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Zorluk: OrtaPerformance and Scalability Optimization

A retail banking platform is launching a feature that gives customers real-time access to their investment portfolios. The platform expects an instantaneous spike of over 200,000200,000 concurrent users daily at exactly 09:00 UTC09:00\text{ UTC} when the market opens. The application is hosted on Amazon EC2 instances within an Auto Scaling group behind an Application Load Balancer (ALB). The portfolio query engine reads data from an Amazon Aurora PostgreSQL database cluster. Which strategy should a Solutions Architect implement to handle this flash traffic without dropping customer requests or degrading query response times?

  1. Configure scheduled scaling for the EC2 Auto Scaling group to scale out before 09:00 UTC09:00\text{ UTC}. Request AWS Support to pre-warm the Application Load Balancer to the expected traffic volume, and configure Amazon Aurora Auto Scaling to scale Aurora Replicas to handle the spike in read queries.Cevap
  2. B
    Configure target tracking scaling policies on the EC2 Auto Scaling group based on CPU utilization, rely on the Application Load Balancer's automatic scaling capabilities to handle the sudden burst, and enable Amazon Aurora Auto Scaling to dynamically provision Aurora Replicas.
  3. C
    Configure scheduled scaling for the EC2 Auto Scaling group to scale out before 09:00 UTC09:00\text{ UTC}. Request AWS Support to pre-warm the Application Load Balancer, and rely on the Aurora PostgreSQL Multi-AZ standby instance in the secondary Availability Zone to serve read traffic during the peak window.
  4. D
    Configure scheduled scaling for the EC2 Auto Scaling group to scale out before 09:00 UTC09:00\text{ UTC}, request AWS Support to pre-warm the Application Load Balancer, and set the Auto Scaling cooldown period to 15 seconds15\text{ seconds} to ensure the EC2 instances scale out rapidly enough to match the traffic rate.

Cevap

Configure scheduled scaling for the EC2 Auto Scaling group to scale out before 09:00 UTC09:00\text{ UTC}. Request AWS Support to pre-warm the Application Load Balancer to the expected traffic volume, and configure Amazon Aurora Auto Scaling to scale Aurora Replicas to handle the spike in read queries.
The correct strategy combines scheduled scaling for the EC2 Auto Scaling group, requesting ALB pre-warming from AWS Support, and using Amazon Aurora Auto Scaling. Scheduled scaling ensures that the EC2 instances are fully provisioned and booted before the predictable spike at 09:00 UTC09:00\text{ UTC}. Requesting AWS Support to pre-warm the ALB ensures the load balancer is provisioned with enough capacity to prevent dropped connections during the instantaneous spike. Configuring Aurora Auto Scaling allows the database to dynamically add Aurora Replicas to handle the surge in read queries.

Adım Adım Çözüm

1
Identify the scaling properties of the front-end load balancer under instant spike conditions.
Standard ALB scaling is reactive and cannot keep up with an instantaneous spike of 200,000200,000 concurrent users, which requires pre-warming by AWS Support.
Pre-warming ensures the ALB is pre-provisioned with adequate capacity to handle the sudden burst without dropping connections.
2
Address compute capacity scaling for predictable daily spikes.
Target tracking or step scaling policies are reactive and would lag, so scheduled scaling must be used to launch EC2 instances before 09:00 UTC09:00\text{ UTC}.
Scheduled scaling guarantees compute capacity is fully booted and ready when the traffic surge arrives.
3
Determine the appropriate database read scaling mechanism.
Aurora Replicas with Auto Scaling are configured to scale horizontally to handle query volume, while the Multi-AZ standby is not queryable.
Amazon Aurora Replicas handle read queries and support auto-scaling, whereas passive standby nodes are strictly for high availability and failover.

Anahtar Kavram

Handling instantaneous flash traffic spikes by combining load balancer pre-warming, scheduled compute scaling, and horizontal read scaling of database replicas.
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Konular

Performance and Scalability Optimization

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