Helidon MP application that uses JPA with an in-memory H2 database.

With JDK21

mvn package
java -jar target/helidon-learning.jar

# Prometheus Format
curl -s -X GET http://localhost:8080/metrics
# TYPE base:gc_g1_young_generation_count gauge
. . .

# JSON Format
curl -H 'Accept: application/json' -X GET http://localhost:8080/metrics
{"base":...
. . .

curl -s -X GET http://localhost:8080/health
{"outcome":"UP",...

The generation of native binaries requires an installation of GraalVM 22.1.0+.

You can build a native binary using Maven as follows:

mvn -Pnative-image install -DskipTests

The generation of the executable binary may take a few minutes to complete depending on your hardware and operating system. When completed, the executable file will be available under the target directory and be named after the artifact ID you have chosen during the project generation phase.

Start your database before running this example.

Example docker commands to start databases in temporary containers:

H2:

docker run --rm --name h2 -p 9092:9082 -p 8082:8082 nemerosa/h2

For details, see https://www.h2database.com/html/cheatSheet.html

docker build -t helidon-learning .

docker run --rm -p 8080:8080 helidon-learning:latest

Exercise the application as described above.

If you don’t have access to a Kubernetes cluster, you can install one on your desktop.

kubectl cluster-info                        # Verify which cluster
kubectl get pods                            # Verify connectivity to cluster

kubectl create -f app.yaml                              # Deploy application
kubectl get pods                                        # Wait for quickstart pod to be RUNNING
kubectl get service  helidon-learning                     # Get service info
kubectl port-forward service/helidon-learning 8081:8080   # Forward service port to 8081

You can now exercise the application as you did before but use the port number 8081.

After you’re done, cleanup.

kubectl delete -f app.yaml

Build the custom runtime image using the jlink image profile:

mvn package -Pjlink-image

This uses the helidon-maven-plugin to perform the custom image generation. After the build completes it will report some statistics about the build including the reduction in image size.

The target/helidon-learning-jri directory is a self contained custom image of your application. It contains your application, its runtime dependencies and the JDK modules it depends on. You can start your application using the provide start script:

./target/helidon-learning-jri/bin/start

Class Data Sharing (CDS) Archive Also included in the custom image is a Class Data Sharing (CDS) archive that improves your application’s startup performance and in-memory footprint. You can learn more about Class Data Sharing in the JDK documentation.

The CDS archive increases your image size to get these performance optimizations. It can be of significant size (tens of MB). The size of the CDS archive is reported at the end of the build output.

If you’d rather have a smaller image size (with a slightly increased startup time) you can skip the creation of the CDS archive by executing your build like this:

mvn package -Pjlink-image -Djlink.image.addClassDataSharingArchive=false

For more information on available configuration options see the helidon-maven-plugin documentation.