Message Batching

Message Batching Overview

Message Batching 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 message batching 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: 'orders', brokers: ['localhost:9092'] });
const producer = kafka.producer();

await producer.connect();
await producer.send({
  topic: 'orders',
  messages: [
    { key: order.id, value: JSON.stringify(order) },
  ],
});
await producer.disconnect();

A KafkaJS producer connects to the brokers and sends keyed messages to a topic.

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

Apache Kafka Cheatsheet

Handy KafkaJS reference related to message batching.

How Message Batching Works in Apache Kafka

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

A KafkaJS producer connects to the brokers and sends keyed messages to a topic.

• 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 Message Batching

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

Key Takeaways

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