New Alpha versions of the Python OpenTelemetry API and SDK packages have just been released, and it’s time to take them for a test drive! This guide walks through installing the required packages, generating tracing information and propagating context across processes via HTTP.
Data pipelines and analytics tooling have become essential parts of modern businesses, so why are error monitoring and observability tooling for data tools still 10 years behind modern application development? Logs are just not good enough to make sure you can quickly and efficiently debug your errors.
Sometimes it’s fun to just nerd out for a moment. Python is one of the most popular scripting languages today and we love it too. That’s one reason why LogicMonitor can execute any script or programming language supported by your environment. That means can enjoy the latest features of your favorite languages in your favorite monitoring tool (right?). If Python is your vice, give Python 3.7’s Data Classes a try!
Python is a powerful and versatile higher-order programming language. Whether you’re developing a web application or working with machine learning, this language has you covered. Python does well at optimizing developer productivity. You can quickly create a program that solves a business problem or fills a practical need. However, the solutions you reach when developing quickly aren’t always optimized for python performance.
Cloud Computing is where the market is migrating. Therefore, we’re here to build an application by leveraging the power of cloud services and see how efficiently we can monitor it.
Python list comprehensions offer a concise method of interacting with each element of a list. Even though they’ve been available since Python 2.0, their syntax often demotivates people from using them. This article aims to introduce List Comprehensions in a friendly way and offer you one more Python feature to add to your scripting toolbox.
A great thing about building applications for the internet is that people from all around the world can benefit from your effort. You can gather new users from Taiwan to Colorado and meet their needs just as effectively. In this global context, it can be good to provide your users with local flavor to help them feel connected to you and your applications. It can also be useful for you to know where your users are coming from to make sure that your infrastructure is configured in the best way.
In my last two posts, I wrote about how to send mass emails with AWS Lambda and the lessons I learned along the way. This post is all about how to take that serverless design to the next level by maximizing my observability into every function running. In this design, three main functions were responsible for getting the emails out the door.
In this post, we’ll learn what Amazon Web Services (AWS) Lambda is, and why it might be a good idea to use for your next project. For a more in-depth introduction to serverless and Lambda, read AWS Lambda: Your Quick Start Guide to Going Serverless. In order to show how useful Lambda can be, we’ll walk through creating a simple Lambda function using the Python programming language. We’ll test it out, as well as take a look at what Lambda provides for metrics and logging.
