PROFESSIONAL-CLOUD-DATABASE-ENGINEER · Question #44
Your organization stores marketing data such as customer preferences and purchase history on Bigtable. The consumers of this database are predominantly data analysts and operations users. You…
The correct answer is C. Use Key Visualizer for Bigtable. Key Visualizer is Bigtable's built-in diagnostic tool that generates heatmaps showing read and write activity across the row key space over time. When a specific cohort (new pilot users with a third-party dataset) experiences poor performance while others do not, it is a…
Question
Your organization stores marketing data such as customer preferences and purchase history on Bigtable. The consumers of this database are predominantly data analysts and operations users. You receive a service ticket from the database operations department citing poor database performance between 9 AM-10 AM every day. The application team has confirmed no latency from their logs. A new cohort of pilot users that is testing a dataset loaded from a third-party data provider is experiencing poor database performance. Other users are not affected. You need to troubleshoot the issue. What should you do?
Options
- AIsolate the data analysts and operations user groups to use different Bigtable instances.
- BCheck the Cloud Monitoring table/bytes_used metric from Bigtable.
- CUse Key Visualizer for Bigtable.
- DAdd more nodes to the Bigtable cluster.
How the community answered
(48 responses)- A4% (2)
- B13% (6)
- C77% (37)
- D6% (3)
Explanation
Key Visualizer is Bigtable's built-in diagnostic tool that generates heatmaps showing read and write activity across the row key space over time. When a specific cohort (new pilot users with a third-party dataset) experiences poor performance while others do not, it is a classic indicator of a row key hotspot - all those users' queries hitting the same narrow key range, overwhelming a few nodes. Key Visualizer will visually pinpoint the hotspot. Checking bytes_used (option B) measures storage, not access patterns. Adding nodes (option D) addresses capacity, not hotspots. Separating user groups (option A) would not fix the underlying data distribution issue.
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