At-Least-Once Delivery

At-Least-Once Delivery Overview

At its core, at-least-once delivery 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 at-least-once delivery pays off across the whole codebase: fewer surprises, easier testing, and smoother onboarding. The snippet below is a solid starting point.

import { Kafka } from 'kafkajs';

const kafka = new Kafka({ clientId: 'orders', brokers: ['localhost:9092'] });
const consumer = kafka.consumer({ groupId: 'order-processors' });

await consumer.connect();
await consumer.subscribe({ topic: 'orders', fromBeginning: false });

await consumer.run({
  eachMessage: async ({ topic, partition, message }) => {
    const order = JSON.parse(message.value.toString());
    console.log({ partition, key: message.key?.toString(), order });
  },
});

A consumer joins a group and processes messages from the partitions it is assigned.

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

Apache Kafka Cheatsheet

Handy KafkaJS reference related to at-least-once delivery.

How At-Least-Once Delivery Works in Apache Kafka

At-Least-Once Delivery 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 consumer joins a group and processes messages from the partitions it is assigned.

• 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 At-Least-Once Delivery

In production, at-least-once delivery needs attention to delivery guarantees, retries, and observability. Make handlers idempotent and monitor consumer lag closely.

Common Mistakes

• Copying at-least-once delivery snippets without understanding what each line does.
• Skipping error handling and edge cases when wiring up at-least-once delivery.
• Leaving at-least-once delivery untested, so regressions slip into production.
• Over-engineering at-least-once delivery before you actually need the extra flexibility.

Key Takeaways

• At-Least-Once Delivery is a core part of working effectively with Apache Kafka.
• Start small and keep at-least-once delivery focused on a single responsibility.
• Apply consistent patterns so at-least-once delivery scales across your project.
• Test and document at-least-once delivery to keep it maintainable over time.