Docs » Configure application receivers » Configure application receivers for orchestration » Kubernetes API server

Kubernetes API server 🔗

Description 🔗

The Splunk Distribution of OpenTelemetry Collector provides this integration as the kubernetes-apiserver monitor type by using the SignalFx Smart Agent Receiver.

Use this integration to retrieve metrics from the API server’s Prometheus metric endpoint.

This monitor is available on Kubernetes, Linux, and Windows.

Benefits 🔗

After you’ve configured the integration, you can:

  • View metrics using the built-in dashboard. For information about dashboards, see View dashboards in Observability Cloud.

  • View a data-driven visualization of the physical servers, virtual machines, AWS instances, and other resources in your environment that are visible to Infrastructure Monitoring. For information about navigators, see Splunk Infrastructure Monitoring navigators.

  • Access Metric Finder and search for metrics sent by the monitor. For information about Metric Finder, see Use the Metric Finder.

Installation 🔗

Follow these steps to deploy the integration:

  1. Deploy the Splunk Distribution of OpenTelemetry Collector to your host or container platform:

  2. Configure the monitor, as described in the next section.

  3. Restart the Splunk Distribution of OpenTelemetry Collector.

Configuration 🔗

This monitor is available in the Smart Agent Receiver, which is part of the Splunk Distribution of OpenTelemetry Collector. The Smart Agent Receiver lets you use existing Smart Agent monitors as OpenTelemetry Collector metric receivers.

Using this monitor assumes that you have a configured environment with a functional Smart Agent release bundle on your system, which is already provided for x86_64/amd64 Splunk Distribution of OpenTelemetry Collector installation paths.

To activate this monitor in the Splunk Distribution of OpenTelemetry Collector, add the following to your configuration file:

    type: kubernetes-apiserver
    ... # Additional config

To complete the monitor activation, you must also include the monitor in a metrics pipeline. To do this, add the monitor to the service > pipelines > metrics > receivers section of your configuration file. For example:

      receivers: [smartagent/kubernetes-apiserver]

See the kubernetes-yaml examples in GitHub for the Agent and Gateway YAML files.

Configuration settings 🔗

The following table shows the configuration options for this monitor:

Option Required Type Description
httpTimeout no int64 HTTP timeout duration for both read and writes. This should be a duration string that is accepted by (default: 10s)
username no string Basic Auth username to use on each request, if any.
password no string Basic Auth password to use on each request, if any.
useHTTPS no bool If true, the agent will connect to the server using HTTPS instead of plain HTTP. (default: false)
httpHeaders no map of strings A map of HTTP header names to values. Comma separated multiple values for the same message-header is supported.
skipVerify no bool If useHTTPS is true and this option is also true, the exporter's TLS cert will not be verified. (default: false)
caCertPath no string Path to the CA cert that has signed the TLS cert, unnecessary if skipVerify is set to false.
clientCertPath no string Path to the client TLS cert to use for TLS required connections
clientKeyPath no string Path to the client TLS key to use for TLS required connections
host yes string Host of the exporter
port yes integer Port of the exporter
useServiceAccount no bool Use pod service account to authenticate. (default: false)
metricPath no string Path to the metrics endpoint on the exporter server, usually /metrics (the default). (default: /metrics)
sendAllMetrics no bool Send all the metrics that come out of the Prometheus exporter without any filtering. This option has no effect when using the prometheus exporter monitor directly since there is no built-in filtering, only when embedding it in other monitors. (default: false)

Example configuration 🔗

The following is an example YAML configuration:

    type: kubernetes-apiserver
    host: localhost
    port: 443
      metric_source: kubernetes-apiserver

The OpenTelemetry Collector has a Kubernetes observer (k8sobserver) that can be implemented as an extension to discover networked endpoints, such as a Kubernetes pod. Using this observer assumes that the OpenTelemetry Collector is deployed in Agent mode, where it is running on each individual node or host instance.

To use the observer, you must create a receiver creator instance with an associated rule. For example:

  # Configures the Kubernetes observer to watch for pod start and stop events.

    # Name of the extensions to watch for endpoints to start and stop.
    watch_observers: [k8s_observer]
        rule: type == "pod" && labels["k8s-app"] == "kube-apiserver"
        type: kubernetes-apiserver
        port: 443
          metric_source: kubernetes-apiserver

        # Configure prometheus scraping if standard prometheus annotations are set on the pod.
        rule: type == "pod" && annotations[""] == "true"
          metrics_path: '`"" in annotations ? annotations[""] : "/metrics"`'
          endpoint: '`endpoint`:`"" in annotations ? annotations[""] : 9090`'

        # If this rule matches an instance of this receiver will be started.
        rule: type == "port" && port == 6379
          # Static receiver-specific config.
          password: secret
          # Dynamic configuration value.
          collection_interval: `pod.annotations["collection_interval"]`
          # Dynamic configuration value.

        # Set a resource attribute based on endpoint value.
        rule: type == "port" && port == 6379
          # Dynamic value.
          app: `pod.labels["app"]`
          # Static value.
          source: redis

    # Name of the extensions to watch for endpoints to start and stop.
    watch_observers: [host_observer]
        # If this rule matches, an instance of this receiver is started.
        rule: type == "port" && port == 6379 && is_ipv6 == true



      receivers: [receiver_creator/1, receiver_creator/2]
      processors: [exampleprocessor]
      exporters: [exampleexporter]
  extensions: [k8s_observer, host_observer]

Metrics 🔗

The following metrics are available for this integration:

Troubleshooting 🔗

You’re getting a “bind: address already in use” error message

If you see an error message such as “bind: address already in use”, another resource is already using the port that the current configuration requires. This resource could be another application, or a tracing tool such as Jaeger or Zipkin.

You can modify the configuration to use another port. You can modify any of these endpoints or ports:

  • Receiver endpoint

  • Extensions endpoint

  • Metrics address (if port 8888)

If you see this error message on Kubernetes and you’re using Helm charts, modify the configuration by updating the chart values for both configuration and exposed ports.

You’re getting a “2021-10-19T20:18:40.556Z info builder/receivers_builder.go:112 Ignoring receiver as it is not used by any pipeline {“kind”: “receiver”, “name”: “” error message

This error occurs when a component (receiver, processor, or exporter) has been configured, but is not used in a receiver pipeline. For example, the following error message tells you that the smartagent/http receiver is configured, but that it is not used by any pipeline:

“2021-10-19T20:18:40.556Z info builder/receivers_builder.go:112 Ignoring receiver as it is not used by any pipeline {"kind": "receiver", "name": "smartagent/http"

Once configured, all components must be enabled by using pipelines in the service section. The service section is used to configure what components are enabled based on the configuration found in the components sections of your configuration file. If a component is configured, but not defined within the service section, then it is not enabled.

Here is a sample configuration:

  # Pipelines can contain multiple subsections, one per pipeline.
    # Traces is the pipeline type.
      receivers: [otlp, jaeger, zipkin]
      processors: [memory_limiter, batch]
      exporters: [otlp, jaeger, zipkin]

See How the OpenTelemetry Collector uses pipelines to process data for more information.

Splunk Distribution of OpenTelemetry Collector is out of memory

If you receive high memory usage or out of memory warnings, do the following before opening a support case:

  1. Verify that you have installed the latest version of Splunk OpenTelemetry Collector for Kubernetes.

  2. Add or change the memory_limiter processor in your configuration file. For example:

         ballast_size_mib: 2000
         check_interval: 5s
            # Check_interval is the time between measurements of memory usage for the  purposes of avoiding going over the limits. 
            # The default is 0. Values below 1s are not recommended, as this can result in unnecessary CPU consumption.
         limit_mib: 4000
            # ​​Maximum amount of memory, in MiB, targeted to be allocated by the process heap. 
            # The total memory usage of the process is typically about 50 MiB higher than this value.
         spike_limit_mib: 500
            # The maximum, in MiB, spike expected between the measurements of memory usage.
         ballast_size_mib: 2000
            # BallastSizeMiB is the size, in MiB, of the ballast size being used by the process. 
            # This must match the value of the mem-ballast-size-mib command line option (if used). 
            # Otherwise, the memory limiter does not work correctly.
  3. Try to reproduce the error and collect a heap dump close to the point where the memory kill occurs: a. Add the pprof extension to the component configuration that is failing. Make sure you enable this extension in a pipeline in the services section. b. Capture the output of the following commands against the problematic pod:

    curl (
    curl > heap.out

For example, if you discover that the pod lasts 5 minutes before it gets killed:

  1. Bounce the pod and collect the first set of data right after the startup.

  2. Wait 3 minutes and collect another set of data (make sure to label the data accordingly).

  3. Collect another set of data right before the crash (if possible).

How long does it take for the pod to be killed due to memory limit? Check the logs at the time of the issue to see if there are any obvious repeating errors.

Gather additional support information, including your end-to-end architecture information.

If you are not able to see your data in Splunk Observability Cloud: