Google PCA Practice Question: Analyze and optimize technical and business processes
A Cloud Spanner instance is experiencing high latency for point reads. The instance has 5 nodes and the read throughput is moderate. The table has a primary key with monotonically increasing values. What is the most likely cause and optimization?
⚠ Common exam trap
Google Cloud often tests the misconception that adding more nodes solves all performance issues, but here the problem is a design flaw (hotspotting) that requires a key distribution strategy, not more capacity.
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
✓
The primary key design causes hotspotting; use a hash prefix or add a leading random value.
The monotonically increasing primary key causes all writes to be directed to the last tablet (splitting point), creating a hotspot on one node. This hotspot leads to high latency for point reads because that node becomes a bottleneck. Adding a hash prefix or a leading random value distributes writes and reads evenly across all nodes, resolving the hotspotting issue.
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 interleaved tables to reduce the number of index lookups.
Why it's wrong here
Interleaving reduces index lookups for parent-child joins, but point reads on a monotonically increasing key suffer from hotspotting on the final split, not from index depth. It is tempting because interleaved tables optimise co-located hierarchical data, and would be correct for parent-child queries rather than single-row key lookups.
- ✗
The instance is underprovisioned; add more nodes.
Why it's wrong here
Five nodes already exceed the moderate read throughput, so adding nodes does not address the hotspot caused by monotonically increasing keys concentrating writes on the last split. It is tempting because underprovisioning commonly causes latency, and adding nodes would be correct if CPU utilisation were genuinely saturated across all splits.
- ✓
The primary key design causes hotspotting; use a hash prefix or add a leading random value.
Why this is correct
Monotonically increasing keys concentrate all writes on the final key range, so a single split absorbs the load. Adding a hash prefix or leading random value distributes writes across splits, removing the hotspot and restoring point-read latency.
- ✗
The instance has too many nodes causing transaction conflicts; reduce nodes.
Why it's wrong here
Monotonically increasing keys create a hotspot on the final split, so latency stems from write contention on one key range, not node count; adding nodes cannot spread that load. Reducing nodes would lower capacity further. More nodes help throughput-bound workloads with well-distributed keys, which is not this scenario.
Visual reference
Go deeper
Related to this question
Key term
Primary key
A primary key is a unique identifier for each record in a database table, ensuring that no two rows have the same value in that column.
Key term
Throughput
Throughput is the rate at which data is successfully transferred from one point to another over a network, typically measured in bits per second.
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