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Difficulty: HardPerformance and Scalability Optimization

A SaaS provider is designing a multi-tenant analytics dashboard. Each tenant's dashboard displays real-time operational metrics gathered from various IoT devices. The metrics are ingested into a central relational database. During a major industry event, the read queries to the database from tenant dashboards are expected to spike from a baseline of 5,0005,000 requests per minute to over 800,000800,000 requests per minute. Concurrently, the ingestion API endpoint will experience an instantaneous volume increase from 1,0001,000 requests per second to 120,000120,000 requests per second. The dashboard is backed by Amazon Aurora PostgreSQL, and the ingestion endpoint uses an Application Load Balancer (ALB) routing to Amazon Elastic Container Service (Amazon ECS) tasks. Which two actions should the Solutions Architect take to optimize the platform's performance and scalability for the upcoming event?

  1. Contact AWS Support to pre-warm the Application Load Balancer (ALB) to handle the expected spike of 120,000120,000 requests per second on the ingestion endpoint.Answer
  2. Deploy Aurora Replicas in the database cluster and configure Aurora Auto Scaling with a target tracking policy based on average reader CPU utilization to scale out read capacity for the dashboards.Answer
  3. C
    Rely on the Application Load Balancer's default automatic scaling capabilities to handle the sudden increase from 1,0001,000 to 120,000120,000 requests per second without pre-provisioning.
  4. D
    Enable Amazon RDS Multi-AZ replication for the Aurora cluster and route the tenant dashboard read queries to the passive standby instance to distribute the load.
  5. E
    Deploy an Amazon ElastiCache for Memcached cluster with multi-AZ replication and persistence enabled to serve as a high-performance caching layer for the tenant metrics.

Answer

Contact AWS Support to pre-warm the Application Load Balancer (ALB) to handle the expected spike of 120,000120,000 requests per second on the ingestion endpoint, and deploy Aurora Replicas in the database cluster and configure Aurora Auto Scaling with a target tracking policy based on average reader CPU utilization.
To handle the sudden traffic spike from 1,0001,000 to 120,000120,000 requests per second, the Solutions Architect must contact AWS Support to pre-warm the Application Load Balancer. Additionally, to scale database read capacity from 5,0005,000 to 800,000800,000 queries per minute, deploying Aurora Replicas and enabling Aurora Auto Scaling based on CPU utilization is the recommended, operationally efficient solution.

Step-by-Step Solution

1
Analyze the load balancer performance requirements.
Identify that the ingestion API endpoint will experience an instantaneous surge from 1,0001,000 to 120,000120,000 requests per second. Default ALB scaling is gradual and cannot cope with this rate of acceleration.
Determining the need for ELB pre-warming to avoid dropped requests during the flash spike.
2
Analyze the database scaling options for read queries.
Determine that read queries will increase from 5,0005,000 to 800,000800,000 requests per minute. Aurora PostgreSQL can scale reads horizontally by adding Aurora Replicas.
Configuring Aurora Auto Scaling to dynamically provision read replicas based on CPU utilization ensures the database handles the query spikes.
3
Evaluate the architectural validity of the remaining options.
Verify that Multi-AZ standby instances in standard RDS are passive and cannot serve reads, and that ElastiCache for Memcached lacks replication and persistence capabilities.
Eliminating options that rely on unsupported engine features or invalid replication architectures.

Key Concept

Handling sudden flash traffic on ELB endpoints via pre-warming and scaling read-heavy relational databases horizontally using Aurora Auto Scaling replicas.
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Topics

Performance and Scalability Optimization

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