Online Courses Scalable Microservices with Kubernetes

Deploying Microservices

So far the Kubernetes examples have been little more than what could be accomplished with Bash, Docker and Jenkins. No we shall look at how Kubernetes can be used for more effective management of application deployment and configuration. Enter Desired State.

Deployments are used to define our desired state then work with replica controllers to ensure desired state is met. A deployment is an abstraction from a pods.

Services are used to group pods and provide an interface to them.

Scaling is up next. Using the deployments configuration file updating the replicas field and running kubectl apply -f <file> is all that needs to be done! Well its not quite that simple. That scales the number of replica pods deployed to our kubernetes cluster. It does not change the amount of machine (VM/Physical) resources in the cluster. So… I would not really call this scaling :(.

Onto updating (patching, new version etc). There are two types of deployments, rollout and blue-green. Rollouts can be conducted by updating the deployment config (container->image) reference then running kubectl apply -f. This will automatically conduct a staged rollout.

OK so that’s the end of the course – it was not very deep, and did not cover anything like dealing with persistent layers. Nonetheless it was good to review the basics. Next step is to understand the architecture or our my application running on Kubernetes in AWS.

At first I read a number of threads stating that kubernetes does not support cross availability zone clusters in AWS. Cross availabilty zone clusters are supported on AWS: Kube-aws supports “spreading” a cluster across any number of Availability Zones in a given region. With that in mind the following architecture is what I will be moving to:

kubernetes high level architecture
Kubernetes high level architecture

Instead of HA Proxy I will stick with out existing NGINX reverse proxy.

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