Post
Ask Question

CQRS Design Pattern and Its Use Case

What is CQRS?

CQRS stands for Command and Query Responsibility Segregation. It emphasizes separating the operations for reading data from those for writing or updating data. Here are the key points:

  1. Separation of Concerns: In CQRS, read and write operations are not kept in the same interface or class. This separation allows different teams to work on these operations independently.
  2. Scalability: Each part (read or write) can scale according to its specific needs. For example, read-heavy parts can be optimized for caching and performance.
  3. Security: Read and write operations can have different security requirements.
  4. Architecture Flexibility: Read operations can have a different architecture (e.g., caching, data transformation) compared to write operations

Let’s explore how you can combine CQRS, two-database CQRS, and materialized views to create a robust system. I’ll provide a use case and technical details for achieving this.

Use Case E-Commerce Inventory Management
 

Problem Statement

Imagine an e-commerce platform that needs to manage its inventory efficiently. The system must handle both read-heavy operations (product listings, stock availability) and write-heavy operations (order processing, stock updates).

Solution Components

  1. CQRS (Command Query Responsibility Segregation)
    • Implement CQRS to separate read and write operations.
    • Define two bounded contexts
      • Inventory Write Context: Responsible for handling stock updates, order processing, and inventory adjustments.
      • Inventory Read Context: Manages product listings, stock availability, and reporting.
  2. Two-Database CQRS
    • Use separate databases for read and write operations:
      • Write Database
        • Stores transactional data related to inventory changes (e.g., stock updates, order processing).
        • Optimized for write-heavy operations.
        • May use a relational database (e.g., SQL Server, PostgreSQL).
      • Read Database
        • Contains materialized views for efficient querying.
        • Optimized for read-heavy operations.
        • May use a NoSQL database (e.g., MongoDB, Cassandra) or a specialized read model database.
  3. Materialized Views
    • Create materialized views in the read database
      • Product Listing View
        • Denormalized view containing product details (name, description, price).
        • Updated asynchronously based on events from the write context.
        • Supports fast product listing queries.
      • Stock Availability View
        • Aggregates stock levels for each product.
        • Updated when stock changes occur.
        • Enables quick stock availability checks.
      • Sales Report View
        • Summarizes sales data (e.g., total sales, popular products).
        • Updated periodically (e.g., daily, hourly).
        • Supports business intelligence queries.

Technical Details

  1. Event Sourcing and Event Bus
    • Use event sourcing in the right context
      • Capture inventory-related events (e.g., stock updated, order placed).
      • Persist events in the write database.
    • Implement an event bus to publish events to subscribers (e.g., materialized views).
  2. Materialized View Updates
    • Subscribe materialized views to relevant events
      • When a stock update event occurs, update the stock availability view.
      • When a product is added or updated, update the product listing view.
      • Periodically update the sales report view.
  3. Read Model API
    • Expose an API for read operations
      • Query product listings, stock availability, and sales reports.
      • Use the read database and materialized views.
  4. Consistency and Retries
    • Handle eventual consistency
      • Inform users that data might be slightly stale.
    • Monitor consistency levels and latency.

Here is an implementation blueprint considering C#.net, SQL Server, MongoDB, and Azure service bus technical components that we can use to handle this use case, but it's not limited to these components and you can use an equivalent tech stack of your choice.

1. C# .NET Implementation
 

Write Context (Commands)

  1. Create a C# .NET Microservice
    • Set up a .NET Core or .NET 5+ project for the write context.
    • Define models for inventory items, orders, and stock updates
  2. Command Handlers
    • Implement command handlers for stock updates, order processing, and inventory adjustments.
    • Use libraries like MediatR1 for handling commands.
  3. Event Sourcing
    • Store inventory-related events (e.g., StockUpdatedEvent, OrderPlacedEvent).
    • Persist events in a SQL Server database.

For e.g.

using MediatR;
using System.Threading;
using System.Threading.Tasks;
public class StockUpdateCommand : IRequest
{
    public int ProductId { get; set; }
    public int Quantity { get; set; }
}

public class StockUpdateCommandHandler : AsyncRequestHandler<StockUpdateCommand>
{
    protected override async Task Handle(StockUpdateCommand request, CancellationToken cancellationToken)
    {
        // Logic to update stock in the write model (e.g., SQL Server)
        // Publish StockUpdatedEvent to Azure Service Bus
    }
}
CREATE TABLE StockEvents
(
    EventId INT PRIMARY KEY,
    EventType VARCHAR(50),
    Timestamp DATETIME,
    Payload NVARCHAR(MAX)
);

2. SQL Server Implementation
 

Write Database

  1. Create a SQL Server Database
    • Set up a database for the write context.
    • Design tables for inventory items, orders, and events.
  2. Event Tables
    • Create tables to store events (e.g., StockEvents, OrderEvents).
    • Include columns for event type, timestamp, and payload.

3. MongoDB Implementation
 

Read Database (Materialized Views)

  1. Create a MongoDB Database
    • Set up a MongoDB instance for the read context.
  2. Collections for Materialized Views
    • Define collections for materialized views (e.g., productListing, stockAvailability, salesReport).
    • Store denormalized data in these collections.

For e.g.

using MongoDB.Driver;
public class ProductListingRepository
{
    private readonly IMongoCollection<ProductListing> _collection;

    public ProductListingRepository(string connectionString, string databaseName)
    {
        var client = new MongoClient(connectionString);
        var database = client.GetDatabase(databaseName);
        _collection = database.GetCollection<ProductListing>("productListing");
    }

    public async Task<IEnumerable<ProductListing>> GetProductListingsAsync()
    {
        return await _collection.Find(_ => true).ToListAsync();
    }
}

4. Azure Service Bus for Evenings

  1. Set Up an Azure Service Bus Namespace
    • Create an Azure Service Bus namespace.
    • Set up queues or topics for asynchronous communication.
  2. Event Publishing
    • In the writing context, publish events (e.g., stock updates, order placements) to the Azure Service Bus.
    • Use the Service Bus SDK for .NET.
  3. Event Subscribers (Materialized Views)
    • In the read context, subscribe to relevant events.
    • Update materialized views based on received events.

Note. Remember that this architecture balances performance, scalability, and complexity. Adjust the design based on your specific requirements and domain.


Similar Articles