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Difficulty: MediumPerformance and Scalability Optimization

A financial services company is launching a personalized dashboard that provides real-time portfolio performance metrics to 2,000,0002,000,000 active customers. During the stock market opening at 9:30 AM, query traffic is projected to surge instantly from 2,0002,000 requests per second to 300,000300,000 requests per second. The application is hosted on Amazon EC2 instances within an Auto Scaling group behind an Application Load Balancer (ALB), and the portfolio data resides in an Amazon RDS for PostgreSQL database. To handle this daily flash traffic spike without dropping connections or incurring high latency, which strategy should a solutions architect implement?

  1. A
    Enable Multi-AZ replication on the Amazon RDS for PostgreSQL database, and configure the application to route read queries to the standby replica. Use target tracking scaling policies based on Application Load Balancer request count per target to dynamically scale the EC2 instances.
  2. B
    Implement an Amazon ElastiCache for Memcached cluster to cache portfolio data across multiple Availability Zones. Configure the EC2 Auto Scaling group cooldown period to 3030 seconds to ensure the fleet scales out quickly enough to match the traffic rate.
  3. Deploy an Amazon ElastiCache for Redis cluster in front of the PostgreSQL database to cache read-heavy queries. Configure the Auto Scaling group to use step scaling policies based on CPU utilization, and contact AWS Support to pre-warm the Application Load Balancer prior to the market opening.Answer
  4. D
    Vertically scale the existing EC2 instances and the Amazon RDS database instance to the largest available instance types before the market opens. Rely on the default auto-scaling behavior of the Application Load Balancer to absorb the sudden load spike at 9:30 AM.

Answer

Deploy an Amazon ElastiCache for Redis cluster in front of the PostgreSQL database to cache read-heavy queries. Configure the Auto Scaling group to use step scaling policies based on CPU utilization, and contact AWS Support to pre-warm the Application Load Balancer prior to the market opening.
The correct strategy combines caching at the database layer (ElastiCache for Redis) to reduce query load, proactive capacity allocation (ALB pre-warming) to handle the instant 150-fold traffic surge, and horizontal step scaling to add EC2 instances in response to CPU metrics. This ensures both the ingress layer (ALB) and the compute/storage layers scale dynamically and performantly.

Step-by-Step Solution

1
Analyze load scaling limits of the Application Load Balancer (ALB).
Identify that a sudden 150-fold traffic spike will outpace the ALB's automatic scaling rate, necessitating pre-warming by AWS Support.
ALB auto-scaling is gradual and cannot react instantly to massive, sub-minute surges.
2
Optimize database read throughput using a caching layer.
Place an Amazon ElastiCache for Redis cluster in front of the RDS PostgreSQL database to handle high-frequency, read-heavy query loads.
Caching prevents database bottlenecking and reduces query response times during peak hours.
3
Configure the Auto Scaling group to react quickly and stably to load surges.
Apply step scaling policies with appropriate warm-up times to scale out EC2 instances in defined increments.
Step scaling is more responsive to large spikes than simple scaling, and proper warm-up times prevent premature scaling evaluation.

Key Concept

Handling instantaneous flash traffic spikes by combining load balancer pre-warming, database query caching, and horizontal step scaling.
Estimated Time:2m 0s
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Topics

Performance and Scalability Optimization

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