Reactive Spring Data in action

To finish this chapter and highlight the benefits of the reactive persistence, let's create a data-intensive reactive application that has to communicate with a database frequently. For example, let's revisit the example from Chapter 6, WebFlux Async Non-Blocking Communication. There, we implemented an alternative read-only web frontend application for the Gitter service (https://gitter.im). The application connects to a predefined chat room and re-streams all the messages to all connected users through Server-Sent Events (SSE). Now, with new requirements, our application has to collect statistics about the most active and the most referenced users in the chat room. Our chat application may use MongoDB to store messages and user profiles. This information may also be used for statistic recalculation purposes. The...