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node-problem-detector

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node-problem-detector aims to make various node problems visible to the upstream layers in cluster management stack. It is a daemon which runs on each node, detects node problems and reports them to apiserver. node-problem-detector can either run as a DaemonSet or run standalone. Now it is running as a Kubernetes Addon enabled by default in the GCE cluster.

Background

There are tons of node problems could possibly affect the pods running on the node such as:

  • Infrastructure daemon issues: ntp service down;
  • Hardware issues: Bad cpu, memory or disk, ntp service down;
  • Kernel issues: Kernel deadlock, corrupted file system;
  • Container runtime issues: Unresponsive runtime daemon;
  • ...

Currently these problems are invisible to the upstream layers in cluster management stack, so Kubernetes will continue scheduling pods to the bad nodes.

To solve this problem, we introduced this new daemon node-problem-detector to collect node problems from various daemons and make them visible to the upstream layers. Once upstream layers have the visibility to those problems, we can discuss the remedy system.

Problem API

node-problem-detector uses Event and NodeCondition to report problems to apiserver.

  • NodeCondition: Permanent problem that makes the node unavailable for pods should be reported as NodeCondition.
  • Event: Temporary problem that has limited impact on pod but is informative should be reported as Event.

Problem Daemon

A problem daemon is a sub-daemon of node-problem-detector. It monitors a specific kind of node problems and reports them to node-problem-detector.

A problem daemon could be:

  • A tiny daemon designed for dedicated usecase of Kubernetes.
  • An existing node health monitoring daemon integrated with node-problem-detector.

Currently, a problem daemon is running as a goroutine in the node-problem-detector binary. In the future, we'll separate node-problem-detector and problem daemons into different containers, and compose them with pod specification.

List of supported problem daemons:

Problem Daemon NodeCondition Description
KernelMonitor KernelDeadlock A system log monitor monitors kernel log and reports problem according to predefined rules.
AbrtAdaptor None Monitor ABRT log messages and report them further. ABRT (Automatic Bug Report Tool) is health monitoring daemon able to catch kernel problems as well as application crashes of various kinds occurred on the host. For more information visit the link.
CustomPluginMonitor On-demand(According to users configuration) A custom plugin monitor for node-problem-detector to invoke and check various node problems with user defined check scripts. See proposal here.

Usage

Flags

  • --version: Print current version of node-problem-detector.
  • --address: The address to bind the node problem detector server.
  • --port: The port to bind the node problem detector server. Use 0 to disable.
  • --system-log-monitors: List of paths to system log monitor configuration files, comma separated, e.g. config/kernel-monitor.json. Node problem detector will start a separate log monitor for each configuration. You can use different log monitors to monitor different system log.
  • --custom-plugin-monitors: List of paths to custom plugin monitor config files, comma separated, e.g. config/custom-plugin-monitor.json. Node problem detector will start a separate custom plugin monitor for each configuration. You can use different custom plugin monitors to monitor different node problems.
  • --apiserver-override: A URI parameter used to customize how node-problem-detector connects the apiserver. The format is same as the source flag of Heapster. For example, to run without auth, use the following config: 
    http://APISERVER_IP:APISERVER_PORT?inClusterConfig=false
    Refer heapster docs for a complete list of available options.
  • --hostname-override: A customized node name used for node-problem-detector to update conditions and emit events. node-problem-detector gets node name first from hostname-override, then NODE_NAME environment variable and finally fall back to os.Hostname.

Build Image

  • go get or git clone node-problem-detector repo into $GOPATH/src/k8s.io or $GOROOT/src/k8s.io with one of the below directions:

    • cd $GOPATH/src/k8s.io && git clone git@github.com:kubernetes/node-problem-detector.git
    • cd $GOROOT/src/k8s.io && git clone git@github.com:kubernetes/node-problem-detector.git
    • cd $GOPATH/src/k8s.io && go get k8s.io/node-problem-detector

  • run make in the top directory. It will:

    • Build the binary.
    • Build the docker image. The binary and config/ are copied into the docker image.

Note: By default node-problem-detector will be built with systemd support with make command. This requires systemd develop files. You should download the systemd develop files first. For Ubuntu, libsystemd-journal-dev package should be installed. For Debian, libsystemd-dev package should be installed.

Push Image

make push uploads the docker image to registry. By default, the image will be uploaded to staging-k8s.gcr.io. It's easy to modify the Makefile to push the image to another registry.

Start DaemonSet

Start Standalone

To run node-problem-detector standalone, you should set inClusterConfig to false and teach node-problem-detector how to access apiserver with apiserver-override.

To run node-problem-detector standalone with an insecure apiserver connection:

node-problem-detector --apiserver-override=http://APISERVER_IP:APISERVER_INSECURE_PORT?inClusterConfig=false

For more scenarios, see here

Try It Out

You can try node-problem-detector in a running cluster by injecting messages to the logs that node-problem-detector is watching. For example, Let's assume node-problem-detector is using KernelMonitor. On your workstation, run kubectl get events -w. On the node, run sudo sh -c "echo 'kernel: BUG: unable to handle kernel NULL pointer dereference at TESTING' >> /dev/kmsg". Then you should see the KernelOops event.

When adding new rules or developing node-problem-detector, it is probably easier to test it on the local workstation in the standalone mode. For the API server, an easy way is to use kubectl proxy to make a running cluster's API server available locally. You will get some errors because your local workstation is not recognized by the API server. But you should still be able to test your new rules regardless.

For example, to test KernelMonitor rules:

  1. make (build node-problem-detector locally)
  2. kubectl proxy --port=8080 (make a running cluster's API server available locally)
  3. Update KernelMonitor's logPath to your local kernel log directory. For example, on some Linux systems, it is /run/log/journal instead of /var/log/journal.
  4. ./bin/node-problem-detector --logtostderr --apiserver-override=http://127.0.0.1:8080?inClusterConfig=false --system-log-monitors=config/kernel-monitor.json --port=20256 (or point to any API server address:port)
  5. sudo sh -c "echo 'kernel: BUG: unable to handle kernel NULL pointer dereference at TESTING' >> /dev/kmsg"
  6. You can see KernelOops event in the node-problem-detector log.
  7. sudo sh -c "echo 'kernel: INFO: task docker:20744 blocked for more than 120 seconds.' >> /dev/kmsg"
  8. You can see DockerHung event and condition in the node-problem-detector log.
  9. You can see DockerHung condition at http://127.0.0.1:20256/conditions.

Note:

Remedy Systems

A remedy system is a process or processes designed to attempt to remedy problems detected by the node-problem-detector. Remedy systems observe events and/or node conditions emitted by the node-problem-detector and take action to return the Kubernetes cluster to a healthy state. The following remedy systems exist:

  • Draino automatically drains Kubernetes nodes based on labels and node conditions. Nodes that match all of the supplied labels and any of the supplied node conditions will be prevented from accepting new pods (aka 'cordoned') immediately, and drained after a configurable time. Draino can be used in conjunction with the Cluster Autoscaler to automatically terminate drained nodes. Refer to this issue for an example production use case for Draino.

Links