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Answer: Place the bulk of the read and write workloads closer to the default leader region., Add more read-write replicas.
To improve write performance in a multi-regional Cloud Spanner instance, it's crucial to minimize the latency caused by the geographical distribution of data. Placing the bulk of the read and write workloads closer to the default leader region (Option A) reduces the distance data needs to travel, thereby decreasing latency and improving write speeds. Adding more read-write replicas (Option C) can also help by distributing the write workload across more nodes, which can handle more write operations in parallel. Keeping the total CPU utilization under 45% in each region (Option D) is a good practice for general performance but does not directly address the write performance issue in a multi-regional setup. Using staleness of at least 15 seconds (Option B) is more relevant for read operations and does not impact write performance. Therefore, the most effective strategies are A and C.
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How can you improve the performance of write workloads in a multi-regional Cloud Spanner instance that are significantly slower compared to a regional instance for a write-heavy application?
A
Place the bulk of the read and write workloads closer to the default leader region.
B
Use staleness of at least 15 seconds.
C
Add more read-write replicas.
D
Keep the total CPU utilization under 45% in each region.