What happens when you enable cache() in PySpark and the dataset exceeds the available memory? How does Spark handle this situation, and what potential issues might arise?
both cache() and persist() store data in memory to speed up the retrieval of intermediate data used for computation. However, persist() is more flexible and allows users to specify storage…