Tutorial - Enabling Tracing for a Java Application with the Admission Controller

Docs > APM > Tracing Guides > Tutorial - Enabling Tracing for a Java Application with the Admission Controller

Overview

This tutorial walks you through the steps to enable tracing for Java Application using the Datadog Admission Controller.

For other scenarios, including on a host, in a container, on cloud infrastructure, and on applications written in other languages, see the other Enabling Tracing tutorials.

See Tracing Java Applications for general comprehensive tracing setup documentation for Java.

Prerequisites

A Datadog account and organization API key
Git
Docker
Curl
Kubernetes v1.14+

Install the sample application

To demonstrate how to instrument your app with the Datadog Admission Controller, this tutorial uses a Java app built with Spring. You can find the code for the app in the springblog GitHub repository.

To get started, clone the repository:

git clone https://github.com/DataDog/springblog.git

The repository contains a multi-service Java application pre-configured to be run within Docker and Kubernetes. The sample app is a basic Spring app using REST.

Start and run the sample application

Switch to to the /k8s subdirectory in the springblog repo:
```
cd springblog/k8s/
```
Deploy the workload with the depl.yaml file:
```
kubectl apply -f ./depl.yaml
```

Verify that it is running with the following command:

kubectl get pods

You should see something like this:

NAME                                       READY   STATUS        RESTARTS        AGE
springback-666db7b6b8-dzv7c                1/1     Terminating   0               2m41s
springfront-797b78d6db-p5c84               1/1     Terminating   0               2m41s

The service is started and listens on port 8080. It exposes an /upstream endpoint.

Check that communication takes place by running the following curl command:

curl localhost:8080/upstream
Quote{type='success', values=Values{id=6, quote='Alea jacta est'}}

To stop the application, run this command from the springblog/k8s directory so you can enable tracing on it:
```
kubectl delete -f ./depl-with-lib-inj.yaml
```

Instrument your app with Datadog Admission Controller

After you have your application working, instrument it using the Datadog Admission Controller. In containerized environments, the process is generally:

Install the Datadog Cluster Agent.
Add Unified Service Tags in pod definition.
Annotate your pod for library injection.
Label your pod to instruct the Datadog Admission controller to mutate the pod.

There’s no need to add the tracing library because it’s automatically injected. You don’t need to redeploy your app yet. This section of the tutorial steps you through the process of adding Datadog variables and deploying a new image or version of your app.

From the k8s subdirectory, use the following command to install the Datadog Cluster Agent, specifying the values-with-lib-inj.yaml config file and your Datadog API key:
```
helm install datadog-agent -f values-with-lib-inj.yaml --set datadog.site='datadoghq.com' --set datadog.apiKey=$DD_API_KEY datadog/datadog
```
For more detailed information, read Installing the Datadog Agent on Kubernetes with Helm

You can check the Datadog Cluster Agent is running with the following command:

kubectl get pods

You should see something like this:

NAME                                                READY   STATUS    RESTARTS  AGE
datadog-agent-4s8rb                                 3/3     Running   0	        30s
datadog-agent-cluster-agent-5666cffc44-d8qxk        1/1     Running   0         30s
datadog-agent-kube-state-metrics-86f46b8484-mlqp7   1/1     Running   0         30s

Add Unified Service Tags to the pod by adding the following block to the depl.yaml file:

labels:
  tags.datadoghq.com/env: "dev"
  tags.datadoghq.com/service: "springfront"
  tags.datadoghq.com/version: "12"

Configure the Datadog Admission Controller to inject a Java tracing library to the app container by adding the following annotation to the pod:

annotations:
  admission.datadoghq.com/java-lib.version: "latest"

This annotation specifies the latest version of the Java tracing library. You can also reference a specific version of the library, such as "v1.5.0".

The final pod definition should look like the excerpt below. See also the full YAML file in the sample repo. The instructions you added to instrument the app are highlighted:

apiVersion: apps/v1
kind: Deployment
metadata:
name: springfront
labels:
    tags.datadoghq.com/env: "dev"
    tags.datadoghq.com/service: "springfront"
    tags.datadoghq.com/version: "12"
spec:
replicas: 1
selector:
    matchLabels:
    name: springfront
minReadySeconds: 15
strategy:
    type: RollingUpdate
    rollingUpdate:
    maxUnavailable: 1
    maxSurge: 1
template:
    metadata:
    labels:
        name: springfront
        tags.datadoghq.com/env: "dev"
        tags.datadoghq.com/service: "springfront"
        tags.datadoghq.com/version: "12"
    annotations:
        admission.datadoghq.com/java-lib.version: "latest"

Run the sample app with the following command:
```
kubectl apply -f depl-with-lib-inj.yaml
```

Run the following command to show that the app and Agent are running:

kubectl get pods

You should see something like this:

NAME                                                READY   STATUS    RESTARTS   AGE
datadog-agent-4s8rb                                 3/3     Running   0          28m
datadog-agent-cluster-agent-5666cffc44-d8qxk        1/1     Running   0          28m
datadog-agent-kube-state-metrics-86f46b8484-mlqp7   1/1     Running   0          28m
springback-666db7b6b8-sb4tp                         1/1     Running   0          27m
springfront-797b78d6db-mppbg                        1/1     Running   0          27m

Run the following command to see details of the pod:

kubectl describe pod springfront

You should see something like this:

Events:
Type    Reason     Age   From               Message
----    ------     ----  ----               -------
Normal  Scheduled  32s   default-scheduler  Successfully assigned default/springfront-797b78d6db-jqjdl to docker-desktop
Normal  Pulling    31s   kubelet            Pulling image "gcr.io/datadoghq/dd-lib-java-init:latest"
Normal  Pulled     25s   kubelet            Successfully pulled image "gcr.io/datadoghq/dd-lib-java-init:latest" in 5.656167878s
Normal  Created    25s   kubelet            Created container datadog-lib-java-init
Normal  Started    25s   kubelet            Started container datadog-lib-java-init
Normal  Pulling    25s   kubelet            Pulling image "pejese/springfront:v2"
Normal  Pulled     2s    kubelet            Successfully pulled image "pejese/springfront:v2" in 22.158699094s
Normal  Created    2s    kubelet            Created container springfront
Normal  Started    2s    kubelet            Started container springfront

As you can see, an init-container is added to your pod. This container includes the Datadog Java tracing libraries to a volume mount. Also JAVA_TOOL_OPTIONS is modified to include javaagent. And Datadog-specific environment variables are added to the container:

Environment:
DD_ENV:              dev
DD_VERSION:          12
DD_SERVICE:          springfront
DD_ENTITY_ID:         (v1:metadata.uid)
DD_TRACE_AGENT_URL:  unix:///var/run/datadog/apm.socket
URL:                 http://springback:8088
JAVA_TOOL_OPTIONS:    -javaagent:/datadog-lib/dd-java-agent.jar
Mounts:
/datadog-lib from datadog-auto-instrumentation (rw)
/var/run/datadog from datadog (rw)
/var/run/secrets/kubernetes.io/serviceaccount from kube-api-access-qvmtk (ro)

Verify that the Datadog tracing library is injected into the pod by checking the pod logs. For example::

kubectl logs -f springfront-797b78d6db-jqjdl

You should see something like this:

Defaulted container "springfront" out of: springfront, datadog-lib-java-init (init)
Picked up JAVA_TOOL_OPTIONS:  -javaagent:/datadog-lib/dd-java-agent.jar

View APM traces in Datadog

Run the following command:
```
curl localhost:8080/upstream
```
Open the Datadog UI and see the two services reporting under the Service Catalog:
Explore Traces and see the associated Service Map:

Clean up the environment

Clean up your environment with the following command:

kubectl delete -f depl-with-lib-inj.yaml

Library injection with the Admission Controller simplifies service instrumentation, enabling you to view APM traces without changing or rebuilding your application. To learn more, read Datadog Library injection.