A company uses Amazon DynamoDB for a shopping cart application. During a flash sale, write requests are throttled due to hot partitions. The access pattern is evenly distributed across items, but the partition key is the customer ID. Which design change would best mitigate throttling?
Adaptive capacity rebalances throughput across partitions.
Why this answer
DynamoDB adaptive capacity automatically adjusts throughput capacity based on traffic patterns, which helps mitigate hot partitions by redistributing unused capacity from less-accessed partitions to heavily accessed ones. This is ideal for the flash sale scenario where write requests are throttled due to uneven access across customer ID partitions, even though the overall access pattern is evenly distributed.
Exam trap
The trap here is that candidates may think increasing provisioned capacity (Option C) is the straightforward fix for throttling, but they overlook that hot partitions require a design-level solution like adaptive capacity or partition key redesign to distribute writes evenly.
How to eliminate wrong answers
Option B is wrong because changing the partition key to a single value for all items would create an extreme hot partition, causing all writes to target one partition and severely throttling the entire table. Option C is wrong because increasing provisioned write capacity to a higher fixed value does not address the root cause of hot partitions; it only increases overall throughput but still allows throttling on individual partitions if the access pattern is skewed. Option D is wrong because adding a DAX cluster in front of DynamoDB is a caching layer that primarily improves read performance and reduces read latency, but it does not mitigate write throttling or hot partition issues on the write path.