A Quick Look at Map and FlatMap

Topics: scala, higher order functions, map, flatmap, for comprehension

I wrote about higher order functions in my last post and I mentioned their importance in Scala, but I didn’t want to digress too much. Now it’s time to introduce two great examples of higher order functions, map and flatMap. Map and flatMap are implemented for all collection types in the Scala collection library. They are quite significant functions in Scala, and functional programming in general, so they rightfully deserve their own post.


Let’s recall the foreach function mentioned in the previous post: list.foreach(print). It is used to execute a function for each element of a collection.

Map is applying a function on each element of a collection similar to foreach, with the difference that map returns a new collection as a result. So we can think of the function supplied to map as a transformation function, which we can use to transform a collection of N elements into a new collection of N transformed elements.

// let's introduce a list of integers
val list = List(1, 2, 4, 8)

// and use a simple increment function
def increment(value: Int): Int = value + 1

// map will call the increment function on each element of the list
// and return a new list with the incremented values
val result = list.map(increment)
// returns: List(2, 3, 5, 9)


FlatMap is similar to map, with the requirement that the elements of the collection are collections as well, and with an additional step of flattening the elements of a collection after executing the transformation function.

Flatten is a standard operation of converting a lists of lists into one list with all the elements. For all collection types.

// if we have a list of lists of integers named, creatively, listOfLists
val listOfLists: List[List[Int]] = List(List(1, 2), List(4, 8), List())

// we can convert it into a list of integers which maintains all the elements of the sublists
val result: List[Int] = listOfLists.flatten
// returns: List(1, 2, 4, 8)

Therefore, flatmap would execute the transformation function on each list element of the listOfLists and return a single flattened list with all the transformed elements.

// if we have the same list of lists of integers
val list = List(List(1, 2), List(4, 8), List())

// let us use the previously defined increment function again
def increment(value: Int): Int = value + 1

// calling flatMap should result in the map functions executed 
// on the sublists List(1, 2) and List(4, 8)
// followed by a flatten
val result = list.flatMap(subList => subList.map(increment))
// returns: List(2, 3, 5, 9)

// if we were to call a map instead of flatMap we would instead increments the integer values
// in the list of lists, without flattening it
// list.map(subList => subList.map(increment))
// returns: List(List(2, 3), List(4, 8), List())

I wouldn’t dive deeper into the theory as I have only started scratching the surface myself, but I was constantly coming across the importance of the flatMap function for defining monads in functional programming. One thing I’ve learned so far is that not every construct that has a flatMap function is a monad, and that Scala has more flexible monad-like types. If interested, you can find more in this Stack Overflow discussion.

More than collections

Map and flatMap functions exist in other classes as well, not just collections. A good example are container classes like Option, Future and Try. And we can implement them for our own classes as well.

I’ve written about Scala Options before but I didn’t mention how useful map and flatMap functions are with the Option container.

Take, for instance, the map function. Calling the map function on an Option (which can be either a Some(value) or None) will execute the function only if the value is an instance of Some. So we can use this to conditionally execute a function without handling the None case.

// let's re-introduce an optional result with the type of Option[Result]
// which can either have the value of Some(Result) or None
val result: Option[Result] = ...

// a doSomeWork function will only be executed if there is Some(Result)
result.map( res => res.doSomeWork() )

On the other hand, the flatMap function can be even more useful with Options, as flattening a list of Option(value) returns a list of values, with only the values which exist, filtering out the None values.

// let's use a list of integers
val list = List(1, 2, 4, 8)

// and let's introduce a function that returns Option[Int]
// such that for any value smaller than or equal 3, it returns None
// otherwise it returns Some(value)
def isBigEnough(value: Int): Option[Int] = if (value > 3) Some(value) else None

// if we call this in a map function
// we will get a list of Option[Int]
val result: List[Option[Int]] = list.map(elem => isBigEnough(elem))
// it returns: List(None, None, Some(4), Some(8))

// but we can call this function in flatMap to return a filtered list of integers
// with only the existing values
val result: List[Int] = list.flatMap(elem => isBigEnough(elem))
// returns: List(4, 8)

I will not get into Try and Futures for now, as they would deserve more than just a few setences each, but I urge you to read more about both them if you’re interested.

The for comprehension

There is a nice syntactic sugar in Scala that allows us to combine map and flatMap functions in a more readable way, using for comprehensions.

It can be explained using the above example and Options:

// let's use a list of integers from the example above
val list = List(1, 2, 4, 8)

// and let's also use the function defined in the example above
def isBigEnough(value: Int): Option[Int] = ...

// we can call the map function from the above example
list.map(elem => isBigEnough(elem))

// but we can also write the same using the for comprehension
for {
  elem <- list  // iterate through the elements
} yield isBigEnough(elem)
// returns: List(None, None, Some(4), Some(8))

// we can call the flatMap function from the above example
list.flatMap(elem => isBigEnough(elem))

// or we can write it using the for comprehension
for {
  elem <- list // iterate through the elements
  option <- isBigEnough(elem) // call isBigEnough for each element
} yield option
// returns: List(4, 8)

So executing a statement on elements inside the for sections is equal to calling it in flatMap, and executing a statement in the yield section is equal to calling the statement in map. Let’s see a more complex example combining everything we’ve seen before.

// let's use the previously introduced list of lists of integers
val listOfLists = List(List(1, 2), List(4, 8), List())

// let's also use the increment function we already know
def increment(value: Int): Int = value + 1

// we can use the for comprehension to write the flatMap execution
// from the beginning of the post: list.flatMap(subList => subList.map(increment))
val transformedList = for {
  subList <- listOfLists
  converted <- subList.map(increment)
} yield converted
// transformedList = List(2, 3, 5, 9)

// and we can also call the isBigEnough function to take only the elements > 3
for {
  converted <- transformedList
  option <- isBigEnough(converted)
} yield option
// returns: List(5, 9)

Parallel execution

Map and flatMap allow for easily processing collections in parallel. One can simply parallelise map and flatMap executions in Scala, by utilising parallel collections. It’s as simple as adding .par to a collection to convert it to a parallel collection. Calling map or flatMap on the newly created parallel collection will now execute in parallel, utilising the number of cores available in the executing machine.

It’s not a surprise that the map function is the key building block of the MapReduce programming model used for batch processing enormous amount of data in parallel in Google and in Apache Hadoop. And both map and flatMap are heavily utilised in Apache Spark as well.

Published on: 10 Apr 2016