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Difficulty: MediumHigh-Performing Database Solutions

A solutions architect is designing a high-performance database tier for a real-time ride-sharing dispatch application. The application must ingest up to 15,00015,000 write operations per second to continuously record coordinate updates from active drivers. At the same time, the system must support high-frequency read queries to locate nearby drivers with sub-millisecond latency. The application workload is highly unpredictable, with sudden traffic surges occurring during rush hours.

Which combination of database configurations will meet these performance and scaling requirements? (Select two.)

  1. Configure the Amazon DynamoDB table with a partition key of driver_id to distribute the write throughput evenly across multiple physical partitions.Answer
  2. Deploy Amazon DynamoDB Accelerator (DAX) to cache read requests for driver coordinates, reducing read latency to microseconds.Answer
  3. C
    Use a partition key based on a monotonically increasing timestamp to maintain chronological ordering of location updates.
  4. D
    Deploy Amazon RDS for PostgreSQL and rely on Read Replicas to handle the high volume of write coordinates and serve as the automatic failover targets.
  5. E
    Set the Amazon DynamoDB table to Provisioned Capacity Mode with fixed read and write units to prevent latency increases during traffic surges.

Answer

Configure the Amazon DynamoDB table with a partition key of driver_id to distribute write throughput, and deploy Amazon DynamoDB Accelerator (DAX) to cache read requests for driver coordinates.
Configuring the Amazon DynamoDB table with a high-cardinality partition key like driver ID ensures that writes are distributed evenly across physical partitions, avoiding hot partitions and write bottlenecks. Utilizing Amazon DynamoDB Accelerator (DAX) provides an in-memory cache that reduces read latencies for driver coordinates to the microsecond range, fulfilling the requirement for sub-millisecond reads.

Step-by-Step Solution

1
Analyze write performance and partition design requirements.
Identify that the system requires up to 15,00015,000 writes per second. A partition key with high cardinality (such as driver_id) is necessary to distribute write operations across multiple physical partitions and prevent hot partitions.
High cardinality keys prevent write throughput throttling and optimize DynamoDB partition usage.
2
Analyze read latency requirements.
Identify that the application needs sub-millisecond read latency. Adding Amazon DynamoDB Accelerator (DAX) caches read operations and brings read latency down to microseconds.
DAX acts as an in-memory write-through cache designed specifically to accelerate DynamoDB reads.
3
Evaluate workload predictability and scaling.
Understand that the workload is highly unpredictable. Provisioned capacity with fixed units or using standard relational databases with read replicas for write scaling is incorrect or inefficient.
Ensures the architecture choice scales dynamically with unpredictable spikes and avoids throttling.

Key Concept

Selecting and configuring high-performing databases to support high write throughput and sub-millisecond read latencies under unpredictable workloads.
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Topics

High-Performing Database Solutions

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