You’ll build images for a sample Go API in several different Docker containers, starting with Ubuntu and language-specific images, then moving on to the Alpine distribution. You will also use multi-stage builds to optimize your images for production. Ubuntu Proposed Main Official: amd64 311: arm64 238: Ubuntu Proposed Multiverse Official: amd64 22: arm64 1: Ubuntu Proposed Universe Official: amd64 137: arm64 133: Ubuntu Restricted Official: amd64 278: arm64 13: Ubuntu Universe Official: amd64 54136: arm64 52988: Ubuntu Updates Main Official: amd64 1869: arm64 1510: Ubuntu Updates Multiverse.

• Ubuntu Alpine Docker Command
• Ubuntu Vs Alpine Docker

When you’re choosing a base image for your Docker image, Alpine Linux is often recommended.Using Alpine, you’re told, will make your images smaller and speed up your builds.And if you’re using Go that’s reasonable advice.

But if you’re using Python, Alpine Linux will quite often:

1. Make your builds much slower.
2. Make your images bigger.
3. Waste your time.
4. On occassion, introduce obscure runtime bugs.

Let’s see why Alpine is recommended, and why you probably shouldn’t use it for your Python application.

Why people recommend Alpine

Let’s say we need to install gcc as part of our image build, and we want to see how Alpine Linux compares to Ubuntu 18.04 in terms of build time and image size.

First, I’ll pull both images, and check their size:

As you can see, the base image for Alpine is much smaller.

Next, we’ll try installing gcc in both of them.First, with Ubuntu:

Note: Outside the very specific topic under discussion, the Dockerfiles in this article are not examples of best practices, since the added complexity would obscure the main point of the article.

To ensure you’re writing secure, correct, fast Dockerfiles, consider my Python on Docker Production Handbook, which includes a packaging process and >70 best practices.

We can then build and time that:

Ubuntu Alpine Docker Command

Now let’s make the equivalent Alpine Dockerfile:

And again, build the image and check its size:

As promised, Alpine images build faster and are smaller: 15 seconds instead of 30 seconds, and the image is 105MB instead of 150MB.That’s pretty good!

But when we switch to packaging a Python application, things start going wrong.

Ubuntu Vs Alpine Docker

Let’s build a Python image

We want to package a Python application that uses pandas and matplotlib.So one option is to use the Debian-based official Python image (which I pulled in advance), with the following Dockerfile:

And when we build it:

The resulting image is 363MB.

Can we do better with Alpine? Let’s try:

And now we build it:

What’s going on?

Standard PyPI wheels don’t work on Alpine

If you look at the Debian-based build above, you’ll see it’s downloading matplotlib-3.1.2-cp38-cp38-manylinux1_x86_64.whl.This is a pre-compiled binary wheel.Alpine, in contrast, downloads the source code (matplotlib-3.1.2.tar.gz), because standard Linux wheels don’t work on Alpine Linux.

Why?Most Linux distributions use the GNU version (glibc) of the standard C library that is required by pretty much every C program, including Python.But Alpine Linux uses musl, those binary wheels are compiled against glibc, and therefore Alpine disabled Linux wheel support.

Most Python packages these days include binary wheels on PyPI, significantly speeding install time.But if you’re using Alpine Linux you need to compile all the C code in every Python package that you use.

Which also means you need to figure out every single system library dependency yourself.In this case, to figure out the dependencies I did some research, and ended up with the following updated Dockerfile:

And then we build it, and it takes…

… 25 minutes, 57 seconds! And the resulting image is 851MB.

Here’s a comparison between the two base images:

Alpine builds are vastly slower, the image is bigger, and I had to do a bunch of research.

Can’t you work around these issues?

Build time

For faster build times, Alpine Edge, which will eventually become the next stable release, does have matplotlib and pandas.And installing system packages is quite fast.As of January 2020, however, the current stable release does not include these popular packages.

Even when they are available, however, system packages almost always lag what’s on PyPI, and it’s unlikely that Alpine will ever package everything that’s on PyPI.In practice most Python teams I know don’t use system packages for Python dependencies, they rely on PyPI or Conda Forge.

Image size

Some readers pointed out that you can remove the originally installed packages, or add an option not to cache package downloads, or use a multi-stage build.One reader attempt resulted in a 470MB image.

So yes, you can get an image that’s in the ballpark of the slim-based image, but the whole motivation for Alpine Linux is smaller images and faster builds.With enough work you may be able to get a smaller image, but you’re still suffering from a 1500-second build time when they you get a 30-second build time using the python:3.8-slim image.

But wait, there’s more!

Alpine Linux can cause unexpected runtime bugs

While in theory the musl C library used by Alpine is mostly compatible with the glibc used by other Linux distributions, in practice the differences can cause problems.And when problems do occur, they are going to be strange and unexpected.

Some examples:

1. Alpine has a smaller default stack size for threads, which can lead to Python crashes.
2. One Alpine user discovered that their Python application was much slower because of the way musl allocates memory vs. glibc.
3. I once couldn’t do DNS lookups in Alpine images running on minikube (Kubernetes in a VM) when using the WeWork coworking space’s WiFi.The cause was a combination of a bad DNS setup by WeWork, the way Kubernetes and minikube do DNS, and musl’s handling of this edge case vs. what glibc does.musl wasn’t wrong (it matched the RFC), but I had to waste time figuring out the problem and then switching to a glibc-based image.
4. Another user discovered issues with time formatting and parsing.

Most or perhaps all of these problems have already been fixed, but no doubt there are more problems to discover.Random breakage of this sort is just one more thing to worry about.

Don’t use Alpine Linux for Python images

Unless you want massively slower build times, larger images, more work, and the potential for obscure bugs, you’ll want to avoid Alpine Linux as a base image.For some recommendations on what you should use, see my article on choosing a good base image.