About this Book
Version 1.0 released in 2017.
When I released The Hacker’s Guide to Python in 2014, I had no idea that I
would be writing a new book so soon. Having worked on OpenStack for a
few more years, I saw how it is easy to struggle with other aspects of Python,
even after being on board for a while.
Nowadays, even if computers are super-fast, no server is fast enough to
handle millions of request per second, which is a typical workload we want
to use them for. Back in the day, when your application was slow, you just
had to optimize it or upgrade your hardware – whichever was cheaper.
However, in a world where you may already have done both, you need to be
able to scale your application horizontally, i.e., you have to make it run on
multiple computers in parallel.
That is usually the start of a long journey, filled with concurrency problems
and disaster scenarios.
Developers often dismiss Python when they want to write performance
enhancing, and distributed applications. They tend to consider the language
to be slow and not suited to that task. Sure, Python is not Erlang, but there’s
also no need to ditch it for Go because of everyone saying it is faster.
I would like to make you aware, dear reader, that a language is never slow.
You would not say that English or French is slow, right? The same applies
for programming languages. The only thing that can be slow is the
implementation of the language – in Python’s case, its reference
implementation is CPython.
Indeed CPython can be quite sluggish, and it has its share of problems.
Every implementation of a programming language has its downside.
However, I think that the ecosystem of Python can make up for that defect.
Python and everything that evolves around it offer a large set of possibilities
to extend your application, so it can manage thousands of requests
simultaneously, compensating for its lack of distributed design or,
