PROFESSIONAL-CLOUD-DATABASE-ENGINEER · Question #157
Your rapidly growing ecommerce company is migrating their analytics workloads to AlloyDB for PostgreSQL. You anticipate a significant increase in reporting queries as the business scales. You need a…
The correct answer is C. Starting with minimal read pools and scaling iteratively based on real-time load is the classic elastic, cost-optimized strategy for a rapidly growing business. AlloyDB read pools support dynamic resizing, so you pay only for capacity you actually need…
Question
Your rapidly growing ecommerce company is migrating their analytics workloads to AlloyDB for PostgreSQL. You anticipate a significant increase in reporting queries as the business scales. You need a read pool strategy to scale your analytics operations in anticipation of future growth while minimizing costs. What should you do-
Options
- ADirect all complex, long-running analytics queries to the primary instance, and only use read pools
- BChange the instance sizes of the read nodes in the read pool.
- CBegin with minimal read pools and iteratively expand or shrink them based on real-time load
- DAssign all reporting queries to a single, large read pool to maximize the combined compute
Explanation
The correct answer is C. Starting with minimal read pools and scaling iteratively based on real-time load is the classic elastic, cost-optimized strategy for a rapidly growing business. AlloyDB read pools support dynamic resizing, so you pay only for capacity you actually need. As analytics query volume grows, you add nodes; during off-peak periods, you shrink them. Option A is wrong because routing long-running analytics queries to the primary competes with write traffic and defeats the purpose of read pools. Option B (resizing node instances) is vertical scaling only and doesn't address elasticity. Option D (one large pool) over-provisions from day one, maximizing cost rather than minimizing it.
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