From Monolith to CQRS: An Evolutionary Guide to Read‑Write Separation

Software design constantly evolves; large systems start small and must be refactored when the existing architecture can no longer meet new requirements. Architects need to balance development speed, technology stack, and team capabilities while choosing evolution paths that solve concrete problems without excessive cost.

The article introduces the core ideas of CQRS (Command Query Responsibility Segregation) and the problems it addresses, illustrating the evolution step by step from a simple monolith.

1. Traditional Monolith Architecture

A typical design consists of a single API server (RESTful) and a database. Clients exchange DTOs (Data Transfer Objects) for both reads and writes. The backend converts DTOs to domain objects for business logic and persistence, then back to DTOs for responses.

Read/write separation is emphasized: the write path handles CUD operations and returns ACK/NACK, while the read path simply retrieves DTOs. However, this model suffers from two major issues:

It becomes an anemic (CRUD) model where business logic is scattered and domain knowledge is lost.

Scalability is limited because the single relational database becomes a bottleneck.

2. Task‑Based Monolith Architecture

To address the above problems, the concept of a domain is introduced. The write path now sends messages (commands) instead of plain DTOs; a message contains an action and its data, e.g., {"rename": "LazyDr"} . This makes it easier for the backend to identify and process domain‑specific actions.

Although the write side now uses commands, the read side still relies on DTOs, creating a gap where additional processing is required to assemble complete read models.

3. CQS (Command‑Query Separation)

CQS solves the read/write pain points by generating pre‑built DTOs (read models) on the write side and storing them in a dedicated read database.

The write side now bears the responsibility of creating these read‑only DTOs, allowing the read side to become a thin layer that only performs pagination, sorting, and simple retrieval.

While this resolves many issues, scalability still requires attention. The article outlines three eventual‑consistency strategies, ordered by latency:

Background thread replication (e.g., Redis).

Message‑queue workers for asynchronous copying.

ETL pipelines (extract‑transform‑load) that may take minutes to hours.

Both state and event data are stored; events enable flexible reconstruction of read views, but rebuilding from scratch each time is inefficient, so a hybrid approach that retains both state and events is recommended.

4. Summary

Domain‑Driven Design (DDD) and CQRS are not magical solutions; they are design choices that address specific problems such as the anemic model and scalability limits, while introducing new concerns like data consistency. Understanding the trade‑offs of each technique allows engineers to select the most appropriate approach for their system.

Ultimately, even when adopting CQRS, practitioners must decide how to achieve eventual consistency—through in‑memory replication, message queues, or ETL processes—recognizing that every architectural evolution brings new challenges.