Functional & Non-Functional Requirements

The primary goal of the Movie Reviews Aggregator System is to provide users with a centralized platform that collects, aggregates, and displays movie reviews from multiple sources. The system should allow users to search for movies, view aggregated reviews, and offer both scores and textual feedback. Additional requirements include user authentication, user-generated reviews, and the ability to categorize movie genres.

Functional requirements involve having a search functionality for users to find movies, displaying ratings from various critics, and providing comprehensive summaries of reviews. Non-functional requirements should focus on performance, ensuring that the system can handle high traffic, particularly during the release of popular films, and also ensuring data consistency and availability.

Capacity Estimation

Considering the complexity of the Movie Reviews Aggregator System, a rough estimation of the time to develop the MVP (Minimum Viable Product) might take around 4 to 6 months. This duration includes phases of requirements gathering, architecture design, implementation, testing, and deployment. The team would include front-end developers, back-end developers, a project manager, and quality assurance testers.

In terms of cost, assuming an agile development approach and the team size mentioned, the estimated budget could range from $200,000 to$300,000. This budget would account for salaries, tools, cloud hosting services, and any third-party APIs utilized for collecting reviews. Additionally, ongoing maintenance costs should also be planned for after the initial launch.

API Design

The Movie Reviews Aggregator System will expose a RESTful API that enables interaction with the application for various functionalities. Key endpoints would include /movies to retrieve movie information, /reviews for fetching aggregated reviews, and /submit-review which would allow authenticated users to submit their own reviews.

Authentication can be handled using OAuth tokens, and each response should be in JSON format to maintain a consistent and easy-to-handle structure. Rate limiting should also be implemented to handle traffic efficiently, especially since reviews will be fetched from external sources.

Database Design

The system’s database will require a relational database management system (RDBMS) like PostgreSQL to store structured data regarding movies and reviews. Key tables may include Movies, Reviews, and Users. The Movies table will store movie details such as title, release date, and genre. The Reviews table will hold review content, source, and associated rating, linked back to the Movies table by a foreign key.

To enhance performance, caching mechanisms like Redis can be employed for frequently accessed queries and data, thus reducing direct database load. In addition to that, a strategy for archiving older reviews can also help maintain database performance over time.

High Level Design

The high-level architecture of the Movie Reviews Aggregator System comprises several key components. At the client-side, users will interact with a web or mobile application. The request will first hit a load balancer that distributes the traffic evenly across multiple application servers. Each server handles requests by interacting with various microservices responsible for different functionalities, such as the movie service, review service, and user service.

A database will store all relevant data with appropriate caching layers implemented to ensure that repeated requests for the same data can be served quickly. Optionally, a message queue can be incorporated to handle asynchronous tasks, such as fetching reviews from third-party sources without blocking user interactions.

Request Flows

When a user searches for a movie, the flow initiates from the client app, sending a request to the load balancer. The load balancer routes this request to one of the available application servers. The server then interacts with the movie service to fetch movie details.

Once the movie details are obtained, the application server queries the review service to compile reviews for that particular movie. After aggregating all reviews, the server compiles the response and sends it back to the client application for display to the user.

Detailed Component Design

Key components of the Movie Reviews Aggregator System include the client application, which is the user interface, and backend services that handle business logic. The application service layer will comprise multiple microservices, each dedicated to a specific function such as the movie service, review service, and user management service.

Additionally, caching services like Redis, database services for persistent storage (like PostgreSQL), and optional message queues for asynchronous processing are vital components that ensure responsive and reliable operations of the system.

Trade-offs & Tech Choices

One of the primary trade-offs of designing this aggregator system is between real-time data versus consistency. While it would be ideal to provide users with real-time reviews as soon as they are available, pulling data from multiple sources entails managing data consistency effectively, especially when reviews can be updated. A possible approach could be to display the most recent data with timestamps and indicate whether the gathered reviews are real-time.

Another consideration is the choice of using microservices versus a monolithic architecture. Microservices offer better scalability and independent deployments but introduce complexity in service management and inter-service communication. In contrast, a monolithic architecture simplifies deployment but could hinder scalability and require more extensive downtime for updates.

Failure Scenarios & Bottlenecks

Several failure scenarios can impact the Movie Reviews Aggregator System. For instance, if one of the third-party review sources goes down, we could encounter issues fetching data. To mitigate this, implementing fallback mechanisms that can either cache the last successful review data or alert users about the temporary inability to fetch new reviews would be essential.

Another potential scenario involves data inconsistency due to delays in updating caches or failures in asynchronous task handling. Implementing mechanisms to continually validate and refresh cache data can help prevent stale data from being shown to users.

Future Improvements

Looking ahead, there are several opportunities for improvement once the initial version is stable. One such area could be integrating advanced sentiment analysis algorithms to provide users with a quantitative score of reviews based on their text. This improvement can enhance user understanding by summarizing the overall sentiment of the reviews fetched.

Another improvement could be introducing features like personalized recommendations based on user preferences, watch history, or even social features like allowing users to follow friends and see their reviews. These enhancements can significantly increase user engagement and return visits to the platform.

High Level Architecture Diagram

Database ER Diagram

Request Flow Sequence Diagram