Java Application

5min
This document explains how to instrument a Java Spring Boot application with KloudMate, using OpenTelemetry.
This instrumentation can be done using the OpenTelemetry operator, which supports injecting and configuring auto-instrumentation libraries for .NET, Java, Node.js, Python, and Go service.
Prerequisites:
Running a Kubernetes cluster.
The cert-manager must be installed. If you use the helm chart, there is an option to generate a self-signed cert instead. 
Step 1: First, install the OpenTelemetry Operator into your cluster. 
You can do this with the Operator release manifest, the Operator helm chart, or with Operator Hub. 
Bash
kubectl apply -f https://github.com/open-telemetry/opentelemetry-operator/releases/latest/download/opentelemetry-operator.yaml
kubectl apply -f https://github.com/open-telemetry/opentelemetry-operator/releases/latest/download/opentelemetry-operator.yaml
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Step 2: Create an OpenTelemetry collector to send telemetry from containers to a collector instead of directly to a backend. 
For example, here is a demo collector:
YAML
kubectl apply -f - <<EOF
apiVersion: opentelemetry.io/v1beta1
kind: OpenTelemetryCollector
metadata:
  name: demo
  namespace: kloudmate-backend #change namespace
spec:
  config:
    receivers:
      otlp:
        protocols:
          grpc:
            endpoint: 0.0.0.0:4317
          http:
            endpoint: 0.0.0.0:4318

    processors:
      memory_limiter:
        check_interval: 1s
        limit_percentage: 75
        spike_limit_percentage: 15
      batch:
        send_batch_size: 10000
        timeout: 10s

    exporters:
      debug: {}
      otlphttp:
        endpoint: 'https://otel.kloudmate.com:4318'
        headers:
          Authorization: 	  

    service:
      pipelines:
        traces:
          receivers: [otlp]
          processors: [memory_limiter, batch]
          exporters: [debug, otlphttp]
        metrics:
          receivers: [otlp]
          processors: [memory_limiter, batch]
          exporters: [debug, otlphttp]
        logs:
          receivers: [otlp]
          processors: [memory_limiter, batch]
          exporters: [debug, otlphttp]

EOF
kubectl apply -f - <<EOF
apiVersion: opentelemetry.io/v1beta1
kind: OpenTelemetryCollector
metadata:
  name: demo
  namespace: kloudmate-backend #change namespace
spec:
  config:
    receivers:
      otlp:
        protocols:
          grpc:
            endpoint: 0.0.0.0:4317
          http:
            endpoint: 0.0.0.0:4318

    processors:
      memory_limiter:
        check_interval: 1s
        limit_percentage: 75
        spike_limit_percentage: 15
      batch:
        send_batch_size: 10000
        timeout: 10s

    exporters:
      debug: {}
      otlphttp:
        endpoint: 'https://otel.kloudmate.com:4318'
        headers:
          Authorization: 	  

    service:
      pipelines:
        traces:
          receivers: [otlp]
          processors: [memory_limiter, batch]
          exporters: [debug, otlphttp]
        metrics:
          receivers: [otlp]
          processors: [memory_limiter, batch]
          exporters: [debug, otlphttp]
        logs:
          receivers: [otlp]
          processors: [memory_limiter, batch]
          exporters: [debug, otlphttp]

EOF
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Note: Change your Kloudmate organization API key to configure the collector to export data to KloudMate. 
Step 3: Configure automatic instrumentation 
To be able to manage automatic instrumentation, the Operator needs to be configured to know what pods to instrument and which automatic instrumentation to use for those pods. This is done via the Instrumentation CRD 
For our example, we have used Java SDK auto instrumentation. 
The following command creates a basic Instrumentation resource that is configured for instrumenting Java services:
YAML
kubectl apply -f - <<EOF
apiVersion: opentelemetry.io/v1alpha1
kind: Instrumentation
metadata:
  name: demo-instrumentation
  namespace: kloudmate-backend  #change namespace
spec:
  exporter:
    endpoint: http://demo-collector:4317
  propagators:
    - tracecontext
    - baggage
  sampler:
    type: parentbased_traceidratio
    argument: "1"
EOF
kubectl apply -f - <<EOF
apiVersion: opentelemetry.io/v1alpha1
kind: Instrumentation
metadata:
  name: demo-instrumentation
  namespace: kloudmate-backend  #change namespace
spec:
  exporter:
    endpoint: http://demo-collector:4317
  propagators:
    - tracecontext
    - baggage
  sampler:
    type: parentbased_traceidratio
    argument: "1"
EOF
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Note: The endpoint used under exporter must be the same as the service name of the collector created in Step 2
Step 4: Now opt-in your services to automatic instrumentation. This is done by updating your service’s spec.template.metadata.annotations to include a language-specific annotation:
java:instrumentation.opentelemetry.io/inject-java: "true" 
To patch your existing Java application with the necessary annotation use the below command:
BASH
kubectl  patch deployment otlp-http-deployment -n kloudmate-backend -p '{"spec":{"template":{"metadata":{"annotations":{"instrumentation.opentelemetry.io/inject-java": "true"}}}}}'    
kubectl  patch deployment otlp-http-deployment -n kloudmate-backend -p '{"spec":{"template":{"metadata":{"annotations":{"instrumentation.opentelemetry.io/inject-java": "true"}}}}}'    
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Following the above steps, you can get the necessary telemetry data on KloudMate. 
Following is a sample YAML file where the annotation is used:
YAML
apiVersion: apps/v1
kind: Deployment
metadata:
  name: spring-boot-k8s
spec:
  selector:
    matchLabels:
      app: spring-boot-k8s
  replicas: 1
  template:
    metadata:
      annotations:
        instrumentation.opentelemetry.io/inject-java: "true"
      labels:
        app: spring-boot-k8s
    spec:
      containers:
        - name: spring-boot-k8s
          image: springboot-instrument:01
          imagePullPolicy: IfNotPresent
          ports:
            - containerPort: 8080

apiVersion: apps/v1
kind: Deployment
metadata:
  name: spring-boot-k8s
spec:
  selector:
    matchLabels:
      app: spring-boot-k8s
  replicas: 1
  template:
    metadata:
      annotations:
        instrumentation.opentelemetry.io/inject-java: "true"
      labels:
        app: spring-boot-k8s
    spec:
      containers:
        - name: spring-boot-k8s
          image: springboot-instrument:01
          imagePullPolicy: IfNotPresent
          ports:
            - containerPort: 8080
﻿
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