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:
- Make your builds much slower.
- Make your images bigger.
- Waste your time.
- 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:
$ docker pull --quiet ubuntu:18.04
docker.io/library/ubuntu:18.04
$ docker pull --quiet alpine
docker.io/library/alpine:latest
$ docker image ls ubuntu:18.04
REPOSITORY TAG IMAGE ID SIZE
ubuntu 18.04 ccc6e87d482b 64.2MB
$ docker image ls alpine
REPOSITORY TAG IMAGE ID SIZE
alpine latest e7d92cdc71fe 5.59MB
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:
FROM ubuntu:18.04
RUN apt-get update &&
apt-get install --no-install-recommends -y gcc &&
apt-get clean && rm -rf /var/lib/apt/lists/*
Note: Outside the 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.
Want a best-practices Dockerfile and build system? Check out my Production-Ready Python Containers product.
We can then build and time that:
$ time docker build -t ubuntu-gcc -f Dockerfile.ubuntu --quiet .
sha256:b6a3ee33acb83148cd273b0098f4c7eed01a82f47eeb8f5bec775c26d4fe4aae
real 0m29.251s
user 0m0.032s
sys 0m0.026s
$ docker image ls ubuntu-gcc
REPOSITORY TAG IMAGE ID CREATED SIZE
ubuntu-gcc latest b6a3ee33acb8 9 seconds ago 150MB
Now let’s make the equivalent Alpine Dockerfile:
FROM alpine
RUN apk add --update gcc
And again, build the image and check its size:
$ time docker build -t alpine-gcc -f Dockerfile.alpine --quiet .
sha256:efd626923c1478ccde67db28911ef90799710e5b8125cf4ebb2b2ca200ae1ac3
real 0m15.461s
user 0m0.026s
sys 0m0.024s
$ docker image ls alpine-gcc
REPOSITORY TAG IMAGE ID CREATED SIZE
alpine-gcc latest efd626923c14 7 seconds ago 105MB
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.
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:
FROM python:3.8-slim
RUN pip install --no-cache-dir matplotlib pandas
And when we build it:
$ docker build -f Dockerfile.slim -t python-matpan.
Sending build context to Docker daemon 3.072kB
Step 1/2 : FROM python:3.8-slim
---> 036ea1506a85
Step 2/2 : RUN pip install --no-cache-dir matplotlib pandas
---> Running in 13739b2a0917
Collecting matplotlib
Downloading matplotlib-3.1.2-cp38-cp38-manylinux1_x86_64.whl (13.1 MB)
Collecting pandas
Downloading pandas-0.25.3-cp38-cp38-manylinux1_x86_64.whl (10.4 MB)
...
Successfully built b98b5dc06690
Successfully tagged python-matpan:latest
real 0m30.297s
user 0m0.043s
sys 0m0.020s
The resulting image is 363MB.
Can we do better with Alpine? Let’s try:
FROM python:3.8-alpine
RUN pip install --no-cache-dir matplotlib pandas
And now we build it:
$ docker build -t python-matpan-alpine -f Dockerfile.alpine .
Sending build context to Docker daemon 3.072kB
Step 1/2 : FROM python:3.8-alpine
---> a0ee0c90a0db
Step 2/2 : RUN pip install --no-cache-dir matplotlib pandas
---> Running in 6740adad3729
Collecting matplotlib
Downloading matplotlib-3.1.2.tar.gz (40.9 MB)
ERROR: Command errored out with exit status 1:
command: /usr/local/bin/python -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/
tmp/pip-install-a3olrixa/matplotlib/setup.py'"'"'; __file__='"'"'/tmp/pip-install-a3olrixa/matplotlib/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'
'"'"', '"'"'
'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' egg_info --egg-base /tmp/pip-install-a3olrixa/matplotlib/pip-egg-info
...
ERROR: Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.
The command '/bin/sh -c pip install matplotlib pandas' returned a non-zero code: 1
What’s going on?
Alpine doesn’t support wheels
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 Alpine doesn’t support standard wheels.
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 they’re disabled.
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 dependency yourself. In this case, to figure out the dependencies I did some research, and ended up with the following update Dockerfile:
FROM python:3.8-alpine
RUN apk --update add gcc build-base freetype-dev libpng-dev openblas-dev
RUN pip install --no-cache-dir matplotlib pandas
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:
| Base image | Time to build | Image size | Research required |
|---|---|---|---|
python:3.8-slim |
30 seconds | 363MB | No |
python:3.8-alpine |
1557 seconds | 851MB | Yes |
Alpine builds are vastly slower, the image is bigger, and I had to do a bunch of research. The image size can be made smaller, for example with multi-stage builds, but that means even more work.
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:
- Alpine has a smaller default stack size for threads, which can lead to Python crashes.
- One Alpine user discovered that their Python application was much slower because of the way musl allocates memory vs. glibc.
- 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.
- 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.