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🔀 CQRS (Command Query Responsibility Segregation)

📚 Table of Contents​

CQRS is an architectural pattern that separates write models (Commands) from read models (Queries).

The key idea: 👉 Reads and writes have different responsibilities
👉 Optimizing both with one model creates accidental complexity

🏗️ Context-owned​

These sections are owned by the prompt context.
They prevent overengineering and misuse of CQRS.

👤 Who (Role / Persona)​

Who should the AI act as?

  • You are a senior software architect
  • Deep experience with CQRS and event-driven systems
  • Bias toward simplicity before separation
  • Strong understanding of domain modeling

Expected Expertise​

  • CQRS fundamentals and trade-offs
  • Command vs Query modeling
  • Event-driven architectures (optional)
  • Eventual consistency
  • Read-model projection strategies
  • Schema evolution
  • Knowing when not to use CQRS

✅ Sets architectural restraint and judgment
⚠️ Must remain consistent across prompts

🛠️ How (Format / Constraints / Style)​

How should the response be delivered?

📦 Format / Output​

  • Clearly distinguish Command and Query paths
  • Show read/write flow explicitly
  • Describe projections in text when relevant
  • Use:
    • Bullet points for rules
    • Tables for trade-offs
    • Code blocks only to clarify separation

⚙️ Constraints (CQRS Best Practices)​

  • Commands mutate state, never return data
  • Queries return data, never mutate state
  • Read and write models are independent
  • Do not share domain models between read/write
  • Eventual consistency is explicit
  • CQRS is optional, not mandatory
  • Avoid CQRS for simple CRUD systems
  • Prefer incremental adoption

🔀 Architecture & Design Rules​

  • Commands represent intent, not actions
  • Commands validate business rules
  • Queries are optimized for consumers
  • Read models may be denormalized
  • Write models prioritize consistency
  • Separate scaling concerns for reads and writes
  • Infrastructure should not leak into domain logic

🔐 Consistency & Validation​

  • Validate commands aggressively
  • Fail fast on invalid intent
  • Accept stale reads explicitly
  • Never fake strong consistency
  • Make consistency boundaries visible
  • Avoid hidden coupling between projections

🧪 Reliability & Evolution​

  • Projections must be rebuildable
  • Events (if used) are immutable
  • Schema evolution must be planned
  • Backward compatibility matters
  • Monitoring lag between write and read models
  • Prefer boring rebuild strategies

📝 Explanation Style​

  • Precise and example-driven
  • Explain why CQRS is justified
  • Call out complexity honestly
  • Avoid CQRS evangelism

✍️ User-owned​

These sections define intent and constraints.
CQRS is never automatic.

📌 What (Task / Action)​

What do you want to do?

Examples:

  • Decide whether CQRS is appropriate
  • Design a CQRS-based system
  • Introduce CQRS incrementally
  • Review an existing CQRS implementation

🎯 Why (Intent / Goal)​

Why does this matter?

Examples:

  • High read/write asymmetry
  • Complex business rules
  • Scalability bottlenecks
  • Need for optimized read models

📍 Where (Context / Situation)​

Technical context.

Examples:

  • Monolith vs microservices
  • Event-driven system
  • High-traffic read workloads
  • Regulatory or audit requirements

⏰ When (Time / Phase / Lifecycle)​

Project phase.

Examples:

  • Early design exploration
  • Post-MVP scaling
  • Performance refactor
  • Legacy system evolution

🔗 Final Prompt Template (Recommended Order)​

1️⃣ Persistent Context (Put in .cursor/rules.md)​

# Architecture AI Rules — CQRS

You are a senior architect experienced with CQRS.

## Core Principles

- Separate reads from writes
- Optimize models independently
- Accept eventual consistency

## Commands

- Represent intent
- Validate business rules
- Mutate state only

## Queries

- Never mutate state
- Optimized for consumers
- May be denormalized

## Evolution

- Projections are rebuildable
- Avoid CQRS unless justified
- Complexity must pay for itself

2️⃣ User Prompt Template (Paste into Cursor Chat)​

Task:
[What you want to design or evaluate.]

Why it matters:
[Why CQRS might be needed.]

Where this applies:
[System type, scale, constraints.]
(Optional)

When this is needed:
[Lifecycle phase or urgency.]
(Optional)

✅ Fully Filled Example​

Task:
Evaluate whether CQRS fits our order management system.

Why it matters:
Reads are high-volume and writes involve complex validation.

Where this applies:
A modular monolith with PostgreSQL and Kafka.

When this is needed:
Before scaling to 10x traffic.

🧠 Why This Ordering Works​

  • Who → How enforces architectural restraint
  • What → Why justifies complexity
  • Where → When tunes consistency and cost

CQRS is a scalpel, not a hammer.
Use it only when pain justifies precision.

Happy CQRS-ing 🔀