Google PCA Cloud Spanner multi-region Practice Question
A global e-commerce platform uses Cloud Spanner in a multi-region configuration across us-central1 (leader) and europe-west1. The application writes all orders to a single table and reads from both regions. During a flash sale, write latency spikes, causing order failures. The team notices that the leader region's CPU utilization is at 95%, while the europe-west1 region is mostly idle. The application uses partitioned DML for batch updates. The development team proposes increasing node count. What should the architect do to reduce write latency while maintaining global read performance?
⚠ Common exam trap
A common misconception is that Cloud Spanner only supports a single leader for writes in any configuration. However, using a dual-region with multiple writable leaders allows both regions to handle writes, distributing the load and reducing latency.
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
✓
Change the placement configuration to use a dual-region with multiple writable leaders.
Configuring a dual-region with multiple writable leaders distributes write operations across both regions, reducing CPU utilization on the original leader region. This approach improves write latency during high-traffic events like a flash sale while maintaining global read performance from both regions. Scaling by adding nodes (as proposed) is also valid, but among the given choices, changing the placement configuration directly addresses the single-leader bottleneck.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Implement manual sharding by splitting the large table into multiple smaller tables across instances.
Why it's wrong here
Manual sharding adds operational complexity and does not directly address the leader region CPU bottleneck; it may introduce consistency challenges.
- ✗
Use interleaved tables to reduce query latency for reads.
Why it's wrong here
Interleaved tables improve read performance by physically co-locating related rows, but they do not reduce write load on the leader region.
- ✗
Create a new node pool with a machine type that has at least 16 vCPUs to handle the write-intensive workload.
Why it's wrong here
Cloud Spanner does not support node pools with different machine types; adding nodes to the instance is the correct scaling method, not creating a node pool with higher vCPUs.
- ✓
Change the placement configuration to use a dual-region with multiple writable leaders.
Why this is correct
Correct. Changing to a dual-region with multiple writable leaders allows both regions to process writes, reducing the load on the original leader and decreasing write latency.
Go deeper
Related to this question
Learn chapter
Introduction to Google Cloud Platform
Key term
Batch
Batch is a cloud computing service that runs large numbers of computing jobs as a group, or batch, without needing to manage individual servers.
Key term
Latency
Latency is the time delay between a request being sent over a network and the response being received, often measured in milliseconds.
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