Movie Central

Abstract

This paper presents the design and implementation of the Movie Response API — a modern, high-performance RESTful service that provides comprehensive movie information, ratings, and recommendations. Implemented using Python and FastAPI, the system integrates third-party data sources and applies lightweight analytics to improve discoverability and user satisfaction. Key contributions include a production-ready API design, efficient caching and concurrency using ASGI, and a modular architecture that supports future integration of machine learning based recommendation modules. Experimental evaluation demonstrates low-latency responses and reliable data retrieval under typical workloads.

Introduction

The increasing consumption of digital media has created a strong demand for accurate, real-time movie information accessible through efficient APIs. This project presents a FastAPI-based Movie Response API designed to provide developers and end-users with a high-performance, easily integrable, and extensible solution for movie search, details, recommendations, and analytics.

The system builds on prior work such as the TMDb and IMDb APIs, incorporating modern API design principles—RESTful semantics, HTTP caching, and statelessness—to ensure scalability. Leveraging asynchronous frameworks like FastAPI and Uvicorn, the architecture supports high concurrency and responsiveness.

The API follows a layered architecture comprising:

1. API Layer (FastAPI): Handles HTTP requests and validation.

2. Service Layer: Manages business logic and external API integrations.

3. Data Layer: Provides caching (Redis) and persistence (PostgreSQL).

Key technologies include Python 3.10+, FastAPI, Pydantic, Uvicorn, Redis, PostgreSQL, Docker, and pytest. Endpoints such as /search, /movie/{id}, /top, and /recommend are implemented asynchronously using httpx/aiohttp, with Pydantic models ensuring robust input/output validation. Caching and rate limiting enhance performance and reliability.

Evaluation results highlight improved developer productivity (via automatic Swagger documentation) and reduced latency due to async processing and caching. Main challenges involved data inconsistencies across providers and API quota management. Overall, the system demonstrates an efficient, scalable, and developer-friendly approach to building modern media information APIs.

Conclusion

The Movie Response API implemented with FastAPI demonstrates a practical approach for delivering fast, reliable movie data and recommendations. The modular design and modern stack enable straightforward extension to advanced recommendation algorithms and integration with streaming platforms.

References

[1] FastAPI Documentation- https://fastapi.tiangolo.com [2] IMDb API Documentation - https://developer.imdb.com [3] The Movie Database (TMDb) API - https://developer.themoviedb.org [4] Fielding, R.T., \'Architectural Styles and the Design of Network-based Software Architectures\', 2000. [5] Pydantic Documentation- https://pydantic-docs.helpmanual.io

Copyright

Copyright © 2025 Prof. Dr. R. V. Babar, Hrishikesh Kulkarni, Kabir Rathod, Rushikesh Gadhave, Mangesh Dalvi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.