In this blog post, I will list the best practices when creating Dockerfiles. By following these points, you can gain more confidence that your docker images are secure and reliable.

Use non-root user

The first and most important point when it comes to creating Dockerfiles is the avoidance of running images using the root user. This is a key point if you want your docker image to be as secure as possible. By default, Docker is using root as an account that is performing all build steps and then running the docker entry-point executable. By leaving the root user as it is and allowing programs inside containers to be run by root, you are exposing yourself to huge risks. There are cases when properly prepared, the exploit can escape the docker container due to root privileges. When you are using a limited privilege account, such incidents are close to impossible. The next thing is the risk when mounting directories to the docker container. This is especially dangerous when you don’t have access to the Dockerfile of the image that you are running, and you don’t know what is going on under the hood. This might cause problems with files on a docker host, for example removing important system configurations. To avoid such problems, always define your custom user in Dockerfile:

FROM ubuntu
RUN useradd -ms /bin/bash myuser
USER myuser

As an alternative, you can use an already existing non-root user from /etc/passwd in base image.

If you want to run a docker image and force a user, you can use -u parameter. It takes on option in the form UID:GID. So if you want to run some command as user which UID and GID is 100 then add -u parameter like that:

docker run -u 100:100 ...

Labels, volume, and expose aka documentation

Try to always define all labels that will help users of your image gain knowledge about it very quickly. For example, you can add an author label to give the user a contact point in case of problems with the image:

LABEL org.opencontainers.image.authors="[email protected]"

Labels are commonly used to store information about the application and dependencies version, for example, the base image tag. Labels have a form of key-value pairs, so you are not limited to any kind of constraints when defining Then.

The next documentation part of the Dockerfile is the VOLUME command. It has two main roles. Firstly, it creates an external volume that will persist after the docker run and makes sure that data that was defined in Dockerfile in the volume path is copied to a new persistent volume. The next thing is information for the user that a given directory in the docker container can be mounted to a custom location and the contents of this directory are persistent.

The last documentation beat is the EXPOSE statement. It has a form of port and optional protocol for example:

EXPOSE 8080/tcp

This statement will help users quickly learn what ports are used by default by applications built using a given Dockerfile. Those ports will also be listed along with docker containers when the user gets the list of them via the docker ps command.

Order of operations and frequency of changes

If you want to build your image as fast as possible, you need to order operations in Dockerfile in a proper way. If you do it like that, then it will be possible to reuse as many docker image layers as possible and run actual building commands only in a few of them. This will improve build speed significantly. A general rule of order of operations is that you need to put layers that will change most often as the last ones in Dockerfile. Let’s see the example of creating a Python docker image with some web application in it:

FROM python:3

# This section will probably not change
RUN mkdir /app

# Dependencies will not change too often
ADD requirements.txt /app
RUN pip install -r /app/requirements.txt

# source code will change very often
ADD /app
CMD [ "python", "./" ]

By writing our Dockerfile like that, we make sure that when the source code is changed ( file in that case) building this image will take a very short. This is because all layers including one with the installation of dependencies are reused in that case. This will also impact network transfer times when you will push or pull your image. If only the last few layers are modified then the rest of them will be reused and downloading and uploading the image will take less time.

Don’t use the latest tag

It might be tempting to always tag your images using latest tags, but you should avoid it. This tag can be very dangerous. Especially when your production build is designed to use it.

In that case, we can imagine a situation where we update our application and the latest tag is pushed. Then we deploy this image in production and there is some kind of bug. Rolling back for this situation is very hard if we had not noted the image identifier of the previous working image. You can optionally use the latest tag as an alias to the tag that is currently deployed to production, but this should be treated only as part of documentation and, you should not use the latest tag in your deployment.

There might be one more dangerous situation associated with using the latest tag. If your docker deploy scripts are configured to always pull images from a remote repository, then this might lead to an accidental unintended update of the application. For example, if you are changing the configuration and this operation is leading to docker container recreation (for example when changing environment vars) then during this recreation, docker will download the latest tag and also update your application when you do now want to do that.

Always cleanup RUN layers

If you want to keep your images as small as possible, you should always clean up the package’s cache after any kind of apt / yum installation operation. This point is not only for the cache cleanup, and it is more general, but we will discuss it, taking that as an example because this technique is most often used if an apt installation scenario. Let’s have a look at Dockerfile describing this situation.

FROM ubuntu

# update packages, install tar, and clean up
RUN apt-get update && apt-get install -y tar \
  && rm -rf /var/lib/apt/lists/*

If we don’t clean up the files in /var/lib/apt/lists/ then there will be no way to clean them up effectively in the next command/layer. Those files will be saved in the apt-get update layer and deleting them from the next layer will not shrink the image size.

Use build arguments if you want to customize your build later

If you want to create reusable Dockerfiles, you can use the ARG command to achieve that. This statement allows us to pass custom variables to the Dockerfile build process. They can be then referenced as any other environment variables to customize the build. Let’s have a look at the example:

FROM busybox
ARG username
USER $username
CMD echo "$username"

Having this Dockerfile if you want to specify username ARG you need to pass --build-arg parameter to docker build command:

docker build --build-arg username=nobody .

In this way, we created a reusable Dockerfile that can be built multiple times producing different images only by changing --build-arg parameters.

Use multi-stage build instead of many Dockerfiles

If you want to separate building your application using build dependencies and running your application using runtime dependencies, then you can create two separate Dockerfile and use than one after another. This approach is very messy and error-prone because you need to make sure that all elements of the build are perfectly suitable for each other. Luckily for us, docker comes with an easy solution for this problem, a multi-stage build. The syntax of this feature is shown in the fragment of an example Dockerfile.

FROM ubuntu AS builder
RUN # build commands go here
# let's assume that the executable is created as /opt/bin/program file

# We are using a smaller runtime image here
FROM alpine:latest
# We are adding the `--from=build` parameter to copy the file from our builder image
COPY --from=builder /opt/bin/program ./
CMD ["./program"]

This Dockerfile will build the first image and expose the results of this image as a reference builder that can be used in the COPY command. Be specifying --from the parameter we tell this layer that files should be copied from the image that was aliased as build