Course: Apache Kafka® 101

Topics

6 min
Tim BerglundSr. Director, Developer Advocacy (Course Presenter)

Kafka Topics

Events have a tendency to proliferate—just think of the events that happened to you this morning—so we’ll need a system for organizing them. Apache Kafka's most fundamental unit of organization is the topic, which is something like a table in a relational database. As a developer using Kafka, the topic is the abstraction you probably think the most about. You create different topics to hold different kinds of events and different topics to hold filtered and transformed versions of the same kind of event.

A topic is a log of events. Logs are easy to understand, because they are simple data structures with well-known semantics. First, they are append only: When you write a new message into a log, it always goes on the end. Second, they can only be read by seeking an arbitrary offset in the log, then by scanning sequential log entries. Third, events in the log are immutable—once something has happened, it is exceedingly difficult to make it un-happen. The simple semantics of a log make it feasible for Kafka to deliver high levels of sustained throughput in and out of topics, and also make it easier to reason about the replication of topics, which we’ll cover more later.

Logs are also fundamentally durable things. Traditional enterprise messaging systems have topics and queues, which store messages temporarily to buffer them between source and destination.

Since Kafka topics are logs, there is nothing inherently temporary about the data in them. Every topic can be configured to expire data after it has reached a certain age (or the topic overall has reached a certain size), from as short as seconds to as long as years or even to retain messages indefinitely. The logs that underlie Kafka topics are files stored on disk. When you write an event to a topic, it is as durable as it would be if you had written it to any database you ever trusted.

The simplicity of the log and the immutability of the contents in it are key to Kafka’s success as a critical component in modern data infrastructure—but they are only the beginning.

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