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name: event-store-design description: Design and implement event stores for event-sourced systems. Use when building event sourcing infrastructure, choosing event store technologies, or implementing event persistence patterns.

Event Store Design

Comprehensive guide to designing event stores for event-sourced applications.

When to Use This Skill

• Designing event sourcing infrastructure
• Choosing between event store technologies
• Implementing custom event stores
• Optimizing event storage and retrieval
• Setting up event store schemas
• Planning for event store scaling

Core Concepts

1. Event Store Architecture

┌─────────────────────────────────────────────────────┐
│                    Event Store                       │
├─────────────────────────────────────────────────────┤
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐ │
│  │   Stream 1   │  │   Stream 2   │  │   Stream 3   │ │
│  │ (Aggregate)  │  │ (Aggregate)  │  │ (Aggregate)  │ │
│  ├─────────────┤  ├─────────────┤  ├─────────────┤ │
│  │ Event 1     │  │ Event 1     │  │ Event 1     │ │
│  │ Event 2     │  │ Event 2     │  │ Event 2     │ │
│  │ Event 3     │  │ ...         │  │ Event 3     │ │
│  │ ...         │  │             │  │ Event 4     │ │
│  └─────────────┘  └─────────────┘  └─────────────┘ │
├─────────────────────────────────────────────────────┤
│  Global Position: 1 → 2 → 3 → 4 → 5 → 6 → ...     │
└─────────────────────────────────────────────────────┘

2. Event Store Requirements

Requirement	Description
Append-only	Events are immutable, only appends
Ordered	Per-stream and global ordering
Versioned	Optimistic concurrency control
Subscriptions	Real-time event notifications
Idempotent	Handle duplicate writes safely

Technology Comparison

Technology	Best For	Limitations
EventStoreDB	Pure event sourcing	Single-purpose
PostgreSQL	Existing Postgres stack	Manual implementation
Kafka	High-throughput streaming	Not ideal for per-stream queries
DynamoDB	Serverless, AWS-native	Query limitations
Marten	.NET ecosystems	.NET specific

Templates and detailed worked examples

Full template library and detailed worked examples live in references/details.md. Read that file when you need the concrete templates.

Best Practices

Do's

• Use stream IDs that include aggregate type - Order-{uuid}
• Include correlation/causation IDs - For tracing
• Version events from day one - Plan for schema evolution
• Implement idempotency - Use event IDs for deduplication
• Index appropriately - For your query patterns

Don'ts

• Don't update or delete events - They're immutable facts
• Don't store large payloads - Keep events small
• Don't skip optimistic concurrency - Prevents data corruption
• Don't ignore backpressure - Handle slow consumers