Apache Kafka - Basics

Basics Overview

At its core, basics is about doing one thing well inside your Apache Kafka project. Once you understand the pattern, you can apply it consistently across features and teams.

Good basics pays off across the whole codebase: fewer surprises, easier testing, and smoother onboarding. The snippet below is a solid starting point.

import { Kafka, logLevel } from 'kafkajs';

const kafka = new Kafka({
  clientId: 'my-app',
  brokers: ['localhost:9092'],
  logLevel: logLevel.INFO,
});

// create producers, consumers, or an admin client from `kafka`

Every KafkaJS app starts from a Kafka client configured with a clientId and broker list.

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

Apache Kafka Cheatsheet

Handy KafkaJS reference related to basics.

How Basics Works in Apache Kafka

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

Every KafkaJS app starts from a Kafka client configured with a clientId and broker list.

• 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 Basics

In production, basics 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 basics.
• Leaving basics untested, so regressions slip into production.
• Over-engineering basics before you actually need the extra flexibility.
• Ignoring documentation, which makes basics hard for the next developer to change.

Key Takeaways

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