CQRS (Command Query Responsibility Segregation) in HLD - Architecture Diagram

System Overview - CQRS (Command Query Responsibility Segregation)

The CQRS system separates the operations that change data (commands) from the operations that read data (queries). This helps improve scalability and performance by allowing each side to be optimized independently. Commands update the write database, while queries read from a separate read database that is kept in sync asynchronously.

Architecture Diagram

User
  |
  v
Load Balancer
  |
  v
API Gateway
  |
  +-------------------+
  |                   |
Command Service    Query Service
  |                   |
Write Database     Read Database
  |                   |
  +--------+----------+
           |
    Event Bus / Message Queue
           |
       Event Handler
           |
       Read DB Updater
           |
        Cache Layer

Components

User

actor

Initiates commands and queries

Load Balancer

load_balancer

Distributes incoming requests evenly to API Gateway instances

API Gateway

api_gateway

Routes commands to Command Service and queries to Query Service

Command Service

service

Handles commands that modify data in the write database

Query Service

service

Handles queries by reading data from the read database

Write Database

database

Stores the authoritative data for writes

Read Database

database

Stores data optimized for queries, updated asynchronously

Event Bus / Message Queue

message_queue

Transfers events from Command Service to update the read database

Event Handler

service

Processes events from the queue to update the read database

Read DB Updater

service

Applies changes to the read database based on events

Cache Layer

cache

Speeds up query responses by caching frequent read data

Request Flow - 15 Hops

User → Load Balancer

Load Balancer → API Gateway

API Gateway → Command Service

Command Service → Write Database

Command Service → Event Bus / Message Queue

Event Bus / Message Queue → Event Handler

Event Handler → Read DB Updater

API Gateway → Query Service

Query Service → Cache Layer

Cache Layer → Query Service

Query Service → Read Database

Read Database → Query Service

Query Service → API Gateway

API Gateway → Load Balancer

Load Balancer → User

Failure Scenario

Component Fails:Write Database

Impact:Commands cannot be persisted, so data updates fail. Queries still work from read database but data becomes stale.

Mitigation:Use database replication and failover to a standby write database. Queue commands temporarily if possible until DB recovers.

Architecture Quiz - 3 Questions

Which component handles data modification requests in CQRS?

A Query Service

B Command Service

C Cache Layer

D Event Handler

Design Principle

CQRS separates commands and queries to optimize each independently. This improves scalability and performance by allowing writes and reads to use different data stores and update mechanisms, reducing contention and enabling asynchronous data synchronization.

Practice

1. What is the main purpose of using CQRS in system design?

easy

A. To encrypt data for security purposes

B. To combine all database operations into a single service

C. To separate read and write operations for better scalability

D. To reduce the number of servers needed

Solution

1. Step 1: Understand CQRS concept

   CQRS stands for Command Query Responsibility Segregation, which means separating commands (writes) from queries (reads).

2. Step 2: Identify the main benefit

   This separation allows each part to be optimized and scaled independently, improving performance and maintainability.

3. Final Answer:

   To separate read and write operations for better scalability -> Option C

4. Quick Check:

   CQRS = Separate reads and writes [OK]

Hint: CQRS splits commands and queries for scaling [OK]

Common Mistakes:

• Thinking CQRS combines operations into one service
• Confusing CQRS with security encryption
• Assuming CQRS reduces server count directly

2. Which of the following is the correct way to describe the role of the 'Command' in CQRS?

easy

A. It processes write operations that change system state

B. It handles read-only queries to fetch data

C. It stores cached data for faster access

D. It manages user authentication and authorization

Solution

1. Step 1: Define Command role in CQRS

   Commands are responsible for write operations that modify the system's state.

2. Step 2: Differentiate from Query

   Queries only read data without changing it, so they are not commands.

3. Final Answer:

   It processes write operations that change system state -> Option A

4. Quick Check:

   Command = Write operations [OK]

Hint: Commands change data; queries only read [OK]

Common Mistakes:

• Confusing commands with queries
• Thinking commands handle caching
• Assuming commands manage security

3. Consider a system using CQRS where the write side updates a user profile and the read side serves user data. If the write side updates the user's email, what is the expected behavior on the read side immediately after the update?

medium

A. The read side instantly shows the updated email without delay

B. The read side deletes the user data until refreshed

C. The read side rejects the query until the write completes

D. The read side may show the old email briefly due to asynchronous update

Solution

1. Step 1: Understand asynchronous update in CQRS

   In CQRS, the read side is often updated asynchronously via events after the write completes.

2. Step 2: Identify read side behavior after write

   Because of this delay, the read side may temporarily show stale data until it receives the update event.

3. Final Answer:

   The read side may show the old email briefly due to asynchronous update -> Option D

4. Quick Check:

   Read side updates asynchronously = possible stale data [OK]

Hint: Reads update asynchronously, so data may lag briefly [OK]

Common Mistakes:

• Assuming immediate read consistency
• Thinking reads block until writes finish
• Believing read data is deleted during update

4. A developer implemented CQRS but notices that the read model is not updating after commands execute. What is the most likely cause?

medium

A. The read model database is corrupted and cannot be read

B. The command handler is not sending events to update the read model

C. The query side is trying to write data instead of reading

D. The system is using synchronous updates causing deadlocks

Solution

1. Step 1: Identify how read model updates in CQRS

   The read model updates via events sent by the command handler after state changes.

2. Step 2: Diagnose missing updates

   If the read model is not updating, likely the events are not being sent or processed properly.

3. Final Answer:

   The command handler is not sending events to update the read model -> Option B

4. Quick Check:

   Missing events cause read model stale data [OK]

Hint: Check if events are sent after commands [OK]

Common Mistakes:

• Blaming database corruption without evidence
• Confusing query side roles
• Assuming synchronous updates cause deadlocks here

5. You are designing a high-traffic e-commerce system using CQRS. Which approach best ensures that the read side remains highly available and scalable while keeping data reasonably fresh?

hard

A. Use event sourcing to asynchronously update read models and deploy multiple read replicas

B. Use a single database for both reads and writes to avoid data duplication

C. Synchronously update the read model within the command transaction to ensure consistency

D. Disable caching on the read side to always fetch fresh data from the write database

Solution

1. Step 1: Understand scalability needs in CQRS

   Separating reads and writes allows scaling read replicas independently to handle high traffic.

2. Step 2: Use event sourcing for asynchronous updates

   Event sourcing helps keep read models updated asynchronously, balancing freshness and availability.

3. Step 3: Evaluate other options

   Single database limits scalability; synchronous updates reduce availability; disabling caching hurts performance.

4. Final Answer:

   Use event sourcing to asynchronously update read models and deploy multiple read replicas -> Option A

5. Quick Check:

   Event sourcing + read replicas = scalable, fresh reads [OK]

Hint: Event sourcing + replicas = scalable reads with freshness [OK]

Common Mistakes:

• Using single DB limits scalability
• Synchronous updates reduce availability
• Disabling cache hurts performance