Kafka Topics

Topics Overview

Topics lets you structure Apache Kafka work so it stays readable, testable, and easy to scale. Instead of ad-hoc code, you follow a clear pattern that other developers can recognise immediately.

The key is to keep topics focused and predictable. Start from the minimal example here, then layer in only the complexity your feature actually needs.

import { Kafka } from 'kafkajs';

const kafka = new Kafka({ clientId: 'admin', brokers: ['localhost:9092'] });
const admin = kafka.admin();

await admin.connect();
await admin.createTopics({
  topics: [{ topic: 'orders', numPartitions: 6, replicationFactor: 3 }],
});
await admin.disconnect();

The admin client creates topics with a chosen partition count and replication factor.

Topics Example

import { Kafka } from 'kafkajs';

const kafka = new Kafka({ clientId: 'app', brokers: ['localhost:9092'] });
const producer = kafka.producer();
const consumer = kafka.consumer({ groupId: 'group' });

• Start from a minimal Topics example and grow it only as needed.
• Keep configuration explicit so Topics behaves the same in every environment.
• Name things clearly so teammates understand your Topics at a glance.
• Add tests around Topics early to lock in expected behaviour.

Apache Kafka Cheatsheet

Handy KafkaJS reference related to topics.

How Topics Works in Apache Kafka

Topics builds on Kafka's log-based design, where producers append events to partitioned topics and consumer groups read them independently, tracking their own offsets.

The admin client creates topics with a chosen partition count and replication factor.

• Topics are split into partitions for parallelism and ordering per key.
• Producers choose a partition, usually by message key.
• Consumer groups share partitions so work scales horizontally.
• Offsets record how far each group has read.

Practical Guidance for Topics

In production, topics needs attention to delivery guarantees, retries, and observability. Make handlers idempotent and monitor consumer lag closely.

Common Mistakes

• Skipping error handling and edge cases when wiring up topics.
• Leaving topics untested, so regressions slip into production.
• Over-engineering topics before you actually need the extra flexibility.
• Ignoring documentation, which makes topics hard for the next developer to change.

Key Takeaways

• Topics is a core part of working effectively with Apache Kafka.
• Start small and keep topics focused on a single responsibility.
• Apply consistent patterns so topics scales across your project.
• Test and document topics to keep it maintainable over time.