Which statement describes the correct use of pyspark.sql.functions.broadcast?
Which statement describes the correct use of pyspark.sql.functions.broadcast?
The correct statement is that it marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join. This optimization technique is used in PySpark to speed up the join process by broadcasting the smaller DataFrame to all executor nodes, thereby avoiding the need to shuffle the larger DataFrame across the cluster.
The broadcast function in PySpark is used in the context of joins. When you mark a DataFrame with broadcast, Spark tries to send this DataFrame to all worker nodes so that it can be joined with another DataFrame without shuffling the larger DataFrame across the nodes. This is particularly beneficial when the DataFrame is small enough to fit into the memory of each node. It helps to optimize the join process by reducing the amount of data that needs to be shuffled across the cluster, which can be a very expensive operation in terms of computation and time.
Marks a DataFrame as small enough for use in broadcast joins.
https://spark.apache.org/docs/3.1.3/api/python/reference/api/pyspark.sql.functions.broadcast.html
Correct Answer: D. It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join. Reference: https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.broadcast.html
Answer is D
The answer is D