CQRS without over‑engineering — Migration Playbook — Practical Guide (Oct 27, 2025)

CQRS without over‑engineering — Migration Playbook

Level: Intermediate Software Engineers

As of October 27, 2025 — aligned with popular frameworks from .NET 8, Spring Boot 3.2, and Node.js 20+

Introduction

Command Query Responsibility Segregation (CQRS) can greatly improve scalability, maintainability, and clarity by separating read and write operations in your applications. However, it’s easy to slip into over-engineering with CQRS — over-separating concerns or bouncing to complex event-driven architectures prematurely.

This article presents a practical CQRS migration playbook focusing on incremental, purposeful adoption, avoiding excessive complexity and ensuring real, valuable benefits.

Prerequisites

Before migrating to CQRS, ensure the following:

Clear domain understanding: Your write and read side business logic and data shapes should be well defined.
Modular codebase: Tightly coupled code complicates CQRS migration. Prepare for code refactoring to separate command and query responsibilities.
Tech stack awareness: Different stacks and versions offer varying support. For example, .NET 8 supports minimal APIs and built-in mediation patterns, Spring Boot 3.2 favours reactive streams, and Node.js 20+ supports advanced streaming.
Version control and CI/CD pipelines: Enable safe rollback and automated testing during migration phases.

Hands-on Steps

1. Identify Commands and Queries

Start by analysing your existing API or service layer. Split methods into two categories:

Commands: Operations that change state (create, update, delete).
Queries: Operations retrieving data without side effects.

Example in a RESTful service:

// Command: updates user email
[HttpPost("users/{id}/email")]
public IActionResult UpdateEmail(Guid id, [FromBody] UpdateEmailRequest req)
{
    _commandHandler.Handle(new UpdateEmailCommand(id, req.Email));
    return NoContent();
}

// Query: gets user details
[HttpGet("users/{id}")]
public IActionResult GetUser(Guid id)
{
    var user = _queryHandler.Handle(new GetUserByIdQuery(id));
    return Ok(user);
}

2. Separate Models and Handlers

Use distinct models and handlers for commands and queries to avoid domain leakage and unnecessary coupling.


// Command DTO & handler
record UpdateEmailCommand(UUID userId, String newEmail);

@Service
public class UpdateEmailHandler {
  public void handle(UpdateEmailCommand cmd) {
     // update domain model and persist
  }
}

// Query DTO & handler
record GetUserByIdQuery(UUID userId);

@Service
public class GetUserByIdHandler {
  public UserDto handle(GetUserByIdQuery query) {
      // fetch read-optimised DTO from DB or cache
  }
}

3. Incrementally Separate Data Stores (Optional)

Initially, commands and queries can share the same database schema with clear read/write boundaries. If scaling requires it, migrate queries and commands to separate stores later — read replicas, document stores, or caches.

This step requires moving towards asynchronous replication or event-sourcing, but it’s often better to delay until bottlenecks appear.

4. Use Mediation or Message Bus Patterns

Introduce a mediator pattern or lightweight message bus between commands and queries to decouple clients from handlers and enable easier observability.


// Using MediatR in .NET 8 for commands and queries
public class UpdateEmailCommand : IRequest { public Guid Id; public string Email; }

public class UpdateEmailHandler : IRequestHandler<UpdateEmailCommand> {
  public Task Handle(UpdateEmailCommand cmd, CancellationToken ct) { /* update logic */ }
}

public class GetUserByIdQuery : IRequest<UserDto> { public Guid Id; }

public class GetUserByIdHandler : IRequestHandler<GetUserByIdQuery, UserDto> {
  public Task<UserDto> Handle(GetUserByIdQuery query, CancellationToken ct) { /* read logic */ }
}

5. Enforce Read‑only Models on Query Side

Keep query models immutable by design and restrict queries to fetching and simple projections only — no business logic or writes. This prevents future drift and bugs.

Common Pitfalls

Over-separation too early: Migrating to multiple microservices or event stores before understanding domain complexity can cause maintenance overhead.
Ignoring eventual consistency: When separating write/read stores, clients must handle the lag. Premature synchronous updates add complexity.
Mixing domain logic in queries: Business rules belong in commands or domain layer, not queries or read models.
Under-automated testing: CQRS increases paths to test—focus on unit, integration, and contract tests.
Dropping versioning or backwards compatibility: Changes to commands or queries must preserve API contracts where clients still rely on old behaviour.

Validation

Validate your CQRS migration by monitoring:

Performance metrics: Requests per second, read latency vs. write latency, and resource utilisation for command and query paths separately.
Consistency guarantees: Confirm eventual consistency expectations hold via logs or user feedback.
Error rates: Track exceptions and failure modes in handlers.
Test coverage: Confirm distinct test suites for command and query layers.

Checklist / TL;DR

Identify and separate commands (mutations) from queries (reads).
Create dedicated models and handlers for each side.
Start with a shared data store; separate only when scaling demands.
Introduce mediator/message bus layers for decoupling.
Keep query models read-only and simple.
Plan and automate comprehensive testing.
Carefully handle eventual consistency and versioning.
Monitor both sides and adjust based on actual bottlenecks.

When to choose CQRS vs simpler CRUD & Templated APIs

If your app has complex domains with separate read/write usage patterns or scaling/multi-team demands, CQRS helps. For simple CRUD and standard REST/GraphQL APIs, CQRS may add unnecessary complexity. Always balance benefits versus added architectural cost.