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Difficulty: HardPerformance and Scalability Optimization

A logistics company is designing a real-time fleet monitoring platform for 1010 million active delivery vehicles. Each vehicle transmits telemetry data every 55 seconds over HTTPS, resulting in a sustained ingestion rate of 22 million requests per second. The peak request volume can suddenly double in less than a minute. The system must process these updates with a sub-second latency envelope and store them in a persistent data store. The engineering team also needs to query the latest vehicle positions with sub-millisecond latency. Which architecture meets these requirements with the highest performance and reliability, while minimizing operational overhead?

  1. A
    Deploy an Application Load Balancer (ALB) to route incoming HTTPS requests to an Amazon Elastic Container Service (ECS) service on AWS Fargate. The ECS tasks process the updates, write the latest positions to an Amazon ElastiCache for Redis replication group with Multi-AZ enabled, and write the historical telemetry to an Amazon DynamoDB table in On-Demand capacity mode.
  2. B
    Deploy a Network Load Balancer (NLB) to route incoming HTTPS requests to an Amazon Elastic Container Service (ECS) service on AWS Fargate. The ECS tasks process the updates, write the latest positions to an Amazon ElastiCache for Memcached cluster with Multi-AZ replication enabled, and write the historical telemetry to an Amazon DynamoDB table in On-Demand capacity mode.
  3. Deploy a Network Load Balancer (NLB) to route incoming HTTPS requests to an Amazon Elastic Container Service (ECS) service on AWS Fargate. The ECS tasks process the updates, write the latest positions to an Amazon ElastiCache for Redis replication group with Multi-AZ enabled, and write the historical telemetry to an Amazon DynamoDB table in On-Demand capacity mode.Answer
  4. D
    Deploy a Network Load Balancer (NLB) to route incoming HTTPS requests to an Amazon Elastic Container Service (ECS) service on AWS Fargate. The ECS tasks process the updates, write the latest positions to an Amazon RDS for PostgreSQL database configured with Multi-AZ, and configure the application to scale read queries for latest positions by directing them to the Multi-AZ standby instance.

Answer

Deploy a Network Load Balancer (NLB) to route incoming HTTPS requests to an Amazon Elastic Container Service (ECS) service on AWS Fargate. The ECS tasks process the updates, write the latest positions to an Amazon ElastiCache for Redis replication group with Multi-AZ enabled, and write the historical telemetry to an Amazon DynamoDB table in On-Demand capacity mode.
The architecture combining a Network Load Balancer (NLB), Amazon ECS on Fargate, Amazon ElastiCache for Redis (Multi-AZ), and Amazon DynamoDB (On-Demand) is correct because it ensures instant scaling to handle massive traffic spikes without pre-warming, provides sub-millisecond read latency for latest positions with high availability, and persists historical telemetry with zero manual capacity scaling overhead.

Step-by-Step Solution

1
Analyze the entry point scaling requirements for the ingestion tier.
Determine that a Network Load Balancer (NLB) is required rather than an Application Load Balancer (ALB) because NLB can handle sudden spikes of millions of requests per second without pre-warming, which is critical since traffic can double from 22 million to 44 million requests per second in less than a minute.
To prevent request drops and high latency during sudden flash traffic surges.
2
Evaluate the caching tier for low-latency queries of the latest vehicle positions.
Identify that Amazon ElastiCache for Redis configured with Multi-AZ replication is required to achieve sub-millisecond query performance and ensure high availability, whereas Memcached lacks replication and Multi-AZ capabilities.
To satisfy the sub-millisecond read latency requirement while ensuring high availability during failovers.
3
Evaluate the persistent database layer for high-throughput writes.
Select Amazon DynamoDB in On-Demand capacity mode to persistently store the historical telemetry, as it handles unpredictable and massive write spikes automatically without manual capacity management, while avoiding RDS Multi-AZ standby instances which cannot serve read traffic to scale query workloads.
To manage operational overhead and handle massive persistent write volume without scale bottlenecks.

Key Concept

Selecting and configuring AWS ingestion, caching, and database services to optimize performance and handle massive, sudden scaling requirements without operational bottlenecks.
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Topics

Performance and Scalability Optimization

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