CQRS Design Pattern

CQRS Design Pattern

In this tutorial, we are going to discuss about the CQRS Design Pattern. CQRS design pattern is a software architectural pattern that separates the concerns of reading and writing data.

In a monolithic application, we usually use one database for everything. This single database handles both read and write operations. That means that the database is both working for complex join queries, and also perform CRUD (Create, Read, Update, Delete) operations. But, when the application gets more complicated, handling these read and write tasks can become really hard to manage.

CQRS stands for Command Query Responsibility Segregation. It’s a design pattern that splits the operations of a system into two distinct parts: commands (which modify data i.e., writes) and queries (which read data i.e., reads). This separation allows for more efficient handling and scaling of each operation.

Why Use CQRS Design Pattern in Microservices?

1. Scalability: Separate scaling of read and write workloads. You can scale your query and command services independently based on demand.
2. Optimized Performance: Each service can be optimized for its specific task (read or write), leading to improved performance.
3. Simplified Complexity: By separating concerns, it becomes easier to manage the complexity of each microservice.
4. Flexibility in Technology Choice: Different technology stacks can be used for the read side and the write side if it suits the specific needs better.

The Problem: Traditional CRUD Operations

Limitations of CRUD

In the ideal world of application development, CRUD (create, read, update, delete) operations provide a simple and intuitive method for handling data. However, complex applications often demand handling numerous concurrent operations, diverse data requirements, and need to scale efficiently. Here, traditional CRUD operations sometimes struggle to keep up.

Consider a large e-commerce platform with a broad user base. The read and write operations for such a system differ drastically. You have thousands of users browsing products (read operations), while concurrently, you have inventory updates, order placements, and product additions (write operations). Can you visualize the pressure on the system? Can a single model efficiently handle these varied data requirements? That’s where the cracks begin to show.

The Problem of Scale and Performance

One significant issue with traditional CRUD operations is scaling. As we’ve seen with our e-commerce example, there’s a considerable difference in the scale of read and write operations. While read operations are significantly more, write operations carry more complexity. Can we scale them independently based on their needs? Unfortunately, with a single data model handling both operations, scaling becomes a tough challenge.

Let’s understand this with an example.

Imagine you’re working on a banking application. This application needs to handle various tasks like processing transactions (like deposits and withdrawals) and keeping track of account balances. Here’s where things get tricky:

• Multiple Operations: In a banking system, you have two main types of operations:
  • Transactions: These are actions like depositing or withdrawing money.
  • Balance Inquiries: Users often want to check their current account balance.
• The Challenge: The problem arises when you try to handle these operations efficiently. Why? Because the needs of reading data (like checking balances) and writing data (like processing transactions) are quite different.
  • Writing Data (Transactions): This involves creating new records or updating existing ones. It needs to be accurate and secure, as it directly affects a user’s finances.
  • Reading Data (Balance Inquiries): This is about fetching data quickly and efficiently. Users expect to see their balance in real-time, without any delay.
• Complexity in Balancing Both: Trying to optimize a system for both reading and writing data can be challenging. If you focus too much on making transactions fast and secure, you might end up making balance inquiries slower. On the other hand, if you optimize for quick balance checks, you might compromise the efficiency or security of processing transactions.

This scenario is a classic example of where a traditional approach to managing data falls short. It becomes difficult to maintain, scale, and ensure the system performs well under different types of loads. That’s where CQRS design pattern comes into play, offering a solution to this dilemma by separating the concerns of reading and writing data.

CQRS Design Pattern: A Solution

Command Query Responsibility Segregation (CQRS) is a design pattern in software architecture that helps solve the kind of problem we just discussed. To understand it better, let’s break down the term:

• Command: This refers to an instruction to do something, like creating, updating, or deleting data (i.e., CUD operations). In our banking example, a command would be processing a deposit or a withdrawal.
• Query: This is a request to retrieve data without changing it. Checking your account balance in the banking app is a query.
• Responsibility Segregation: This means dividing responsibilities. In CQRS, it’s about separating the responsibilities of handling commands (writes) and queries (reads).

Now, how does CQRS design pattern work in simple terms?

• Separation of Concerns: CQRS design pattern divides the system into two parts:
  • The Command Side: This part handles all the write operations. It’s responsible for updating data, like processing transactions in our banking app.
  • The Query Side: This part deals with all the read operations. It’s optimized for fetching data quickly, like showing your current account balance.

Here is how CQRS design pattern looks like with a single Read/Write model with single database.

• State Change: The separation of concerns also distinguishes between methods that change the state and those that don’t. This implies that each method of an object falls into one of these categories, but not both:
  • Query: Returns a result. Doesn’t change the system’s state or cause any side effects that change the state.
  • Command (also known as modifiers or mutators): Changes the state of a system. Doesn’t return a value, only an indication of success or failure.
• Why is this Useful?
  • Optimization: By separating reads and writes, you can optimize each operation independently. The command side can focus on security and accuracy, while the query side can be optimized for speed.
  • Simplicity: It simplifies the design. Each side (command and query) can be developed and maintained separately, reducing complexity.
  • Scalability: As your application grows, you can scale the read and write operations independently, based on their specific demands.

In essence, CQRS design pattern acknowledges that the needs for reading and writing data are different and allows you to treat them separately. This leads to more efficient, maintainable, and scalable systems.

Conclusion

CQRS design pattern is a powerful architectural pattern that can enhance the scalability and maintainability of applications, especially those with complex domains or high read/write discrepancies. However, it introduces complexity that must be managed carefully. By applying best practices and leveraging complementary patterns like event sourcing, you can effectively implement CQRS in your software architecture.

That’s all about the CQRS Design Pattern overview. If you have any queries or feedback, please write us email at contact@waytoeasylearn.com. Enjoy learning, Enjoy Microservices..!!