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cluster-troubleshooting.md

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WARNING WARNING WARNING WARNING WARNING

PLEASE NOTE: This document applies to the HEAD of the source tree

If you are using a released version of Kubernetes, you should refer to the docs that go with that version.

The latest 1.0.x release of this document can be found [here](http://releases.k8s.io/release-1.0/docs/admin/cluster-troubleshooting.md).

Documentation for other releases can be found at releases.k8s.io.

Cluster Troubleshooting

This doc is about cluster troubleshooting; we assume you have already ruled out your application as the root cause of the problem you are experiencing. See the application troubleshooting guide for tips on application debugging. You may also visit troubleshooting document for more information.

Listing your cluster

The first thing to debug in your cluster is if your nodes are all registered correctly.

Run

kubectl get nodes

And verify that all of the nodes you expect to see are present and that they are all in the Ready state.

Looking at logs

For now, digging deeper into the cluster requires logging into the relevant machines. Here are the locations of the relevant log files. (note that on systemd-based systems, you may need to use journalctl instead)

Master

  • /var/log/kube-apiserver.log - API Server, responsible for serving the API
  • /var/log/kube-scheduler.log - Scheduler, responsible for making scheduling decisions
  • /var/log/kube-controller-manager.log - Controller that manages replication controllers

Worker Nodes

  • /var/log/kubelet.log - Kubelet, responsible for running containers on the node
  • /var/log/kube-proxy.log - Kube Proxy, responsible for service load balancing

A general overview of cluster failure modes

This is an incomplete list of things that could go wrong, and how to adjust your cluster setup to mitigate the problems.

Root causes:

  • VM(s) shutdown
  • Network partition within cluster, or between cluster and users
  • Crashes in Kubernetes software
  • Data loss or unavailability of persistent storage (e.g. GCE PD or AWS EBS volume)
  • Operator error, e.g. misconfigured Kubernetes software or application software

Specific scenarios:

  • Apiserver VM shutdown or apiserver crashing
    • Results
      • unable to stop, update, or start new pods, services, replication controller
      • existing pods and services should continue to work normally, unless they depend on the Kubernetes API
  • Apiserver backing storage lost
    • Results
      • apiserver should fail to come up
      • kubelets will not be able to reach it but will continue to run the same pods and provide the same service proxying
      • manual recovery or recreation of apiserver state necessary before apiserver is restarted
  • Supporting services (node controller, replication controller manager, scheduler, etc) VM shutdown or crashes
    • currently those are colocated with the apiserver, and their unavailability has similar consequences as apiserver
    • in future, these will be replicated as well and may not be co-located
    • they do not have their own persistent state
  • Individual node (VM or physical machine) shuts down
    • Results
      • pods on that Node stop running
  • Network partition
    • Results
      • partition A thinks the nodes in partition B are down; partition B thinks the apiserver is down. (Assuming the master VM ends up in partition A.)
  • Kubelet software fault
    • Results
      • crashing kubelet cannot start new pods on the node
      • kubelet might delete the pods or not
      • node marked unhealthy
      • replication controllers start new pods elsewhere
  • Cluster operator error
    • Results
      • loss of pods, services, etc
      • lost of apiserver backing store
      • users unable to read API
      • etc.

Mitigations:

  • Action: Use IaaS provider's automatic VM restarting feature for IaaS VMs

    • Mitigates: Apiserver VM shutdown or apiserver crashing
    • Mitigates: Supporting services VM shutdown or crashes

  • Action use IaaS providers reliable storage (e.g GCE PD or AWS EBS volume) for VMs with apiserver+etcd

    • Mitigates: Apiserver backing storage lost

  • Action: Use (experimental) high-availability configuration

    • Mitigates: Master VM shutdown or master components (scheduler, API server, controller-managing) crashing
      • Will tolerate one or more simultaneous node or component failures
    • Mitigates: Apiserver backing storage (i.e., etcd's data directory) lost
      • Assuming you used clustered etcd.

  • Action: Snapshot apiserver PDs/EBS-volumes periodically

    • Mitigates: Apiserver backing storage lost
    • Mitigates: Some cases of operator error
    • Mitigates: Some cases of Kubernetes software fault

  • Action: use replication controller and services in front of pods

    • Mitigates: Node shutdown
    • Mitigates: Kubelet software fault

  • Action: applications (containers) designed to tolerate unexpected restarts

    • Mitigates: Node shutdown
    • Mitigates: Kubelet software fault

  • Action: Multiple independent clusters (and avoid making risky changes to all clusters at once)

    • Mitigates: Everything listed above.

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