Do you think IIT Guwahati certified course can help you in your career?
Introduction
Welcome to the world of Kafka brokers!
Have you ever wondered how large amounts of data are efficiently handled, processed, and distributed in real-time? Well, that's where Kafka brokers come into play.
A Kafka broker is an Apache Kafka component that stores, manages, and distributes messages in the Kafka messaging system.
This article will help you clear your questions what is a Kafka broker?
You'll explore more about this topic with the help of its architecture with diagrams and examples and many more.
So let us begin with the topic!!
Apache Kafka
Apache Kafka is similar to a super-fast, well-organized warehouse where a large amount of information continuously comes in from various sources. It ensures that all information is kept and processed in the correct order, allowing us to analyze and understand what is happening in real-time. It's a great tool for dealing with massive amounts of data that keep flowing constantly.
For example, Consider a large river into which thousands of various colored balls are regularly thrown. Kafka is like a special machine that catches each ball sorts it by color, and places it in a different container. We can find and analyze the balls based on their colors.
Alright!! Now let us look at what Kafka broker is.
Kafka Broker
A Kafka broker allows the exchange of information between producers and consumers. Brokers handle all client requests to write and read events. A Kafka cluster is simply a collection of one or more Kafka brokers. A unique numeric ID identifies a cluster broker.
For example, in this, we have a cluster of 3 brokers having unique IDs.
Next, we will look at the architecture of Kafka Broker Architecture for a better understanding.
Kafka Broker Architecture
The Kafka broker architecture contains some components, which are discussed below.
Kafka Broker: A Kafka broker is a single instance or node in the Kafka system. It is in charge of receiving incoming messages, storing them, and serving them to consumers.
Cluster: A cluster is a set of Kafka brokers that interact with each other. A Kafka cluster is extremely scalable, allowing you to add or remove brokers as needed to accommodate changing data volumes or processing requirements.
Topic: In Kafka, a topic is a category or stream of messages. Producers publish Messages on a specific subject, and consumers subscribe to one or more topics to receive the messages. The topic helps in better parallel processing with partitioned processing.
Partitions: Each topic has one or more partitions. Partitions enable horizontal scaling and parallel message processing. Each division is kept on a separate Kafka broker.
Alright!! Let us now look at the working of Kafka Broker.
Working of Kafka Broker
Here is the working of the Kafka broker:
Producers send messages: Producers are applications that send messages to Kafka brokers. These messages can contain any data, including logs, events, and records.
Message storage: Kafka brokers accept and store messages in a distributed fault-tolerant fashion. They ensure that the messages are safely kept and accessible.
Topics and partitions: Kafka brokers group messages into topics that serve as categories or streams. Each topic is subdivided further into divisions. Partitions provide parallel processing as well as scalability.
Replication for reliability: Kafka brokers duplicate each partition over many brokers to ensure fault tolerance. This means that even if a broker fails, the data is still accessible through replicas.
Leaders and followers: Within each partition, one broker is designated as the leader and handles read and write requests. The other duplicates are followers, constantly replicating the data of the leader.
Consumer consumption: Topics are subscribed to by consumers, and messages are delivered to them by Kafka brokers. Consumers can view the messages in the generated sequence, allowing for sequential and real-time processing.
Feature of Kafka Broker
Here are some features of Kafka broker:
Scalability: Kafka brokers may be scaled horizontally by adding more broker instances to the cluster. This enables handling increased data quantities and managing growing workloads without sacrificing performance.
Fault Tolerance: Kafka allows fault tolerance through data replication. Each partition has several replicas spread among various brokers. If a broker fails, one replica can easily take over as the leader, assuring ongoing operation and data availability.
Durability: Kafka brokers store messages on disk, ensuring data durability even in a failure. Messages are kept for a configurable amount of time or according to retention standards, allowing for historical data analysis.
Parallel Processing: Messages can be processed in parallel using Kafka's partitioning technique. Multiple consumers can process every partition independently, allowing efficient and scalable data processing
Frequently Asked Question
Can a Kafka broker have multiple topics?
Yes, In Apache Kafka, we can create multiple topics, each with its unique name. Topics are divided into partitions, and each partition is ordered. Messages within a partition are assigned an id called Offset.
How many partitions can a broker have?
The number of partitions per broker has no fixed numbers, and it can be scalable and adjustable as per the requirement. The number of partitions is often determined by criteria such as data volume, processing requirements, and hardware capability.
Can Kafka deliver the same message twice?
No, Kafka provides at-least-once message delivery semantics, which ensures that messages are not lost. However, due to retries or failures, Kafka may sometimes deliver the same message twice, requiring users to deal with possible duplicates.
Conclusion
This article has cleared your question, What is a Kafka Broker?
And also, you have learned briefly about Apache broker, what a Kafka broker is, the architecture of a Kafka broker, it's working, and some features of it.
If you want to learn more, refer to these articles:
34+ registered 
