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Answer: Decrease the number of input partitions to reduce resource consumption and improve query performance, by finding an optimal balance between parallelism and resource usage.
The BEST strategy to optimize query performance in the context of over-partitioning, while also considering scalability, cost-effectiveness, and compliance, is to decrease the number of input partitions. This approach reduces resource consumption and improves query performance by finding an optimal balance between parallelism and resource usage. Option A is incorrect because increasing the number of partitions without considering resource consumption can exacerbate the problem. Option C is incorrect as it addresses the symptom (small files) rather than the root cause (over-partitioning). Option D is incorrect because partition pruning improves performance by reading fewer partitions but does not reduce the overhead associated with over-partitioning.
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In a scenario where you are processing a large dataset using Spark Structured Streaming, you notice that the performance of your queries is not as expected due to over-partitioning. The dataset is expected to grow significantly over time, and you need to ensure that your solution is cost-effective, scalable, and complies with data governance policies. Which of the following strategies would BEST optimize the performance of your queries by addressing the issues caused by over-partitioning, while also considering the future growth of the dataset? Choose one option.
A
Increase the number of input partitions to maximize parallelism and potentially improve query performance, without considering the impact on resource consumption.
B
Decrease the number of input partitions to reduce resource consumption and improve query performance, by finding an optimal balance between parallelism and resource usage.
C
Implement file concatenation or bucketing strategies to reduce the number of small files, which indirectly addresses the symptoms of over-partitioning but not the root cause.
D
Apply partition pruning techniques to selectively read only the necessary partitions, which improves query performance but does not directly reduce the overhead of over-partitioning.