The answer is to add a random prefix (salting) to the row key. This fixes Bigtable hotspotting because Cloud Bigtable partitions rows lexicographically by row key, so a monotonically increasing timestamp prefix funnels all new writes to a single tablet server, overwhelming it. By salting the row key—prepending a random or hashed value—you distribute writes evenly across multiple tablet servers, as each salted key hashes to a different partition. On the Google Professional Cloud Database Engineer exam, this scenario tests your understanding of row key design patterns for write-heavy workloads, often appearing as a trap where candidates might suggest increasing node count instead of fixing the key structure. The common mistake is to overlook that hotspotting is a data distribution problem, not a capacity one. Memory tip: think “salt spreads the heat”—just as salt scatters across food, salting scatters writes across nodes.
PCDE Plan and manage database infrastructure Practice Question
This PCDE practice question tests your understanding of plan and manage database infrastructure. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A Cloud Bigtable instance is experiencing hotspotting on a single node during heavy write traffic. The row keys are based on a timestamp prefix. Which change should they make to the row key design to distribute writes evenly?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Add a random prefix (salting) to the row key
Adding a random prefix (salting) to the row key distributes writes across multiple tablet servers by ensuring that consecutive timestamps do not all hash to the same node. This prevents hotspotting because Cloud Bigtable partitions rows lexicographically by row key; a monotonically increasing timestamp prefix causes all new writes to land on a single tablet server. Salting spreads the write load uniformly across the cluster.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 reverse timestamp (e.g., MAX_TIMESTAMP - timestamp)
Why it's wrong here
Reverse timestamp only reorders rows but does not break the sequential pattern causing hotspotting.
✗
Increase the number of nodes in the cluster
Why it's wrong here
Adding nodes does not fix an unbalanced row key design; hotspotting will persist.
✓
Add a random prefix (salting) to the row key
Why this is correct
Salting distributes writes across nodes by randomizing the start of the row key.
Related concept
Read the scenario before looking for a memorised answer.
✗
Enable replication across zones
Why it's wrong here
Replication helps with durability and read latency but does not solve write hotspotting.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that scaling infrastructure (adding nodes or replication) can fix a design-level hotspotting issue, when the correct solution is to modify the row key schema to distribute the load.
Detailed technical explanation
How to think about this question
Cloud Bigtable uses a consistent hash of the row key to assign tablets to tablet servers. Without salting, a timestamp prefix creates a sequential key pattern that maps to a single tablet server until a split occurs, but during heavy writes the split cannot keep up, causing hotspotting. Salting with a random prefix (e.g., a hash of the timestamp modulo the number of nodes) ensures writes are spread across all tablet servers from the start, though it complicates range scans. In practice, a field like user_id or a hashed prefix is often used instead of purely random values to preserve some locality.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Plan and manage database infrastructure — This question tests Plan and manage database infrastructure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Add a random prefix (salting) to the row key — Adding a random prefix (salting) to the row key distributes writes across multiple tablet servers by ensuring that consecutive timestamps do not all hash to the same node. This prevents hotspotting because Cloud Bigtable partitions rows lexicographically by row key; a monotonically increasing timestamp prefix causes all new writes to land on a single tablet server. Salting spreads the write load uniformly across the cluster.
What should I do if I get this PCDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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