Learn how to leverage Amazon Aurora’s Auto Scaling capabilities by creating policies based on the DatabaseConnections metric to dynamically adjust resources and maintain consistent read performance as user demand fluctuates.
Table of Contents
Question
An application team uses an Amazon Aurora MySQL DB cluster with one Aurora Replica. The application team notices that the application read performance degrades when user connections exceed 200. The number of user connections is typically consistent around 180, with occasional sudden increases above 200 connections. The application team wants the application to automatically scale as user demand increases or decreases.
Which solution will meet these requirements?
A. Migrate to a new Aurora multi-master DB cluster. Modify the application database connection string.
B. Modify the DB cluster by changing to serverless mode whenever user connections exceed 200.
C. Create an auto scaling policy with a target metric of 195 DatabaseConnections.
D. Modify the DB cluster by increasing the Aurora Replica instance size.
Answer
C. Create an auto scaling policy with a target metric of 195 DatabaseConnections.
Explanation
Amazon Aurora is a MySQL and PostgreSQL-compatible relational database built for the cloud. It supports Auto Scaling, which allows you to dynamically adjust the computing resources allocated to your Aurora DB cluster based on workload demands.
By creating an auto scaling policy with a target metric of 195 DatabaseConnections, the Aurora DB cluster will automatically scale up or down based on the number of user connections. Specifically:
- Set up an Amazon CloudWatch metric for “DatabaseConnections” on the Aurora DB cluster.
- Create an Auto Scaling policy for the Aurora DB cluster.
- Set the target value for the “DatabaseConnections” metric to 195.
- Configure the Auto Scaling policy to add or remove Aurora Replicas when the metric value exceeds or falls below the target value.
With this solution, when the number of user connections exceeds 195, Aurora will automatically scale out by adding new Aurora Replicas to handle the increased read workload. Conversely, when the number of connections drops below 195, Aurora will scale in by removing unnecessary Replicas, optimizing resource utilization and costs.
By automatically scaling based on the DatabaseConnections metric, the application’s read performance will remain consistent, even during sudden spikes or drops in user demand.
Other options are not applicable or do not address the scaling requirement:
A. Migrating to a multi-master DB cluster does not provide automatic scaling capabilities.
B. Modifying the DB cluster to serverless mode is not a suitable solution for scaling based on user connections.
D. Increasing the Aurora Replica instance size does not address the need for automatic scaling based on user demand.
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