Examkin

Soru

Zorluk: OrtaHigh-Performing Database Solutions

An enterprise IoT platform receives telemetry data from 50,00050,000 connected sensors. The platform experiences highly unpredictable and spiky write traffic that peaks at 8,5008,500 writes per second, and requires microsecond read response times for active sensor queries. The payload of each sensor includes a sequential timestamp and a device ID. A team wants to implement a database architecture that maximizes throughput and minimizes read latency without introducing hot partitions.

Which database configuration should be implemented to meet these requirements?

  1. Store the sensor data in Amazon DynamoDB configured with on-demand capacity mode. Design the partition key as a composite value of the device ID and timestamp to distribute the write load, and enable Amazon DynamoDB Accelerator (DAX) to cache read operations.Cevap
  2. B
    Store the sensor data in Amazon DynamoDB configured with on-demand capacity mode. Design the partition key using the monotonically increasing timestamp to keep data chronologically sorted, and configure Amazon DynamoDB Accelerator (DAX) to cache read operations.
  3. C
    Store the sensor data in Amazon RDS for MySQL. Configure Multi-AZ for high availability, and deploy a Read Replica that serves as the primary automated disaster recovery failover target while handling the read traffic.
  4. D
    Store the sensor data in Amazon DynamoDB configured with provisioned capacity mode. Design the partition key as a composite value of the device ID and timestamp, and set a static provisioning value of 8,5008,500 write capacity units to optimize cost.

Cevap

Storing the sensor data in Amazon DynamoDB configured with on-demand capacity mode, using a composite partition key of device ID and timestamp, and enabling DynamoDB Accelerator (DAX) to cache reads.
The correct design uses Amazon DynamoDB with a composite partition key (device ID and timestamp) to distribute write workloads across partitions, preventing partition hot spots. It leverages on-demand capacity mode to scale dynamically with unpredictable traffic spikes, and integrates Amazon DynamoDB Accelerator (DAX) to achieve microsecond read latency.

Adım Adım Çözüm

1
Analyze the access pattern and latency requirements.
Identify that the system needs to support high-velocity writes up to 8,5008,500 writes/sec with microsecond-level read latency.
Microsecond read latency requires a caching tier such as DynamoDB Accelerator (DAX), and high-velocity writes are best handled by DynamoDB's distributed architecture.
2
Select the partition key strategy to avoid write bottlenecks.
Choose a composite key using both device ID and timestamp instead of just a sequential timestamp.
Using only sequential timestamps results in a monotonically increasing key sequence, which directs all concurrent writes to a single partition, creating a hot key bottleneck.
3
Determine the capacity management mode for DynamoDB.
Configure the DynamoDB table with on-demand capacity mode.
On-demand capacity mode is ideal for unpredictable, spiky workloads as it dynamically scales to accommodate surges without requiring manual provisioning or risking throttling.

Anahtar Kavram

High-performing NoSQL database design with partition key optimization and caching for low-latency operations.
Bu soruyu puanla

Konular

High-Performing Database Solutions

Bu konudan daha fazla soru