Now an important aspect of how mod_wsgi daemon process groups work, is that the step of setting up a daemon process groups is separate to the step of saying what WSGI application should actually run in that daemon process group. What this means is that it is possible to tell mod_wsgi to create a daemon process group, but then never actually run a WSGI application in it.
Combining that with the ability of mod_wsgi to load and run a specific Python script in the context of the processes making up a daemon process group when those processes are started, it is actually possible to use a daemon process group to run other Python based services instead and have Apache manage that service. This could for example be used to implement a mini background task execution service in Python allowing you to offload work from the WSGI application processes, with it all managed as part of the Apache instance.
As far as mod_wsgi is concerned it doesn’t really care what the process does though, it will simply create the process and trigger the loading of the initial Python script. It doesn’t even really care if that Python script performs an ‘exec()’ to run a completely different program, thus replacing the Python process with something else. It is this latter trick of being able to run a separate program that we can use to have Apache manage the life of the Docker instance created from our container image.
Quelle: Graham Dumpleton: Using Apache to start and manage Docker containers.
This handcrafted guide exists to provide both novice and expert Python developers a best practice handbook to the installation, configuration, and usage of Python on a daily basis.This guide is opinionated in a way that is almost, but not quite, entirely unlike Python’s official documentation. You won’t find a list of every Python web framework available here. Rather, you’ll find a nice concise list of highly recommended options.
Quelle: The Hitchhiker’s Guide to Python! — The Hitchhiker’s Guide to Python
A lot of information on AWS is already written. Most people learn AWS by reading a blog or a “getting started guide” and referring to the standard AWS references. Nonetheless, trustworthy and practical information and recommendations aren’t easy to come by. AWS’s own documentation is a great but sprawling resource few have time to read fully, and it doesn’t include anything but official facts, so omits experiences of engineers. The information in blogs or Stack Overflow is also not consistently up to date.
This guide is by and for engineers who use AWS. It aims to be a useful, living reference that consolidates links, tips, gotchas, and best practices. It arose from discussion and editing over beers by several engineers who have used AWS extensively.
Source: The Open Guide to Amazon Web Services
Hey, have you heard of the new AWS services: ContainerCache, ElastiCast and QR72? Of course not, I just made those up.But with 50 plus opaquely named services, we decided that enough was enough and that some plain english descriptions were needed.
Source: AWS in Plain English
We recently introduced Instant Messaging on LinkedIn, complete with typing indicators and read receipts. To make this happen, we needed a way to push data from the server to mobile and web clients over persistent connections instead of the traditional request-response paradigm that most modern applications are built on. In this post, we’ll describe the mechanisms we use to instantly send messages, typing indicators, and read receipts to clients as soon as they arrive. We’ll describe how we used the Play Framework and the Akka Actor Model to manage Server-sent events-based persistent connections. We’ll also provide insights into how we did load testing on our server to manage hundreds of thousands of concurrent persistent connections in production. Finally, we’ll share optimization techniques that we picked up along the way.
Source: Instant Messaging at LinkedIn: Scaling to Hundreds of Thousands of Persistent Connections on One Machine | LinkedIn Engineering