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Microservicessystem_design~12 mins

Data consistency challenges in Microservices - Architecture Diagram

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System Overview - Data consistency challenges

This system demonstrates common data consistency challenges in microservices architecture. It highlights how multiple services maintain their own databases and the difficulties in keeping data synchronized across them. The key requirement is to ensure eventual consistency while handling asynchronous communication.

Architecture Diagram
User
  |
  v
Load Balancer
  |
  v
API Gateway
  |
  +------------------+------------------+
  |                  |                  |
Service A          Service B          Service C
(Database A)       (Database B)       (Database C)
  |                  |                  |
  +------------------+------------------+
           |                  |
           v                  v
       Message Queue (Event Bus)
           |
           v
       Cache Layer
Components
User
client
Initiates requests to the system
Load Balancer
load_balancer
Distributes incoming requests evenly to API Gateway instances
API Gateway
api_gateway
Routes requests to appropriate microservices and handles authentication
Service A
service
Handles specific business logic and owns Database A
Service B
service
Handles another domain and owns Database B
Service C
service
Handles additional domain logic and owns Database C
Database A
database
Stores data owned by Service A
Database B
database
Stores data owned by Service B
Database C
database
Stores data owned by Service C
Message Queue (Event Bus)
message_queue
Enables asynchronous communication and event propagation between services
Cache Layer
cache
Caches frequently accessed data to reduce database load and latency
Request Flow - 12 Hops
User → Load Balancer
Load Balancer → API Gateway
API Gateway → Service A
Service A → Database A
Service A → Message Queue (Event Bus)
Message Queue (Event Bus) → Service B
Service B → Database B
Service B → Cache Layer
Service A → Cache Layer
Service A → API Gateway
API Gateway → Load Balancer
Load Balancer → User
Failure Scenario
Component Fails:Message Queue (Event Bus)
Impact:Events between services are delayed or lost, causing data inconsistency and stale data in downstream services.
Mitigation:Implement message queue replication and persistence; use retry mechanisms and dead-letter queues to handle failures and ensure eventual consistency.
Architecture Quiz - 3 Questions
Test your understanding
Which component ensures that user requests are distributed evenly to prevent overload?
ALoad Balancer
BAPI Gateway
CMessage Queue
DCache Layer
Design Principle
This architecture demonstrates the challenge of maintaining data consistency across multiple microservices with independent databases. It uses asynchronous event-driven communication via a message queue to propagate changes, accepting eventual consistency rather than immediate synchronization. Caches improve read performance but add complexity to consistency management.