Escape the Micro-Maze: Build Fast, Scalable Streaming Services with Apache Flink

So, you're building microservices, and if you're like me, you've probably found yourself wrestling with Kubernetes, trying to manage state, handle failures, and figure out scaling for each service. Someone inevitably says, "Just build it stateless!" and I always think, "I'd love to see that work seamlessly in the real world." I believe there's a more straightforward way to build fast, resilient user experiences.

In this talk, I want to share a somewhat radical idea for those of us tired of the traditional microservice shuffle: building our operational logic, and even entire microservices, directly in Apache Flink. I'm not just talking about data pipelines; I'm proposing we start "going operational with Flink," moving beyond its traditional analytical domain.

I'll dig into why I think Flink offers a distinct advantage for application development. First, Flink was born for state, and I'll show you how its robust state backends can simplify what's often a major headache in microservice architectures. Then, we'll look at how Flink's inherent fault tolerance and scaling mechanisms can apply to our application logic, not just data processing – meaning less ops and more dev for us. Finally, I'll discuss practical approaches for handling point-to-point calls, comprehensive state management, and general application development patterns within Flink. I've come to think of Flink as an application server, supercharged for streams and state.

Join me to see how Apache Flink can simplify our architectures, make our user experiences faster, and potentially let us bid farewell to some of those microservice complexities. And with a bit of help From Kafka streams, we'll see it action