Given a Bigtable with row keys designed as region#userID#timestamp, what will be the effect on data locality when scanning all users in a single region?

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GCP - Cloud Firestore and Bigtable

Given a Bigtable with row keys designed as region#userID#timestamp, what will be the effect on data locality when scanning all users in a single region?

ARows are scattered randomly, causing slow scans

BRows for the region are stored together, enabling efficient scans

CRows are grouped by timestamp only

DRows are grouped by userID only

Step-by-Step Solution

Solution:

Step 1: Analyze row key structure
Row keys start with region, so all rows for a region are stored contiguously.
Step 2: Understand scan impact
Scanning by region benefits from locality, making scans efficient.
Final Answer:
Rows for the region are stored together, enabling efficient scans -> Option B
Quick Check:
Prefix grouping improves scan locality = B [OK]

Quick Trick: Prefix keys group data for fast regional scans [OK]

Common Mistakes:

Assuming random scattering despite prefix
Thinking grouping is by timestamp or userID only
Ignoring row key order importance

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Given a Bigtable with row keys designed as region#userID#timestamp, what will be the effect on data locality when scanning all users in a single region?

Step 1: Analyze row key structure

Step 2: Understand scan impact

Final Answer:

Quick Check:

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