Narrow Transformations: Each Piece Stays Home

A narrow transformation is one where each output partition is built from exactly one input partition. Think of filter: to decide which rows of a partition to keep, an executor only needs the rows already sitting in front of it. It never has to look at any other partition, on any other machine. The same is true of select, which picks columns, and withColumn, which computes a new one, and map, which transforms each row. Every one of these works on a partition in place, using only what is already there. Because no information has to cross between partitions, narrow operations need no coordination and no network. Each executor runs the operation on its own partitions independently, in parallel, and nothing waits on anything else. This is the cheap, embarrassingly-parallel half of Spark, and it

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