What are partitions in Kafka? How do they differ from topics and what are they used for?

In Apache Kafka, a topic is a logical category or stream of messages to which data is published. A topic can be thought of as a channel for transmitting messages.

Partitions are the physical subdivisions of a topic. Each topic is divided into one or more partitions, which are stored on different Kafka brokers. Partitions are ordered and allow for scaling data processing and provide parallelism.

Main differences and purposes:

• A topic is an abstraction for organizing messages by categories.
• A partition is a specific log of sequential messages within a topic.
• Partitions enable load distribution among brokers and consumers.
• Parallel processing is achieved because different partitions can be read and written simultaneously.
• Message order is guaranteed only within a single partition, not across the entire topic.

Example: If you have a topic "orders" with 3 partitions, then order messages will be distributed across these 3 partitions, allowing for parallel processing of orders and system scaling.