Moshe Zadka - DevOps in Python: Infrastructure as Python

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Python began as a language to automate an operating system: the Amoeba. A typical Unix shell would be ill-suited since it had an API, not just textual file representations. The Amoeba OS is a relic now. However, Python continues to be a useful tool for automation of operationsthe heart of typical DevOps work.

It is easy to learn and easy to write readable code is a necessity when a critical part of the work is responding to a 4 a.m. alert and modifying some misbehaving program.

It has powerful bindings to C and C++, the universal languages of the operating systemand yet is natively memory-safe, leading to few crashes at the automation layer.

Finally, although not true when it was created, Python is one of the most popular languages. This means that it is relatively easy to hire people with Python experience and easy to get training materials and courses for people who need to learn on the job.

This book guides you through how to take advantage of Python to automate operations.

To get the most out of the book, you need to be somewhat familiar with Python. If you are new to Python, there are many great resources to learn it, including the official Python tutorial at docs.python.org. You also need to be somewhat familiar with Unix-like operating systems like Linux, especially how to use the command line.

Thanks to my wife, Jennifer Zadka, without whose support I could not have written this book.

Thanks to my parents, Yaacov and Pnina Zadka, who taught me how to learn.

Thanks to my advisor, Yael Karshon, who taught me how to write.

Thanks to Mahmoud Hashemi for inspiration and encouragement.

Thanks to Mark Williams for being there for me.

Thanks to Glyph Lefkowitz for teaching me about Python, programming, and being a good person.

Thanks to Brennon Church and Andrea Ross, who supported my personal growth and learning journey.

Before you can use Python, you need to install it. Some operating systems, such as macOS and some Linux variants, have Python preinstalled. Those versions of Python, colloquially called system Python, often make poor defaults for people who want to develop in Python.

The version of Python installed is often behind the latest practices. System integrators often patch Python in ways that can lead to surprises. For example, Debian-based Python is often missing modules like venv and ensurepip. macOS Python links against a Mac shim around its native SSL library. Those things mean, especially when starting and consuming FAQs and web resources, it is better to install Python from scratch.

This chapter covers a few ways to do so and the pros and cons of each.

Volunteers have built ready-to-install packages for some of the more popular operating systems.

On macOS, the Homebrew third-party package manager has up-to-date Python packages. An introduction to Homebrew is beyond the scope of this book. Homebrew being a rolling release, the Python version is upgraded from time to time. While this means that it is a useful way to get an up-to-date Python, it makes it a poor target for reliably distributing tools.

It also has some downsides for doing day-to-day development. Since it upgrades quickly after a new Python release, development environments can break quickly and without warning. It also means that sometimes code can stop working; even if you are careful to watch upcoming Python versions for breaking changes, not all packages will. Homebrew is a good fit when needing a well-built up-to-date Python interpreter for a one-off task. Writing a quick script to analyze data, or automate some APIs, is a good use of Homebrew Python.