event-store-design

SKILL.md

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