Design Parking Lot Management System | System Design + LLD <> HLD

Introduction

Building scalable microservices is one of the most challenging aspects of modern software engineering. In this post, I'll share insights from designing a platform that handles millions of requests daily, covering key architectural patterns and the trade-offs we encountered.

Key Design Principles

1. Single Responsibility Principle

Each microservice should have one clear responsibility. This makes services easier to:

• Understand: Clear boundaries and purpose
• Scale: Target specific bottlenecks
• Maintain: Smaller, focused codebases
• Deploy: Independent release cycles

2. Database Per Service

One of the fundamental principles we followed was giving each service its own database. This approach:

• Eliminates tight coupling between services
• Allows for technology diversity (SQL vs NoSQL)
• Enables independent scaling of data layers
• Prevents cascading failures

Architectural Patterns We Implemented

Event-Driven Architecture

We implemented an event-driven system using:

• Apache Kafka for message streaming
• Event sourcing for audit trails
• CQRS for read/write separation

// Example: Order service publishing events
class OrderService {
  async createOrder(orderData) {
    const order = await this.orderRepository.save(orderData);
    
    // Publish event for other services to consume
    await this.eventPublisher.publish('order.created', {
      orderId: order.id,
      customerId: order.customerId,
      totalAmount: order.total,
      timestamp: new Date()
    });
    
    return order;
  }
}

API Gateway Pattern

We centralized cross-cutting concerns through an API Gateway:

• Authentication & Authorization
• Rate Limiting
• Request/Response Transformation
• Circuit Breaking

Service Mesh

For service-to-service communication, we implemented Istio:

• Traffic Management: Load balancing, routing
• Security: mTLS, RBAC
• Observability: Metrics, tracing, logging

Scaling Challenges & Solutions

Challenge 1: Data Consistency

Problem: Maintaining consistency across distributed services Solution: Implemented the Saga pattern for distributed transactions

Challenge 2: Service Discovery

Problem: Services need to find and communicate with each other Solution: Used Kubernetes service discovery with health checks

Challenge 3: Monitoring & Debugging

Problem: Distributed tracing across multiple services Solution: Implemented OpenTelemetry with Jaeger for distributed tracing

Performance Optimizations

Caching Strategy

• Redis for session storage and frequently accessed data
• CDN for static assets
• Application-level caching for expensive computations

Database Optimization

• Read replicas for read-heavy workloads
• Database sharding for horizontal scaling
• Connection pooling to manage database connections

Lessons Learned

1. Start Simple: Don't over-engineer from day one
2. Monitor Everything: Observability is crucial in distributed systems
3. Embrace Failure: Design for failure scenarios
4. Automate Deployment: CI/CD is essential for microservices
5. Team Structure: Conway's Law applies - organize teams around services

Conclusion

Building scalable microservices requires careful planning, the right tools, and continuous iteration. The patterns and practices outlined here have served us well, but remember that every system is unique. Start with your specific requirements and scale incrementally.

The key is to balance complexity with maintainability, ensuring your architecture supports both current needs and future growth.