Common Challenge for Monolithic Application

• Scalability: how to scale individual components within a single monolithic system (MUST SCALE THE ENTIRE SYSTEM)
• Performance Bottlenecks: the entire application depends on the services that with the worst performance
• Maintainability: Codebase becomes larger and harder to manage & New features increase the risk of breaking shit

Monolithic vs. Microservices

• Monolithic: Single application with tightly coupled modules with One codebase, one database
• Microservices Architecture: independent services, each with its own functionality and database. Services communicate via APIs

  Definition of Distributed System

• A system where multiple services run independently across different machines
• Different services work together to achieve a common goal. 
• Pro 
  • Scalability — can scale certain service individually 
  • Flexibility services can be deployed and updated independently 
  • Fault Tolerance — if one service fails, others can continue operating 
• Con
  • Complexity — requires managing and coordinating multiple services 
  • Network Latency — adds overhead due to internal service communication 
  • Data Consistency — hard to keep data synchronized across multiple services

    Distributed System vs. Microservices

    Distributed system is a broader concept where components run on multiple machines. And Microservices are one kind where services are small, independent and loosely coupled.

    Breaking Monolithic Architecture

    Common SOP

• Identify boundaries: separated by business functionality or bounded context
• Service independence
  • Independently deployable and scalable
  • Avoid tight coupling between services
• Service communication
  • Service interact via REST APIs, gRPC, or message queues
  • Synchronous vs. Asynchronous communication trades offs Question to ask
• What services should be independent?
• How do we ensure smooth communication?
• How do we manage data consistency across services?

  Microservices 101

Database management

• each Microservice manage its own database and schema
• Potential Challenges:
  • Transaction management issues — 2 PC design, SAGA design
  • Data duplication 
  • Slow performance —Asyn, Multi-threading

    Components

• Service Discovery: enable services to find each other dynamically as the service scale dynamically 
• Service Registry: centralized directory where services register their locations with a service name
  • no hardcode IP address
  • enable automatic load balancing
• API Gateway: manage external request and routes them to services (Client <–> Backend)
  • Example: Spring Cloud Gateway
    • Route: defines where and how request should be forward
    • Predicate: a condition that matches incoming HTTP requests 
    • Filter: modify requests or responses before forwarding to the destination service
• Core Services: connect directly to database but not to other services
• Composite services: connect to one or more core services but not connect to database

  Observability

  It is extremely important to implement Logging, Monitoring, and Tracing because we want the ability to monitor and understand the internal state of a system from the outside based on the data it generates

  Real-world tools

• Logging: ELK Stack
• Metrics Monitoring: Kubesphere
• Tracing and Service Monitoring: Kiali

https://medium.com/@jyoti1308/designing-proximity-service-yelp-7472e1f20cee