Introduction to Apache Kafka: A Distributed Streaming Platform

Introduction

Kafka is a widely‑used distributed, horizontally‑scalable, fault‑tolerant commit‑log platform that stores massive amounts of data, provides a high‑throughput message bus and supports real‑time stream processing.

How It Works

Producers send records to Kafka brokers; records are stored in topics that are split into partitions. Within a partition, messages are ordered by offset. Consumers subscribe to topics and poll for new records, forming consumer groups where each partition is read by a single consumer instance.

Kafka persists all records on disk using an append‑only log, enabling O(1) reads and writes independent of data size. It leverages page‑cache, zero‑copy, batch protocols and linear disk I/O to achieve near‑network speed.

Scalability and Fault Tolerance

Horizontal scaling is achieved by adding more brokers; replication of partitions across multiple brokers ensures that if a leader fails, a follower can take over. Metadata such as leader election is stored in ZooKeeper, a distributed key‑value store.

Streams API

Kafka Streams provides a client‑side library for stateful and stateless stream processing, with concepts of KStream and KTable that illustrate the duality of streams and tables. State is kept locally (e.g., RocksDB) and can be restored by replaying the underlying topic.

KSQL

KSQL offers a SQL‑like language for defining simple streaming jobs on top of the Streams API, making stream processing accessible to non‑developers.

When to Use Kafka

Kafka serves as a central event‑driven backbone for micro‑service architectures, enabling decoupled communication, high availability and massive throughput, which is why it is adopted by thousands of companies worldwide.

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