DevOps

Application development and deployment have come a long way over the years. From manual processes to scripting, to full Continuous Integration and Continuous Deployment (or Delivery) processes along with pipelining tools. Using CI/CD has helped automate and streamline application development and deployment processes. It has also helped improve the general operations process. GitOps (Infrastructure updates via pull request, while using Git as the source of truth for all configurations) helps continuously manage not only the application configuration but also the business and operations configurations governing the application and the resources its using.

Cloud Native applications have become a norm these days. Developers and Operations engineers are continuously finding ways to improve the quality and speed of deploying and maintaining these applications. It started with folks writing bash scripts, migrating to python like scripting and then over to configuration management and infrastructure as code tools. The philosophy behind these Infrastructure as Code and Configuration Management tools is to treat your infrastructure as programmable assets. That also means use of version control systems like Git.

In a previous blog we discussed the configuration and use of fluentbit with AWS elasticsearch. https://medium.com/@bahubalishetti/configuring-fluentbit-on-kubernetes-for-aws-elasticsearch-bec486bcc727 It helped provide a basic configuration of “logging” from a Kubernetes cluster. “Logging” is one aspect of “Observability” in Kubernetes. Lets review: Observability for the cluster and the application covers three areas: Monitoring metrics — Pulling metrics from the cluster, through cAdvisor, metrics server, and/or prometheus, along with application data which can be aggregated across clusters in Wavefront by VMware.

As noted in one of my earlier blogs, one of the key issues with managing Kubernetes is observability. Observability is the ability to gain insight into multiple data points/sets from the Kubernetes cluster and analyze this data in resolving issues. As review, observability for the cluster and application covers three areas: Monitoring metrics — Pulling metrics from the cluster, through cAdvisor, metrics server, and/or prometheus, along with application data which can be aggregated across clusters in Wavefront by VMware.