Create the architectural design for Peloton class rankings.

This question prompts you to create a system architecture for ranking Peloton classes, likely influenced by user engagement, interactions, and feedback. Peloton, a fitness technology company, provides live and on-demand fitness classes that users can take remotely from their homes. Important factors that could impact the rankings include the total number of participants in a class, the average user rating, the workout completion rate, and social interactions, such as the 'high fives' exchanged during a class. Your objective in responding to this question is to showcase your knowledge of system architecture, database design, and ranking algorithms. Illustrate your ability to decompose a complex system into its individual components and explain how they work together.

A suitable architecture for this use case could be a Microservices architecture, combined with Event-Sourcing and CQRS patterns. By employing a microservices approach, each functional component of the application will operate independently, enabling the scaling, modification, and replacement of individual components without impacting others. This architecture will include modules for User Participation, Class Interaction, and Ratings. The User Participation microservice will be tasked with monitoring the classes that users have attended and whether they have completed them. The Class Interaction microservice will track all interactions that take place during a class, including the number of 'high fives' and messages sent in the live chat. The Ratings microservice will manage user ratings for each class. Additionally, we will implement the Event-Sourcing pattern to log events such as users joining or completing a class, which will help maintain a record of all activities and facilitate analysis and debugging. Lastly, we will utilize the CQRS (Command Query Responsibility Segregation) pattern to differentiate between read and update operations for a data store. A distinct rating calculation model will be employed to rank classes based on the data gathered from the microservices.

Alternatively, the system could be structured using a Serverless architecture, which enables us to scale the service as needed. The application design would mainly include an API gateway, multiple AWS Lambda functions, and an Amazon RDS database. The API Gateway would manage incoming requests and direct them to the corresponding Lambda functions. These functions might cover tracking class participation, user interactions during classes, and user ratings. The Class Participation function would be tasked with monitoring user attendance in classes. The Class Interaction function would facilitate social interactions within the class, such as 'high fives' and messaging. The Ratings function would log user ratings for each class. Amazon RDS would act as our main database, containing information about users, classes, participation, interactions, and ratings. A distinct Ranking function would execute at regular intervals (for example, each night) to refresh a class ranking table in the database. This function would analyze all collected data to establish a ranking for each class based on an algorithm we create. This algorithm could consider user participation, interactions, and ratings, assigning different weights to each factor as we deem appropriate. This approach would allow us to maintain an up-to-date ranking of Peloton classes for users to access.