What Are Narrow Dependencies?Transformations consisting of narrow dependencies [we’ll call them narrow transformations] are those where each input partition will contribute to only one output partition.
What Are Wide Dependencies?A wide dependency [or wide transformation] style transformation will have input partitions contributing to many output partitions. You will often hear this referred to as a shuffle where Spark will exchange partitions across the cluster. With narrow transformations, Spark will automatically perform an operation called pipelining on narrow dependencies, this means that if we specify multiple filters on DataFrames they’ll all be performed in-memory. The same cannot be said for shuffles. When we perform a shuffle, Spark will write the results to disk. You’ll see lots of talks about shuffle optimization across the web because it’s an important topic but for now all you need to understand are that there are two kinds of transformations.
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