Monitor Kubernetes: Observe the health and performance of your Kubernetes deployments

Applications running in a containerized environment like Kubernetes pose a unique monitoring challenge: how do you diagnose and resolve issues with hundreds of microservices on thousands (or millions) of containers, running in ephemeral and disposable pods?

A successful Kubernetes monitoring solution has a few requirements:

This guide describes how to use Elastic Observability to observe all layers of your application, including the orchestration software itself:

This guide describes how to deploy Elastic monitoring agents as DaemonSets using kubectl and the Beats GitHub repository manifest files. For other deployment options, see the Kubernetes operator and custom resource definitions from Elastic Cloud on Kubernetes (ECK) or the Helm charts.

The Elastic Stack provides 4 main components for monitoring Kubernetes:

Beats agents are deployed to Kubernetes as DaemonSets. This deployment architecture ensures that the agents are available to capture both system and application-level observability data.

Filebeat: Collects logs from pods, containers, and applications running on Kubernetes.

Filebeat communicates with the Kubernetes API server to retrieve information about the pods running on the host, all the metadata annotations, and the location of the log files.

When autodiscovery is configured, Filebeat automatically discovers what kind of components are running in a pod and applies the logging modules needed to capture logs for those components.

Metricbeat: Collects and preprocesses system and service metrics, such as information about running processes, as well as CPU, memory, disk, and network utilization numbers.

Because Metricbeat runs on each node, it can collect metrics from the Kubelet API. These metrics provide important information about the state of the Kubernetes nodes, pods, containers, and other resources.

For cluster-wide metrics, Metricbeat accesses the kube-state-metrics service directly or gets metrics scraped by Prometheus.

When hints-based autodiscovery is configured, Metricbeat looks for hints in Kubernetes pod annotations or Docker labels and launches the proper configuration to collect application metrics.

Other Beats (not shown): Collect and process other types of data, such as Uptime data and network traffic.

All Beats agents provide processors for adding metadata to events. The metadata is valuable for grouping and exploring related data. For example, when you’re analyzing container logs, you want to know the host and container name, and you want to be able to correlate logs, metrics, and traces.

The default deployments include processors, when needed, for enriching events with cloud, Kubernetes, and host metadata.

Now that you have a basic understanding of the monitoring architecture, let’s learn how to deploy monitoring to your Kubernetes environment.

To monitor Kubernetes, you need Elasticsearch for storing and searching your observability data, and Kibana for visualizing and managing it. For more information, see Spin up the Elastic Stack.

Collecting and analyzing logs of both Kubernetes Core components and various applications running on top of Kubernetes is a powerful tool for Kubernetes observability. Containers running within Kubernetes pods publish logs to stdout or stderr. These logs are written to a location known to Kubelet.

To collect pod logs, all you need is Filebeat running as a DaemonSet in your Kubernetes cluster. You configure Filebeat to communicate with the Kubernetes API server, get the list of pods running on the current host, and collect the logs the pods are producing. Those logs are annotated with all the relevant Kubernetes metadata, such as pod ID, container name, container labels and annotations, and so on.

To start collecting logs, deploy and run an instance of Filebeat on each Kubernetes host. Filebeat communicates with the Kubernetes API server to retrieve information about the pods running on the host and all the metadata annotations.

To deploy Filebeat to your Kubernetes cluster:

To make deployment easier, Elastic provides a YAML file that defines all the required deployment settings. In many cases, you can change the connection details and deploy with default settings to get started quickly.

curl -L -O https://raw.githubusercontent.com/elastic/beats/8.1/deploy/kubernetes/filebeat-kubernetes.yaml

By default Filebeat sends events to an existing Elasticsearch deployment on elasticsearch:9200, if present. To specify a different destination, change the following settings in the filebeat-kubernetes.yaml file:

env:
- name: ELASTICSEARCH_HOST
  value: elasticsearch
- name: ELASTICSEARCH_PORT
  value: "9200"
- name: ELASTICSEARCH_USERNAME
  value: elastic 
- name: ELASTICSEARCH_PASSWORD
  value: changeme
- name: ELASTIC_CLOUD_ID 
  value:
- name: ELASTIC_CLOUD_AUTH 
  value:

To avoid exposing sensitive data, you can base64 encode the string, then store it in a Kubernetes secret. For example:

$ echo -n 'changeme' | base64
Y2hhbmdlbWU=
$ kubectl create secret generic es-secret --from-literal=password='Y2hhbmdlbWU=' --namespace=kube-system 

To use the secret, change the env setting in the manifest file:

env:
- name: ELASTICSEARCH_PASSWORD
  valueFrom:
    secretKeyRef:
      name: es-secret
      key: password

To collect container logs, each Filebeat instance needs access to the local log’s path, which is actually a log directory mounted from the host. The default deployment manifest already contains the configuration to do this:

filebeat.inputs:
- type: container
  paths:
    - /var/log/containers/*.log

With this configuration, Filebeat can collect logs from all the files that exist under the /var/log/containers/ directory.

This configuration assumes you know what kinds of components are running in a pod and where the container logs are stored. Later you’ll learn how to use autodiscovery to automatically discover containers.

Filebeat provides processors that you can use in your configuration to enrich events with metadata coming from Docker, Kubernetes, hosts, and cloud providers.

The add_kubernetes_metadata processor is already specified in the default configuration. This processor adds Kubernetes and container-related metadata to the logs:

filebeat.inputs:
- type: container
  paths:
    - /var/log/containers/*.log
  processors:
    - add_kubernetes_metadata:
        host: ${NODE_NAME}
        matchers:
        - logs_path:
            logs_path: "/var/log/containers/"

In the previous steps, you learned how to collect container logs and enrich them with metadata. However you can take it further by leveraging the autodiscovery mechanism in Filebeat. With autodiscovery, Filebeat can automatically discover what kind of components are running in a pod and apply the logging modules needed to capture logs for those components.

To configure autodiscovery, you can use static templates. For example, the template in this example configures Filebeat to collect NGINX logs from any pod labeled as app: nginx.

filebeat.autodiscover:
  providers:
    - type: kubernetes
      node: ${NODE_NAME}
      templates:
        - condition:
            equals:
              kubernetes.labels.app: "nginx"
          config:
            - module: nginx
              access:
                input:
                  type: container
                  stream: stdout
                  paths:
                    - /var/log/containers/*${data.kubernetes.container.id}.log
              error:
                input:
                  type: container
                  stream: stderr
                  paths:
                    - /var/log/containers/*${data.kubernetes.container.id}.log

This is good, but requires advanced knowledge of the workloads running in Kubernetes. Each time you want to monitor something new, you’ll need to re-configure and restart Filebeat. To avoid this, you can use hints-based autodiscovery:

filebeat.autodiscover:
  providers:
    - type: kubernetes
      node: ${NODE_NAME}
      hints.enabled: true
      hints.default_config:
        type: container
        paths:
          - /var/log/containers/*${data.kubernetes.container.id}.log

Then annotate the pods accordingly:

apiVersion: v1
kind: Pod
metadata:
  name: nginx-autodiscover
  annotations:
    co.elastic.logs/module: nginx
    co.elastic.logs/fileset.stdout: access
    co.elastic.logs/fileset.stderr: error

With this setup, Filebeat identifies the NGINX app and starts collecting its logs by using the nginx module.

You can enrich your configuration with additional processors to drop unwanted events. For example:

processors:
- drop_event:
      when:
        - equals:
              kubernetes.container.name: "metricbeat"

You can also enrich events with cloud and host metadata by specifying the add_cloud_metadata and add_host_metadata processors. These processors are already specified in the default configuration:

processors:
- add_cloud_metadata:
- add_host_metadata:

If you’re using Red Hat OpenShift, you need to specify additional settings in the manifest file and enable the container to run as privileged.

To view the log data collected by Filebeat, open Kibana and go to Observability > Logs.

The Logs app in Kibana allows you to search, filter, and tail all the logs collected into the Elastic Stack. Instead of having to ssh into different servers and tail individual files, all the logs are available in one tool under the Logs app.

Explore the Logs app:

Filebeat ships with a variety of pre-built Kibana dashboards that you can use to visualize logs from Kubernetes Core components and applications running on top of Kubernetes. If these dashboards are not already loaded into Kibana, you must run the Filebeat setup job.

After loading the dashboards, navigate to Kibana > Dashboards and search for the services you want to monitor, like MySQL or NGINX.

Notice that modules capture more than logs. You can also use them to capture metrics.

Collecting metrics about Kubernetes clusters and the workloads running on top of them is a key aspect of Kubernetes observability. However, collecting metrics from Kubernetes poses some challenges. You need to collect metrics about the resources running on "physical" machines as well as the containers and pods. Specifically, you need to monitor the health and performance of:

Instead of using multiple technologies to collect metrics, you deploy Metricbeat to monitor all layers of your technology stack.

You’ll use Metricbeat to collect metrics from pods running in your Kubernetes cluster as well as metrics from the Kubernetes cluster itself.

Metricbeat modules provide a quick and easy way to pick up metrics from various sources and ship them to Elasticsearch as ECS-compatible events, ready to be correlated with logs, uptime, and APM data.

To deploy Metricbeat to your Kubernetes cluster:

To make deployment easier, Elastic provides a YAML file that defines all the required deployment settings. In many cases, you can change the connection details and deploy with default settings to get started quickly.

curl -L -O https://raw.githubusercontent.com/elastic/beats/8.1/deploy/kubernetes/metricbeat-kubernetes.yaml

Metricbeat sends events to an existing Elasticsearch deployment, if present. To specify a different destination, change the following parameters in the metricbeat-kubernetes.yaml file:

env:
- name: ELASTICSEARCH_HOST
  value: elasticsearch
- name: ELASTICSEARCH_PORT
  value: "9200"
- name: ELASTICSEARCH_USERNAME
  value: elastic 
- name: ELASTICSEARCH_PASSWORD
  value: changeme
- name: ELASTIC_CLOUD_ID 
  value:
- name: ELASTIC_CLOUD_AUTH 
  value:

To avoid exposing sensitive data, you can base64 encode the string, then store it in a Kubernetes secret. For example:

$ echo -n 'changeme' | base64
Y2hhbmdlbWU=
$ kubectl create secret generic es-secret --from-literal=password='Y2hhbmdlbWU=' --namespace=kube-system 

To use the secret, change the env setting in the manifest file:

env:
- name: ELASTICSEARCH_PASSWORD
  valueFrom:
    secretKeyRef:
      name: es-secret
      key: password

Metricbeat will run on each node in the cluster as a Daemonset’s pod. To collect system-level metrics, key paths are mounted from the host to the pod:

- name: proc
  hostPath:
  path: /proc
- name: cgroup
  hostPath:
  path: /sys/fs/cgroup

To collect system-level metrics from the running node, configure the system module. The metricsets that you’re likely to want are already enabled in the manifest. Modify the settings as required for your environment:

- module: system
  period: 10s
  metricsets:
    - cpu
    - load
    - memory
    - network
    - process
    - process_summary

Because Metricbeat is running on each node, you can collect metrics from the Kubelet API. These metrics provide important information about the state of the Kubernetes node, pods, containers, and other resources.

To collect these metrics, configure the kubernetes module. The metricsets that you’re likely to want are already enabled in the manifest. Modify the settings as required for your environment:

- module: kubernetes
  metricsets:
    - node
    - system
    - pod
    - container
    - volume

These metricsets collect metrics from the Kubelet API and therefore require access to the specific endpoints. Depending on the version and configuration of Kubernetes nodes, kubelet might provide a read-only HTTP port (typically 10255), which is used in some configuration examples. But in general, and lately, this endpoint requires SSL (HTTPS) access (to port 10250 by default) and token-based authentication.

Metricbeat gets some metrics from kube-state-metrics. If kube-state-metrics is not already running, deploy it now. To learn how, see the Kubernetes deployment docs.

To collect state metrics:

Because the kube-state-metrics service provides cluster-wide metrics, there’s no need to fetch them per node. To use this singleton approach, Metricbeat leverages a leader election method, where one pod holds a leader lock and is responsible for collecting cluster-wide metrics. For more information about leader election settings, see Autodiscover.

metricbeat.autodiscover:
    providers:
    - type: kubernetes
      scope: cluster
      node: ${NODE_NAME}
      unique: true
      templates:
        - config:
            - module: kubernetes
              hosts: ["kube-state-metrics:8080"]
              period: 10s
              add_metadata: true
              metricsets:
                - state_node
                - state_deployment
                - state_daemonset
                - state_replicaset
                - state_pod
                - state_container
                - state_cronjob
                - state_resourcequota
                - state_statefulset

Metricbeat supports autodiscovery based on hints from the provider. The hints system looks for hints in Kubernetes pod annotations or Docker labels that have the prefix co.elastic.metrics. When a container starts, Metricbeat checks for hints and launches the proper configuration. The hints tell Metricbeat how to get metrics for the given container. To enable hint-based autodiscovery, set hints.enabled: true:

metricbeat.autodiscover:
  providers:
    - type: kubernetes
      hints.enabled: true

By labeling Kubernetes pods with the co.elastic.metrics prefix you can signal Metricbeat to collect metrics from those pods using the appropriate modules:

apiVersion: v1
kind: Pod
metadata:
    name: nginx-autodiscover
    annotations:
        co.elastic.metrics/module: nginx
        co.elastic.metrics/metricsets: stubstatus
        co.elastic.metrics/hosts: '${data.host}:80'
        co.elastic.metrics/period: 10s

To enrich your collection resources, you can use the prometheus module to collect metrics from every application that runs on the cluster and exposes a Prometheus exporter. For instance, let’s say that the cluster runs multiple applications that expose Prometheus metrics with the default Prometheus standards. Assuming these applications are annotated properly, you can define an extra autodiscovery provider to automatically identify the applications and start collecting exposed metrics by using the prometheus module:

metricbeat.autodiscover:
  providers:
    - type: kubernetes
      include_annotations: ["prometheus.io.scrape"]
      templates:
        - condition:
            contains:
              kubernetes.annotations.prometheus.io/scrape: "true"
          config:
            - module: prometheus
              metricsets: ["collector"]
              hosts: "${data.host}:${data.port}"

This configuration launches a prometheus module for all containers of pods annotated with prometheus.io/scrape: "true".

Metricbeat provides processors that you can use in your configuration to enrich events with metadata coming from Docker, Kubernetes, hosts, and cloud providers.

The add_cloud_metadata and add_host_metadata processors are already specified in the default configuration:

processors:
- add_cloud_metadata:
- add_host_metadata:

This metadata allows correlation of metrics with the hosts, Kubernetes pods, Docker containers, and cloud-provider infrastructure metadata and with other pieces of observability puzzle, such as application performance monitoring data and logs.

To deploy Metricbeat to Kubernetes, run:

kubectl create -f metricbeat-kubernetes.yaml

To check the status, run:

$ kubectl --namespace=kube-system  get ds/metricbeat

NAME       DESIRED   CURRENT   READY     UP-TO-DATE   AVAILABLE   NODE-SELECTOR   AGE
metricbeat   32        32        0         32           0           <none>          1m

Metrics should start flowing to Elasticsearch.

If you’re using Red Hat OpenShift, you need to specify additional settings in the manifest file and enable the container to run as privileged.

To view the performance and health metrics collected by Metricbeat, open Kibana and go to Observability > Metrics.

On the Inventory page, you can switch between different views to see an overview of the containers and pods running on Kubernetes:

On the Metrics Explorer page, you can group and analyze metrics for the resources that you are monitoring.

Notice how everywhere you go in Kibana, there is a search bar that allows you to, you know, search for things. It’s a great way to filter views and zoom into things when you’re looking for that needle in a haystack.

Metricbeat ships with a variety of pre-built Kibana dashboards that you can use to visualize metrics about your Kubernetes environment. If these dashboards are not already loaded into Kibana, you must run the Metricbeat setup job.

On the Kubernetes overview dashboard, you can see an overview of all the nodes, deployments, and pods running on your Kubernetes cluster:

You can use these dashboards as they are, or as a starting point for custom dashboards tailored to your needs.

Quickly triage and troubleshoot application performance problems with the help of Elastic application performance monitoring (APM).

Think of a latency spike — APM can help you narrow the scope of your investigation to a single service. Because you’ve also ingested and correlated logs and metrics, you can then link the problem to CPU and memory utilization or error log entries of a particular Kubernetes pod.

Application monitoring data is streamed from your applications running in Kubernetes to APM Server, where it is validated, processed, and transformed into Elasticsearch documents.

There are many ways to deploy APM Server when working with Kubernetes, but this guide assumes that you’re using our hosted Elasticsearch Service on Elastic Cloud. If you haven’t done so already, enable APM Server in the Elasticsearch Service console.

If you want to manage APM Server yourself, there are a few alternative options:

A secret token is used to secure communication between APM agents and APM Server. On the Elastic Cloud deployment page, select APM and copy your APM Server secret token. To avoid exposing the secret token, you can store it in a Kubernetes secret. For example:

kubectl create secret generic apm-secret --from-literal=ELASTIC_APM_SECRET_TOKEN=asecretpassword --namespace=kube-system 

If you’re managing APM Server yourself, see secret token for instructions on how to set up your secret token.

If you are using ECK to set up APM Server, the operator automatically generates an {APM-server-name}-apm-token secret for you.

In most cases, setting up APM agents and thereby instrumenting your applications is as easy as installing a library and adding a few lines of code.

Select your application’s language for details:

go get go.elastic.co/apm

import (
	"net/http"

	"go.elastic.co/apm/module/apmhttp"
)

func main() {
	mux := http.NewServeMux()
	...
	http.ListenAndServe(":8080", apmhttp.Wrap(mux))
}

        # ...
        - name: ELASTIC_APM_SERVER_URL
          value: "apm-server-url-goes-here" 
        - name: ELASTIC_APM_SECRET_TOKEN
          valueFrom:
            secretKeyRef:
              name: apm-secret
              key: ELASTIC_APM_SECRET_TOKEN 
        - name: ELASTIC_APM_SERVICE_NAME
          value: "service-name-goes-here" 

    # ...
    spec:
      volumes:
      - name: elastic-apm-agent 
        emptyDir: {}
      initContainers:
      - name: elastic-java-agent
        image: docker.elastic.co/observability/apm-agent-java:1.12.0 
        volumeMounts:
        - mountPath: /elastic/apm/agent
          name: elastic-apm-agent
        command: ['cp', '-v', '/usr/agent/elastic-apm-agent.jar', '/elastic/apm/agent'] 

      # ...
      containers:
      - name: your-app-container
        env:
        # ...
        - name: JAVA_TOOL_OPTIONS 
          value: -javaagent:/elastic/apm/agent/elastic-apm-agent.jar

        # ...
        - name: ELASTIC_APM_SERVER_URLS
          value: "apm-server-url-goes-here" 
        - name: ELASTIC_APM_SECRET_TOKEN
          valueFrom:
            secretKeyRef:
              name: apm-secret
              key: ELASTIC_APM_SECRET_TOKEN 
        - name: ELASTIC_APM_SERVICE_NAME
          value: "service-name-goes-here" 
        - name: ELASTIC_APM_APPLICATION_PACKAGES
          value: "org.springframework.samples.petclinic" 
        - name: JAVA_TOOL_OPTIONS 
          value: -javaagent:/elastic/apm/agent/elastic-apm-agent.jar

    # ...
    spec:
      volumes:
      - name: elastic-apm-agent 
        emptyDir: {}
      initContainers:
      - name: elastic-dotnet-agent
        image: busybox
        command: ["/bin/sh","-c"] 
        args: 
          - wget -qO './elastic-apm-agent/ElasticApmAgent.zip' https://github.com/elastic/apm-agent-dotnet/releases/download/1.7.0/ElasticApmAgent_1.7.0.zip;
            cd elastic-apm-agent;
            cat ElasticApmAgent.zip | busybox unzip -;
        volumeMounts:
        - mountPath: /elastic-apm-agent
          name: elastic-apm-agent

      # ...
      containers:
      - name: your-app-container
        volumeMounts:
        - mountPath: /elastic-apm-agent
          name: elastic-apm-agent
        env:
        # ...
        - name: DOTNET_STARTUP_HOOKS
          value: "/elastic-apm-agent/ElasticApmAgentStartupHook.dll"

        # ...
        - name: ELASTIC_APM_SERVER_URLS
          value: "apm-server-url-goes-here" 
        - name: ELASTIC_APM_SECRET_TOKEN
          valueFrom:
            secretKeyRef:
              name: apm-secret
              key: ELASTIC_APM_SECRET_TOKEN 
        - name: ELASTIC_APM_SERVICE_NAME
          value: "service-name-goes-here" 
        - name: DOTNET_STARTUP_HOOKS 
          value: "/elastic-apm-agent/ElasticApmAgentStartupHook.dll"

npm install elastic-apm-node --save

var apm = require('elastic-apm-node').start()

        # ...
        - name: ELASTIC_APM_SERVER_URL
          value: "apm-server-url-goes-here" 
        - name: ELASTIC_APM_SECRET_TOKEN
          valueFrom:
            secretKeyRef:
              name: apm-secret
              key: ELASTIC_APM_SECRET_TOKEN 
        - name: ELASTIC_APM_SERVICE_NAME
          value: "service-name-goes-here" 

rpm -ivh <package-file>.rpm

dpkg -i <package-file>.deb

apk add --allow-untrusted <package-file>.apk

        # ...
        - name: ELASTIC_APM_SERVER_URL
          value: "apm-server-url-goes-here" 
        - name: ELASTIC_APM_SECRET_TOKEN
          valueFrom:
            secretKeyRef:
              name: apm-secret
              key: ELASTIC_APM_SECRET_TOKEN 
        - name: ELASTIC_APM_SERVICE_NAME
          value: "service-name-goes-here" 

# Django
pip install elastic-apm

# Flask
pip install elastic-apm[flask]

INSTALLED_APPS = (
   # ...
   'elasticapm.contrib.django',
)

from elasticapm.contrib.flask import ElasticAPM

app = Flask(__name__)

apm = ElasticAPM(app)

        # ...
        - name: ELASTIC_APM_SERVER_URL
          value: "apm-server-url-goes-here" 
        - name: ELASTIC_APM_SECRET_TOKEN
          valueFrom:
            secretKeyRef:
              name: apm-secret
              key: ELASTIC_APM_SECRET_TOKEN 
        - name: ELASTIC_APM_SERVICE_NAME
          value: "service-name-goes-here" 

gem 'elastic-apm'

# config.ru

app = lambda do |env|
  [200, {'Content-Type' => 'text/plain'}, ['ok']]
end

# Wraps all requests in transactions and reports exceptions
use ElasticAPM::Middleware

# Start an instance of the Agent
ElasticAPM.start()

run app

# Gracefully stop the agent when process exits.
# Makes sure any pending transactions have already sent.
at_exit { ElasticAPM.stop }

        # ...
        - name: ELASTIC_APM_SERVER_URL
          value: "apm-server-url-goes-here" 
        - name: ELASTIC_APM_SECRET_TOKEN
          valueFrom:
            secretKeyRef:
              name: apm-secret
              key: ELASTIC_APM_SECRET_TOKEN 
        - name: ELASTIC_APM_SERVICE_NAME
          value: "service-name-goes-here" 

In most instances, APM agents automatically read Kubernetes data from inside the container and send it to APM Server. If this is not the case, or if you wish to override this data, you can set environment variables for the agents to read. These environment variable are set via the Downward API in your kubernetes pod spec:

      # ...
      containers:
      - name: your-app-container
        env:
        # ...
        - name: KUBERNETES_NODE_NAME
          valueFrom:
            fieldRef:
              fieldPath: spec.nodeName
        - name: KUBERNETES_POD_NAME
          valueFrom:
            fieldRef:
              fieldPath: metadata.name
        - name: KUBERNETES_NAMESPACE
          valueFrom:
            fieldRef:
              fieldPath: metadata.namespace
        - name: KUBERNETES_POD_UID
          valueFrom:
            fieldRef:
              fieldPath: metadata.uid

The table below maps these environment variables to the APM metadata event field:

APM agents are deployed with your application.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: <<your-app>>
  namespace: kube-system
  labels:
    app: <<your-app>>
    service: <<your-app>>
spec:
  replicas: 1
  selector:
    matchLabels:
      app: <<your-app>>
  template:
    metadata:
      labels:
        app: <<your-app>>
        service: <<your-app>>
    spec:
      dnsPolicy: ClusterFirstWithHostNet
      volumes:
      - name: elastic-apm-agent
        emptyDir: {}
      initContainers:
      - name: elastic-java-agent
        image: docker.elastic.co/observability/apm-agent-java:1.12.0
        volumeMounts:
        - mountPath: /elastic/apm/agent
          name: elastic-apm-agent
        command: ['cp', '-v', '/usr/agent/elastic-apm-agent.jar', '/elastic/apm/agent']
      containers:
      - name: <<your-app>>
        image: <<your-app>>
        volumeMounts:
        - mountPath: /elastic/apm/agent
          name: elastic-apm-agent
        env:
        - name: ELASTIC_APM_SERVER_URL
          value: "apm-server-url-goes-here"
        - name: ELASTIC_APM_SECRET_TOKEN
          valueFrom:
            secretKeyRef:
              name: apm-secret
              key: ELASTIC_APM_SECRET_TOKEN
        - name: ELASTIC_APM_SERVICE_NAME
          value: "petclinic"
        - name: ELASTIC_APM_APPLICATION_PACKAGES
          value: "org.springframework.samples.petclinic"
        - name: ELASTIC_APM_ENVIRONMENT
          value: test
        - name: JAVA_TOOL_OPTIONS
          value: -javaagent:/elastic/apm/agent/elastic-apm-agent.jar
        - name: KUBERNETES_NODE_NAME
          valueFrom:
            fieldRef:
              fieldPath: spec.nodeName
        - name: KUBERNETES_POD_NAME
          valueFrom:
            fieldRef:
              fieldPath: metadata.name
        - name: KUBERNETES_NAMESPACE
          valueFrom:
            fieldRef:
              fieldPath: metadata.namespace
        - name: KUBERNETES_POD_UID
          valueFrom:
            fieldRef:
              fieldPath: metadata.uid

kubectl apply -f demo.yml

To view your application’s trace data, open Kibana and go to Observability > APM.

The APM app allows you to monitor your software services and applications in real-time: visualize detailed performance information on your services, identify and analyze errors, and monitor host-level and agent-specific metrics like JVM and Go runtime metrics.

Having access to application-level insights with just a few clicks can drastically decrease the time you spend debugging errors, slow response times, and crashes.

Best of all, because Kubernetes environment variables have been mapped to APM metadata events, you can filter your trace data by Kubernetes namespace, node.name, pod.name, and pod.uid.