All programming languages provide efficient data structures that allow you to logically or mathematically organize and model your data. Most of us are familiar with simpler data structures like lists (or arrays) and dictionaries (or associative arrays), but these basic array-based data structures act more as generic solutions to your programming needs and aren’t really optimized for performance on custom implementations. There’s much more than programming languages bring to the table.

Web frameworks are powerful tools. They abstract the common aspects of building web sites and APIs and allow us to build richer, more stable applications with less effort. A broad range of web frameworks is available to us in Python. Some are proven favorites with large ecosystems and communities. Others excel in niche use cases or for specific kinds of development. Still, others are up-and-comers with compelling new reasons to be considered.

Our instrumentation uses built-in extension mechanisms where possible, such as Django’s database instrumentation. But often libraries have no such mechanisms, so we resort to wrapping third party libraries’ functions with our own decorators. For example, we instrument jinja2 ’s Template.render() function with a decorator to measure template rendering time. We value the correctness of our instrumentation a lot so that we do not affect our users’ applications.

Webhooks run a large portion of the "magic" that happens between applications. They are sometimes called reverse APIs, callbacks, and even notifications. Many services, such as SendGrid, Stripe, Slack, and GitHub use events to send webhooks as part of their API. This allows your application to listen for events and perform actions when they happen. In a previous article, we looked at how to consume webhooks with Node.js and Express.

The Sigma IDE has added Alexa skill development support that makes skill development in NodeJS or Python a breeze! This post includes the tutorial walk-through for the YouTube video which you can find below.

This guide is focused on how to log in Python using the built-in support for logging. It introduces various concepts that are relevant to understanding Python logging, discusses the corresponding logging APIs in Python and how to use them, and presents best practices and performance considerations for using these APIs.

About 90% of all Lambda functions monitored by Dashbird on AWS Lambda are running Nodejs and Python runtimes. Is this purely a reflection of the general popularity of these programming languages?