[performance] Apache Spark: map vs mapPartitions?

What's the difference between an RDD's map and mapPartitions method? And does flatMap behave like map or like mapPartitions? Thanks.

(edit) i.e. what is the difference (either semantically or in terms of execution) between

  def map[A, B](rdd: RDD[A], fn: (A => B))
               (implicit a: Manifest[A], b: Manifest[B]): RDD[B] = {
    rdd.mapPartitions({ iter: Iterator[A] => for (i <- iter) yield fn(i) },
      preservesPartitioning = true)
  }

And:

  def map[A, B](rdd: RDD[A], fn: (A => B))
               (implicit a: Manifest[A], b: Manifest[B]): RDD[B] = {
    rdd.map(fn)
  }

Imp. TIP :

Whenever you have heavyweight initialization that should be done once for many RDD elements rather than once per RDD element, and if this initialization, such as creation of objects from a third-party library, cannot be serialized (so that Spark can transmit it across the cluster to the worker nodes), use mapPartitions() instead of map(). mapPartitions() provides for the initialization to be done once per worker task/thread/partition instead of once per RDD data element for example : see below.

val newRd = myRdd.mapPartitions(partition => {
  val connection = new DbConnection /*creates a db connection per partition*/

  val newPartition = partition.map(record => {
    readMatchingFromDB(record, connection)
  }).toList // consumes the iterator, thus calls readMatchingFromDB 

  connection.close() // close dbconnection here
  newPartition.iterator // create a new iterator
})

Q2. does flatMap behave like map or like mapPartitions?

Yes. please see example 2 of flatmap.. its self explanatory.

Q1. What's the difference between an RDD's map and mapPartitions

map works the function being utilized at a per element level while mapPartitions exercises the function at the partition level.

Example Scenario : if we have 100K elements in a particular RDD partition then we will fire off the function being used by the mapping transformation 100K times when we use map.

Conversely, if we use mapPartitions then we will only call the particular function one time, but we will pass in all 100K records and get back all responses in one function call.

There will be performance gain since map works on a particular function so many times, especially if the function is doing something expensive each time that it wouldn't need to do if we passed in all the elements at once(in case of mappartitions).

map

Applies a transformation function on each item of the RDD and returns the result as a new RDD.

Listing Variants

def map[U: ClassTag](f: T => U): RDD[U]

Example :

val a = sc.parallelize(List("dog", "salmon", "salmon", "rat", "elephant"), 3)
 val b = a.map(_.length)
 val c = a.zip(b)
 c.collect
 res0: Array[(String, Int)] = Array((dog,3), (salmon,6), (salmon,6), (rat,3), (elephant,8)) 

mapPartitions

This is a specialized map that is called only once for each partition. The entire content of the respective partitions is available as a sequential stream of values via the input argument (Iterarator[T]). The custom function must return yet another Iterator[U]. The combined result iterators are automatically converted into a new RDD. Please note, that the tuples (3,4) and (6,7) are missing from the following result due to the partitioning we chose.

preservesPartitioning indicates whether the input function preserves the partitioner, which should be false unless this is a pair RDD and the input function doesn't modify the keys.

Listing Variants

def mapPartitions[U: ClassTag](f: Iterator[T] => Iterator[U], preservesPartitioning: Boolean = false): RDD[U]

Example 1

val a = sc.parallelize(1 to 9, 3)
 def myfunc[T](iter: Iterator[T]) : Iterator[(T, T)] = {
   var res = List[(T, T)]()
   var pre = iter.next
   while (iter.hasNext)
   {
     val cur = iter.next;
     res .::= (pre, cur)
     pre = cur;
   }
   res.iterator
 }
 a.mapPartitions(myfunc).collect
 res0: Array[(Int, Int)] = Array((2,3), (1,2), (5,6), (4,5), (8,9), (7,8)) 

Example 2

val x = sc.parallelize(List(1, 2, 3, 4, 5, 6, 7, 8, 9,10), 3)
 def myfunc(iter: Iterator[Int]) : Iterator[Int] = {
   var res = List[Int]()
   while (iter.hasNext) {
     val cur = iter.next;
     res = res ::: List.fill(scala.util.Random.nextInt(10))(cur)
   }
   res.iterator
 }
 x.mapPartitions(myfunc).collect
 // some of the number are not outputted at all. This is because the random number generated for it is zero.
 res8: Array[Int] = Array(1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 5, 7, 7, 7, 9, 9, 10) 

The above program can also be written using flatMap as follows.

Example 2 using flatmap

val x  = sc.parallelize(1 to 10, 3)
 x.flatMap(List.fill(scala.util.Random.nextInt(10))(_)).collect

 res1: Array[Int] = Array(1, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10) 

Conclusion :

mapPartitions transformation is faster than map since it calls your function once/partition, not once/element..

Further reading : foreach Vs foreachPartitions When to use What?

Map:

Map transformation.

The map works on a single Row at a time.

Map returns after each input Row.

The map doesn’t hold the output result in Memory.

Map no way to figure out then to end the service.

// map example

val dfList = (1 to 100) toList

val df = dfList.toDF()

val dfInt = df.map(x => x.getInt(0)+2)

display(dfInt)

MapPartition:

MapPartition transformation.

MapPartition works on a partition at a time.

MapPartition returns after processing all the rows in the partition.

MapPartition output is retained in memory, as it can return after processing all the rows in a particular partition.

MapPartition service can be shut down before returning.

// MapPartition example

Val dfList = (1 to 100) toList

Val df = dfList.toDF()

Val df1 = df.repartition(4).rdd.mapPartition((int) => Iterator(itr.length))

Df1.collec()

//display(df1.collect())

For more details, please refer to the Spark map vs mapPartitions transformation article.

Hope this is helpful!

Map :

1. It processes one row at a time , very similar to map() method of MapReduce.
2. You return from the transformation after every row.

MapPartitions

1. It processes the complete partition in one go.
2. You can return from the function only once after processing the whole partition.
3. All intermediate results needs to be held in memory till you process the whole partition.
4. Provides you like setup() map() and cleanup() function of MapReduce

Map Vs mapPartitions http://bytepadding.com/big-data/spark/spark-map-vs-mappartitions/

Spark Map http://bytepadding.com/big-data/spark/spark-map/

Spark mapPartitions http://bytepadding.com/big-data/spark/spark-mappartitions/