Which solution will meet these requirements?

A company has an application that customers use to search for records on a website. The application’s data is stored in an Amazon Aurora DB cluster. The application’s usage varies by season and by day of the week.

The website’s popularity is increasing, and the website is experiencing slower performance because of increased load on the DB cluster during periods of peak activity. The application logs show that the performance issues occur when users are searching for information. The same search is rarely performed multiple times.

A SysOps administrator must improve the performance of the platform by using a solution that maximizes resource efficiency.

Which solution will meet these requirements?
A . Deploy an Amazon ElastiCache for Redis cluster in front of the DB cluster. Modify the application to check the cache before the application issues new queries to the database. Add the results of any queries to the cache.
B . Deploy an Aurora Replica for the DB cluster. Modify the application to use the reader endpoint for
search operations. Use Aurora Auto Scaling to scale the number of replicas based on load. Most Voted
C . Use Provisioned IOPS on the storage volumes that support the DB cluster to improve performance sufficiently to support the peak load on the application.
D . Increase the instance size in the DB cluster to a size that is sufficient to support the peak load on the application. Use Aurora Auto Scaling to scale the instance size based on load.

View Answer

Answer: A

Explanation:

Step-by-Step

Understand the Problem:

The application experiences slower performance during peak activity due to increased load on the Amazon Aurora DB cluster.

Performance issues occur primarily during search operations.

The goal is to improve performance and maximize resource efficiency.

Analyze the Requirements:

The solution should improve the performance of the platform.

It should maximize resource efficiency, which implies cost-effective and scalable options.

Evaluate the Options:

Option A: Deploy an Amazon ElastiCache for Redis cluster.

ElastiCache for Redis is a managed in-memory caching service that can significantly reduce the load on the database by caching frequently accessed data.

By modifying the application to check the cache before querying the database, repeated searches for the same information will be served from the cache, reducing the number of database reads.

This is efficient and cost-effective as it reduces database load and improves response times.

Option B: Deploy an Aurora Replica and use Auto Scaling.

Adding Aurora Replicas can help distribute read traffic and improve performance.

Aurora Auto Scaling can adjust the number of replicas based on the load.

However, this option may not be as efficient in terms of resource usage compared to caching because it still involves querying the database.

Option C: Use Provisioned IOPS.

Provisioned IOPS can improve performance by providing fast and consistent I/O.

This option focuses on improving the underlying storage performance but doesn’t address the inefficiency of handling repeated searches directly.

Option D: Increase the instance size and use Auto Scaling.

Increasing the instance size can provide more resources to handle peak loads.

Aurora Auto Scaling can adjust instance sizes based on the load.

This option can be costly and may not be as efficient as caching in handling repeated searches.

Select the Best Solution:

Option A is the best solution because it leverages caching to reduce the load on the database, which directly addresses the issue of repeated searches causing performance problems. Caching is generally more resource-efficient and cost-effective compared to scaling database instances or storage.

Reference: Amazon ElastiCache for Redis Documentation

Amazon Aurora Documentation

AWS Auto Scaling

Using ElastiCache for Redis aligns with best practices for improving application performance by offloading repetitive read queries from the database, leading to faster response times and more efficient resource usage.