Overview

This project is a robust Python-based API built using FastAPI, designed to power a mobile application developed in Flutter. The application’s primary objective is to provide users with live ratings for nearby locations based on user-generated reviews. The API leverages various cutting-edge technologies, including MongoDB, Kafka, AWS services, and Google Maps API, to deliver fast, reliable, and scalable functionality.

Due to the size and complexity of the project, a monolithic architecture was chosen, ensuring streamlined development and simplified management. The monolithic structure effectively accommodates the requirements of a tightly integrated mobile application.

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Features

1. Location-Based Ratings:

   • Retrieves live ratings for locations using Google Maps API to find nearby points of interest based on user location.

2. User Reviews Management:

   • Accepts and processes user reviews to calculate dynamic ratings for locations.

3. Real-Time Data Processing:

   • Utilizes Kafka for efficient real-time data processing of user reviews and ratings updates.

4. Image Recognition:

   • Integrates AWS Rekognition to analyze user-uploaded images for content validation and to associate images with locations.

5. Cloud Storage:

   • Stores user-generated media such as images and videos on AWS S3 for high availability and scalability.

6. Database:

   • Employs MongoDB for managing dynamic, unstructured data related to locations, reviews, and ratings.
   • Utilizes MongoDB Realm for serverless functions and data triggers, enabling automation of specific database operations.

7. Deployment and Scalability:

   • API hosted on AWS EC2 for high-performance computing.
   • Scalable microservices run on AWS Fargate, ensuring flexibility and reliability in handling traffic spikes.

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Technologies Used

Core Framework and Programming Language:

• FastAPI: Chosen for its speed, asynchronous capabilities, and built-in support for OpenAPI documentation.
• Python: Provides flexibility and a vast ecosystem of libraries to support complex requirements.

Database:

• MongoDB: Schema-less, highly scalable, and suitable for storing unstructured and hierarchical data.
• MongoDB Realm: Automates database triggers and offers serverless backend capabilities.

Message Queue:

• Apache Kafka: Handles asynchronous message processing for real-time data synchronization and updates.

Cloud Services:

• AWS Rekognition: Provides advanced image analysis for validating and associating user-uploaded images with locations.
• AWS S3: Ensures reliable storage for media files with low-latency access.
• AWS EC2: Offers robust hosting for the monolithic API, ensuring high performance.
• AWS Fargate: Runs containerized services for task-based microservices, providing scalability and reduced operational overhead.

Geospatial Data:

• Google Maps API: Facilitates location-based searches and retrieves nearby points of interest.

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Requirements

Prerequisites:

• Python 3.9+: Ensure you have Python installed on your system.
• MongoDB: Use MongoDB Atlas or a local instance for database management.
• Kafka Cluster: Set up or connect to an existing Kafka cluster for message queuing.
• AWS Credentials: Ensure you have IAM roles with the necessary permissions for:
  • AWS Rekognition
  • AWS S3
  • AWS EC2
  • AWS Fargate

Scalability and Future Enhancements:

While the current monolithic architecture suits the project scope, future iterations could consider transitioning to a microservices architecture to further enhance scalability and maintainability.

Potential Enhancements:

Implement GraphQL to optimize API queries. Add real-time WebSocket updates for dynamic rating changes. Integrate a machine learning model for predicting location popularity trends.

Conclusion

The Live Ratings API is a high-performance, feature-rich backend solution designed to power location-based, real-time applications. By leveraging modern frameworks and cloud services, it offers scalability, reliability, and ease of integration with client-side applications.