#130: Different File Encodings Between Windows and Linux
While Python works great on Linux, Windows and Mac, there are tiny differences that may have big impacts on your code. I found this out the annoying way with a crashing application.
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#129: Copy & Paste With Python
For some problems it would be great if our application could access the clipboard of the current user and do some copy & paste actions. Let's look how we can access the clipboard from Python.
#128: Periodic Tasks in Celery
If you need to run a task every few seconds or create a report every week, you can use Celery and its feature for periodic tasks – at least if we use Linux or WSL on Windows.
#127: Track the State of a Celery Task
So far our Celery tasks where fire-and-forget jobs. We put a task in a queue and go to the next activity. However, sometimes we need to know if a task succeeded before we can do more work. Let us take a look on how Celery tracks tasks.
#126: Flask and Celery
Flask is a great web framework. The only problem, as with all web applications, is the processing of long-running tasks. Luckily for us, we can use our knowledge of Celery and combine it with Flask to process the long-running tasks asynchronously. Even better, we already know everything we need to do this.
#125: Logging in Celery
Errors happen, especially when you have a distributed application. Let's look how Celery allows us to log messages into the Celery logger.
#124: Delay Jobs in Celery
One big benefit of asynchronous jobs is that they do not need to be processed right away. Let's look how we can tell Celery to keep our jobs around for a while before starting the work.
#123: Running Celery on Windows
If we try to run Celery on Windows, we will run into a problem: Windows is not officially supported by Celery. However, that does not mean that it will not work. Let's look what we need to do to run Celery on Windows.
#122: Asynchronous Jobs With Celery
RabbitMQ is a great tool for asynchronous jobs. It works well but we need to do all the infrastructure parts on our own. Celery can run on top of RabbitMQ and offers us a lot more comfort for our asynchronous jobs. However, that comfort comes with the price of additional complexity that we need to handle. In this post we start with the basic set-up and over the next weeks we take a deeper look at the features of Celery.
#121: RabbitMQ and Python
The series on Tweepy showed my that I have a big gap in my Python knowledge: How can I separate work in an asynchronous way? In this post I look at RabbitMQ and how we can use it to get more flexibility in our Python applications.