Software architecture is the high-level structure or blueprint of a software system. It defines how different parts of an application are organized, how they communicate with each other.

• Why its matters: Architecture impacts scalability, performance, and maintainability.
• Importance in system design and Key design Consideration.
  • Scalability: Ability to handle increased data or traffic.
  • Maintainability: How easily the system can be updated or modified.
  • Performance: Efficiency and responsiveness under load.
• Keep in mind , the architecture choices you make directly Influence system Behaviour.

Software Architecture pattern and styles.

1. Monolithic Architecture

• Definition: A system where components communicate through events(message) instead of direct calls, enabling loose coupling.
• Pros:
  • Highly decoupled architecture.
  • Excellent for handling asynchronous workflows.
  • Better scalability for high-traffic systems.
• Cons:
  • Debugging and tracing becomes more complex.
  • Difficult to ensure data consistency across services.
• Use Cases: Real-time systems, IOT applications, financial trading platforms.

Multi-Tier Architecture

• Multi-Tier Architecture is a software design pattern that structures applications into multiple layers, each responsible for specific functions, this separation enhances scalability, maintainability, and security.
• Key points:
  • Organizes applications into independent layers.
  • Separates concerns: UI , business logic , and data storage.
  • Enables better scalability, performance, and security.
  • Used in web applications enterprise systems and cloud architecture.

2. Microservices Architecture

Definition: Microservices architecture is a software design pattern where applications are structured as a collection of small, loosely coupled services, each responsible for a specific function.

Characteristics:

• Independently deployable services
• Loosely coupled services
• Scalable and fault tolerant.

1 How to Identify Microservices.

• Business Capabilities: Each service should align with a specific business function.
• Single Responsibility Principle: A Microservices should do one thing well.
• Data Ownership: Each Microservices owns its own database – avoid shared databases.
• Independently Deployable: should be deployable and scalable without affecting others.
• How to structure Microservices
• Decompose by business Domain: Use Domain driven design (DDD) to group services logically.
• Define clear APIS: Services should communicate via well-defined APIS.
• Avoid making microservices too large or too small.
• Implement observably: Use logging, monitoring. And tacking to track service interactions.

2 How to structure Microservices.

• Decompose by business Domain: Use Domain driven design (DDD) to group services logically.
• Define clear APIS: Services should communicate via well-defined APIS.
• Avoid making microservices too large or too small.
• Implement observably: Use logging, monitoring. And tacking to track service interactions.

3.Communication in Microservices.

• Synchronous Communication
  • REST APIS (Simple, widely used , but high latency)
  • gRPC (Efficient , binary format, better performance)
• Asynchronous Communication.
  • Event-Driven Messaging (Kafka, RabbitMQ)

4. Challenges of Microservices 

• Data Consistency: Distributed database — > Eventually consistency
• Distributed Tracing: Difficult to debug & track requests.
• Network Overhead: More Api calls à Increase latency
• Security: Authentication, authorization, data protection.

5.Scaling strategies in Microservices.

• Horizontal Scaling: Add more instances of service.
• Auto-scaling: Scale up/down automatically.
• Sharding & Database Scaling: split databases for high traffic services.

6. Real World Example of Microservices

• Netflix: Uses microservices for video streaming & personalization.
• Uber: Scales ride-matching, payments and navigation independently.
• Amazon: Each Services run separately.

3. Event Driven Architecture

1. Introduction to Event-Driven Architecture

Definition: A system design where components communicate through events rather than direct calls.

Key Characteristics:

• Asynchronous
• Loose coupling
• Scalability & Flexibility.

Why Used it?.

• Enhances system responsiveness
• Enables real-time event processing
• Supports complex workflows

Synchronous Vs Asynchronous Systems.

1. Synchronous Communication (Request-response Model)
   1. Blocking calls
   1. Tight coupling
   1. Traditional HTTP Apis
2. Asynchronous Communication (Event-Driven model)
   1. Non-blocking
   1. Decouple components
   1. Message queue & event brokers

2. Pub-sub vs Event Streaming

• Publish subscribe Model (pub-sub)et
  • Events are broadcasted to multiple subscribers
  • Each subscriber get the events once.
  • RabbitMq and AWS SNS.
• Event Streaming
  • Events are stored and consumed in order.
  • Consumer process events at different time.
  • Kafka, AWS Kinesis.

3.Key component of an Event Driven System

• Event producers (Generate Event)
• Event Brokers (Transmit and Store events)- Example Kafka, RabbitMQ , Aws
• Event bridge
• Event consumers (React to events)
• Event Storage (Log-based persistence for replaying events)

4. Best Practice

• Use idempotent event processing to avoid duplicates
• Implement dead letter queue for failed message
• Choose the right event broker based on systems needs
• Ensure event versioning to handle schema changes

5. Use cases of Event-Driven Architecture

• Logging and Auditing
• Real time Notification
• Microservices decoupling
• IoT System  
• E- commerce order processing