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Architecture Overview

The Tracker GraphQL API is built with a modern, scalable architecture designed to handle real-time tracking data efficiently.

System Architecture Diagram​

Tracker Architecture Diagram

System Components​

Core Components​

1. GraphQL API Server​

  • Built with Python and Uvicorn
  • Uses Ariadne for GraphQL schema implementation
  • Handles authentication and request validation
  • Manages database operations through SQLAlchemy

2. Database Layer​

  • PostgreSQL with PostGIS extension
  • Stores:
    • Tracker information
    • Location history
    • User and client data
    • Production and delivery data

3. Caching System​

  • Redis-based caching
  • Caches:
    • Geocoding results
    • Frequently accessed queries
    • Session data
  • Implements cache invalidation strategies

4. Real-time Communication​

  • Socket.IO server for real-time updates
  • Handles:
    • Live location updates
    • Status changes
    • Notifications
  • Maintains client sessions

5. Background Processing​

  • Redis-based queue system
  • Processes:
    • Geocoding tasks
    • Data aggregation
    • Cleanup operations
  • Scalable worker processes

Data Flow​

  1. Client Request Flow
  1. Real-time Update Flow

Security Architecture​

  • JWT-based authentication
  • Client-specific data filtering
  • Rate limiting
  • CORS configuration
  • Input validation
  • SQL injection prevention

Scalability Considerations​

  1. Horizontal Scaling

    • Stateless API servers
    • Redis cluster for caching
    • PostgreSQL replication
    • Multiple worker processes

  2. Performance Optimization

    • Query batching
    • Efficient caching
    • Connection pooling
    • Background processing

  3. High Availability

    • Load balancing
    • Failover configurations
    • Health monitoring
    • Automated recovery

Development Architecture​

Monitoring and Logging​

  • Structured logging
  • Error tracking
  • Real-time monitoring
  • Health checks

Performance Monitoring​

The system includes a comprehensive performance monitoring architecture:

The performance monitoring system provides:

  • Resolver Metrics: Tracks execution time of GraphQL resolvers
  • Database Metrics: Monitors query execution times through a custom SQLAlchemy session
  • Cache Metrics: Records cache hit/miss rates and timing
  • Configurable Thresholds: Customizable thresholds for identifying slow operations
  • In-Response Metrics: Optional performance data included in GraphQL responses
  • Retention Policies: Configurable data retention for metrics

This architecture allows for:

  1. Real-time performance monitoring
  2. Identification of bottlenecks
  3. Optimization of slow queries and resolvers
  4. Historical performance analysis
  5. Proactive system scaling based on performance trends

Future Considerations​

  1. Scalability

    • Microservices architecture
    • GraphQL federation
    • Global distribution

  2. Features

    • Enhanced analytics
    • Machine learning integration
    • Advanced geofencing

  3. Integration

    • Third-party services
    • External APIs
    • Custom plugins