CodeMosa

Master LeetCode Patterns

Advanced

Event Sourcing

Messaging & Async

Store state changes as an append-only sequence of events and build projections

Core Idea

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Persist domain events as the source of truth. Reconstruct current state by replaying events and maintain materialized views for queries.

When to Use

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When auditability and temporal queries are required, or complex domains benefit from CQRS and projections.

Recognition Cues

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Indicators that this pattern might be the right solution
  • Strict audit/logging requirements
  • Need to time-travel or rebuild state
  • Multiple read models per domain

Pattern Variants & Approaches

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Overview

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Append immutable domain events to an event store; project into read models for queries and rebuild by replay.

Overview Architecture

AppendStreamUpdateQuery⚙️Application💾Event Store⚙️Projector💾Read Model

When to Use This Variant

  • Strict audit trail
  • Temporal queries
  • Multiple read models

Use Case

Financial ledgers, audit-heavy systems, and complex domains with evolving views.

Advantages

  • Complete history
  • Optimized projections
  • Independent read models

Implementation Example

# Event store interactions
eventStore.append(event)
projector.on(event, updateReadModel)
readModel.query(params)

Tradeoffs

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Pros

  • Full audit trail and temporal insights
  • Multiple optimized read models (CQRS)
  • Easier to evolve projections independently

Cons

  • High conceptual and operational complexity
  • Event evolution and migration costs
  • Eventual consistency and replay times

Common Pitfalls

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  • Event schema evolution and upcasting complexity
  • Long replays without snapshots/compaction
  • Out-of-order or duplicate event handling
  • Assuming exactly-once delivery

Design Considerations

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  • Immutable, versioned event contracts
  • Snapshots and log compaction for fast recovery
  • Idempotent projections and replay tooling
  • Partitioning by aggregate for ordering
  • Checksum/validation and dead letter handling

Real-World Examples

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EventStoreDB

Purpose-built event store

Clusters with billions of events
Banks

Ledgering via event streams

Regulated, audit-heavy systems
Microservices

Kafka-backed event-sourced services

Large distributed estates

Complexity Analysis

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Scalability

Partitioned logs with compaction

Implementation Complexity

High - Contracts, snapshots, replay

Cost

Medium - Storage and tooling

Related Patterns

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CQRSOutboxSaga PatternCDC