Kafka with Microservices

with Microservices sits at the heart of microservices in Apache Kafka. This guide walks through the concept step by step, with examples, a cheatsheet, and common mistakes to avoid.

with Microservices Overview

with Microservices is a building block you will reach for often in Apache Kafka. It keeps related logic together and makes your intent obvious to reviewers and future maintainers.

When you learn with microservices properly, you avoid the guesswork that leads to bugs and rework. The example below shows the shape you will use in most real Apache Kafka projects.

// each service reacts to events and emits new ones
await consumer.subscribe({ topic: 'payment-completed' });
await consumer.run({
  eachMessage: async ({ message }) => {
    const payment = JSON.parse(message.value.toString());
    await producer.send({
      topic: 'order-confirmed',
      messages: [{ key: payment.orderId, value: JSON.stringify(payment) }],
    });
  },
});

Event-driven services stay decoupled by reacting to and emitting Kafka events.

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

Apache Kafka Cheatsheet

Handy KafkaJS reference related to with microservices.

Task	Example	Purpose
Create client	`new Kafka({ clientId, brokers })`	Connect to the cluster
Produce	`producer.send({ topic, messages })`	Publish events
Consume	`consumer.run({ eachMessage })`	Process events
Subscribe	`consumer.subscribe({ topic })`	Choose topics to read
Group	`kafka.consumer({ groupId })`	Scale consumers
Admin	`admin.createTopics(...)`	Manage topics
Commit offset	auto-commit or `commitOffsets`	Track progress

How with Microservices Works in Apache Kafka

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

Event-driven services stay decoupled by reacting to and emitting Kafka events.

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 with Microservices

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

Concern	Recommendation
Ordering	Key related events so they land on one partition
Reliability	Use acks=all and idempotent producers
Idempotency	Handle duplicate deliveries safely
Monitoring	Track consumer lag and error rates

Common Mistakes

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

Key Takeaways

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

Pro Tip

Pair with microservices with automated tests from day one. It is far cheaper to catch Apache Kafka regressions in CI than in production.