Modern applications demand high scalability, resiliency, and a full audit trail of changes. Event sourcing is a powerful architectural pattern that meets these needs by persisting state changes as a sequence of immutable events.

Apache Kafka is an ideal foundation for event sourcing thanks to its durable, append-only log, distributed storage, and real-time stream processing capabilities.

In this blog, we’ll explore how to design and implement event-sourced systems using Kafka, covering architecture, data modeling, stream processing, and best practices for event-driven microservices.

What is Event Sourcing?

In traditional systems, we persist only the current state (e.g., account balance). With event sourcing, we persist all state changes as a sequence of events:

  • AccountCreated
  • MoneyDeposited
  • MoneyWithdrawn

The current state is reconstructed by replaying events.

Benefits:

  • Full auditability
  • Simplified state rollback
  • Enhanced debugging and traceability
  • Enables CQRS (Command Query Responsibility Segregation)

Why Kafka for Event Sourcing?

Apache Kafka offers several advantages:

Feature Benefit
Immutable log Perfect fit for storing event history
Partitioned topics Scales event streams horizontally
Durable and replicated Guarantees event persistence
Replayable topics Supports event reprocessing and rebuilding
Stream processing support Enables real-time projections and enrichment

Kafka enables temporal reconstruction of state by replaying messages from offset 0.

Designing Event Topics in Kafka

  1. Use event-specific topics per entity type:
  • customer-events
  • order-events
  • payment-events
  1. Key events by entity ID for ordering:
  • Key = customerId
  • Ensures all events for the same entity go to the same partition
  1. Use Avro/JSON schema for consistency:
{
"eventType": "OrderPlaced",
"orderId": "123",
"customerId": "456",
"items": [...],
"timestamp": "2024-04-10T10:30:00Z"
}

Building the Event Store

Kafka becomes your event store, replacing traditional databases for write-heavy operations.

Best practices:

  • Configure infinite retention (or long-term archival)
  • Use compacted topics if you want latest state alongside full event stream
  • Use schema registry for version control

Rebuilding State from Events

To compute current state:

  1. Consume events from the beginning
  2. Apply business logic per event type
  3. Update in-memory cache or materialized view (e.g., Redis, Postgres, Elasticsearch)

You can also use Kafka Streams or ksqlDB for real-time materialization:

CREATE TABLE order_summary AS
SELECT orderId, SUM(item_price) AS total
FROM order-events
GROUP BY orderId;

CQRS with Kafka

Event sourcing pairs naturally with CQRS, separating:

  • Command model (write side): emits events
  • Query model (read side): listens to events and builds projections

Example:

  • Order Service emits OrderPlaced
  • Billing Service listens and processes payment
  • Analytics Service creates revenue dashboard

This decouples systems and enables independent scalability.

Idempotency and Deduplication

Ensure your consumers are idempotent, as event reprocessing may occur:

  • Use event UUIDs or sequence numbers
  • Track processed offsets or hashes
  • Use Kafka transactional APIs for exactly-once semantics (EOS)

Handling Event Evolution

Event schemas change over time. To manage:

  • Use Schema Registry with backward compatibility checks
  • Include event version in payload
  • Write transformation logic for legacy versions

Error Handling and Replay

  • Use dead-letter queues (DLQ) for poison messages
  • Log and alert on deserialization or processing failures
  • Reprocess events by resetting consumer group offsets:
kafka-consumer-groups.sh --reset-offsets --to-earliest \
--group order-consumer-group \
--topic order-events --execute

When Not to Use Event Sourcing

Event sourcing introduces complexity. Avoid it if:

  • You don’t need historical data
  • Your system doesn’t support eventual consistency
  • Your use case is CRUD-heavy with minimal state transitions

Conclusion

Event sourcing with Kafka empowers you to build robust, scalable, and auditable event-driven systems. By capturing every state change as an event, you unlock powerful capabilities like time travel, real-time analytics, and domain decoupling.

Whether you’re building microservices, fintech platforms, or IoT architectures, Kafka provides the ideal backbone for event sourcing at scale.

Start with clear event design, ensure schema evolution, and embrace Kafka’s stream-processing capabilities to bring your event-driven vision to life.