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.
Back when starting my first software development job I thought my success at work would be directly determined by my programming skills. Of course, that's not the case. One can be the most productive and clever programmer in the world, but that alone isn't sufficient for a satisfactory career in software development. You'll need to get along with people, know which problems not to fix, deal with pressure, etc.
Hello again! In this part of the series, we'll finally get our hands dirty using Skaffold to build, push and deploy applications on Kubernetes. We'll deploy a Postgres database on our local Minikube cluster. Along the way, we'll learn Kubernetes concepts such as ConfigMaps, Secrets, Persistent Volumes and Persistent Volume Claims, StatefulSets, and Services.
This is the first part of a series on my journey in learning functional programming (FP). In this first part, I'd like to share why I spend time on learning functional programming in the first place.
In Part 1 of this series, we installed all tools required for developing our Django application on local Kubernetes with Skaffold. In this part, we'll create the Django application. In the next part, we'll finally get to the fun part: defining Kubernetes manifests and Skaffold configuration file.
Containerization has been a revolution in software development. Technologies such as Docker allow developers package their software and all its dependencies in containers runnable in any computing environment, be it your local desktop, public cloud or your company's datacenter. This has drastically accelerated deploying software.
I had two courses in programming at the university, in Java and C. The Java course taught object-oriented programming: objects, classes, inheritance and all that.
Property-based testing is a testing paradigm supporting regular example-based unit tests. In the Pragmatic Programmer book, the authors recommend to use property-based testing for verifying assumptions about their code. It forces you to think about the actual preconditions, postconditions and invariants of your code instead of implicitly coming up with such rules through hard-coded examples.
Optics are a functional programming toolbox for zooming into nested data structures. They are designed for composability, allowing you to create complex operations step-by-step by composing simple components. Optics also never modify their input data structures, ensuring your objects stay nice and immutable.