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A fleet logistics company is migrating its real-time telemetry processing platform to AWS. The platform receives data updates from 2,500,0002,500,000 active vehicles globally. Each vehicle transmits updates every 1010 seconds, resulting in a write throughput of 250,000250,000 writes per second. Each update consists of GPS coordinates, speed, and engine metrics. The monitoring dashboard requires real-time access to the most recent coordinate updates per vehicle with sub-millisecond read latency. A solutions architect must design a high-performing database architecture that avoids partition throttling and supports this read/write workload. Which two configurations should the solutions architect implement to meet these requirements? (Select TWO.)

  1. Provision an Amazon DynamoDB table with a partition key of `VehicleID` and a sort key of `Timestamp` to distribute writes evenly across partitions.Cevap
  2. Deploy an Amazon DynamoDB Accelerator (DAX) cluster to cache read requests and deliver microsecond response times for the dashboard coordinate lookups.Cevap
  3. C
    Provision an Amazon DynamoDB table using `Timestamp` as the partition key and `VehicleID` as the sort key to optimize coordinate queries chronologically.
  4. D
    Configure an Amazon RDS for PostgreSQL database in a Multi-AZ deployment, and configure the application to promote a Read Replica to the primary instance automatically during write spikes to handle write latency issues.
  5. E
    Deploy an Amazon CloudFront distribution in front of the dashboard API with a default Time-to-Live (TTL) of zero seconds to cache the real-time coordinates, ensuring the API origin is queried for every read request to maintain coordinate accuracy.

Cevap

To support a write volume of 250,000250,000 writes per second and sub-millisecond read latency, the solutions architect should provision an Amazon DynamoDB table with `VehicleID` as the partition key and `Timestamp` as the sort key, and deploy an Amazon DynamoDB Accelerator (DAX) cluster to cache read requests.
To scale to 250,000250,000 writes per second, Amazon DynamoDB is the ideal database choice. Choosing `VehicleID` as the partition key is highly effective because there are 2,500,0002,500,000 unique vehicle identifiers, resulting in a well-distributed hash key structure that avoids write hot spots. To meet the dashboard requirement for sub-millisecond (microsecond) read latency for lookup operations, deploying Amazon DynamoDB Accelerator (DAX) is the best choice because it provides an in-memory caching tier that sits directly in front of the DynamoDB table.

Adım Adım Çözüm

1
Analyze write throughput requirements and calculate partition key distribution.
A write throughput of 250,000250,000 writes per second requires a database that scales horizontally. Amazon DynamoDB scales dynamically, but to avoid throttling, the partition key must be highly distributed. Using a unique `VehicleID` (with 2,500,0002,500,000 possibilities) as the partition key distributes writes evenly across partitions.
Ensures writes do not bottleneck on a single partition.
2
Analyze read latency requirements.
The dashboard requires sub-millisecond read latency. Standard DynamoDB queries return data in single-digit milliseconds, but microsecond response times require an in-memory caching tier.
Reduces read response time to microseconds.
3
Evaluate caching options for DynamoDB.
Amazon DynamoDB Accelerator (DAX) is an in-memory cache specifically designed for DynamoDB. It integrates seamlessly without requiring application-level caching logic changes and handles heavy read volumes at microsecond latency.
Directly meets the sub-millisecond lookup requirement for the dashboard.

Anahtar Kavram

Designing high-performing, horizontally scalable database solutions in AWS using DynamoDB partitioning strategies and in-memory caching with DAX to prevent write hotspots and read latency bottlenecks.
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