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Difficulty: MediumOptimizing Performance with Caching and DAX

An online learning platform stores quiz questions and metadata in an Amazon DynamoDB table. During exams, a surge in user traffic causes high read latency and ProvisionedThroughputExceededException errors when the application retrieves quiz details. The developer wants to introduce Amazon DynamoDB Accelerator (DAX) to optimize query performance with minimal latency and minimal application rewrite. The application currently retrieves quiz details using strongly consistent reads and executes frequent Scan operations to populate list views. Which combination of actions should the developer take to resolve the latency and throttling issues? (Select two.)

  1. Modify the application's read requests to use eventually consistent reads so that the DynamoDB Accelerator (DAX) client can serve them from the item cache.Answer
  2. Replace the Scan operations with Query operations to allow DAX to store and serve the retrieved quiz details from the query cache.Answer
  3. C
    Deploy an Amazon ElastiCache for Memcached cluster to act as an API-compatible write-through cache for the DynamoDB table.
  4. D
    Keep using strongly consistent reads for quiz metadata retrieval to ensure DAX caches the most up-to-date data.
  5. E
    Use Scan operations with DAX, relying on DAX to automatically index the results into the item cache for subsequent single-item lookups.

Answer

The correct actions are to modify the read requests to use eventually consistent reads and to replace Scan operations with Query operations.
To resolve the read throttling and latency issues, the developer must configure the read requests to use eventually consistent reads, because DynamoDB Accelerator (DAX) only caches eventually consistent reads; strongly consistent reads are passed directly through to DynamoDB and do not benefit from caching. Additionally, replacing Scan operations with Query operations ensures that the retrieved data is cached in the DAX query cache, which dramatically improves performance compared to executing expensive Scan operations.

Step-by-Step Solution

1
Analyze the consistency requirements for caching with DAX.
Identify that strongly consistent reads bypass the DAX cache, meaning the application must be modified to use eventually consistent reads to leverage DAX.
DAX only caches eventually consistent reads in its item and query caches.
2
Evaluate the retrieval method used in the application.
Identify that Scan operations are inefficient and do not populate the item cache; they should be replaced with Query operations.
Query operations are more efficient, target specific partition keys, and populate the DAX query cache.

Key Concept

DAX Caching Behavior and Query Optimization
Estimated Time:2m 0s
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Topics

Optimizing Performance with Caching and DAX

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