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Zorluk: OrtaPerformance and Scalability Optimization

A media company is planning the launch of a new subscription-based video streaming service. During a major live sporting event broadcast, a promotional code will be displayed on screen. The company expects the traffic to the subscription registration endpoint to instantly spike from a baseline of 100100 requests per second (RPS) to over 150,000150,000 RPS within a 3030-second window. The registration application is hosted on a fleet of Amazon EC2 instances in an Auto Scaling group behind an Application Load Balancer (ALB), and it queries an Amazon Aurora PostgreSQL database for promotion eligibility checks. Which design should a solutions architect implement to ensure the architecture scales to meet this peak demand without dropping requests or experiencing latency degradation?

  1. A
    Rely on the default self-scaling behavior of the Application Load Balancer to handle the incoming traffic spike dynamically. Configure the EC2 Auto Scaling group with a target tracking policy based on Average CPU Utilization. Route the database read queries to the Amazon Aurora Multi-AZ standby instance to offload the primary writer.
  2. B
    Request AWS Support to pre-warm the Application Load Balancer to the expected peak capacity. Configure the EC2 Auto Scaling group with a step scaling policy to scale out quickly. Deploy an Amazon ElastiCache for Memcached cluster with multi-AZ replication enabled to cache promotion eligibility queries and reduce the load on the Aurora database.
  3. Submit a support ticket to AWS to pre-warm the Application Load Balancer to the target throughput of 150,000150,000 requests per second. Configure a scheduled scaling policy for the EC2 Auto Scaling group to launch the required number of instances 3030 minutes before the promotional broadcast. Deploy Amazon Aurora Replicas and configure Aurora Auto Scaling based on reader average CPU utilization to scale the database tier.Cevap
  4. D
    Rely on the Application Load Balancer to scale automatically to meet the traffic demands. Configure a target tracking scaling policy for the EC2 Auto Scaling group and set the scaling cooldown period to 1010 seconds to ensure rapid deployment of new instances. Route all database traffic to the primary writer and rely on Aurora storage auto-scaling to handle the increased query load.

Cevap

The architecture must use a pre-warmed Application Load Balancer, a scheduled scaling policy for EC2, and Aurora Replicas with Aurora Auto Scaling.
The correct design resolves all scaling bottlenecks. For a massive, near-instantaneous traffic spike (100100 to 150,000150,000 RPS), an Application Load Balancer requires pre-warming to avoid dropping connection requests. An EC2 Auto Scaling group cannot scale out fast enough using dynamic scaling policies due to instance launch and bootstrapping times, so a scheduled scaling policy must be configured to provision capacity before the event. Amazon Aurora Replicas with Aurora Auto Scaling successfully distribute the read load horizontally.

Adım Adım Çözüm

1
Analyze the load profile and load balancer scaling limitations.
The traffic spikes from 100100 to 150,000150,000 RPS in 3030 seconds. Because this rate of increase exceeds the default scaling rate of an Application Load Balancer, the architect must request ALB pre-warming from AWS Support.
Prevents connection timeouts and dropped packets at the load balancer entry point during the sudden traffic spike.
2
Address backend compute scaling delays.
EC2 instance boot times and application bootstrapping typically take several minutes, meaning dynamic reactive scaling policies (like target tracking or step scaling) will react too slowly, causing service degradation. The architect must configure a scheduled scaling policy to pre-provision EC2 instances 3030 minutes before the event.
Ensures sufficient backend compute capacity is fully active and ready to process requests before the promotional code is broadcast.
3
Optimize the database read tier for increased query volumes.
The promotion eligibility queries are read-heavy. The architect must deploy Aurora Replicas and configure Aurora Auto Scaling based on reader average CPU utilization.
Allows the database to scale read capacity horizontally to handle the influx of queries without overloading the primary writer instance.

Anahtar Kavram

Handling flash traffic spikes by combining load balancer pre-warming, scheduled instance scaling, and database read replica auto-scaling.
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Performance and Scalability Optimization

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