Storing User Profile Data for Online Multiplayer Games - Best Practices

Best Practices for Storing User Profile Data for Online Multiplayer Games

Question

A game development company is developing a new online multiplayer game.

Game user profile data will contain the user's match history.

Match history data is expected to be in size on the order of 500KB.

Additionally, the user profile data schema is expected to go through revisions as new game features are implemented.

How should the team store the user profile data?

Answers

Explanations

Click on the arrows to vote for the correct answer

A. B. C. D.

Answer: D.

Option A is incorrect because the maximum item size in DynamoDB is 400KB.Option B is incorrect because the user profile schema will undergo frequent updates.

Therefore, it is not suitable for a relational database.

Option C is incorrect because this solution does not relate the user profile to the user's match history.

Option D is CORRECT because a recommended approach to storing items greater than 400KB in DynamoDB is to store them in S3, and link the S3 object as an attribute in DynamoDB.Reference:

https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/bp-use-s3-too.html

Option A: Use Amazon DynamoDB to store user profile data DynamoDB is a NoSQL database service provided by AWS that can handle large amounts of data and scale dynamically as the data size grows. DynamoDB supports flexible data modeling and provides low latency, high-performance queries. Since the user profile data schema is expected to go through revisions as new game features are implemented, DynamoDB's flexibility in handling schema changes can be useful. However, DynamoDB has a limit of 400KB per item, which means that the match history data, which is expected to be around 500KB, cannot be stored as a single item.

Option B: Use Amazon RDS to store user profile data Amazon RDS is a managed relational database service that provides scalability, high availability, and security features. Since the user profile data schema is expected to go through revisions, RDS provides flexibility in handling schema changes, and it supports various SQL query languages. However, RDS has limitations in handling large volumes of data, and it may not be a cost-effective solution for small to medium-sized data sets. Also, match history data, which is expected to be around 500KB, may not fit in RDS's storage limit.

Option C: Use Amazon DynamoDB to store user profile data. Use Amazon RDS to store user match history In this option, DynamoDB can be used to store user profile data, and RDS can be used to store match history data. DynamoDB's flexibility in handling schema changes and RDS's scalability and SQL query support make this option suitable for storing user profile data and match history data, respectively.

Option D: Use Amazon DynamoDB to store user profile data. Use S3 to store user match history. Store a link to the S3 object as an attribute in DynamoDB In this option, user profile data can be stored in DynamoDB, and match history data can be stored in S3. DynamoDB's flexibility in handling schema changes and S3's scalability in handling large data volumes can make this option suitable for storing user profile data and match history data, respectively. However, storing a link to the S3 object as an attribute in DynamoDB may increase the overall cost and complexity of the solution.

Conclusion: Based on the requirements of the game development company, Option A (using DynamoDB to store user profile data) may not be sufficient as it cannot handle match history data, which is expected to be around 500KB. Option B (using RDS to store user profile data) may not be suitable as it may not handle large volumes of data efficiently, and match history data may not fit in RDS's storage limit. Option C (using DynamoDB for user profile data and RDS for match history data) is the best option as it leverages the strengths of both services and provides a cost-effective and scalable solution. Option D is also a possible solution, but it may increase complexity and cost.