In this lesson you will learn choreography-based saga in Apache Kafka, why it matters within saga pattern, and how to use it correctly with clear, copy-ready examples.
Choreography-Based Saga Overview
At its core, choreography-based saga 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 choreography-based saga pays off across the whole codebase: fewer surprises, easier testing, and smoother onboarding. The snippet below is a solid starting point.
// 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.
Start from a minimal Choreography-Based Saga example and grow it only as needed.
Keep configuration explicit so Choreography-Based Saga behaves the same in every environment.
Name things clearly so teammates understand your Choreography-Based Saga at a glance.
Add tests around Choreography-Based Saga early to lock in expected behaviour.
Apache Kafka Cheatsheet
Handy KafkaJS reference related to choreography-based saga.
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 Choreography-Based Saga Works in Apache Kafka
Choreography-Based Saga 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 Choreography-Based Saga
In production, choreography-based saga 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
Copying choreography-based saga snippets without understanding what each line does.
Skipping error handling and edge cases when wiring up choreography-based saga.
Leaving choreography-based saga untested, so regressions slip into production.
Over-engineering choreography-based saga before you actually need the extra flexibility.
Key Takeaways
Choreography-Based Saga is a core part of working effectively with Apache Kafka.
Start small and keep choreography-based saga focused on a single responsibility.
Apply consistent patterns so choreography-based saga scales across your project.
Test and document choreography-based saga to keep it maintainable over time.
Pro Tip
Bookmark this choreography-based saga pattern and reuse it. Consistency across your Apache Kafka codebase is worth more than clever one-off solutions.
You now understand choreography-based saga in Apache Kafka and how to apply it in real projects. Next, continue with Orchestration-Based Saga to keep building your skills.