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PCDE Practice Question: Design innovative, scalable, and highly available cloud database solutions
You are running Cloud Bigtable for time-series analytics. Each row represents a metric and uses a row key of format 'metricID#timestamp' (e.g., 'cpu_usage#2023-08-01T00:00:00Z'). You notice that writes are concentrated on a small number of nodes. What is the most effective way to distribute writes more evenly?
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 hash prefix of the metricID to the row key
The row key design is poor because metricID is a limited set (e.g., cpu_usage) and timestamp is increasing, so all writes for a metric go to a single tablet. Salting by prepending a hash of the metricID (or using a field promotion with a hash prefix) distributes writes across tablets. Reversing timestamp helps with reads but not write distribution. Using a different column family does not affect row key distribution. Increasing nodes only helps if data is distributed, but the hotspot will remain.
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 a different column family for each metric
Why it's wrong here
Column families do not affect row key distribution.
- ✗
Increase the number of nodes
Why it's wrong here
More nodes do not solve hotspotting if the row key concentrates writes.
- ✓
Add a hash prefix of the metricID to the row key
Why this is correct
Salting with a hash prefix distributes writes across all nodes.
- ✗
Reverse the timestamp in the row key
Why it's wrong here
Reversing timestamp helps with reads (most recent first) but does not distribute writes because metricID still dominates.
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