What is CQRS?

CQRS stands for Command Query Responsibility Segregation. It is a design pattern that splits a system into two parts: one that handles commands (writes or changes) and another that handles queries (reads data).

Let's break it down

  • Command: an action that changes something, like adding a new order.
  • Query: a request to get information, like looking up the status of an order.
  • Responsibility Segregation: keeping the “write” work separate from the “read” work, so each can be built and optimized on its own.
  • Pattern: a reusable solution that developers apply to solve a common problem.

Why does it matter?

Separating reads from writes lets you scale each side independently, improve performance, and simplify the code because each part only has one job to do. This can make large, data-heavy applications faster and easier to maintain.

Where is it used?

  • E-commerce platforms where order placement (command) and product browsing (query) have very different load patterns.
  • Banking systems that need strict consistency for transactions (commands) but fast, cached lookups for account balances (queries).
  • Social media feeds that write new posts or likes (commands) while millions of users read timelines (queries).
  • IoT dashboards that record sensor data (commands) and display real-time charts (queries).

Good things about it

  • Scalability: you can add more resources to the read side without affecting writes.
  • Performance: reads can use optimized data models or caches, making them faster.
  • Clearer code: each component has a single purpose, reducing complexity.
  • Flexibility: you can use different storage technologies for reads and writes (e.g., relational DB for writes, NoSQL for reads).
  • Better security: you can apply stricter controls on the write side while keeping the read side more open.

Not-so-good things

  • Increased complexity: you now have two separate codebases and data flows to manage.
  • Eventual consistency: reads may show stale data because writes need time to propagate.
  • Higher operational overhead: more services, databases, and monitoring are required.
  • Learning curve: teams need to understand the pattern and its trade-offs before using it effectively.