Your team is developing an online bidding application which has used DynamoDB tables to store customers' information.
The application requires the fastest possible response time for reads in microseconds.
However, it seems that the existing DynamoDB tables cannot provide such performance.
Which approach is the best to improve the read performance greatly without modifying existing application logic?
Click on the arrows to vote for the correct answer
A. B. C. D.Correct Answer - D.
DAX is a DynamoDB service to provide access to eventually consistent data with microsecond latency.
Refer to https://docs.amazonaws.cn/en_us/amazondynamodb/latest/developerguide/DAX.html#DAX.use-cases.
for its use cases.
Option A is incorrect: This option can scale up/down the read/write capacity however it cannot improve the performance dramatically.
Option B is incorrect: Global Secondary Index cannot guarantee that the performance will be improved.
It is also up to how application code reads the DynamoDB table.
Option C is incorrect: This case does not mention that multi-region table is required.
It cannot improve the performance if the application only runs in a region.
Option D is CORRECT: DAX is a fully managed, highly available, in-memory cache that has a 10x performance improvement for DynamoDB.
Besides, as DAX is compatible with existing DynamoDB API calls, there is no need to modify application code for this approach.
To improve the read performance of the existing DynamoDB tables, the best approach without modifying the existing application logic would be to use a DynamoDB Accelerator (DAX) cluster. The correct answer is D.
DynamoDB Accelerator (DAX) is an in-memory caching service that provides sub-millisecond response times to applications that are using DynamoDB tables. By using DAX, you can improve the read performance of DynamoDB tables by up to 10 times.
DAX is easy to set up and use. You can simply create a DAX cluster and configure your application to use it. Once the cluster is up and running, it will automatically cache frequently accessed data from the DynamoDB table in memory, reducing the number of reads required to the actual DynamoDB table.
Option A, which is to configure an on-demand read/write capacity mode for the DynamoDB table, can help scale the table automatically based on the traffic. But it may not provide the fastest possible response time for reads in microseconds.
Option B, which is to create a Global Secondary Index for the DynamoDB table, can improve query efficiency for specific query patterns, but it may not improve read performance greatly.
Option C, which is to create a read replica table in another region (Global Table), can improve read capacity, but it may introduce additional latency due to data replication between the two regions. Moreover, it may not provide the fastest possible response time for reads in microseconds.