Introduction to Reactive Kafka

David Landup
Arpendu Kumar Garai

In the previous chapter, we took a look at the Spring Cloud Stream binders for Kafka. In this chapter, we will familiarize ourselves with the “reactive” architecture pattern and explore Reactive Kafka.

Traditional applications often deal with blocking calls, or synchronous calls, which means if we want to access a particular entity or information from a system where most of the threads are busy, the application would block the new one or wait until the previous threads complete processing their requests. When handling large streams of data, it is essential to process them with tremendous speed and agility. That's when software developers realized they needed a multi-threaded environment capable of handling asynchronous and non-blocking calls to make the best use of processing data streams.

Overview of Reactive Programming Paradigm

A Stream of data is a sequential set of information or records that are transferred from one system to another. They are usually processed using the FIFO (First-In-First-Out) pattern. The same blocking methodology of data streaming often prevents a system from processing real-time data while streaming records. As a result, a group of prominent developers realized they needed an approach to build a “reactive” systems architecture that would facilitate data processing while streaming. Hence, they signed a manifesto, popularly known as the Reactive Manifesto.

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