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:
Adding local secondary indexes (LSIs) can help improve query performance for specific access patterns but will not necessarily reduce data retrieval time significantly across the board. LSIs also require careful planning and may increase write latency, which could still result in higher latency overall.
Storing trading data in Amazon S3 and using S3 Transfer Acceleration is not suitable for sub-millisecond latency requirements. S3 is optimized for high-throughput, large-scale data storage rather than low-latency access, making it unsuitable for real-time trading applications.
Adding retries with exponential backoff may help in cases of transient errors, but it does not address the root cause of higher data retrieval times. This could potentially lead to longer overall latency in processing requests, as it introduces additional wait times during retries.
Using DynamoDB Accelerator (DAX) provides an in-memory caching layer for DynamoDB that reduces the time taken for read operations to microseconds, thus meeting the sub-millisecond latency requirement for processing trade requests effectively with minimal effort.
I organize that the answer is:
Use DynamoDB Accelerator (DAX) to cache the trading data.
I suspect that the answer is:
Use DynamoDB Accelerator (DAX) to cache the trading data.