Why we use Kafka in microservices? (2023)

Why do you use Kafka?

Why would you use Kafka? Kafka is used to build real-time streaming data pipelines and real-time streaming applications. A data pipeline reliably processes and moves data from one system to another, and a streaming application is an application that consumes streams of data.

Popular use cases of Kafka include: Traditional messaging, to decouple data producers from processors with better latency and scalability. Site activity tracking with real-time publish-subscribe feeds. As a replacement for file-based log aggregation, where event data becomes a stream of messages.

The strong and scalable Kafka is the best choice for the majority of microservices use cases. It solves many of the challenges with microservice orchestration while providing features that microservices strive for, such as scalability, efficiency, and speed.

The purpose of APIs is to essentially provide a way to communicate between different services, development sides, microservices, etc. The REST API is one of the most popular API architectures out there. But when you need to build an event streaming platform, you use the Kafka API.

The Kafka REST Proxy is a RESTful web API that allows your application to send and receive messages using HTTP rather than TCP. It can be used to produce data to and consume data from Kafka or for executing queries on cluster configuration.

Apache Kafka does not provide out-of-the-box capabilities of an API Management solution. API Management solutions do not provide event streaming capabilities to continuously send, process, store and handle millions of events in real time (aka stream processing / streaming analytics).

Memory. Kafka relies heavily on the filesystem for storing and caching messages. All data is immediately written to a persistent log on the filesystem without necessarily flushing to disk.

Apache Kafka is frequently deployed on the Kubernetes container management system, which is used to automate deployment, scaling, and operation of containers across clusters of hosts. Apache Kafka on Kubernetes goes hand-in-hand with cloud-native development, the next generation of application development.

It is more fault-tolerant than databases. Unlike databases, it is possible to scale Kafka's storage independent of memory and CPU. Thus, it is a long-term storage solution due to its higher flexibility than databases.

How to use Kafka with microservices?

Apache Kafka architecture for microservices utilizes an application setup where Microservices communicate with each other through Kafka. This is made possible by the publish-subscribe approach of Apache Kafka for handling record writing and reading.

Kafka supports a publish-subscribe model that handles multiple message streams. These message streams are stored as a first-in-first-out (FIFO) queue in a fault-tolerant manner.

Franz Kafka's work is characterized by anxiety and alienation, and his characters often face absurd situations. He is famous for his novels The Trial, in which a man is charged with a crime that is never named, and The Metamorphosis, in which the protagonist wakes to find himself transformed into an insect.

Kafka takes on the burden of handling all the problems related to distributed computing: node failures, replication or ensuring data integrity. It makes Kafka a great candidate for the fundamental piece of architecture, a central log that can serve as a source of truth for other services.

Apache Kafka scales well and may track events but lacks some message simplification and granular security features. It is perhaps an excellent choice for teams that emphasize performance and efficiency. IBM MQ is a powerful conventional message queue system, but Apache Kafka is faster.

Apache Kafka is an open-source streaming platform that enables the development of applications that ingest a high volume of real-time data. It was originally built by the geniuses at LinkedIn and is now used at Netflix, Pinterest and Airbnb to name a few.