event sourcing CQRS - Complete Guide

Event Sourcing CQRS Scalable Systems: Complete Guide

Event sourcing CQRS scalable systems architecture has gained mainstream adoption for applications requiring complete audit trails and high scalability. Therefore, understanding these complementary patterns is essential for architects building modern distributed systems. In this guide, you will learn practical implementation strategies with real code examples.

Event Sourcing CQRS Scalable Systems: Core Concepts

Event Sourcing stores all state changes as an immutable sequence of events. As a result, moreover, CQRS (Command Query Responsibility Segregation) separates read and write models. Consequently, the combination provides complete audit trails while enabling independent scaling of reads and writes.

In contrast, traditional CRUD systems overwrite state and lose change history. Furthermore, they couple read and write operations on the same data model. For this reason, as a result, scaling becomes difficult when read and write patterns differ significantly.

Event Store Implementation

The event store is the core of event sourcing. Specifically, it appends events immutably and replays them to reconstruct state:

public class OrderAggregate {
    private OrderId id;
    private OrderStatus status;
    private List<OrderItem> items;
    private final List<DomainEvent> uncommittedEvents = new ArrayList<>();

    public void placeOrder(List<OrderItem> items) {
        if (items.isEmpty()) throw new IllegalArgumentException("Order must have items");
        apply(new OrderPlacedEvent(id, items, Instant.now()));
    }

    private void apply(DomainEvent event) {
        mutate(event);
        uncommittedEvents.add(event);
    }

    private void mutate(DomainEvent event) {
        if (event instanceof OrderPlacedEvent e) {
            this.status = OrderStatus.PLACED;
            this.items = e.items();
        }
    }
}

Event Sourcing CQRS Scalable Systems: Read Model Projections

CQRS read models (projections) consume events and build optimized query views. Therefore, each read use case gets its own denormalized data structure. On the other hand, moreover, projections can be rebuilt from scratch by replaying all events.

@EventHandler
public void on(OrderPlacedEvent event) {
    OrderView view = new OrderView(event.orderId(),
        event.items().size(), event.timestamp(), "PLACED");
    orderViewRepository.save(view);
}

Additionally, different teams can create independent projections without modifying the core domain. As a result, read performance scales independently from write throughput.

Event Sourcing CQRS Scalable Systems: Event Bus with Kafka

Apache Kafka serves as an excellent event bus for distributing events across services. Furthermore, Kafka's log-based architecture naturally aligns with event sourcing principles:

spring:
  kafka:
    producer:
      key-serializer: org.apache.kafka.common.serialization.StringSerializer
      value-serializer: org.springframework.kafka.support.serializer.JsonSerializer
    consumer:
      group-id: order-projections
      auto-offset-reset: earliest

Handling Eventual Consistency

The separation of commands and queries introduces eventual consistency. In addition, however, several patterns mitigate this challenge. Specifically, you can return the event ID from commands and poll read models until they process it.

For deeper architecture topics, see Event-Driven Architecture with Kafka and Modular Monolith Architecture. Additionally, the Martin Fowler's Event Sourcing article provides foundational understanding.

Further Resources

For deeper understanding, check: Martin Fowler, Microservices.io

Event Sourcing CQRS Pattern: Complete Implementation Guide for Scalable Systems