What is Narrow Dependency in Apache Spark?

In Apache Spark, a narrow dependency refers to a specific type of relationship between two Resilient Distributed Datasets (RDDs). It describes how partitions in a child RDD rely on data from the parent RDD.

• One-to-one Mapping: Each partition in the child RDD depends on at most one partition from the parent RDD. This means a child partition only processes data from a single corresponding parent partition.
• Faster Execution: Narrow dependencies enable optimizations like pipelining. In pipelining, the output of one transformation can be used as the input for the next transformation without waiting for the entire parent RDD to be processed. This improves efficiency.
• Reduced Shuffling: Since each child partition has a specific parent partition to access, there’s no need to shuffle data across the network. Shuffling refers to the movement of data between different worker nodes in the Spark cluster.

Examples of Transformations that create Narrow Dependencies in Apache Spark

• Map: Applies a function to each element in an RDD, resulting in a new RDD with one output element for each input element.
• Filter: Selects elements based on a predicate function, keeping only those that meet the criteria.
• Union: Combines two compatible RDDs into a single RDD.
• Join: Joins can be either wide or narrow depending on the partitioning scheme. If the parent datasets are partitioned on the join key, the join becomes a narrow dependency because each child partition only needs data from the corresponding partition of each parent.

Apache Spark, a powerful distributed computing framework, is designed to process large-scale datasets efficiently across a cluster of machines. However, Dependencies play a crucial role in Spark’s performance, particularly concerning shuffling operations. Shuffling, which involves moving data across the network, can significantly impact latency and efficiency. Understanding dependencies helps anticipate when shuffling might occur. Wide and Narrow dependencies define how data is partitioned and transferred between different stages of a Spark job.

The degradation for the performance is primarily due to the nature of dependencies between RDDs (Resilient Distributed Datasets) in Spark transformations.

RDDs (Resilient Distributed Datasets) in Apache Spark are composed of four parts namely:

• Partitions : RDDs are divided into smaller, distributed chunks called partitions , and these partitions are distributed across multiple nodes in a cluster (the workers).
• Dependencies : They model relationships between a RDD and its partitions , with the RDD it was derived from.
• Functions : They represent operations applied to RDD partitions to derive new RDDs. (such as map or filter).
• Metadata : It contains information about partitioning scheme and placement of data within partitions.

In Apache Spark, understanding the concepts of wide and narrow dependencies is crucial for optimizing performance, especially when dealing with large-scale datasets. In this article we will focus on the dependencies in Spark.