Patterns

35 items

System Design Pattern

Communicationevent-sourcingevent-storeaudit-logreplayimmutableadvanced

Event Sourcing Pattern

Storing state as a sequence of events

Used in: Banking Systems, Order Management, Audit Systems|30 min read

Summary

Event Sourcing stores application state as a sequence of immutable events rather than as current state in a database. Instead of updating records in place, every state change is appended as an event to an event store. The current state is derived by replaying events from the beginning. This provides a complete audit trail, enables time travel, and supports event-driven architectures naturally.

Key Takeaways

Events as Source of Truth

In event sourcing, the database stores every event that ever happened. Current state is derived by replaying these events. This means you have a complete history of how the system reached its current state.

Immutability and Append-Only Storage

Events are immutable facts about the past—once written, they never change. The event store is append-only. Instead of fixing data by updating records, you append compensating events.

Time Travel and Replay

Since all historical events are preserved, you can reconstruct any past state by replaying events up to a point in time. This enables debugging production issues by replaying past events.

Event Schema Evolution

Unlike database migrations, you cannot change past events. This requires careful versioning: include a version field in every event, write code that handles multiple versions.

Snapshots for Performance

Replaying thousands of events to reconstruct state is too slow. Snapshots periodically capture state, allowing replay to start from the snapshot rather than the beginning.

Separation from CQRS

Event sourcing and CQRS are often used together but are distinct patterns. Event sourcing is about how you store data. CQRS is about separating read and write models.

Pattern Details

Traditional CRUD systems store only current state. When you update a record, the previous value is lost forever. This makes it impossible to answer questions about past states or maintain audit trails.

Trade-offs

Aspect	Advantage	Disadvantage

Patterns

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Retry with Backoff Pattern

Replication Pattern

Caching Strategies Pattern

Persistent Connections Pattern

Load Balancing Pattern

Fan-out Pattern

Fan-in Pattern

Circuit Breaker Pattern

Eventual Consistency Pattern

Queue-based Load Leveling Pattern

Bloom Filters Pattern

Time-Series Storage Pattern

Bulkhead Pattern

Batch Processing Pattern

Write-Ahead Log Pattern

API Gateway Pattern

Backend for Frontend Pattern

Sidecar Pattern

Idempotency Pattern

Rate Limiting Pattern

Backpressure Pattern

Pub/Sub Pattern

Strong Consistency Pattern

Conflict Resolution Pattern

Leader Election Pattern

Consensus Protocols Pattern

CQRS Pattern

LSM Trees Pattern

Sharding Pattern