Java Application

6min

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
﻿
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
﻿
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
﻿
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"}}}}}'    
﻿
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
﻿
﻿
JVM Metrics
Name
Description
Unit
jvm.memory.used
Measure of memory used.
Bytes
jvm.memory.committed
Measure of memory committed.
Bytes
jvm.memory.limit
Measure of max obtainable memory.
Bytes
jvm.memory.used_after_last_gc
Measure of memory used, as measured after the most recent garbage collection event on this pool.
Bytes
jvm.gc.duration
Duration of JVM garbage collection actions.
seconds
jvm.thread.count
Number of executing platform threads.
thread
jvm.class.loaded
Number of classes loaded since JVM start.
class
jvm.class.unloaded
Number of classes unloaded since JVM start.
class
jvm.class.count
Number of classes currently loaded.
class
jvm.cpu.time
CPU time used by the process as reported by the JVM.
seconds
jvm.cpu.count
Number of processors available to the Java virtual machine.
cpu
jvm.cpu.recent_utilization
Recent CPU utilization for the process as reported by the JVM.
one
jvm.memory.init
Measure of initial memory requested.
Bytes
jvm.system.cpu.utilization
Recent CPU utilization for the whole system as reported by the JVM.
one
jvm.system.cpu.load_1m
Average CPU load of the whole system for the last minute as reported by the JVM.
run_queue_item
jvm.buffer.memory.used
Measure of memory used by buffers.
Bytes
jvm.buffer.memory.limit
Measure of total memory capacity of buffers.
Bytes
jvm.buffer.count
Number of buffers in the pool.
buffer
﻿

Name	Description	Unit
jvm.memory.used	Measure of memory used.	Bytes
jvm.memory.committed	Measure of memory committed.	Bytes
jvm.memory.limit	Measure of max obtainable memory.	Bytes
jvm.memory.used_after_last_gc	Measure of memory used, as measured after the most recent garbage collection event on this pool.	Bytes
jvm.gc.duration	Duration of JVM garbage collection actions.	seconds
jvm.thread.count	Number of executing platform threads.	thread
jvm.class.loaded	Number of classes loaded since JVM start.	class
jvm.class.unloaded	Number of classes unloaded since JVM start.	class
jvm.class.count	Number of classes currently loaded.	class
jvm.cpu.time	CPU time used by the process as reported by the JVM.	seconds
jvm.cpu.count	Number of processors available to the Java virtual machine.	cpu
jvm.cpu.recent_utilization	Recent CPU utilization for the process as reported by the JVM.	one
jvm.memory.init	Measure of initial memory requested.	Bytes
jvm.system.cpu.utilization	Recent CPU utilization for the whole system as reported by the JVM.	one
jvm.system.cpu.load_1m	Average CPU load of the whole system for the last minute as reported by the JVM.	run_queue_item
jvm.buffer.memory.used	Measure of memory used by buffers.	Bytes
jvm.buffer.memory.limit	Measure of total memory capacity of buffers.	Bytes
jvm.buffer.count	Number of buffers in the pool.	buffer

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