![]() #Update container image from kitematic how toTogether they’re designed to get you started, especially if you’ve never worked with Docker before or are relatively new to Docker-although they assume you already know and understand how to work with Jenkins. Part two is about laying the groundwork to wrap this image in your own Dockerfile and taking more elegant control of the image. First up, part one is getting your development environment ready and learning to work with the default Jenkins Docker container that Cloudbees offers. Each is sized to take about 30 minutes to complete. This first blog is split into two lesson plans. You can find their documentation here and their Git repo/Dockerfile here. This was my reference point when first thinking about running Jenkins in a Docker container and for many people this might be sufficient. If you haven’t taken a look at the Cloudbees Jenkins Docker image, start there as it’s really quite good. Handling “secret” data with images and Jenkins Minimizing image dependencies on public imagesĬreating and using Data-Volumes, including backupsĬreating containerized “Build Environments” using containers This entire blog series will cover the following Docker concepts: Then we’ll move on to dealing with data persistence and adding a web proxy with NGINX. For starters, we’ll create a Jenkins master server in a Docker container. Over this series of blog posts I’ll be covering ways to think about all of the above as containers and finish with an advanced look at ways to use Docker containers as build slaves. NGINX web proxy (we use SSL certs etc, with NGINX is an easy choice here)īuild slave agents (machines either being SSH’d into, or JNLP connecting to, Jenkins Master) Jenkins master data (Plugins, Job Definitions, etc) We deploy Jenkins with these architectural components in mind: Jenkins is also a great application to demonstrate one way to think about “Dockerizing” your applications. ![]() #Update container image from kitematic softwareThis presents challenges to applications with data persistence needs or multi-process architectures.Īs I mentioned in my last post, we use Jenkins as a foundational piece of open source software on top of which we build our automation. Productionizing applications with Docker containers requires adjusting to their ephemeral nature and single-process focus. ![]() When I first started learning about Docker a year ago and exploring its use I had trouble finding great documentation and examples - even today many describe simple use cases that ultimately aren’t production ready. In this first tutorial in the Docker series, you’ll learn: ![]()
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