When to use CQRS on your Clean Arch .NET project

When you first scaffold a .NET Core Web API, you’re greeted by the familiar trio: Controllers → Services → Repositories. Everything feels orderly, maintainable and predictable, until your project grows, performance hiccups sneak in, and your “simple” CRUD endpoints start to buckle under real-world demands. That’s when you start hearing whispers of Commands and Queries, or the buzzword CQRS, and suddenly your once-rock-solid clean architecture feels… incomplete.

In this post, I’ll take you on a journey from _“I’ve already got clean architecture, why complicate things?” to “Aha! This is exactly what our team needed.” You’ll discover the precise moments when introducing CQRS not only makes sense, but becomes a lifesaver:

• When read and write paths start cutting in on each other’s performance
• When an ever-growing service layer turns into a dreaded maintenance mountain
• When scaling demands and rich reporting threaten your transactional workflows

Stick around, and by the end you’ll have a clear checklist of when to hold off on CQRS, and, more importantly, when to embrace it so your clean-architected codebase stays lean, lightning-fast, and a joy to work on.

🚀 When to introduce CQRS in a Clean Architecture

1. Divergent Read vs. Write Requirements

   • Your read side needs very different data shapes (DTOs, projections, denormalized views) than your write side’s entity model.
   • You find yourself repeatedly mapping or reshaping the same tables for reporting or UIs.

2. Read or Write Performance Bottlenecks

   • Complex JOINs or heavy reporting queries slow down transactional operations.
   • High write throughput causes locking/contention on tables that serve your reports.

3. Independent Scalability Needs

   • You want to scale reads (e.g. read replicas, caching layers) separately from writes (primary database).
   • Traffic patterns show read:write ratios that justify different resource allocations.

4. Complex Domain Logic & Validation

   • Your write use-cases involve multiple aggregates, invariants and side-effects (email, external APIs, workflows).
   • Encapsulating each write scenario as its own Command + Handler makes business rules easier to isolate and test.

5. Rich Reporting / Analytics / Dashboards

   • You need real-time or near-real-time reporting views that would bloat or complicate your transactional schema.
   • You’d benefit from a read-optimized data store (e.g. materialized views, CQRS read database) without hurting your write performance.

6. Audit Trail & Event Sourcing

   • You require full history of state changes (for compliance, debugging or replay scenarios).
   • You’re moving toward Event Sourcing where each Command emits domain events, and the read model subscribes to them.

7. Large Teams or Bounded Contexts

   • Different teams own read-heavy features (analytics) vs. write-heavy features (order processing).
   • You want clear code ownership: Command handlers live in one module, Query handlers in another.

8. Gradual Adoption in a Monolith

   • You can start by extracting just the most problematic use-case into YourUseCaseCommand/QueryHandler while the rest remains service-based.
   • Clean Architecture’s layering makes it easy to route some operations through a mediator (e.g. MediatR) and leave others on the service layer.

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⚠️ When not to introduce CQRS

1. Simple CRUD Applications

   • Only basic create/read/update/delete and no heavy reporting — a traditional service + repository is sufficient.

2. Low Traffic & Small Data Volumes

   • No performance issues on reads or writes; premature split adds needless complexity.

3. Strong Consistency Requirements Everywhere

   • CQRS often implies eventual consistency on the read side. If you need every read to reflect the latest write instantly, CQRS may complicate matters.

4. Early-Stage or Prototype Projects

   • Focus on delivering features quickly; refactor toward CQRS only when real-world usage reveals pain points.

5. Limited Team Bandwidth or DevOps Maturity

   • CQRS often requires more infrastructure (multiple data stores, messaging, read replicas). If you can’t support that, stick to simpler patterns.

6. Regulatory or Transactional Simplicity

   • If your domain demands multi-table transactions covering both reads and writes in one atomic operation, splitting models can get tricky.

7. Avoid Premature Optimization

   • If you haven’t yet measured a bottleneck, don’t optimize for hypothetical complexity.

🎯 The Main Trigger for CQRS in Clean Architecture

When a single service’s read-or-write side becomes a maintenance or performance hotspot, it’s time to extract that use-case into a CQRS pipeline:

1. Identify the specific endpoint or operation that struggles (slow queries, tangled business logic, hard-to-test methods).
2. Define it as a Command or Query object.
3. Implement a focused handler for that use-case, isolating all DB calls, external integrations, validation, mapping, etc.
4. Gradually migrate other operations only as they exhibit similar pain points.

By evolving incrementally, only splitting off the problematic flows into CQRS handlers, you keep your Clean Architecture lean where it works well, and apply CQRS only where it delivers real benefits.

So tell me, is there something else I am missing in the list? Please let me know in the comments to know what is the best place to start implementing CQRS.