.NET Core 3 was recently released and brought with it a bunch of innovations, including a brand new JSON (de)serializer System.Text.Json. We're going to compare this serializer with Newtonsoft.Json and other major .NET serializers. Check out this epic performance battle.

In the 3rd part of the series we'll see how to create asynchronous steps in the pipeline with TPL Dataflow. We'll also see a new implementation using the Disruptor-net library.

In the First Part of the series, we talked about the Pipeline Pattern in programming, also known as the Pipes and Filters design pattern. In this part, we'll see how to implement such a pipeline with TPL Dataflow.

The Pipeline pattern is a powerful tool in programming. The idea is to chain a group of functions in a way that the output of each function is the input the next one. The concept is pretty similar to an assembly line where each step manipulates and prepares the product for the next step.

Extension methods are awesome, right? They are probably most widely used in the LINQ feature. But when should we use them? And when shouldn't we? Let's talk guidelines.

One of the most commonly used patterns in software development is Caching. It's a simple, yet extremely effective concept. The idea is reuse of results. When performing a heavy operation, we will save the result in said cache

I'd like to tackle an old dilemma: Class instantiation. Which pattern do you use to create a class? Do you always use a new statement? Do we still need to use Singleton or Factory? Should we always use dependency injection? How about static classes, are they truly evil?

I recently wrote 3 blog posts on different Producer/Consumer (Job Queues) implementations. In this article, we will compare performance of all the approaches, including...

In this article, we'll see how to implement Job Queues with TPL Dataflow, including implementations of several of the said variations. We will dive into the Dataflow mindset along the way, figuring out this awesome library.