Kafka-Guide

A comprehensive guide to understanding Apache Kafka, including its architecture, installation steps, and a practical use case demonstration.

Kafka is a message broker using a commit log, often associated with infrastructure data, microservices, and event-driven architectures.

Architecture

Schema:

Kafka Cluster Components

• Broker: Acts as a node with SSD storage.
• Partition: Ensures high availability and fault tolerance of data. We could have multiple partitions replicated across brokers (Leader and Followers).
• Topic: Similar to a table in a database, it defines a data stream.
• Offset: The unique identifier for a message/event within a topic.
• Producer: Publishes messages to Kafka.
• Consumer: Reads messages from a topic.
• Consumer Group: Multiple instances of the same application (e.g., App C with 3 instances) can consume messages from specific partitions with coordination.

Key Features

• Replications: Ensures data redundancy and availability.
• Partitions: Allows for data distribution and parallel processing.
• Scalability: Easily scalable to handle large volumes of data.
• Performance: Optimized for high-throughput and low-latency message processing.

Kafka operates asynchronously, and consumers can reread messages since events are stored with offsets.

Partitioning

Partitions logically divide topics and are distributed across the cluster. Each message within a partition is associated with a unique key.

Alternatives to Kafka

• RabbitMQ
• ActiveMQ
• ZeroMQ

Installing Kafka with Kubernetes

Instructions for installing Kafka using Kubernetes will be provided in this section.

Real-World Example

Tracking a bus to see its real-time position using Flask, JavaScript, and leafletjs .