#24: 10 Majorly Used Transformations in RDDs (Resilient Distributed Datasets)

Certainly! Here are 10 majorly used transformations in RDDs (Resilient Distributed Datasets) in Apache Spark:

map(func):

• Applies a function to each element of the RDD.
• Example: rdd.map (lambda x: x * 2)

filter(func):

• Filters elements based on a predicate function.
• Example: rdd.filter(lambda x: x % 2 == 0)

flatMap(func):

• Similar to map, but each input item can be mapped to 0 or more output items.
• Example: rdd.flatMap(lambda x: (x, x*2))

reduceByKey(func):

• Combines values with the same key using a specified reduce function.
• Example: rdd.reduceByKey(lambda x, y: x + y)

groupByKey():

• Groups the values for each key in the RDD into a single sequence.
• Example: rdd.groupByKey()

sortByKey():

• Sorts the RDD by key.
• Example: rdd.sortByKey()

join(otherRDD):

• Performs an inner join between two RDDs based on their keys.
• Example: rdd1.join(rdd2)

distinct():

• Returns a new RDD containing distinct elements from the original RDD.
• Example: rdd.distinct()

mapPartitions(func):

• Similar to map, but operates on each partition of the RDD separately.
• Example: rdd.mapPartitions(lambda partition: [x*2 for x in partition])

cogroup(otherRDD):

• Groups the values for each key in both RDDs and performs a cogroup operation.
• Example: rdd.cogroup(otherRDD)

These transformations are commonly used in Spark applications for various data processing tasks, such as filtering, mapping, aggregating, joining, and sorting data distributed across a cluster.