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.
Thundra’s Python agent recently had important additions to its feature set. We made observing applications with distributed components easier with distributed tracing support. Also, it now has automatic tracing for SqlAlchemy, Elasticsearch, and MongoDB to ease observing interactions with your datastores. Moreover, we added sampling support for log, metric and trace plugins to filter data intelligently under heavy load.
If you’re a Python developer building serverless applications, you’ve probably seen our series, “The Right Way™ to do Serverless in Python.” However, it’s been a while since our last major feature release of our IOpipe Python agent. In that launch, we introduced auto HTTP tracing and have since made a number of refinements.
Python has rapidly grown into one of the most popular languages for automation. That’s why LogicMonitor supports Python when it comes to managing your LM portal and expanding your monitoring coverage with scripts. During your scripting adventures, you may find yourself wanting to take your work to the next level. One widely-applicable improvement is credential abstraction.
We’re pleased to announce that the Lumigo serverless intelligence platform now supports the Python microframework, AWS Chalice. Chalice was created by AWS to simplify the process of writing serverless apps in Python. Similar in structure to the Flask web framework, Chalice handles much of the configuration on behalf of the user, including automated IAM role policy generation.
Many people writing about AWS Lambda view Node as the code-default. I’ve been guilty of this in my own articles, but it’s important to remember that Python is a ‘first-class citizen’ within AWS and is a great option for writing readable Lambda code. Take a look at these two starter examples of writing functionality in Python.
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.
