Analyze compute footprint of Spring Boot & Quarkus Application, by deploying on Openshift

Deployment Steps :

Post verify the applications in local system it is time to deploy all in cloud (in our case it is Red Hat Openshift). We will have one namespace and will use S2I method to deploy all the services in same namespace.

  1. Create and logged in to the namespace ‘springboot2quarkus’. Snapshot below.
Web console of RedHat Openshift displaying deployed applications
Openshift web-console displaying deployed workloads
Above command create image stream to pull bnase images for quarkus app s2i deployment

Analyze compute footprint and compare both apps:

At this stage we have two front end application (spring boot & Quarkus) connecting to same backend service and provide same functionality and deployed in Openshift. We can log in to metric dashboard and get their compute footprint.

spring boot front end app restart logs
Quarkus front end app restart logs


On comparing the footprint, we found that runtime is very less in Quarkus and very resilient in cloud. If we develop the app from scratch, it will be much easier in Quarkus and even if we migrate from existing spring boot app it is straightforward. The API dependencies play a big role in that. Except for explicitly setting the parameter name, it is seamless. I ’ve managed to complete the migration in one week. It was very interesting and challenging week and I have learned a lot for Quarkus during this time and I am sure that the next micro-service migration will go much more smoothly. And based on compute footprint analysis it will be wiser to select Quarkus in my next java based micro-service project.



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abhiroop ghatak

abhiroop ghatak

Cloud Developer Advocate. Post Graduate in Computer Applications with 11+ years of experience in Software Development & cloud onboarding.