Design innovative, scalable, and highly available cloud database solutions →mediumMultiple ChoiceObjective-mapped
PCDE Practice Question: Design innovative, scalable, and highly available cloud database solutions
You need to design a Bigtable row key for a time-series application that records temperature readings from thousands of sensors. The most common query is 'get all readings for a specific sensor in the last hour'. Which row key design is optimal?
⚠ Common exam trap
Candidates often choose sensorID#timestamp (Option B) thinking it groups data correctly, but they overlook that Bigtable's lexicographic ordering places older data first, making 'last hour' queries require scanning the entire row or using a reverse scan, which is less efficient than reverse timestamps.
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
✓
sensorID#reverse_timestamp
Optimal because it groups all readings for a sensor together (via sensorID as the row key prefix) while using reverse timestamps to ensure the most recent data appears first within each row. This design allows Bigtable to efficiently scan a single row for the last hour's readings using a prefix scan on sensorID with a timestamp range filter, minimizing the number of rows accessed.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
timestamp#sensorID
Why it's wrong here
Does not group by sensor.
- ✗
sensorID#timestamp
Why it's wrong here
Most recent data is scattered if timestamp is ascending.
- ✓
sensorID#reverse_timestamp
Why this is correct
Groups by sensor and puts recent data first.
- ✗
hash(sensorID)#timestamp
Why it's wrong here
Distributes writes well but makes range scans inefficient.
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