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Difficulty: MediumPerformance and Scalability Optimization

A financial media platform publishes quarterly earnings reports. During a major company's earnings release, traffic to the historical stock data service spikes from a baseline of 2,0002,000 requests per second to over 120,000120,000 requests per second within 3030 seconds. The application tier runs on Amazon EC2 instances in an Auto Scaling group behind an Application Load Balancer (ALB). The database tier currently uses an Amazon RDS for PostgreSQL Multi-AZ deployment. Which strategy should a solutions architect recommend to scale both the database read capacity and the load balancer to handle this sudden traffic surge?

  1. Migrate the database to Amazon Aurora PostgreSQL, configure Aurora Auto Scaling to dynamically provision Aurora Replicas to handle the read load, and request AWS Support to pre-warm the ALB prior to the scheduled earnings release.Answer
  2. B
    Keep the database on Amazon RDS for PostgreSQL, configure the Multi-AZ standby instance to serve read traffic during the peak window, and configure the EC2 Auto Scaling group to handle the ALB scaling automatically.
  3. C
    Migrate the database to Amazon Aurora PostgreSQL, configure the Multi-AZ standby replica to serve read traffic, and request AWS Support to pre-warm the ALB prior to the scheduled earnings release.
  4. D
    Keep the database on Amazon RDS for PostgreSQL, enable Aurora Auto Scaling for the RDS instance to dynamically scale read replicas, and rely on the ALB's default auto-scaling to absorb the sudden traffic surge.

Answer

Migrate the database to Amazon Aurora PostgreSQL, configure Aurora Auto Scaling to dynamically provision Aurora Replicas to handle the read load, and request AWS Support to pre-warm the ALB prior to the scheduled earnings release.
The correct strategy migrates the database to Amazon Aurora PostgreSQL, enabling dynamic write/read splitting and horizontal scaling through Aurora Auto Scaling replicas. It also addresses the sudden, extreme spike in front-end traffic by requesting ALB pre-warming, which ensures the load balancer has pre-allocated capacity ready to handle the immediate surge to 120,000120,000 requests per second without dropping packets.

Step-by-Step Solution

1
Evaluate the database scaling requirements.
Determine that Amazon RDS for PostgreSQL Multi-AZ standbys are purely passive and cannot serve read traffic, requiring a transition to Aurora Replicas or RDS Read Replicas to handle read-heavy traffic scaling.
To scale read operations dynamically, we need an architecture that supports active replicas and auto-scaling, which Amazon Aurora PostgreSQL provides.
2
Address the rapid traffic spike at the load balancer tier.
Identify that the surge from 2,0002,000 to 120,000120,000 requests per second in 3030 seconds is too rapid for the Application Load Balancer's standard automated scaling algorithm to handle without dropping requests.
ALBs scale gradually based on incoming traffic patterns. Sudden spikes require requesting pre-warming from AWS Support to allocate adequate capacity beforehand.
3
Combine the database and load balancer optimizations into a unified strategy.
Combine ALB pre-warming and Aurora Replica Auto Scaling to address both the sudden entry point load and the backend database read amplification.
This dual approach ensures both the ingestion tier and the data storage tier scale efficiently to handle flash traffic.

Key Concept

Scaling read capacity dynamically and handling sudden traffic spikes requires a combination of database read replicas and pre-warming load balancers.
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Topics

Performance and Scalability Optimization

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