I recently finished reading Clean Architecture by Robert C. Martin. This book accompanied with Clean Code and Clean Coder are very useful reading for any professional software developer, even though they are getting old and there are better books available out there. This post briefly summarizes Clean Architecture.
In this article, we'll see how to deploy the Django application built in Part 2 of this series to local Kubernetes cluster. We'll be using Skaffold for the deployment. Skaffold offers support for multiple profiles, making it useful both local development with hot code reloading as well as production deployments.
In the previous part, we created a custom user model in Django. In this part, I'd like to show how to roll custom authentication. Neither custom user model nor custom authentication are required for the granular role-based access control, but I'd like this series to be a complete tour of authentication and authorization in Django. The code accompanying the series can be found in GitHub. So let's get started!
In this short series of articles, I'd like to share how to implement granular, resource-level role-based access control in Django. We'll build a REST API that returns 401s (Unauthorized) for unauthenticated users, 404s for authenticated users not authorized to view given resources, and 403s (Forbidden) for users authorized to view resources but forbidden to perform given actions.
I've had the privilege to participate in recruitment in two companies. I've seen a hundred CVs, read dozens of cover letters, and interviewed many applicants. Based on this experience, I can share a few learnings on how to write a good job application.
Hi! In the previous article, we saw how to speed up I/O-bound tasks with multithreading without any callbacks. In this article, I'd like to share how to speed up compute-intensive (CPU-bound) tasks with ProcessPoolExecutor, again using asyncio high-level methods to keep the code readable and without a single callback.
Recently I had to perform a batch processing task where a thousands of images were downloaded from S3, the images were processed and then uploaded to a new bucket in S3. As the processing was relatively lightweight, most of the computation time was spent on downloading and uploading images, that is, I/O. Such I/O bound tasks are a great fit for multithreading (CPU-bound tasks better fit multiprocessing, with all its quirks related to serialization). In this post, I'd like to share a small example how to run tasks in a thread pool.
The Five Dysfunctions of a Team by Patrick Lencioni is a classic in teamwork literature. Recently I've been reading the author's follow-up book The Ideal Team Player. I've only started reading the fable, but I couldn't help taking a peek at the end of the book where I found an interesting self-evaluation test for anyone interested in improving their team work skills. I thought it's quite cool so I'll share it here!
MapReduce was revolutionary when it was first published in 2004. It provided a programming model for batch processing datasets with terabytes of data. MapReduce was built on three seemingly simple phases: map, sort, and reduce. It used the general-purpose HDFS (Hadoop Distributed File System) file system for I/O and was therefore capable of processing almost any kind of data.
Hi! In this post, I'd like to present my favorite functional programming techniques (FP) for Python. I'm a big fan of FP as I've found that by following FP principles I can write code that is more readable and easier to debug. Python is not a functional programming language (and it never will be), but I think there are still many things we can learn from languages such as Haskell that are beneficial also in Python.