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Zorluk: ZorPerformance and Scalability Optimization

A major airline is preparing to launch a flash promotional event where discounted international flight tickets will be released at a specific second. The marketing department expects an immediate surge in traffic, with concurrent user connections scaling from fewer than 100100 to over 450,000450,000 within the first 6060 seconds of the launch. The backend application, running on Amazon ECS, queries flight availability from an Amazon Aurora PostgreSQL database (with a read-to-write ratio of 10:110:1). The system must also store transient flight search filters and user session data, which requires a caching solution that supports Multi-AZ replication and data persistence to survive cache node failures. Which architecture optimization strategy will handle the peak traffic spike while maintaining low latency and preventing request failures?

  1. A
    Configure the Application Load Balancer to scale dynamically using target tracking policies, route the read queries to an Amazon RDS PostgreSQL Multi-AZ standby instance to handle the catalog read load, and implement an Amazon ElastiCache for Redis cluster to maintain persistent user sessions.
  2. B
    Request AWS Support to pre-warm the Application Load Balancer to the expected traffic volume, scale the Aurora database reads horizontally by adding Aurora Replicas under an Auto Scaling policy, and deploy an Amazon ElastiCache for Memcached cluster to manage the user session states across multiple Availability Zones.
  3. Request AWS Support to pre-warm the Application Load Balancer to the expected traffic volume, scale the Aurora database reads horizontally by configuring Aurora Auto Scaling with Aurora Replicas, and use Amazon ElastiCache for Redis with Multi-AZ enabled to store the persistent user session state.Cevap
  4. D
    Rely on the Application Load Balancer's automatic scaling to dynamically handle the incoming connection surge, scale the Aurora database reads by configuring Aurora Auto Scaling with Aurora Replicas, and utilize an Amazon ElastiCache for Redis cluster with Multi-AZ and auto-failover to manage session data.

Cevap

The strategy that pre-warms the Application Load Balancer, utilizes Aurora Auto Scaling with Aurora Replicas for read scaling, and deploys Amazon ElastiCache for Redis with Multi-AZ for session persistence.
The correct strategy addresses all three scaling constraints. First, requesting AWS Support to pre-warm the Application Load Balancer is essential because the expected flash traffic spike (from 100100 to 450,000450,000 concurrent connections within 6060 seconds) happens too quickly for standard load balancer auto-scaling to react, which would cause HTTP 503 / connection errors. Second, configuring Aurora Auto Scaling with Aurora Replicas allows the database tier to scale read operations horizontally to match the 10:110:1 read-heavy workload. Third, Amazon ElastiCache for Redis supports Multi-AZ replication and data persistence, ensuring that user session states are preserved and highly available in the event of a cache node failure.

Adım Adım Çözüm

1
Address the immediate, massive connection surge from the flash traffic profile.
Determine that the Application Load Balancer (ALB) must be pre-warmed via an AWS Support request.
An instantaneous jump from 100100 to 450,000450,000 concurrent users in 6060 seconds exceeds the default scaling rate of ALBs, leading to HTTP 503 errors and dropped requests if not pre-warmed.
2
Design the scaling strategy for the Amazon Aurora database reads.
Configure Aurora Auto Scaling to dynamically provision Aurora Replicas based on CPU or connection metrics.
The database workload is read-heavy (10:110:1 read-to-write ratio). Horizontal scaling with Aurora Replicas offloads read traffic from the writer node. RDS Multi-AZ standby nodes cannot serve read traffic.
3
Select the appropriate caching technology to handle transient user session states and filters.
Select Amazon ElastiCache for Redis with Multi-AZ and replication enabled.
ElastiCache for Memcached lacks persistence and built-in Multi-AZ replication/failover features, making Redis the correct choice to meet the resilience and durability requirements.

Anahtar Kavram

Load balancer pre-warming, horizontal database read scaling with Aurora Replicas, and selecting caching engines based on replication and persistence requirements.
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Performance and Scalability Optimization

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