The CQRS Mistakes I Keep Seeing in Real-World Systems

December 9, 2025 · Asad Ali

Every few years, CQRS becomes fashionable again. Architects rediscover it, conferences hype it, and developers start splitting their codebases into commands and queries… even when they don’t need to. I’ve implemented CQRS in high-scale distributed systems, and I’ve also ripped it out of projects where it did more harm than good. This post is about the mistakes I see repeatedly — even in senior teams — and how to avoid them.

When CQRS solves the wrong problem

One of the earliest production outages I encountered was caused by a team that introduced CQRS simply because it “felt clean”. The system didn’t need decoupling, event sourcing, or read-optimised views — but the team built all three. The result: slow writes, inconsistent reads, and a debugging nightmare.

Warning: CQRS is not an architecture. It’s a pattern. Only use it when you have problems it actually solves.

You should consider CQRS only when:

  • Write complexity is fundamentally different from read complexity
  • You need to scale reads independently from writes
  • Your domain uses invariants that require explicit command handling
  • Read views must be denormalized or optimized for queries

Otherwise—save yourself the pain.

Accidental event sourcing — without understanding event sourcing

This is the most common CQRS mistake I see: engineers assume CQRS implies event sourcing. It doesn’t. And if you implement event sourcing without knowing the operational costs, things will fall apart quickly.

Event sourcing brings:

  • Replay costs
  • Event versioning complexity
  • Migration burdens
  • Stale read models
  • Operational overhead of projections
Anti-pattern: Implementing event sourcing when all you needed was a database row and a few domain events.

In one system I audited, building projections took 45 minutes on startup because the team didn’t understand event replay costs. A single corrupted event caused a chain reaction that broke three microservices.

Command handlers turning into god services

When teams adopt CQRS, they often move all business logic into command handlers. On paper it looks clean; in practice it degenerates into massive, untestable classes.

I’ve seen command handlers that:

  • Make API calls
  • Update multiple aggregates
  • Send emails
  • Publish events
  • Write audit logs
  • Call workflows

All from one method.

Warning: Command handlers should orchestrate, not contain business logic. Domain models must enforce invariants.

If your domain objects are anemic, CQRS won’t save you. It will simply add more ceremony around already-weak models.

Ignoring eventual consistency — then being shocked by it

CQRS almost always introduces eventual consistency. Your write model and read model update at different times. Yet many teams still design their UI and workflows as if everything were synchronous.

Result: users click “Save”, refresh the page, and see stale data.

Architecture insight: If your users cannot tolerate eventual consistency, CQRS may not be appropriate for your domain.

In one production system, the read model lagged behind the write model by 5+ seconds under load. The customer perception was that data was “randomly missing”. The architecture wasn’t wrong — but the user experience was never designed for eventual consistency.

Building overcomplicated read models for trivial screens

Teams often build dozens of read models: dashboards, grids, reports, search views. But many of these screens don’t need specialized read models — a simple database query would suffice.

Best practice: Use read models only when necessary. Default to simple queries unless you have performance or structure requirements.

Every read model is another moving piece. Another projection. Another rebuild during replays. Overusing them leads to operational drag.

Not designing for projection failure modes

Projection handlers (the part that updates read models) will fail at some point. Schema mismatches, missing fields, corrupted events, out-of-order arrival — I’ve seen them all.

But the biggest mistake? Teams build projections assuming perfect event streams.

In production you must design for:

  • Reprocessing events
  • Deduplication
  • Out-of-order arrival
  • Poison events
  • Projection rebuilds
Debugging insight: Most projection bugs start with one malformed event. Add validation and fallbacks early.

Using CQRS inside a single monolith — and making it harder

CQRS isn’t a microservices-exclusive pattern. You can absolutely use it inside monoliths. But many teams adopt it prematurely, thinking it magically gives scalability or domain clarity.

Inside monoliths, CQRS often becomes:

  • More layers
  • More abstractions
  • More mapping
  • More ceremony
  • The same performance constraints as before

If your monolith doesn’t have read-write divergence problems, splitting it into commands and queries won’t add value.

Confusing CQRS with microservices or domain-driven design

CQRS is compatible with DDD and microservices, but it’s not required by either. I’ve seen many teams bind these concepts together so tightly that they can’t reason about architecture anymore.

Some common misconceptions:

  • “If we do DDD, we must do CQRS” — No.
  • “If we implement CQRS, we must split into microservices” — No.
  • “CQRS gives us scalability automatically” — Also no.
Warning: CQRS is orthogonal to your deployment topology. Don’t force alignments that don’t exist.

My rule of thumb after years of building distributed systems

Here’s when CQRS is a good choice:

  • Reads outnumber writes by several orders of magnitude
  • You need denormalized data for fast queries
  • Write workflows have complex business invariants
  • You already operate an event-driven ecosystem

Here’s when it’s the wrong choice:

  • Your CRUD system is already simple and stable
  • You don’t have scalability pain — you just expect to someday
  • Your team doesn’t understand eventual consistency deeply
  • You don’t have operational tooling or monitoring for projections

Final thoughts

CQRS can be incredibly powerful — I’ve seen it unlock scalability trajectories that traditional CRUD architectures couldn’t support. But it’s also one of the most abused patterns in modern software systems. Most failures stem from misapplied ambition, not from the pattern itself.

If you treat CQRS as a hammer, everything looks like a command or a query. If you treat it as a scalpel, it will give you surgical precision in domains that truly need it.

— Asad