A shuffle-heavy silver aggregation repeatedly fails with a java.lang.OutOfMemoryError raised inside an executor while tasks build their aggregation buffers; the driver stays healthy. The compute runs a worker configuration with a small heap per executor. Which configuration change addresses the root cause most directly?
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Correct answer: Increase executor memory (larger worker type or higher spark.executor.memory)
Reference: https://docs.databricks.com/aws/en/compute/configure
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Official Answer (A): Increase executor memory (larger worker type or higher spark.executor.memory)
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