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Zorluk: KolayHigh-Performing Database Solutions

An enterprise application utilizes an Amazon Aurora PostgreSQL database cluster for transactional operations and an Amazon DynamoDB table for user session state. During peak traffic periods, the Aurora database experiences high latency on read queries, while the DynamoDB table encounters throttling on a small group of frequently accessed user profiles. Which two configurations should a solutions architect implement to optimize database performance? (Select TWO.)

  1. Create Amazon Aurora Read Replicas to offload read traffic from the primary database instanceCevap
  2. Enable Amazon DynamoDB Accelerator (DAX) to cache frequently accessed user profilesCevap
  3. C
    Redesign the DynamoDB partition key to use a monotonically increasing creation timestamp
  4. D
    Configure Aurora Read Replicas as the primary disaster recovery failover mechanism for write traffic

Cevap

Creating Amazon Aurora Read Replicas and enabling Amazon DynamoDB Accelerator (DAX) are the correct configurations to optimize the performance.
Creating Amazon Aurora Read Replicas allows the application to redirect read traffic from the primary writer database instance, reducing latency. Additionally, enabling Amazon DynamoDB Accelerator (DAX) introduces an in-memory cache that resolves read throttling on frequently accessed keys (hot keys).

Adım Adım Çözüm

1
Analyze the read latency issue on the Amazon Aurora PostgreSQL database.
Identify that read operations are saturating the primary writer instance, indicating a need to scale read capacity.
Creating Aurora Read Replicas offloads read traffic from the primary writer instance, lowering read latency.
2
Analyze the read throttling issue on the Amazon DynamoDB table.
Identify that a subset of frequently accessed keys (hot keys) is causing performance bottlenecks.
Enabling Amazon DynamoDB Accelerator (DAX) provides an in-memory cache that intercepts reads for popular keys, preventing throttling and offering microsecond response times.

Anahtar Kavram

Scaling read capacity with database replicas and using in-memory caching for key-value databases to handle hot keys.
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