Which Docker Base Image Is Best for Python Apps? A Comprehensive Comparison

Introduction

In the early days of Python development I relied on virtualenvwrapper for environment isolation, but with Python 3 the built‑in venv became the standard. Modern applications now require complex environments (e.g., Redis, PostgreSQL) and Docker has become essential for building and deploying Python services.

Requirements for Docker Base Images

We need a base image that satisfies both general operating‑system expectations and the specific needs of Python applications.

Key requirements include stability, timely security updates, up‑to‑date system libraries, a rich set of packages, the latest Python version, and a small image footprint.

Long‑term support (LTS) is also important for production deployments.

Long‑term support (LTS) is a software lifecycle policy that extends maintenance periods, reduces update risk, and improves reliability.

Linux Image Options

Option 1: Traditional Linux distributions – Ubuntu LTS, CentOS, Debian

Ubuntu 18.04 (LTS) receives security updates until 2023, CentOS 8 is supported until 2024 (maintenance until 2029), and Debian 10 (Buster) is supported until 2024. These images often ship older Python versions (e.g., Ubuntu 18.04 includes Python 3.6.7), so the Dockerfile must install or upgrade Python manually.

Option 2: Official Docker Python images

The official Python images come with pre‑installed Python (3.7, 3.8, 3.9) and multiple variants such as Alpine, Debian Buster, and slim builds. Choosing the correct variant is crucial because size and available libraries differ significantly.

Option 3: Cloud Linux images – Amazon Linux 2

Amazon Linux 2 is an AWS‑optimized, LTS, security‑focused, free Linux distribution that integrates well with Docker and offers good performance for Python workloads.

Option 4: Alpine

Alpine Linux is only 5.59 MB (≈8.7 % of Ubuntu 18.04) because it uses the lightweight musl C library and BusyBox utilities. However, many pre‑compiled Python wheels are built against glibc, so Alpine often requires source compilation and additional build tools, leading to longer build times.

Comparison – Docker Base Image Size

Image sizes (as of the test date 2020‑02‑28):

Ubuntu 18.04: 64.2 MB

Alpine: 5.59 MB

Debian buster: 114 MB

CentOS 8: 237 MB

Amazon Linux 2: 163 MB

python:3.7: 919 MB

python:3.7‑slim: 179 MB

When deploying many containers, smaller images reduce storage costs and start‑up latency.

Comparison – Docker Image Build Time

The following simple Flask Dockerfile is used for all tests:

# Dockerfile-flask
# Simply inherit the Python 3 image.
FROM python:3
ENV APP /app
RUN mkdir $APP
WORKDIR $APP
EXPOSE 5000
COPY requirements.txt .
RUN pip install --upgrade pip
COPY . .
# Finally, run the application (omitted for brevity)

Measured build times (seconds):

ubuntu:18.04 – 91.0 s

amazonlinux:2 – 30.9 s

debian:buster – 52.2 s

python:3.7 – 35.8 s

python:3.7‑slim – 53.5 s

alpine:latest – 1483 s (≈24 min)

The Alpine build is dramatically slower because it must install gcc, make, musl-dev, and compile many Python packages from source.

The long build time is caused by Alpine’s use of musl instead of glibc, which forces source compilation of wheels that expect glibc.

Conclusion

Alpine is unsuitable as a base for most Python applications due to poor binary‑wheel support despite its tiny size; it may be better for Go or other languages.

Amazon Linux 2 offers a stable, secure, high‑performance Python base in AWS environments.

Ubuntu 18.04 and Debian 10 provide balanced size and compatibility; Debian 10 is slightly newer and a solid default choice.

The official Docker Python images do not show clear advantages in security or maintenance for Python workloads.

Consistency of the Linux distribution across large deployments is important to avoid unforeseen risks.