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Difficulty: MediumOptimizing Performance with Caching and DAX

A multiplayer gaming platform operates a matchmaking lobby service that frequently retrieves game mode configurations from an Amazon DynamoDB table. During peak traffic hours, player sign-ins spike, leading to high read latency and ProvisionedThroughputExceededException errors on the table due to the volume of read requests. The development team decides to deploy an Amazon DynamoDB Accelerator (DAX) cluster to cache these configurations. Which of the following implementation steps must the developers perform to successfully resolve the latency issue using DAX caching? (Select two.)

  1. Configure the application code to use the DAX SDK client instead of the standard DynamoDB SDK client.Answer
  2. Modify the application's query requests to use eventually consistent reads instead of strongly consistent reads.Answer
  3. C
    Convert the query operations to Scan operations to force DAX to populate its item cache for all game configurations.
  4. D
    Scale up the DynamoDB table's provisioned read capacity units (RCUs) to handle the cache miss rate during peak hours.
  5. E
    Hardcode the DAX cluster primary endpoint and IAM access key credentials directly in the application's SDK client initialization block.

Answer

To successfully optimize read latency using DAX, the application must instantiate the DAX SDK client instead of the standard DynamoDB SDK client, and all read queries must be configured as eventually consistent reads.
To cache query results using DAX, the application must point to the DAX cluster using the specialized DAX SDK client. Furthermore, DAX is designed to cache eventually consistent reads; executing queries with strong consistency forces DAX to bypass its cache and query DynamoDB directly, failing to resolve the latency bottleneck.

Step-by-Step Solution

1
Integrate the DAX SDK into the application dependencies.
The application has access to the DAX client classes.
The standard AWS SDK for DynamoDB is not aware of DAX cluster endpoints and cannot route caching calls correctly without the DAX SDK wrapper.
2
Replace the instantiation of the standard DynamoDB client with the DAX client pointing to the cluster endpoint.
All client calls now target the DAX cluster.
This enables the application to write through and read from the DAX cluster nodes automatically.
3
Verify and change the read consistency settings on the query calls from strongly consistent to eventually consistent.
Queries hit the DAX query cache when available.
Strongly consistent queries bypass the DAX cache to guarantee the latest data from the DynamoDB table, which prevents caching from resolving the latency bottleneck.

Key Concept

Integrating the Amazon DynamoDB Accelerator (DAX) SDK and understanding the requirement of eventually consistent reads for caching behavior.
Estimated Time:2m 0s
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Topics

Optimizing Performance with Caching and DAX

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