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Zorluk: ZorPerformance and Scalability Optimization

A metropolitan transit authority is launching a digital boarding verification system across its entire rail network. The system processes QR code passenger scans from 300,000300,000 active turnstiles. During morning rush hour, boarding verification requests surge from a baseline of 2,0002,000 requests per second to a peak of 120,000120,000 requests per second within 45 seconds45\text{ seconds}. The system must validate passenger ticket balances, update transit logs, and return boarding decisions with sub-millisecond database read response times to prevent passenger queues.

Which architecture scales to meet this sudden workload surge while maintaining performance and operational efficiency?

  1. Deploy a Network Load Balancer (NLB) to distribute the verification requests to an Amazon ECS service running on AWS Fargate. Store ticket and balance information in an Amazon Aurora PostgreSQL DB cluster. Configure Aurora Auto Scaling to dynamically scale the number of Aurora Replicas, and route the boarding validation read queries to the DB cluster's reader endpoint.Cevap
  2. B
    Deploy an Application Load Balancer (ALB) to distribute the verification requests to an Amazon ECS service running on AWS Fargate, relying on default ALB scaling during the rush-hour surge. Store ticket and balance information in an Amazon Aurora PostgreSQL DB cluster. Configure Aurora Auto Scaling to dynamically add Aurora Replicas, and route read queries to the DB cluster's reader endpoint.
  3. C
    Deploy a Network Load Balancer (NLB) to distribute the verification requests to an Amazon ECS service running on AWS Fargate. Store ticket and balance information in an Amazon RDS for PostgreSQL DB instance configured with a Multi-AZ deployment. Scale the database read throughput during peak hours by routing the boarding validation read queries to the standby DB instance.
  4. D
    Deploy an Application Load Balancer (ALB) to distribute the verification requests to an Amazon ECS service running on AWS Fargate, relying on default ALB scaling. Store ticket and balance information in an Amazon RDS for PostgreSQL DB instance in a Multi-AZ deployment, and configure the application to route validation read queries to the secondary standby instance during the rush-hour surge.

Cevap

Deploy a Network Load Balancer (NLB) to distribute the verification requests to an Amazon ECS service running on AWS Fargate. Store ticket and balance information in an Amazon Aurora PostgreSQL DB cluster. Configure Aurora Auto Scaling to dynamically scale the number of Aurora Replicas, and route the boarding validation read queries to the DB cluster's reader endpoint.
The correct architecture uses a Network Load Balancer (NLB) to handle the sudden, extreme traffic spike without requiring pre-warming, routing requests to Amazon ECS tasks. On the database tier, it stores data in Amazon Aurora PostgreSQL and uses Aurora Auto Scaling to dynamically provision read replicas, routing read traffic to the reader endpoint. This scales the read workload efficiently while maintaining low latency.

Adım Adım Çözüm

1
Analyze load balancer scaling requirements.
Identify that the system must scale from a baseline of 2,0002,000 requests per second to 120,000120,000 requests per second in 45 seconds45\text{ seconds}.
An Application Load Balancer (ALB) cannot scale this fast natively without pre-warming, leading to dropped requests. A Network Load Balancer (NLB) is required to handle sudden, extreme traffic spikes.
2
Evaluate database horizontal scaling limitations for RDS.
Determine that Amazon RDS Multi-AZ deployments use a passive standby instance.
The standby instance in an RDS Multi-AZ configuration only serves as a failover target and does not accept read connections, making it unable to scale query throughput.
3
Evaluate Amazon Aurora read scaling mechanisms.
Leverage Amazon Aurora Auto Scaling with Aurora Replicas.
Amazon Aurora can dynamically add replicas to handle spikes in read queries, and routes traffic through the reader endpoint, solving the database read bottleneck.

Anahtar Kavram

Handling sudden traffic bursts at the entry tier using Network Load Balancers (NLB) and scaling the database read capacity using Amazon Aurora Replicas behind a reader endpoint.
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Performance and Scalability Optimization

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