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Apache Spark: MapPartitions — A Powerful Narrow Data Transformation

MapPartitions is a powerful transformation available in Spark which programmers would definitely like. It gives them the flexibility to process partitions as a whole by writing custom logic on lines of single-threaded programming. This story today highlights the key benefits of MapPartitions

Apache Spark, on a high level, provides two types of data transformation for use in data analytics programs, the Narrow ones, and the Wide ones. Narrow ones compute a data partition of a Spark Dataset/RDD only from a single partition of a parent Spark Dataset/RDD, while the wide ones compute a data partition of a Spark Dataset/RDD from multiple partitions of a parent Spark Dataset/RDD.

Considering the Narrow transformations, Apache Spark provides a variety of such transformations to the user, such as map, maptoPair, flatMap, flatMaptoPair, filter, etc. Among all of these narrow transformations, mapPartitions is the most powerful and comprehensive data transformation available to the user. This particular transformation, if used judiciously, can speed up the performance and efficiency of the underlying Spark Job manifold.

mapPartitions transformation is applied to each partition of the Spark Dataset/RDD as opposed to most of the available narrow transformations which work on each element of the Spark Dataset/RDD partition. mapPartitions takes an iterator to the data collection in a partition and returns an iterator to a new data collection. The size could of the input and output data collection differ.

mapPartitions provide 7 key benefits which are listed below:

Get a copy of my recently published book on Spark Partitioning: https://www.amazon.com/dp/B08KJCT3XN/

Big Data Architect, Apache Spark Specialist, https://www.linkedin.com/in/ajaywlan/