Scala: using functional programming on the JVM – part 3

Standard

Hi, dear readers! Welcome to my blog. On this post, the last on this series, we will continue to see more features from the Scala language. If you haven’t read the previous post, please go to the “programming languages” menu option to find all of the series. So, without further delay, let’s begin!

Collections

Collections, as the name implies, are data structures where we can store and organize data. There is various types of Collections that can be used on the Scala language, all common from any programming language and with all the standard behavior from their types, such as lists, sets and maps.

On the next sections, we will see the major methods that Scala offers us to work with their collections.

So, let’s fire up the Scala REPL and begin!

Filter

As the name, implies, filter can be used to filter data from a collection, generating a subset. Let’s begin by creating a List:

val mylist = List[Integer](1,2,3,4,5)

Next, we create a function that returns if a number is even:

def isEven(n:Integer) = n % 2 == 0

And finally, we used the filter function, printing on console the even numbers:

scala> mylist.filter(n => isEven(n)).foreach(println(_))

2

4

As we can see, it printed only 2 and 4 from our list, proving that our filtering was successful.

One important thing to note on this and the other methods is that none of the methods changes the original collection, they always create and returns a new one, since they are designed to work with immutables. We can check this by printing the list:

scala> print(mylist)

List(1, 2, 3, 4, 5)

Find

The find method is similar to the filter one, but instead of returning a subset, it returns only a element from the collection. The return is a optional, typed from the same type of the element type from the collection.

For this example, we will use the same collection from our previous example. If we wanted to return only the number 2 element and print on console, all we have to do is this:

scala> println(mylist.find(n => n == 2).getOrElse(0))

2

Map

The map is another common method for collections on programming languages. His objective is to take a collection and transform his elements on new elements, that could be from a different type, generating a new collection. Let’s see a example.

On our example, we will take the numbers from our previous list and create a new list, where the numbers are transformed on strings on the format “the number is x”. If we wanted to do this transformation and print the result on console, we can do the following:

scala> mylist.map(n => "the number is " + n).foreach(println(_))

the number is 1

the number is 2

the number is 3

the number is 4

the number is 5

Flatmap

Another interesting method is the flatmap. The flatmap is similar to a map, but with one difference: when used against complex objects of nested collections, this method denormalize the results, generating a flat collection. Let’s see a example.

First we create a class:

case class classA(val a: String, val b : List[String])

Then, we create a list with objects from our class:

scala> val myobjectlist = List(classA("A",List("A","B","C")),classA("B",List("A","C")),classA("C",List("C")),classA("D",List("A","B")))

myobjectlist: List[classA] = List(classA(A,List(A, B, C)), classA(B,List(A, C)), classA(C,List(C)), classA(D,List(A, B)))

Now, let’s see the result on the REPL, if we try to map our list, using only the b attribute:

scala> val mapobjectlist = myobjectlist.map(n => n.b)

mapobjectlist: List[List[String]] = List(List(A, B, C), List(A, C), List(C), List(A, B))

As we can see above, the result is a list of lists. This gives us a extra complexity to iterate over our results, since we will need to access each internal list individually in order to obtain all the elements.

Now let’s see the same result, using flatmap this time:

scala> val mapobjectflatlist = myobjectlist.flatMap(n => n.b)

mapobjectflatlist: List[String] = List(A, B, C, A, C, C, A, B)

Now, the list is flatten to a single List, allowing us to iterate over the elements much easier.

Reduce

Another useful feature when working with collections is the reduce method. With result, as on map’s case, we make a transformation on a list, but on this case, instead of generating a new collection, we aggregate the collection, generating a new value.

The simplest and easier example we can demonstrate is simply summing up the values. If we wanted to sum up all the values from our numeric list, all we need to do is this:

scala> println(mylist.reduce((sum,n) => sum+n))

15

A important thing to take note is that, on this case, the order from which the numbers will be iterate is from left to right. If we would like to explicit this ordering or reverse it, we could do this by using the reduceLeft or reduceRight methods instead.

Fold

Fold is pretty similar to the reduce method, but with a fundamental difference: while reduce obligates us that the result must be from the same type of the source elements, fold doesn’t. Let’s see a example to better understand it.

Let’s suppose that, different from our previous example, we wanted to generate a string from the numbers of our numeric collection, separated by parentheses. We can do this using the following:

scala>  val foldlistStr = mylist.fold("")((sum,n) => sum+"("+n+")")

foldlistStr: Comparable[_ >: Integer with String <: Comparable[_ >: Integer with String <: java.io.Serializable] with java.io.Serializable] with java.io.Serializable = (1)(2)(3)(4)(5)

scala> println(foldlistStr)

(1)(2)(3)(4)(5)

scala>

As we can see, on this case, we not only had to declare the folding method, but also a empty string at beginning. That it was the aggregator variable, which is then used at each iteration to form the aggregation. This is necessary in order to allow Scala to infer what it will be the type of the result of our folding operation.

Conclusion

And so we conclude our trip on the Scala language. I hope I could bring for the reader a glimpse of the language and all his power. While is not as popular as languages such as Java or C#, it is definitely a good language worthy to be considered, specially on distributed systems where it could be used with distributed tools, such as Akka.

Thank you for following me on my series, see you next time!

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