When I started my journey with Apache Kafka, JSON was already everywhere. From Javascript UIs, through API calls, and even databases - it became a lingua franca of data exchange. For many organizations adopting Kafka I worked with, the decision to combine it with JSON was a no-brainer.
Yet, this post is not really about Kafka itself. It’s not another how-to guide either. Using Kafka made me less enthusiastic about JSON and more focused on data evolution aspects.
There seems to be a lot of interest in improving application startup time these days, driven by the discussions on languages and technologies (like Go, Node, or GraalVM), that provide almost instant startup for serverless functions (a.k.a. lambdas) being run in the cloud. However, such a speedup brings benefits to other types of processing as well.
In this post, I’d like to present a different perspective on the topic of application startup time.
Two days ago I’ve participated in a JUG meeting in Poznań which special guest was famous Java rock star Adam Bien with his presentation called Microservices in 2016 — What Worked Well. The session was focused on the typical Bien’s stack: JEE microservices & Docker and overlapped with his presentation on Devoxx Poland 2016. But don’t get me wrong - it was totally worth it!
The Bien’s way One of the most controversial topics during the session was the preffered size and the granularity of microservices.