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Answer: Implement file concatenation or bucketing strategies to reduce the number of tiny files, thereby decreasing I/O operations and improving query performance without significantly increasing costs.
The MOST effective strategy to address the performance degradation caused by tiny files in a cost-sensitive environment is to implement file concatenation or bucketing. This approach reduces the number of tiny files by combining them into larger files or organizing them into buckets, which decreases the number of I/O operations required during query processing. This method directly tackles the root cause of the performance issue without significantly increasing costs or sacrificing scalability. Option A suggests increasing the number of input partitions, which may improve parallelism but does not address the tiny files issue and could lead to higher resource consumption. Option B proposes decreasing the number of input partitions, which might reduce resource usage but at the expense of parallelism and performance. Option D, partition pruning, is a technique to optimize query performance by reading only necessary partitions but does not solve the problem of tiny files.
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In a scenario where you are processing a large dataset using Spark Structured Streaming in a cost-sensitive environment, you notice that the performance of your queries is significantly degraded due to the presence of numerous tiny files. These files are a result of frequent small writes to the storage system. Considering the need to optimize query performance while minimizing costs and ensuring scalability, which of the following strategies would be the MOST effective in addressing the issues caused by tiny files? Choose one option.
A
Increase the number of input partitions to improve parallelism and query performance, despite the potential increase in resource consumption.
B
Decrease the number of input partitions to reduce resource consumption, accepting a potential decrease in parallelism and query performance.
C
Implement file concatenation or bucketing strategies to reduce the number of tiny files, thereby decreasing I/O operations and improving query performance without significantly increasing costs.
D
Apply partition pruning to selectively read only the required partitions, ignoring the issue of tiny files altogether.
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