Which solution meets these requirements?
Add local secondary indexes (LSIs) for the trading data.
Store the trading data in Amazon S3, and use S3 Transfer Acceleration.
Add retries with exponential backoff for DynamoDB queries.
Use DynamoDB Accelerator (DAX) to cache the trading data.
Explanations:
Local secondary indexes (LSIs) improve query flexibility by allowing different sort keys for the same partition key, but they do not specifically reduce data retrieval time. They could improve performance in some cases, but DAX is better suited for reducing latency.
Storing data in Amazon S3 is not suitable for stock trading data that needs fast, low-latency access. S3 Transfer Acceleration only speeds up uploads and downloads over long distances, which does not meet the sub-millisecond latency requirement.
Adding retries with exponential backoff is a technique for improving reliability in case of request failures but does not reduce the data retrieval time or improve the latency of DynamoDB queries.
DynamoDB Accelerator (DAX) is an in-memory caching service designed to improve DynamoDB read performance. It can provide microsecond response times, which is ideal for the low-latency requirements of stock trading applications.
Based on what I know, the answer is:
Use DynamoDB Accelerator (DAX) to cache the trading data.
I reckon the answer is:
Use DynamoDB Accelerator (DAX) to cache the trading data.