In my previous blog post I showed how to use the Kops tool to create a production ready Kubernetes cluster on Amazon Web Services (AWS). In this follow-up post I will show how to install Elasticsearch and its graphical counterpart Kibana in the cluster, in order to be able to collect and store logs from your cluster and search/read them. We will also install Fluentd as this component is responsible for transmitting the standard Kubernetes logs to Elasticsearch. This is generally known as the ELK stack, which stands for Elasticsearch, Logstash (precursor to Fluentd) and Kibana.

Today I will be talking about Kops, which is an official tool for creating Kubernetes clusters on AWS, with support for GCE and VMware vSphere in alpha. It takes a whole lot of the pain out of setting up a Kubernetes cluster yourself, but still presents many challenges to overcome and a great degree of freedom in how you configure the cluster.

I have recently created a Kubernetes cluster on AWS for a client, where I used the Kops tool for the very first time and I will here present what I learnt about implementing best practices with this technology stack. Given the rapid development of Kubernetes itself, and how relatively young Kops is, it proved to be far from a walk in the park to create a production-grade cluster. Documentation is often relatively poor, or just plain missing. The intention of this blog post is to make it easier for others going down the same route.