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Answer: Manually clearing broadcast variables after use to release memory resources.
The correct approach to manage broadcast variables and prevent resource exhaustion is to manually un-persist them after use to free up memory. Broadcasting excessively large datasets that exceed executor memory limits can lead to resource exhaustion, as these variables are stored in memory and replicated across all cluster nodes. It's essential to monitor memory usage and optimize resource utilization to avoid job failures. If a dataset is too large, consider alternative methods like caching or partitioning to minimize memory usage.
Author: LeetQuiz Editorial Team
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When utilizing broadcast variables to optimize a Spark job's performance, what is a crucial factor to consider to avoid resource exhaustion?
A
Employing broadcast variables within iterative algorithms to reduce task serialization time.
B
Restricting the use of broadcast variables to operations that do not alter the broadcasted data, ensuring it remains immutable.
C
Broadcasting datasets that are too large and surpass executor memory limits.
D
Manually clearing broadcast variables after use to release memory resources.
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