This article focuses on advanced Apache Spark interview questions relevant for senior data scientist or engineer roles. It highlights important differences between caching methods and the role of Spark Broadcast variables. The article explains the use of cache() and persist() functions for managing data storage effectively, enabling candidates to optimize for performance. It also discusses how Spark Broadcast variables facilitate efficient data distribution, making it easier for candidates to prepare for interviews by emphasizing key concepts and practical applications essential in the field.
Both cache() and persist() store an RDD/DataFrame/Dataset in memory (or disk) to avoid recomputation. cache() is shorthand for persist(StorageLevel.MEMORY_ONLY), while persist() offers more control.
A Spark Broadcast variable allows you to efficiently send a read-only variable to all worker nodes. Instead of shipping a copy of the variable each time it is needed, it distributes a single copy.
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