For example, if you need to install curl, wget, and git, instead of doing this: The easiest way to decrease the number of layers is to chain similar instructions. This means, ideally, you’d want to have as few layers as possible. Every instruction in the Dockerfile, however, creates a new layer on top of the base Docker image. On top of the base image you probably want to install some software, add some files, configure some parameters, and so on. I explained the base image in a previous section. Dockerfile Instructions Chainingīefore I give you the next tip, let me explain what happens with every new instruction you put into Dockerfile. By redesigning your CI/CD so images are preloaded on the machine, you can save a lot of time. Imagine every time you run your pipeline, Docker attempts to download multiple images (which then get lost after the CI/CD pipeline is finished). But in clustered systems or CI/CD pipelines, it can make a huge difference. Maybe it’s won’t make a huge difference on your local machine since Docker, after downloading the image, will automatically save it on your machine. So, easy performance gains can be achieved by making sure the machine already has the necessary images. If not, Docker will contact DockerHub (or another registry, if specified) and will attempt to download it. Whenever you execute docker run or docker build, the first thing Docker does is look to see if the specified image is already downloaded on the machine. Docker Image CachingĪnother easy performance improvement can be achieved by preloading used Docker images on the machine. Usually, lightweight images are tagged :alpine or :slim. This significant difference can make your builds faster. Image node-alpine, however, is 9x smaller. If you’re developing a node.js-based application, you’re probably using an official node image (FROM node). So, the size of your final image will depend not only on how much you put inside, but also which base image you use. If you use a small base image, however, you’ll get a very small container. So, if you use a large base image, you’ll end up with a big container. Everything you want to have in your container will be added “on top” of the base Docker image. Every Dockerfile (which is a definition of your container) starts with the keyword “FROM.” This instruction tells Docker which base image to use to build your container. Let me briefly explain how Docker containers work to better understand this. The first, and the easiest, way to improve your container build and startup time is to use a slim Docker base image. Therefore, it’s important to have a good monitoring system, which can help you find where your performance is lacking. So, if you suffer from long application startup or restarts, it can be either Docker or the application itself that loads for a long time. Most of the performance improvements to Docker improve container build and startup time only. You should keep in mind, however, that the performance of your application running inside the container isn’t really influenced by Docker itself. Other, more advanced options require a bit more effort-they can’t be applied to every setup, but they bring even more advantages. They don’t typically require many changes to your containers, so it’s an easy win. But if you’re looking for maximum performance, where every second counts, you’ll need to learn how to improve Docker performance even more.Ī few general and easy-to-implement performance tips can be applied pretty much universally. Containers usually start in a few seconds and deploying newer versions of a container doesn’t take much longer. This is due to the way containers work-they’re really fast. Using Docker containers is one of the most popular ways to build modern software these days.
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