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The application is modelled in multiple deployments which each deploy replica sets.

Replica Sets are a way of what pods it should be running and can be scaled independently from other Replica Sets; either manually or automatically. In our scenario we will have a replica set for our API & our asynchornous worker so that they can be scaling based on their specific needs. In our scenario we would prefer to scale the worker out when messages on the queue pile up while we'd like to scale our API based on CPU and/or memory.

A pod can have multiple containers running next to each other and form a scale unit. In our case, this is represented by our API container which is using the orders validator sidecar.


Unfortunately Kuberenetes does not have the concept of "an application" which represents a group of replica sets (at least not to my knowledge). However, it allows you to create isolated namespaces which isolate entities from another namespace so that they don't interfer with each other or another person changes your application.

Another interesting aspect is that every entity is also capable of providing metadata about itself by assinging labels. In our scenario every entity is deployed in a dedicated namespace and has at least the following labels - app, microservice, service, type.

By using a service we can expose our API to the internet. This entity is using the labels that we have assigned to forward traffic to the internal port of the container inside the pod by using port forwarding. Given we want to expose this outside of the cluster, the type of the service is LoadBalancer which the routing.

We're also running a Redis cache Pod which has service linked to it. This allows the pod to open up a port for other pods to communicate with it, in our case the API. Given this is only for local communication we use the default ClusterIP service type with port forwarding.