Comparison Between Spark Map And Flatmap
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Apache Spark supports the various transformation techniques. In this blog, we will learn about the Apache Spark Map and FlatMap Operation and Comparison between Apache Spark map vs flatmap transformation methods. This article is all about, how to learn map operations on RDD. Afterwards, we will learn how to process data using flatmap transformation. We will also cover the difference between Spark map ( ) and flatmap transformations in Spark.
2. Introduction – Apache Spark Map and FlatMap Operation
There are following methods which we use as transformation operations in Apache Spark flatmap and Map are some of them. Both map and flatmap are similar operations in both we apply operations on the input. Map operations is a process of one to one transformation. It operates each and every element of RDD one by one and produces new RDD out of it. While the flatmap operation is a process of one to many transformations. It operates every element of RDD but produces zero, one, too many results to create RDD.
Let’s discuss Spark map and flatmap in detail.
2.1 Map ( ) Transformation
Map transformation means to apply operation on each element of the collection. As a result, a map will return a whole new collection of transformed elements. When we apply map transformation on RDD, it transforms each and every element. In this process, operation applies one by one on each element. In this process, User can define his own custom business logic to the RDD. That means that same logic will be applied to entire RDD.
In this Process, it takes one element from the input and returns one transformed element at the same time. By this operation, resulted RDD will be as same as the RDD on which we performed the transformation. Resulted RDD will be of a similar length of the parent RDD.
2.2 FlatMap ( ) Transformation
FlatMap is also a transformation operation. When we perform the operation on it, it applies on each RDD and produces new RDD out of it. It is quite similar to map function. The difference is, FlatMap operation applies to one element but gives many results out of it. That means from single element we may get zero, one, two etc. many results. Flatmap transformation is one step ahead of Map operation.
– Important points about flatmap transformation:
– This transformation is lazily evaluated due to its spark transformation operation.
– It provides flatten output.
– It does not shuffle the data from one to another partition because it is a narrow operation.
– This parameter returns an array, list or sequences.
3. Difference: FlatMap vs Spark Map Transformation
When we apply map(func), it returns a new distributed dataset after the transformation process. This is possible to form by passing each element of the source through map function.
– FlatMap (func)
Flatmap is somehow similar to map, but in this operation, each input item can be mapped to 0 or more output items. As they are more than single outputs, results should be in sequence, not as a single value.
4. FlatMap vs Apache Spark Map – Parallel Execution
In both the transformation operations, we can easily process collections in parallel. Through scala, we can simply parallelize map and flatmap executions. It is easy to convert whole into parallel just by adding .par to a collection.
5. Map and FlatMap – Conclusion
As a result, we have seen that It’s not a surprise that the map function is the key building block in Apache spark RDD. Both Transformation operations are used to create RDDs but with different styles. We have also seen that map() and flatMap() transformation methods are high in use in Apache Spark and also learned the whole comparison on Apache Spark Map vs FlatMap Operation.
Reference – Apache Spark
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