CQRS (Command Query Responsibility Segregation)- When One Model is Not Enough

aruva - empowering ideas
11 min readMay 24

Embark on a journey into the dynamic realm of CQRS, a significant architectural pattern rapidly gaining traction in designing intricate business applications. In this blog post, we’ll unfurl the core principles of CQRS and illuminate how it can revolutionize how you manage and access your application’s data.

Photo by Shahadat Rahman on Unsplash

The Limitations of Traditional Data Management

Traditional software development primarily relies on CRUD (Create, Read, Update, Delete) operations for data management. This approach is ingrained in most developers’ workflow as it’s straightforward, intuitive, and effective in various applications. However, like any other system, it’s imperfect, and certain limitations begin to surface when dealing with complex, high-volume systems.

To illustrate this, let’s look at the banking industry — a field that deals with numerous and complex daily transactions. Millions of transactions, from account deposits and withdrawals to loan payments and balance inquiries, are processed daily. Applying CRUD operations on a single data model can prove challenging with such a high volume of concurrent operations.

The same data model is used for writing (creating, updating, and deleting records) and reading data in a CRUD-based model. This means that every time a user initiates a transaction (a write operation), the data model needs to be locked to prevent other users from reading inconsistent data and vice versa. This concurrent access to the data model creates a bottleneck, leading to latency in data processing.

For example, during peak banking hours, the volume of transactions significantly increases. Suppose many users are trying to access their account balance (a read operation) while others are updating their account information or making transactions (write operations). In that case, the system will need help to keep up. This can lead to slow processing times, causing frustration among the user base and potentially damaging the bank’s reputation.

Furthermore, the CRUD model tends to be rigid, making it harder to adapt to changing business rules and requirements. As businesses grow and evolve, so do their data needs. A bank might start offering new…

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