This chapter will provide a brief overview on the basics of using the Scala shell and introduce you to functional programming with collections.

If you’re already comfortable with Scala or plan on using the Python shell for the interactive Spark sections of this mini course, you may not need to spend time here

This exercise is based on a great tutorial, First Steps to Scala. However, reading through that whole tutorial and trying the examples at the console may take considerable time, so we will provide a basic introduction to the Scala shell here. Do as much as you feel you need (in particular you might want to skip the final “bonus” question).

Note that we will be using the sbt program to launch the scala interpreter here. This is a non-standard way of launching a scala REPL, so be aware that it is only a convenience based on what was packaged on the USB.

1. Launch the Scala console by typing:

``````usb/\$ sbt/sbt console
``````
2. Declare a list of integers as a variable called “myNumbers”.

```scala> val myNumbers = List(1, 2, 5, 4, 7, 3)
myNumbers: List[Int] = List(1, 2, 5, 4, 7, 3)
```
3. Declare a function, `cube`, that computes the cube (third power) of an Int. See steps 2-4 of First Steps to Scala.

```scala> def cube(a: Int): Int = a * a * a
cube: (a: Int)Int
```
4. Apply the function to `myNumbers` using the `map` function. Hint: read about the `map` function in the Scala List API and also in Table 1 about halfway through the First Steps to Scala tutorial.

```scala> myNumbers.map(x => cube(x))
res: List[Int] = List(1, 8, 125, 64, 343, 27)
// Scala also provides some shorthand ways of writing this:
// myNumbers.map(cube(_))
// myNumbers.map(cube)
```
5. Then also try writing the function inline in a `map` call, using closure notation.

```scala> myNumbers.map{x => x * x * x}
res: List[Int] = List(1, 8, 125, 64, 343, 27)
```
6. Define a `factorial` function that computes n! = 1 * 2 * … * n given input n. You can use either a loop or recursion, in our solution we use recursion (see steps 5-7 of First Steps to Scala). Then compute the sum of factorials in `myNumbers`. Hint: check out the `sum` function in the Scala List API.

```scala> def factorial(n:Int):Int = if (n==0) 1 else n * factorial(n-1) // From http://bit.ly/b2sVKI
factorial: (Int)Int
scala> myNumbers.map(factorial).sum
res: Int = 5193
```
7. BONUS QUESTION. This is a more challenging task and may require 10 minutes or more to complete, so you should consider skipping it depending on your timing so far. Do a wordcount of a textfile. More specifically, create and populate a Map with words as keys and counts of the number of occurrences of the word as values.

You can load a text file as an array of lines as shown below:

```import scala.io.Source
```

Then, instantiate a `collection.mutable.HashMap[String,Int]` and use functional methods to populate it with wordcounts. Hint, in our solution, which is inspired by this solution online, we use `flatMap` and then `map`.

```scala> import scala.io.Source
import scala.io.Source

lines: Array[String] = Array(# Apache Spark, "", Lightning-Fast Cluster Computing - <http://spark.apache.org/>, "", "", ## Online Documentation, "", You can find the latest Spark documentation, including a programming, guide, on the project webpage at <http://spark.apache.org/documentation.html>., This README file only contains basic setup instructions., "", ## Building Spark, "", Spark is built on Scala 2.10. To build Spark and its example programs, run:, "", "    ./sbt/sbt assembly", "", (You do not need to do this if you downloaded a pre-built package.), "", ## Interactive Scala Shell, "", The easiest way to start using Spark is through the Scala shell:, "", "    ./bin/spark-shell", "", Try the following command, which should return 1000:, "", "    scala> sc.parallelize(1 to 1000).co...

scala> val counts = new collection.mutable.HashMap[String, Int].withDefaultValue(0)
counts: scala.collection.mutable.Map[String,Int] = Map()

scala> lines.flatMap(line => line.split(" ")).foreach(word => counts(word) += 1)

scala> counts
res1: scala.collection.mutable.Map[String,Int] = Map(request, -> 1, Documentation -> 1, requires -> 1, their -> 1, ./sbt/sbt -> 2, MRv1, -> 1, instructions. -> 1, basic -> 1, SPARK_HADOOP_VERSION=2.0.0-cdh4.2.0

```

Or, a purely functional solution:

```scala> import scala.io.Source
import scala.io.Source

lines: Array[String] = Array(# Apache Spark, "", Lightning-Fast Cluster Computing - <http://spark.apache.org/>, "", "", ## Online Documentation, "", You can find the latest Spark documentation, including a programming, guide, on the project webpage at <http://spark.apache.org/documentation.html>., This README file only contains basic setup instructions., "", ## Building Spark, "", Spark is built on Scala 2.10. To build Spark and its example programs, run:, "", "    ./sbt/sbt assembly", "", (You do not need to do this if you downloaded a pre-built package.), "", ## Interactive Scala Shell, "", The easiest way to start using Spark is through the Scala shell:, "", "    ./bin/spark-shell", "", Try the following command, which should return 1000:, "", "    scala> sc.parallelize(1 to 1000).co...

scala> val emptyCounts = Map[String,Int]().withDefaultValue(0)
emptyCounts: scala.collection.immutable.Map[String,Int] = Map()

scala> val words = lines.flatMap(line => line.split(" "))
words: Array[String] = Array(#, Apache, Spark, "", Lightning-Fast, Cluster, Computing, -, <http://spark.apache.org/>, "", "", ##, Online, Documentation, "", You, can, find, the, latest, Spark, documentation,, including, a, programming, guide,, on, the, project, webpage, at, <http://spark.apache.org/documentation.html>., This, README, file, only, contains, basic, setup, instructions., "", ##, Building, Spark, "", Spark, is, built, on, Scala, 2.10., To, build, Spark, and, its, example, programs,, run:, "", "", "", "", "", ./sbt/sbt, assembly, "", (

scala> val counts = words.foldLeft(emptyCounts)({(currentCounts: Map[String,Int], word: String) => currentCounts.updated(word, currentCounts(word) + 1)})
counts: scala.collection.immutable.Map[String,Int] = Map(Please -> 1, Contributing -> 1, 2.10. -> 1, application -> 1, please -> 1, "" -> 149, for -> 1, find -> 1, Apache -> 6, test -> 1, adding -> 1, `SPARK_YARN=true`: -> 1, Hadoop, -> 1, any -> 2, Once -> 1, For -> 5, name -> 1, this -> 4, protocols -> 1, in -> 4, "local...

scala> counts
```
Hands-on Exercises