This project is a personal solution for building a scalable Logistics Platform using Apache Kafka and other microservices.

graph TB
    subgraph Client
        CL[Client Applications]
    end

    subgraph Gateway
        LB[Nginx Load Balancer]:::infra
        AG["API Gateway"]:::gateway
        WS["WebSocket Gateway"]:::gateway
    end

    subgraph Services
        BS["Booking Service"]:::service
        DS["Driver Service"]:::service
        TS["Tracking Service"]:::service
        PS["Pricing Service"]:::service
        AS["Analytics Service"]:::service
        ADS["Admin Service"]:::service
        CS["Communication Service"]:::service
        ASS["Assignment Service"]:::service
    end

    subgraph "Message Broker"
        KB["Apache Kafka"]:::broker
    end

    subgraph "Background Processing"
        AC["Analytics Consumer"]:::worker
        BC["Booking Consumer"]:::worker
        DC["Demand Consumer"]:::worker
        LC["Location Consumer"]:::worker
        CW["Celery Workers"]:::worker
    end

    subgraph "Data Layer"
        PG[(PostgreSQL)]:::database
        PGB[PgBouncer]:::database
        RD[(Redis Cache)]:::cache
    end

    %% Client Layer Connections
    CL --> LB
    LB --> AG
    LB --> WS

    %% Gateway to Services
    AG --> BS
    AG --> DS
    AG --> TS
    AG --> PS
    AG --> AS
    AG --> ADS
    WS --> CS

    %% Service Interconnections
    BS --> ASS
    ASS --> DS
    TS --> KB
    DS --> KB

    %% Message Broker to Consumers
    KB --> AC
    KB --> BC
    KB --> DC
    KB --> LC

    %% Consumers to Services
    AC --> AS
    BC --> BS
    LC --> TS

    %% Services to Data Layer
    BS --> PGB
    DS --> PGB
    TS --> PGB
    PS --> PGB
    AS --> PGB
    ADS --> PGB
    PGB --> PG

    %% Cache Connections
    BS --> RD
    DS --> RD
    TS --> RD
    PS --> RD

    %% Styles
    classDef service fill:#90EE90,stroke:#333,stroke-width:2px
    classDef gateway fill:#87CEEB,stroke:#333,stroke-width:2px
    classDef broker fill:#FFA500,stroke:#333,stroke-width:2px
    classDef database fill:#DDA0DD,stroke:#333,stroke-width:2px
    classDef cache fill:#E6E6FA,stroke:#333,stroke-width:2px
    classDef worker fill:#FFD700,stroke:#333,stroke-width:2px
    classDef infra fill:#FF6B6B,stroke:#333,stroke-width:2px

    %% Click Events
    click LB "https://github.com/kshitijk4poor/logistics-platform/blob/main/nginx.conf"
    click AG "https://github.com/kshitijk4poor/logistics-platform/blob/main/app/main.py"
    click WS "https://github.com/kshitijk4poor/logistics-platform/blob/main/app/routes/websockets.py"
    click BS "https://github.com/kshitijk4poor/logistics-platform/blob/main/app/services/booking/booking_service.py"
    click DS "https://github.com/kshitijk4poor/logistics-platform/blob/main/app/services/drivers/driver_service.py"
    click TS "https://github.com/kshitijk4poor/logistics-platform/blob/main/app/services/tracking/tracking_service.py"
    click PS "https://github.com/kshitijk4poor/logistics-platform/blob/main/app/services/pricing/pricing_service.py"
    click AS "https://github.com/kshitijk4poor/logistics-platform/blob/main/app/services/analytics/analytics_service.py"
    click ADS "https://github.com/kshitijk4poor/logistics-platform/blob/main/app/services/admin/admin_service.py"
    click CS "https://github.com/kshitijk4poor/logistics-platform/blob/main/app/services/communication/notification.py"
    click ASS "https://github.com/kshitijk4poor/logistics-platform/blob/main/app/services/assignment/driver_assignment.py"
    click KB "https://github.com/kshitijk4poor/logistics-platform/blob/main/app/services/messaging/kafka_service.py"
    click RD "https://github.com/kshitijk4poor/logistics-platform/blob/main/app/services/caching/cache.py"
    click AC "https://github.com/kshitijk4poor/logistics-platform/blob/main/app/services/analytics/analytics_consumer.py"
    click BC "https://github.com/kshitijk4poor/logistics-platform/blob/main/app/services/booking/booking_consumer.py"
    click DC "https://github.com/kshitijk4poor/logistics-platform/blob/main/app/services/demand/demand_consumer.py"
    click LC "https://github.com/kshitijk4poor/logistics-platform/blob/main/app/services/tracking/location_consumer.py"
    click PGB "https://github.com/kshitijk4poor/logistics-platform/blob/main/pgbouncer.ini"
    click PG "https://github.com/kshitijk4poor/logistics-platform/blob/main/db/database.py"

• 🚗 Real-time driver availability tracking
• 📊 Efficient data processing with Apache Kafka
• 🔄 Asynchronous task processing
• 🚀 Highly scalable microservices architecture
• 🧪 Comprehensive test suite
• 🌍 Real-time location tracking and geospatial indexing
• 💰 Dynamic pricing system
• 📅 Booking management with support for immediate and scheduled bookings
• 📈 Analytics service for data insights
• 🔔 Real-time notifications via WebSockets

This service is built with scalability in mind, utilizing a microservices architecture, Apache Kafka for efficient message processing, and FastAPI for high-performance API development.

1. FastAPI Backend: Asynchronous Python web framework for high-performance API development.
2. Apache Kafka: Distributed event streaming platform for handling high-volume data streams.
3. PostgreSQL: Robust relational database for persistent storage.
4. Redis: In-memory data store for caching and real-time data processing.
5. Nginx: High-performance load balancer and reverse proxy.
6. Docker & Docker Compose: Containerization for easy deployment and scaling.
7. Celery: Distributed task queue for background job processing.

1. Driver Availability Service: Handles real-time updates of driver availability
2. Booking Service: Manages creation and processing of bookings
3. Pricing Service: Calculates dynamic pricing based on various factors
4. Driver Assignment Service: Efficiently assigns drivers to bookings
5. Tracking Service: Manages real-time location updates from drivers
6. Analytics Service: Processes and analyzes logistics data
7. Communication Service: Manages WebSocket connections for real-time updates
8. Admin Service: Provides endpoints for managing vehicles and other administrative tasks

For detailed information on each service and its workflows, please refer to the Technical Documentation.

• Microservices Architecture: Allows independent scaling of services.
• Asynchronous Processing: Utilizes FastAPI's asynchronous capabilities for non-blocking I/O operations.
• Message Streaming: Kafka for distributed event streaming and processing.
• Caching: Redis for fast data retrieval and reducing database load.
• Load Balancing: Nginx for distributing incoming traffic across multiple backend instances.
• Containerization: Docker for easy scaling of individual services.
• Geospatial Indexing: H3 for efficient location-based queries and operations.
• Task Scheduling: Celery for managing future bookings and related tasks.

1. Advanced Database Scaling:

   • Implement database replication for improved read performance and fault tolerance.
   • Set up geolocation-based sharding to optimize query performance for location-based operations.
   • Explore multi-region database clusters for global scalability and reduced latency.

2. Enhanced Caching Strategy:

   • Implement a distributed caching layer using Redis Cluster for improved cache performance and scalability.
   • Develop intelligent cache invalidation mechanisms to ensure data consistency.

3. Serverless Computing Integration:

   • Explore serverless options (e.g., AWS Lambda, Google Cloud Functions) for specific microservices to improve scalability and reduce operational costs.

4. Advanced Monitoring and Observability:

   • Implement distributed tracing using tools like Jaeger or Zipkin for better visibility into microservices interactions.
   • Set up advanced alerting and anomaly detection systems.

5. Edge Computing:

   • Implement edge computing solutions for reduced latency in real-time location tracking and processing.

These future implementations will further enhance the scalability, performance, and feature set of the Driver Availability Service, ensuring its readiness for global-scale operations and emerging technologies.

1. Clone the repository
2. Set up environment variables (refer to .env.example)
3. Run docker-compose up --build to start all services
4. Access the API at http://localhost:8000

Once the server is running, you can access the interactive API documentation at:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

We use pytest for unit and integration testing. Run tests with:

pytest

For more details on our testing strategy, including unit, integration, and end-to-end testing, please refer to the Technical Documentation.

This project implements various security measures, including:

• JWT-based authentication for API and WebSocket connections
• Rate limiting to prevent abuse
• Data encryption at rest and in transit
• Role-Based Access Control (RBAC)

For more information on security measures, please see the Technical Documentation.

The system is designed with performance and resilience in mind, implementing:

• Circuit breakers to prevent cascading failures
• Intelligent retry mechanisms for transient failures
• Comprehensive logging for all errors and exceptions

For more details, refer to the Technical Documentation.

The project uses:

• Docker for containerization
• Kubernetes for container orchestration and scaling
• Prometheus and Grafana for monitoring and alerting

For more information on deployment and DevOps practices, see the Technical Documentation.

While this is a personal project, I welcome any feedback or suggestions you might have.

This project implements advanced features such as real-time location tracking, geospatial indexing, and an advanced microservices architecture. For detailed information on these implementations, please refer to the Technical Documentation.