Serious software development calls for performance optimization. When you start optimizing application performance, you can’t escape looking at profilers. Whether monitoring production servers or tracking frequency and duration of method calls, profilers run the gamut. In this article, I’ll cover the basics of using a Python profiler, breaking down the key concepts, and introducing the various libraries and tools for each key concept in Python profiling.

Around 1996, when I attended my first programming classes, C++ was the language of choice if you wanted to have a job in this industry. The Internet was young and not as widely available as it is now. Ruby and Python were still in their infancy. Now, in 2018, both have evolved and matured well enough to be in the top 10 most in-demand programming languages. In this article, I’m going to highlight features and contrasts between Python and Ruby.

PM2 is a production-grade process manager that makes management of background process easy. In the Python world we could compare PM2 to Supervisord, but PM2 has some nifty features you might like. With PM2, rolling restarts, monitoring, checking logs and even deploying application has never been that simple. We really value CLI UX, so PM2 is really simple to use and master.

Flask is a Python framework known for its ease of use. It inherits Python’s advantages of extensibility, broad support, and relative simplicity. It’s known as a microframework because it relies on extensions for much of its functionality. Flask avoids constraining the developer to a predetermined database or authentication mechanism, for example, and instead leaves room for choice.

We’ve been on a roll this year with Beelines, our integrations for quick, easy, and automagic instrumentation of your apps. You may have already seen our Node.js, Ruby, and Go beelines – today, we’re excited to announce the release of the Honeycomb Beeline for Python!