What is the difference between map and flatMap and a good use case for each

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Can someone explain to me the difference between map and flatMap and what is a good use case for each   What does  flatten the results  mean  What is it good for

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map  It returns a new RDD by applying a function to each element of the RDD    Function in  map can return only one item   flatMap  Similar to map  it returns a new RDD by applying a function to each element of the RDD  but the output is flattened    Also  function in flatMap can return a list of elements  0 or more   For Example    sc parallelize  3 4 5   map lambda x  range 1 x   collect        Output    1  2    1  2  3    1  2  3  4     sc parallelize  3 4 5   flatMap lambda x  range 1 x   collect        Output   notice o p is flattened out in a single list  1  2  1  2  3    1  2  3  4    Source https   www linkedin com pulse difference-between-map-flatmap-transformations-spark-pyspark-pandey

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RDD map returns  all elements in single array  RDD flatMap returns  elements in Arrays of array  let s assume we have text in text txt file as  Spark is an expressive framework This text is to understand map and faltMap functions of Spark RDD   Using map  val text sc textFile  text txt   map   split       collect   output   text    Array Array String       Array Array Spark  is  an  expressive  framework   Array This  text  is  to  understand  map  and  faltMap  functions  of  Spark  RDD     Using flatMap   val text sc textFile  text txt   flatMap   split       collect   output    text    Array String      Array Spark  is  an  expressive  framework  This  text  is  to  understand  map  and  faltMap  functions  of  Spark  RDD

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Use test md as a example      spark-1 6 1 cat test md This is the first line  This is the second line  This is the last line   scala gt  val textFile   sc textFile  test md   scala gt  textFile map line   gt  line split       count   res2  Long   3  scala gt  textFile flatMap line   gt  line split       count   res3  Long   15  scala gt  textFile map line   gt  line split       collect   res0  Array Array String     Array Array This  is  the  first  line    Array This  is  the  second  line    Array This  is  the  last  line     scala gt  textFile flatMap line   gt  line split       collect   res1  Array String    Array This  is  the  first  line   This  is  the  second  line   This  is  the  last  line     If you use map method  you will get the lines of test md  for flatMap method  you will get the number of words   The map method is similar to flatMap  they are all return a new RDD  map method often to use return a new RDD  flatMap method often to use split words

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map func  Return a new distributed dataset formed by passing each element of the source through a function func declared so map  is  single term   whiles   flatMap func  Similar to map  but each input item can be mapped to 0 or more output items so func should return a Sequence rather than a single item

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map and flatMap are similar  in the sense they take a line from the input RDD and apply a function on it  The way they differ is that the function in map returns only one element  while function in flatMap can return a list of elements  0 or more  as an iterator   Also  the output of the flatMap is flattened  Although the function in flatMap returns a list of elements  the flatMap returns an RDD which has all the elements from the list in a flat way  not a list

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map    is a higher-order method that takes a function as input and applies it to each element in the source RDD   http   commandstech com difference-between-map-and-flatmap-in-spark-what-is-map-and-flatmap-with-examples   flatMap   a higher-order method and transformation operation that takes an input function

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It boils down to your initial question  what you mean by flattening       When you use flatMap  a  multi-dimensional  collection becomes  one-dimensional  collection    val array1d   Array   1 2 3    4 5 6    7 8 9       array1d is an array of strings  val array2d   array1d map x   gt  x split         array2d will be   Array  Array 1 2 3   Array 4 5 6   Array 7 8 9     val flatArray   array1d flatMap x   gt  x split         flatArray will be   Array  1 2 3 4 5 6 7 8 9       You want to use a flatMap when       your map function results in creating multi layered structures but all you want is a simple - flat - one dimensional structure  by removing ALL the internal groupings

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Flatmap and Map both transforms the collection   Difference   map func  Return a new distributed dataset formed by passing each element of the source through a function func   flatMap func  Similar to map  but each input item can be mapped to 0 or more output items  so func should return a Seq rather than a single item    The transformation function  map  One element in -  one element out  flatMap  One element in -  0 or more elements out  a collection

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Difference in output of map and flatMap   1 flatMap  val a   sc parallelize 1 to 10  5   a flatMap 1 to    collect     Output    1  1  2  1  2  3  1  2  3  4  1  2  3  4  5  1  2  3  4  5  6  1  2  3  4  5  6  7  1  2  3  4  5  6  7  8  1  2  3  4  5  6  7  8  9  1  2  3  4  5  6  7  8  9  10   2 map   val a   sc parallelize List  dog    salmon    salmon    rat    elephant    3   val b   a map   length  collect     Output   3 6 6 3 8

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If you are asking the difference between RDD map and RDD flatMap in Spark  map transforms an RDD of size N to another one of size N    eg   myRDD map x   gt  x 2    for example  if myRDD is composed of Doubles    While flatMap can transform the RDD into anther one of a different size   eg    myRDD flatMap x   gt new Seq 2 x 3 x     which will return an RDD of size 2 N  or   myRDD flatMap x   gt if x lt 10 new Seq 2 x 3 x  else new Seq x

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all examples are good    Here is nice visual illustration    source courtesy   DataFlair training of spark  Map   A map is a transformation operation in Apache Spark  It applies to each element of RDD and it returns the result as new RDD  In the Map  operation developer can define his own custom business logic  The same logic will be applied to all the elements of RDD   Spark RDD map function takes one element as input process it according to custom code  specified by the developer  and returns one element at a time  Map transforms an RDD of length N into another RDD of length N  The input and output RDDs will typically have the same number of records       Example of map using scala    val x   spark sparkContext parallelize List  spark    map    example     sample    example    3  val y   x map x   gt   x  1   y collect    res0  Array  String  Int            Array  spark 1    map 1    example 1    sample 1    example 1       rdd y can be re writen with shorter syntax in scala as  val y   x map     1   y collect    res1  Array  String  Int            Array  spark 1    map 1    example 1    sample 1    example 1       Another example of making tuple with string and it s length val y   x map x   gt   x  x length   y collect    res3  Array  String  Int            Array  spark 5    map 3    example 7    sample 6    example 7     FlatMap     A flatMap is a transformation operation  It applies to each element of RDD and it returns the result as new RDD  It is similar to Map  but FlatMap allows returning 0  1 or more elements from map function  In the FlatMap operation  a developer can define his own custom business logic  The same logic will be applied to all the elements of the RDD      What does  flatten the results  mean    A FlatMap function takes one element as input process it according to custom code  specified by the developer  and returns 0 or more element at a time  flatMap   transforms an RDD of length N into another RDD of length M     Example of flatMap using scala     val x   spark sparkContext parallelize List  spark flatmap example     sample example    2      map operation will return Array of Arrays in following case   check type of res0 val y   x map x   gt  x split          split      returns an array of words y collect    res0  Array Array String          Array Array spark  flatmap  example   Array sample  example       flatMap operation will return Array of words in following case   Check type of res1 val y   x flatMap x   gt  x split       y collect   res1  Array String         Array spark  flatmap  example  sample  example      RDD y can be re written with shorter syntax in scala as  val y   x flatMap   split       y collect   res2  Array String         Array spark  flatmap  example  sample  example

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Here is an example of the difference  as a spark-shell session   First  some data - two lines of text   val rdd   sc parallelize Seq  Roses are red    Violets are blue        lines  rdd collect      res0  Array String    Array  Roses are red    Violets are blue     Now  map transforms an RDD of length N into another RDD of length N   For example  it maps from two lines into two line-lengths   rdd map   length  collect      res1  Array Int    Array 13  16    But flatMap  loosely speaking  transforms an RDD of length N into a collection of N collections  then flattens these into a single RDD of results    rdd flatMap   split       collect      res2  Array String    Array  Roses    are    red    Violets    are    blue     We have multiple words per line  and multiple lines  but we end up with a single output array of words  Just to illustrate that  flatMapping from a collection of lines to a collection of words looks like     aa bb cc        dd     gt     aa   bb   cc        dd      gt    aa   bb   cc   dd     The input and output RDDs will therefore typically be of different sizes for flatMap   If we had tried to use map with our split function  we d have ended up with nested structures  an RDD of arrays of words  with type RDD Array String    because we have to have exactly one result per input   rdd map   split       collect      res3  Array Array String     Array                                       Array Roses  are  red                                         Array Violets  are  blue                                       Finally  one useful special case is mapping with a function which might not return an answer  and so returns an Option  We can use flatMap to  filter out the elements that return None and extract the values from those that return a Some   val rdd   sc parallelize Seq 1 2 3 4    def myfn x  Int   Option Int    if  x  lt   2  Some x   10  else None  rdd flatMap myfn  collect      res3  Array Int    Array 10 20     noting here that an Option behaves rather like a list that has either one element  or zero elements

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For all those who ve wanted PySpark related   Example transformation  flatMap   gt  gt  gt  a  hello what are you doing   gt  gt  gt  a split       hello    what    are    you    doing     gt  gt  gt  b   hello what are you doing   this is rak    gt  gt  gt  b split     Traceback  most recent call last     File     line 1  in  AttributeError   list  object has no attribute  split    gt  gt  gt  rline sc parallelize b   gt  gt  gt  type rline       gt  gt  gt  def fwords x           return x split      gt  gt  gt  rword rline map fwords   gt  gt  gt  rword collect        hello    what    are    you    doing      this    is    rak      gt  gt  gt  rwordflat rline flatMap fwords   gt  gt  gt  rwordflat collect       hello    what    are    you    doing    this    is    rak    Hope it helps

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Generally we use word count example in hadoop  I will take the same use case and will use map and flatMap and we will see the difference how it is processing the data   Below is the sample data file   hadoop is fast hive is sql on hdfs spark is superfast spark is awesome   The above file will be parsed using map and flatMap   Using map   gt  gt  gt  wc   data map lambda line line split         gt  gt  gt  wc collect    u hadoop is fast   u hive is sql on hdfs   u spark is superfast   u spark is awesome     Input has 4 lines and output size is 4 as well  i e   N elements     N elements   Using flatMap   gt  gt  gt  fm   data flatMap lambda line line split         gt  gt  gt  fm collect    u hadoop   u is   u fast   u hive   u is   u sql   u on   u hdfs   u spark   u is   u superfast   u spark   u is   u awesome     The output is different from map     Let s assign 1 as value for each key to get the word count    fm  RDD created by using flatMap wc  RDD created using map      gt  gt  gt  fm map lambda word    word 1   collect     u hadoop   1    u is   1    u fast   1    u hive   1    u is   1    u sql   1    u on   1    u hdfs   1    u spark   1    u is   1    u superfast   1    u spark   1    u is   1    u awesome   1     Whereas flatMap on RDD wc will give the below undesired output    gt  gt  gt  wc flatMap lambda word    word 1   collect     u hadoop   u is   u fast    1   u hive   u is   u sql   u on   u hdfs    1   u spark   u is   u superfast    1   u spark   u is   u awesome    1    You can t get the word count if map is used instead of flatMap   As per the definition  difference between map and flatMap is      map  It returns a new RDD by applying given function to each element   of the RDD    Function in map returns only one item       flatMap  Similar to map  it returns a new RDD by applying a function   to each element of the RDD  but output is flattened

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map returns RDD of equal number of elements while flatMap may not   An example use case for flatMap Filter out missing or incorrect data   An example use case for map Use in wide variety of cases where is the number of elements of input and output are the same   number csv  1 2 3 - 4 - 5   map py adds all numbers in add csv   from operator import    def f row     try      return float row    except Exception      return 0  rdd   sc textFile  a csv   map f   print rdd count           7 print rdd reduce add      15 0   flatMap py uses flatMap to filtered out missing data before addition   Less numbers are added compared to the previous version   from operator import    def f row     try      return  float row     except Exception      return     rdd   sc textFile  a csv   flatMap f   print rdd count           5 print rdd reduce add      15 0

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The difference can be seen from below sample pyspark code   rdd   sc parallelize  2  3  4   rdd flatMap lambda x  range 1  x   collect   Output   1  1  2  1  2  3    rdd map lambda x  range 1  x   collect   Output    1    1  2    1  2  3

[apache-spark] What is the difference between map and flatMap and a good use case for each?

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