The multistage build pattern is evolved from the concept of builder pattern where we use different Dockerfiles for building and packaging the application code. Those images would be available in the common organization docker private repository. In some organizations, the security team itself publishes base images every month after testing & security scanning. You need to get approval from the enterprise security team to use the base image. Note:You cannot directly use the publicly available base images in project environments. So, you might ask, then how do we debug applications? They have the debug version of the same image that comes with the busybox for debugging.Īlso, most of the distributions now have their minimal base images. Distroless base images are available for java, nodejs, python, Rust, etc.ĭistroless images are so minimal that they don’t even have a shell in them. It is a stripped-down version of the operating system. You can further reduce the base image size using distroless images. alpine latest c059bfaa849c 5.59MBīy default, it comes with the sh shell that helps debug the container by attaching it. It’s not just small it’s very secure as well. Your first focus should be on choosing the right base image with a minimal OS footprint. You can clone it and follow along the tutorial. The following are the methods by which we can achieve docker image optimization.ĭocker Excercise Files: All the application code, Dockerfiles, and configs used in this article are hosted this Github repository. Additionally, we have given practical examples to understand docker image optimization in real time.Įither you use the examples given in the article or try the optimization techniques on existing Dockerfiles. Let’s look at different established methods of optimizing Docker images. So it all boils down to how efficiently we can manage these resources inside the container image. If we take a container image of a typical application, it contains a base image, Dependencies/Files/Configs, and cruft (unwanted software). Not just for production environments, at every stage in the CI/CD process, you should optimize your docker images.Īlso, with container orchestration tools like Kubernetes, it is best to have small-sized images to reduce the image transfer and deploy time. Therefore, DevOps engineers must optimize the docker images to ensure that the docker image is not getting bloated after application builds or future releases. I have seen cases where the initial application image started with 350MB, and over time it grew to more than 1.5 GB.Īlso, by installing unwanted libraries, we increase the chance of a potential security risk by increasing the attack surface. This introduces a challenge in Docker image build, as now – the same code requires more time & resources to be built before it can be shipped as a container. In typical software development, each service will have multiple versions/releases, and each version requires more dependencies, commands, and configs. Need for Docker Image OptimizationĮven though the Docker build process is easy, many organizations make the mistake of building bloated Docker images without optimizing the container images. First, we add all the build instructions, such as the code dependencies, commands, and base image details, in Dockerfile. The whole concept of “run anywhere” images starts from a simple configuration file called Dockerfile. It enables engineers to collect all the code dependencies and files into a single location which can be run anywhere, quite quickly & easily. We will also look at some of the best tools for Docker Image Optimization.ĭocker as a container engine makes it easy to take a piece of code & run it inside a container. This blog talks about different optimization techniques that you can quickly implement to make the smallest and minimal docker image. If you want to reduce docker image size, you need to use the standard best practices in building a Docker Image.
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