Implementing a Scalable Caching Solution for Read-Heavy Applications
Question
A solution architect would like to implement a caching solution for an application.
The application is read-heavy performing frequent read operations.
The application load is very unpredictable.
So the solution architect would like to be able to horizontally scale the caching system up and down as required, while keeping the infrastructure costs minimal.
What is the optimal solution to this problem?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: A.
Option A is CORRECT because ElastiCache for Redis (Cluster Mode Disabled) clusters support scaling up or down of read capacity by creating or deleting read replicas within the cluster.
Option B is incorrect because horizontally scaling ElastiCache for Redis (Cluster Mode Enabled) clusters can be a complex activity as it can require re-sharding of the clusters.
Therefore, this is not the ideal solution.
Option C is incorrect because Amazon DAX is a caching solution specific to DynamoDB.Option D is incorrect because Write-Through caching strategy is related to the consistency of data in cache in relation to the base source database.
It is not relevant to the scenario.
Reference:
https://docs.aws.amazon.com/AmazonElastiCache/latest/red-ug/Replication.Redis-RedisCluster.htmlThe optimal solution for implementing a caching solution for a read-heavy application that has an unpredictable load while keeping infrastructure costs minimal is to deploy Amazon ElastiCache for Redis (Cluster Mode Disabled) cluster (option A).
Amazon ElastiCache is a fully managed in-memory caching service provided by AWS that supports two popular open-source in-memory caching engines, Redis and Memcached. Amazon ElastiCache for Redis offers horizontal scaling by providing two different cluster modes: cluster mode enabled and cluster mode disabled.
In cluster mode disabled, you can create a Redis cluster without sharding, which means you can horizontally scale the caching nodes up and down without the need to reshard data across nodes. This option allows the caching nodes to be added or removed without data redistribution, making it ideal for applications with unpredictable traffic patterns.
On the other hand, cluster mode enabled allows you to shard your data across multiple Redis nodes, providing scalability and high availability. However, it requires more complex management and is not the optimal solution for this use case.
Amazon DAX is another caching service provided by AWS that is designed to improve the performance of read-intensive applications. However, it is not the optimal solution in this case because it is not as cost-effective as ElastiCache for Redis and does not support horizontal scaling without data redistribution.
Write-through caching strategy involves writing data to both the cache and backend storage, which can improve read performance by reducing the number of reads from the backend storage. However, it is not a solution for scaling the caching infrastructure up and down, which is the requirement in this use case.
In conclusion, the optimal solution for this use case is to deploy Amazon ElastiCache for Redis (Cluster Mode Disabled) cluster (option A) to provide horizontal scaling without data redistribution and keep infrastructure costs minimal.
