OpenShift for Developers, 2nd Edition

Chapter 8. Production Deployment and Scaling

Now that you have deployed the Noted application with a database, we can talk about some basic tasks that you might need to perform to make the platform work for your app. First you will need to scale the quarkus-backend component to run multiple instances and handle more load. Since a few instances of your backend component will be running, we will discuss how OpenShift can deploy updates to the fleet and potentially roll out an update to your app with zero downtime using the proper deployment strategy for your specific application. OpenShift also has robust health checking built in to make sure things are running as expected, which we’ll cover in this chapter as well.

Application Scaling

OpenShift has some powerful built-in mechanisms in place that allow your application to scale by replicating. When a deployment scales upward its replica set creates additional pods for an application. The service associated with this deployment will perform the simple task of spreading the load across the replica set. The number of replicas that a deployment has can be configured manually or automatically based on CPU, memory, or concurrency metrics, as you will see and configure in the sections that follow.

Manual Scaling

Manually scaling the quarkus-backend deployment is a quick and easy way for your application to be able to handle more load.

Open the Topology view to manually scale the quarkus-backend. Select “quarkus-backend,” and click the ...