DBS-C01 Workload-Specific Database Design Practice Question
A startup is building a social media application that stores user posts in Amazon DynamoDB. The access pattern is to retrieve posts by user_id (partition key) sorted by post_timestamp (sort key) in descending order. The table has a global secondary index (GSI) with the same key structure but with different projection. The application reads from the GSI. Recently, the team noticed that writes to the base table are throttled during peak hours. The write capacity is balanced across partitions. Which design change should be made to reduce write throttling?
⚠ Common exam trap
Watch out — candidates often assume increasing capacity or switching to on-demand mode will solve all throttling issues, but they overlook the fundamental partition-level throughput limits that cause hot partition throttling.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Add a write sharding pattern by appending a random suffix to the partition key.
The write throttling is caused by a hot partition, where a single partition key (user_id) receives a disproportionate number of writes. By appending a random suffix to the partition key, the writes are distributed evenly across multiple partitions, eliminating the hot spot. This is a well-known sharding pattern for DynamoDB when access patterns create uneven write traffic, and it does not require changing the read logic because the GSI can be queried with a sort key condition on post_timestamp.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use DynamoDB Accelerator (DAX) for writes.
Why it's wrong here
DAX caches reads, not writes.
- ✗
Increase the write capacity units (WCUs) on the base table.
Why it's wrong here
This may help temporarily but does not address the root cause if throttling is due to hot partitions.
- ✗
Switch to on-demand capacity mode.
Why it's wrong here
On-demand handles spikes but can be costly; it does not address hot partitions.
- ✓
Add a write sharding pattern by appending a random suffix to the partition key.
Why this is correct
Sharding distributes writes across partitions, reducing hot spots.
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