When you pull a Docker image, you will notice that it is pulled as different layers. Also, when you create your own Docker image, several layers are created. In this post we will try to get a better understanding of Docker layers.
In a previous post, we talked about how we can check our Docker images for any known vulnerabilities by means of Anchore Engine. This still required a manual action. Wouldn’t it be great if we could incorporate Anchore Engine into our Jenkins CI build job or pipeline? In this post, we will take a look at how we can accomplish this by means of the Anchore Container Image Scanner Jenkins Plugin.
When using Docker containers in production, we need to ensure that we are following best practices. In this post, we will focus on Ensure images are scanned and rebuilt to include security patches from the CIS Docker Community Benchmark which we discussed previously. The item states that you should scan your images “frequently” for any vulnerabilities and then take the necessary actions to mitigate these vulnerabilities. We will use Anchore Engine in order to accomplish this.
Do you want to experiment with Jenkins CI in a local setup? In this post we will setup a local Jenkins CI server, create a build job for a simple Spring Boot Maven project and push the created Docker image to DockerHub. It will be a setup for local experimenting only, but really handy if you want to try out a Jenkins plugin for example.
You are using Docker for development and testing purposes but did not yet take the step to use it in production? Then read on, because in this blog post we will take a look at how you can ensure that you run your Docker containers in a secure way.
Assume a new developer or test engineer is added to your team. You develop an application with obviously some kind of database and you want them to get up to speed as soon as possible. You could ask them to install the application and database themselves or you could support them with it, but this would cause a lot of effort. What if you handed them over a simple YAML file which would get them up to speed in a few minutes? In this post we will explore some of the capabilities of Docker Compose in order to accomplish this.
In the first part of this post, we explained the Performance Diagnostic Methodology (PDM) and how to use it. But, the proof of the pudding is in the eating and therefore it is now time to apply the methodology. Of course, the best proof would be to apply the methodology to a real world performance issue, but instead of waiting for that, we will try to simulate some performance issues and verify whether the methodology can work.