DBS-C01 Workload-Specific Database Design Practice Question
A startup is building a real-time chat application that requires storing messages with high write throughput and low-latency reads. The data model is simple: each message has a conversation ID, timestamp, and content. Which database design is MOST appropriate?
⚠ Common exam trap
Candidates often choose Amazon RDS for MySQL because they are familiar with relational databases and indexes, but they overlook the fundamental scalability limitations of a single-node RDS instance for high-write workloads, which DynamoDB's distributed architecture solves natively.
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
✓
Amazon DynamoDB with conversation_id as partition key and timestamp as sort key
Amazon DynamoDB with conversation_id as partition key and timestamp as sort key is the most appropriate design because it directly supports high write throughput and low-latency reads for a real-time chat application. The partition key enables even distribution of writes across partitions, while the sort key allows efficient range queries for messages within a conversation ordered by time, matching the access pattern perfectly.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon RDS for MySQL with a single table and indexes on conversation_id and timestamp
Why it's wrong here
RDS may not scale to high write throughput without sharding.
- ✗
Amazon Timestream to store messages as time-series data
Why it's wrong here
Timestream is optimized for IoT and operational metrics, not chat messages.
- ✗
Amazon Redshift with columnar storage and compression
Why it's wrong here
Redshift is a data warehouse, not designed for real-time transactional workloads.
- ✓
Amazon DynamoDB with conversation_id as partition key and timestamp as sort key
Why this is correct
This model supports high write throughput and efficient queries by conversation.
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