What is the difference between narrow dependencies and wide dependencies in Spark?
In Spark, narrow dependencies and wide dependencies refer to the different relationships between RDDs. Their difference lies in how operations are executed and how data is partitioned.
- Narrow dependency refers to each partition of a parent RDD being used by only one partition of a child RDD. More specifically, when each partition of an RDD is used by only one partition of a child RDD, it is considered a narrow dependency. In this scenario, Spark can perform transformations between the parent and child RDD on the same node without the need for data shuffling.
- Wide Dependency:
Wide dependency refers to the scenario where each partition of a parent RDD can be used by multiple partitions of a child RDD. Specifically, when each partition of an RDD is used by multiple partitions of a child RDD, it is referred to as a wide dependency. In this case, Spark needs to perform a shuffle operation to reorganize the data into partitions in order to accurately compute the results.
In general, narrow dependencies can execute operations more efficiently in Spark because they do not require data shuffling, while wide dependencies may lead to a performance decrease because they require data shuffling. Avoiding wide dependencies as much as possible can improve the performance of the program.
