dnsConfig block in the Kubernetes deployment manifests
   • corrected the maximum connection pool size in the Redis library, cutting Redis connections by about 70%

   Action items

   • Test Debian glibc-based base images added to the image library to see whether they resolve the DNS problems
   • Update dependencies to newer Netty versions

   Lessons

   With ndots:5, a name outside the cluster that has fewer than five dots is first tried with every cluster search domain. Adding a trailing dot avoids that, but only if every library in the path accepts it, and the Netty version in use did not. The ndots change is a workaround that will not hold forever. The author would like better alerting for this kind of failure but notes that alerts on one-off DNS failures are not wanted.

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4. IL-0051 A third-party log shipping client leaked file descriptors with every new container until nodes failed with too many open files

   thredUP2018

   Symptoms

   While thredUP's move to Kubernetes seemed to be going well, Datadog showed the number of open files on the nodes climbing steadily, with no obvious reason.

   Service outageFile descriptor exhaustion

   Trigger

   Containers starting on a node, which in Kubernetes happens constantly with every deploy and every pod that moves between nodes.

   Root cause

   Software defect

   Component

   Telemetry agent

   Contributing factor

   Unverified assumption

   Detection

   Datadog showed the number of open files on the nodes rising.

   Remediation

   • killed the affected node, brought up a new one and redeployed

   Action items

   • Move away from the log aggregation vendor

   Lessons

   Libraries and agents that worked with the EC2 deployment lifecycle may not cope with Kubernetes, where containers start, stop and move between nodes constantly. Each one needs to be checked against the new lifecycle instead of being trusted because it worked before.

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5. IL-0036 A reindex Job that kept failing exhausted one node after another until the whole cluster was down

   Universe2017

   Symptoms

   Shortly after a search reindex job was started, problems with the Kubernetes cluster hit Universe's reporting and search APIs and the Kubernetes cluster UI itself. The status page opened an investigation into reports of search and reporting being unavailable at 14:37 EDT on 2017-07-21.

   Service outageNode NotReadyNode recreation loop

   Trigger

   A change to the search index definition, which removed some fields from full-text indexing, was shipped, and a reindex job was started as a Kubernetes Job to apply it.

   Root cause

   Capacity exhaustion

   Component

   Jobs and CronJobsPod scheduling and placement

   Contributing factor

   Unverified assumptionAutomation amplificationExcessive blast radius

   Detection

   The team saw problems with the Kubernetes cluster shortly after starting the job, and the status page cites reports of search and reporting being unavailable.

   Remediation

   • removed the reindex Job from the scheduler
   • let the nodes auto-heal back to healthy

   Lessons

   restartPolicy: Never on the pod template does not stop a Kubernetes Job from retrying: a Job replaces failed pods and may run more than once, so a job that can exhaust a node will keep moving to new nodes.

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