Tired of waiting for sluggish HTTP requests to complete before your backend code can proceed with other things? Sanic is an asynchronous web framework in Python, that is built to be fast. In a world where Flask and Django are the most preferred web development options in Python, Sanic is the new kid on the block. It’s a promising alternative that is not only faster but also delivers efficiency, simplicity, and scalability.

Gunicorn is a popular application server for Python applications. It uses the Web Server Gateway Interface (WSGI), which defines how a web server communicates with and makes requests to a Python application. In production, Gunicorn is often deployed behind an NGINX web server. NGINX proxies web requests and passes them on to Gunicorn worker processes that execute the application.

There are dozens of great languages to learn. Today we’ll be breaking down the differences between two of the best: PHP vs Python. Which one is best for your application? Which is going to give you the best return on your development time? We’ll pit these two head-to-head and find out which is the best choice for you.

Python powers many applications we use in our day-to-day like Reddit, Instagram, Dropbox, and Spotify. The adoption of Python 3 has been a subject of debate in the Python community. While Python 3 has been out for more than a decade now, there wasn’t much incentive to migrate from the stable Python 2.7 in the earlier releases. If you’re still running on legacy python, it’s high time to migrate as it has reached the end of its life since Jan 2020.

Over the past 24 months or so, I have been studying investing/trading while also working to become more proficient with Splunk. I like to combine activities and gain momentum, so I decided stock market and economic data would be the perfect way to dig deeper into Splunk and hopefully improve my investing/trading. In the beginning, I only looked at it as a way to learn more about Splunk while using data that was interesting to me.

InfluxData prides itself on prioritizing developer happiness. A large part of maintaining developer happiness is providing client libraries that allow users to interact with the database through the language and library of their choosing. Data analysis is the task most broadly associated with Python use cases, accounting for 58% of Python tasks, so it makes sense that Pandas is the second most popular library for Python users.

We’re big fans of AWS Lambda at Honeycomb. As you may have read, we recently made some major improvements to our storage engine by leveraging Lambda to process more data in less time. Making a change to a complex system like our storage engine is daunting, but can be made less so with good instrumentation and tracing. For this project, that meant getting instrumentation out of Lambda and into Honeycomb.