A live online game uses DynamoDB instances in the backend to store real-time scores of the participants as they compete against each other from various parts of the world.
Which data consistency option is the most appropriate to implement?
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A. B. C. D.Correct Answer - A.
Since the gamers are from geographically distinct locations, the data will need to be immediately readable within a second as soon as it is written.
Therefore strongly consistency is needed.
https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadConsistency.htmlOption B is INCORRECT because the scenarios outline that the participants of the game are live.
It will not suffice if any of them get updates on scores in less than real-time.
Option C is INCORRECT because strong eventual consistency is not applicable in DynamoDB.Option D is INCORRECT because only two data consistency models are available with the DynamoDB service.
Optimistic consistency is not supported.
In this scenario, the online game is using DynamoDB as its backend data store to store real-time scores of the participants. Since DynamoDB is a highly available and scalable NoSQL database service provided by AWS, it offers different data consistency options to choose from.
Data consistency refers to the level of accuracy and correctness of the data returned by a database system. DynamoDB provides four types of data consistency models, namely, strongly consistent, eventually consistent, strongly eventual consistency, and optimistic consistency.
In the context of the given scenario, the most appropriate data consistency option to implement is "strongly consistent" (Option A). Here's why:
Strongly Consistent: In a strongly consistent system, every read operation is guaranteed to return the most up-to-date data. This means that every read operation will see the latest changes made to the data, and there is no delay or lag in the retrieval of data. Strong consistency is the most stringent and reliable consistency model available in DynamoDB.
Since the online game is live and real-time, it is crucial to ensure that the scores are accurately updated in real-time, and there is no delay or lag in the retrieval of data. Thus, implementing strongly consistent data consistency ensures that the game's scores are accurately updated in real-time, and every read operation returns the most up-to-date data.
Eventually Consistent: In an eventually consistent system, every read operation is not guaranteed to return the most up-to-date data. There is a slight delay in the retrieval of data, and multiple read operations might return different versions of data. This consistency model is suitable for systems where the data is not time-sensitive, and the system can tolerate some inconsistency for a short period.
Strong Eventual Consistency: Strong eventual consistency is a variation of eventual consistency where the system guarantees that all updates will propagate to all nodes eventually. This model is suitable for systems that can tolerate some inconsistency for a short period, but eventually, all nodes will have the same version of data.
Optimistic Consistency: Optimistic consistency is a consistency model where the system assumes that there will be no conflicts in the data and allows multiple writes to proceed concurrently. The system then detects and resolves any conflicts that might occur after the writes are complete. This model is suitable for systems where conflicts are rare, and the system can tolerate some inconsistency for a short period.
In conclusion, the most appropriate data consistency option to implement in the given scenario is strongly consistent. This ensures that every read operation returns the most up-to-date data and the scores are accurately updated in real-time, which is critical for a live online game.