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Difficulty: EasyPerformance and Scalability Optimization

A company is designing a new portal that will experience highly variable read traffic to its relational database. The database tier must scale horizontally and automatically to handle peak read loads while maintaining high availability. Which configuration should a solutions architect recommend to scale the database's read capacity?

  1. Deploy Amazon Aurora MySQL with Aurora Replicas and configure an Application Auto Scaling policy to dynamically scale the number of replicas based on CPU utilization.Answer
  2. B
    Deploy Amazon Aurora MySQL in a Multi-AZ configuration and configure the application to send read queries to the standby writer instance in the secondary Availability Zone during peak hours.
  3. C
    Deploy an Application Load Balancer in front of the database and request AWS Support to pre-warm it before peak hours to handle the increased read query volume.
  4. D
    Configure Aurora Auto Scaling to add Aurora Replicas, setting the scale-out cooldown period to 10 seconds so that the cluster launches multiple replicas immediately during a traffic spike before the first replica finishes provisioning.

Answer

Deploy Amazon Aurora MySQL with Aurora Replicas and configure an Application Auto Scaling policy to dynamically scale the number of replicas based on CPU utilization.
Deploying Amazon Aurora MySQL with Aurora Replicas and configuring an Application Auto Scaling policy based on CPU utilization is the standard AWS best practice for handling highly variable read workloads. Aurora Replicas share the same underlying storage volume as the writer instance, resulting in sub-10ms replication lag, and can be dynamically scaled up and down based on target tracking policies.

Step-by-Step Solution

1
Identify the primary performance bottleneck and scaling requirements.
The workload is read-heavy and highly variable, requiring dynamic horizontal read scaling for a relational database with high availability.
This establishes that the solution must support scaling reads independently of writes and react to fluctuating utilization.
2
Select a database engine and topology that supports read replicas.
Amazon Aurora MySQL with Aurora Replicas is selected as it supports up to 15 low-latency reader endpoints.
Unlike traditional active-passive standby databases, Aurora reader nodes are fully active and capable of serving read queries.
3
Configure the scaling mechanism and policy settings.
An Application Auto Scaling policy is applied to the Aurora reader fleet using CPU utilization as the scaling metric, with a reasonable cooldown period.
This automates the provisioning of reader nodes to handle peaks and deletes them during low-traffic periods to optimize costs, while a proper cooldown prevents rapid, redundant scaling events.

Key Concept

Horizontal read scaling in Amazon Aurora using reader replicas and Application Auto Scaling.
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Topics

Performance and Scalability Optimization

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