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Zorluk: ZorCost-Optimized Database Design and Capacity Planning

A company is redesigning its database tier to optimize costs. The application uses two main tables: a user session table that experiences highly unpredictable, spiky write traffic when new media is released but remains idle for long periods, and a product catalog table that experiences a highly predictable, constant baseline read and write traffic. Currently, both tables are hosted on a single Amazon RDS for PostgreSQL DB instance, which must be constantly scaled for peak load, resulting in high idle costs. Which TWO actions should a solutions architect take to meet these requirements in the most cost-effective manner while maintaining high availability? (Select TWO.)

  1. Migrate the user session table to an Amazon DynamoDB table configured with On-Demand capacity mode.Cevap
  2. Migrate the product catalog table to an Amazon DynamoDB table configured with Provisioned capacity mode and auto scaling enabled.Cevap
  3. C
    Migrate the user session table to an Amazon DynamoDB table configured with Provisioned capacity mode set to the maximum expected peak traffic.
  4. D
    Migrate both tables to Amazon DynamoDB tables configured with On-Demand capacity mode.
  5. E
    Keep both tables on the Amazon RDS DB instance, and configure an Amazon RDS Read Replica in a different Availability Zone to act as the primary automatic failover target.

Cevap

Migrate the user session table to an Amazon DynamoDB table configured with On-Demand capacity mode, and migrate the product catalog table to an Amazon DynamoDB table configured with Provisioned capacity mode and auto scaling enabled.
Migrating the user session table to DynamoDB configured with On-Demand capacity mode ensures that the company only pays for the actual database requests during spiky periods and pays nothing when the table is idle. Migrating the product catalog table to DynamoDB configured with Provisioned capacity mode with auto scaling optimizes costs for the stable, predictable baseline by leveraging lower per-request rates while scaling dynamically to handle gradual load changes.

Adım Adım Çözüm

1
Analyze the workload characteristics of each database table to identify patterns.
The user session table has highly unpredictable, spiky write traffic with long idle periods, while the product catalog table has a highly predictable, constant baseline read and write traffic.
Choosing the cost-optimal capacity model requires matching the database scaling configuration to the workload's specific traffic profile.
2
Evaluate the capacity options for the user session table.
Amazon DynamoDB with On-Demand capacity mode is chosen.
For highly spiky and unpredictable workloads that are frequently idle, On-Demand capacity is the most cost-effective since it bills purely on request volume without incurring costs for idle provisioned capacity.
3
Evaluate the capacity options for the product catalog table.
Amazon DynamoDB with Provisioned capacity mode and auto scaling is chosen.
For predictable baseline workloads, Provisioned capacity mode offers a much lower cost per request than On-Demand mode, and auto scaling accommodates any gradual variations safely.
4
Examine options involving Amazon RDS Read Replicas for high availability.
Reject using RDS Read Replicas as automatic failover targets.
Standard RDS Read Replicas are designed for scaling read traffic, not as primary high-availability failover destinations, which requires Multi-AZ deployments.

Anahtar Kavram

Selecting and configuring database capacity models (On-Demand versus Provisioned with auto scaling) to align with workload traffic patterns (predictable baseline versus unpredictable spiky) for cost optimization.
Tahmini Süre:2m 30s
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Cost-Optimized Database Design and Capacity Planning