Google PCA Design and plan a cloud solution architecture Practice Question
A global e-commerce platform is experiencing intermittent latency spikes during flash sales. The application is deployed on Google Kubernetes Engine (GKE) with a regional cluster. The architecture includes a frontend service, a product catalog service using Cloud Spanner, and an order processing service using Cloud Pub/Sub. During high load, the catalog service shows increased query latency, and some requests time out. What should the architect prioritize to address the issue?
⚠ Common exam trap
A common mix-up: candidates confuse horizontal scaling (adding nodes) with database optimization, overlooking that Cloud Spanner performance issues require schema-level tuning rather than infrastructure scaling.
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
✓
Enable Cloud Spanner interleaved tables and add secondary indexes for common query filters.
The issue is specifically with Cloud Spanner query latency under high load. Enabling interleaved tables and adding secondary indexes optimizes data locality and query performance, reducing the need for expensive cross-table joins and full table scans. This directly addresses the root cause of increased latency and timeouts in the catalog service.
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 Cloud CDN to cache product catalog responses.
Why it's wrong here
Cloud CDN caches static HTTP responses at edge locations; it cannot cache Spanner query results, so the catalog service still executes the same reads under load. CDN is correct for serving cacheable product images or static assets, not for relieving database query latency driven by concurrent reads.
- ✗
Increase the number of nodes in the GKE node pool.
Why it's wrong here
Adding nodes scales compute capacity for pods, but the bottleneck is Spanner query latency, not insufficient CPU or memory. Node scaling is right when pods are pending or CPU-bound; here it leaves the database contention untouched and the timeouts persist.
- ✓
Enable Cloud Spanner interleaved tables and add secondary indexes for common query filters.
Why this is correct
Cloud Spanner query latency under flash-sale load stems from scanning non-interleaved tables and full-table reads. Interleaving co-locates child rows with parents, and secondary indexes accelerate the catalog's common filter queries, cutting the data scanned and reducing timeouts at the database layer.
- ✗
Migrate the catalog service from Cloud Spanner to Cloud Bigtable for better read performance.
Why it's wrong here
Bigtable suits high-throughput key-based lookups, not the relational queries and secondary indexes a product catalog uses. Migrating also discards Spanner's strong consistency and SQL, and Bigtable would not resolve the contention; Spanner's own scaling or query tuning is the actual fix.
Go deeper
Related to this question
Learn chapter
Load Balancing and Autoscaling
Key term
Spanner
Google Cloud's globally distributed, strongly consistent, and horizontally scalable database service designed for mission-critical, transactional workloads.
Key term
GKE
GKE is Google's managed Kubernetes service that automates deploying, scaling, and managing containerized applications in the cloud.
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