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🛠 Reactive Streams with Kotlin, Webflux, and Rsocket-JS
The concept of reactive programming has enjoyed a resurgence in the last few years. The post Notes on Reactive Programming calls out that:
the origins of Reactive Programming can probably be traced to the 1970s or even earlier
But it is only relatively recently that Java and its ecosystem provided first-class support for reactive programming, with Java 9 introducing reactive streams, and Spring 5 introducing WebFlux.
In this post, we'll dive into a simple example application implementing a WebFlux server accessed from a web page via RSockets. But before we go into the code, it is worth providing a quick overview of what reactive programming is all about.
Imagine you are a reseller and have been given the task of contacting your supplier to find the status of your orders. So you pick up the phone and call your supplier. A customer service representative takes the call and places you on hold while they query their system for the order status. You listen to the hold music for a few minutes, after which the customer service representative picks up again and informs you of the order status.
This interaction was not very efficient. It forces you to sit on hold, during which time you are not all that occupied, but still you are unable to walk away and perform other useful tasks. You have essentially been blocked as you wait for a response to your query.
Let's now imagine another scenario where instead of calling your supplier, you send them an email with your request. Once the email is sent, you can move on to other tasks. When you receive a response, you have the luxury of finishing what you were doing before reading the email reply.
This style of interaction is much more efficient than waiting on the phone, as you are free to perform whatever tasks you need to while waiting for an email response. You have effectively been unblocked and been granted the freedom to perform tasks asynchronously.
Traditional web servers and clients work in a synchronous, blocking fashion. A client will make a request and block execution until a response is provided. The server, in turn, will spawn a thread to process the request, perform any work, and provide the response to the client.
Reactive applications are built around the idea of servers and clients interacting in an asynchronous and non-blocking fashion. This allows for much more efficient use of resources, allowing a server to process more clients.
There is much more to reactive programming than the simple analogy above, but this is enough to understand the sample application we'll explore next.
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