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Difficulty: EasyOptimizing Performance with Caching and DAX

A financial technology application uses an Amazon DynamoDB table to retrieve real-time stock price data. During periods of high market activity, the application experiences increased read latency due to a massive spike in repeat query requests for popular stock symbols, leading to Read Capacity Unit (RCU) throttling on the table. A developer decides to deploy an Amazon DynamoDB Accelerator (DAX) cluster to resolve this issue. Which two of the following benefits does deploying a DAX cluster provide to resolve this throughput and latency bottleneck? (Select two.)

  1. It provides sub-millisecond response times for cached read requests.Answer
  2. It reduces the read load on the DynamoDB table by serving repeat read requests from the cache.Answer
  3. C
    It automatically increases the provisioned read capacity units (RCUs) of the table to handle hot partition keys.
  4. D
    It converts DynamoDB Query operations into Scan operations to pre-populate the cache.
  5. E
    It serves as a secure store to cache database access credentials and rotate them automatically.

Answer

Deploying a DAX cluster provides sub-millisecond response times for cached read requests and reduces the read load on the DynamoDB table by serving repeat read requests from the cache.
Deploying a DAX cluster acts as an in-memory cache that serves repeat read requests with sub-millisecond latency. Since these cached reads are resolved within the DAX cluster, they do not consume the underlying table's provisioned Read Capacity Units (RCUs), resolving the throttling bottleneck.

Step-by-Step Solution

1
Identify the performance bottleneck in the DynamoDB table.
The bottleneck is caused by a massive spike in repeat query requests, leading to RCU throttling and latency.
Understanding the nature of the bottleneck is necessary to choose the correct optimization approach.
2
Evaluate the capabilities of Amazon DynamoDB Accelerator (DAX) to resolve read latency and capacity bottlenecks.
DAX provides an API-compatible, in-memory caching layer that serves repeat read requests with sub-millisecond latency and offloads read traffic from the database table.
This confirms that DAX directly mitigates both the read latency and RCU throttling issues.

Key Concept

Using Amazon DynamoDB Accelerator (DAX) to cache read requests, reducing latency and table read throughput load.
Estimated Time:1m 0s
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Topics

Optimizing Performance with Caching and DAX

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