A function is a group of statements that perform a task. You can divide up your code into separate functions. How you divide up your code among different functions is up to you, but logically, the division usually is so that each function performs a specific task.

Scala has both functions and methods and we use the terms method and function interchangeably with a minor difference. A Scala method is a part of a class which has a name, a signature, optionally some annotations, and some bytecode where as a function in Scala is a complete object which can be assigned to a variable. In other words, a function, which is defined as a member of some object, is called a method.

A function definition can appear anywhere in a source file and Scala permits nested function definitions, that is, function definitions inside other function definitions. Most important point to note is that Scala function’s name can have characters like +, ++, ~, &,-, –, \, /, :, etc.

Function Declarations

A Scala function declaration has the subsequent form −

def functionName ([list of parameters]) : [return type]

Methods are implicitly declared abstract if you don’t use the equals sign and the method body.

Function Definitions

A Scala function definition has the subsequent form −

Syntax

def functionName ([list of parameters]) : [return type] = {

   function body

   return [expr]

}

Here, return type could be any valid Scala data type and list of parameters will be a list of variables separated by comma and list of parameters and return type are optional. Very similar to Java, a return statement can be used along with an expression in case function returns a value. Subsequent is the function which will add two integers and return their sum −

Syntax

object add {

   def addInt( a:Int, b:Int ) : Int = {

      var sum:Int = 0

      sum = a + b

      return sum

   }

}

A function, that does not return anything can return a Unit that is equivalent to void in Java and indicates that function does not return anything. The functions which do not return anything in Scala, they are called procedures.

Syntax

Here is the syntax −

object Hello{

   def printMe( ) : Unit = {

      println("Hello, Scala!")

   }

}

Calling Functions

Scala provides a number of syntactic variations for invoking methods. Subsequent is the standard way to call a method −

functionName( list of parameters )

If a function is being called using an instance of the object, then we would use dot notation similar to Java as follows −

[instance.]functionName( list of parameters )

Try the subsequent example program to define and then call the equivalent function.

Example

object Demo {

   def main(args: Array[String]) {

      println( "Returned Value : " + addInt(5,7) );

   }

  

   def addInt( a:Int, b:Int ) : Int = {

      var sum:Int = 0

      sum = a + b




      return sum

   }

}

Save the above program in Demo.scala. The subsequent commands are used to compile and execute this program.

Command

\>scalac Demo.scala

\>scala Demo

Output

Returned Value : 12

Scala functions are the heart of Scala programming and that’s why Scala is assumed as a functional programming language. Subsequent are few important concepts related to Scala functions which should be understood by a Scala programmer.

Functions Call-by-Name	Functions with Named Arguments
Function with Variable Arguments	Recursion Functions
Default Parameter Values	Higher-Order Functions
Nested Functions	Anonymous Functions
Partially Applied Functions	Currying Functions

Scala – Closures

A closure is a function, whose return value depends on the value of one or more variables declared outside this function.

The subsequent piece of code with anonymous function.

val multiplier = (i:Int) => i * 10

Here the only variable used in the function body, i * 10 , is i, which is defined as a parameter to the function. Try the subsequent code −

val multiplier = (i:Int) => i * factor

There are two free variables in multiplier: i and factor. One of them, i, is a formal parameter to the function. Hence, it is bound to a new value each time multiplier is called. However, factor is not a formal parameter, then what is this? Let us add one more line of code.

var factor = 3

val multiplier = (i:Int) => i * factor

Now factor has a reference to a variable outside the function but in the enclosing scope. The function references factor and reads its current value each time. If a function has no external references, then it is trivially closed over itself. No external context is required.

Try the subsequent example program.

Example

object Demo {

   def main(args: Array[String]) {

      println( "multiplier(1) value = " +  multiplier(1) )

      println( "multiplier(2) value = " +  multiplier(2) )

   }

   var factor = 3

   val multiplier = (i:Int) => i * factor

}

Save the above program in Demo.scala. The subsequent commands are used to compile and execute this program.

Command

\>scalac Demo.scala

\>scala Demo

Output

multiplier(1) value = 3

multiplier(2) value = 6

So, this brings us to the end of blog. This Tecklearn ‘How to use Functions in Scala Programming Language’ helps you with commonly asked questions if you are looking out for a job in Apache Spark and Scala and Big Data Developer. If you wish to learn Apache Spark and Scala and build a career in Big Data Hadoop domain, then check out our interactive, Apache Spark and Scala Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

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What you will Learn in this Course?

Introduction to Scala for Apache Spark

What is Scala
Why Scala for Spark
Scala in other Frameworks
Scala REPL
Basic Scala Operations
Variable Types in Scala
Control Structures in Scala
Loop, Functions and Procedures
Collections in Scala
Array Buffer, Map, Tuples, Lists

Functional Programming and OOPs Concepts in Scala

Functional Programming
Higher Order Functions
Anonymous Functions
Class in Scala
Getters and Setters
Custom Getters and Setters
Constructors in Scala
Singletons
Extending a Class using Method Overriding

Introduction to Spark

Introduction to Spark
How Spark overcomes the drawbacks of MapReduce
Concept of In Memory MapReduce
Interactive operations on MapReduce
Understanding Spark Stack
HDFS Revision and Spark Hadoop YARN
Overview of Spark and Why it is better than Hadoop
Deployment of Spark without Hadoop
Cloudera distribution and Spark history server

Basics of Spark

Spark Installation guide
Spark configuration and memory management
Driver Memory Versus Executor Memory
Working with Spark Shell
Resilient distributed datasets (RDD)
Functional programming in Spark and Understanding Architecture of Spark

Playing with Spark RDDs

Challenges in Existing Computing Methods
Probable Solution and How RDD Solves the Problem
What is RDD, It’s Operations, Transformations & Actions Data Loading and Saving Through RDDs
Key-Value Pair RDDs
Other Pair RDDs and Two Pair RDDs
RDD Lineage
RDD Persistence
Using RDD Concepts Write a Wordcount Program
Concept of RDD Partitioning and How It Helps Achieve Parallelization
Passing Functions to Spark

Writing and Deploying Spark Applications

Creating a Spark application using Scala or Java
Deploying a Spark application
Scala built application
Creating application using SBT
Deploying application using Maven
Web user interface of Spark application
A real-world example of Spark and configuring of Spark

Parallel Processing

Concept of Spark parallel processing
Overview of Spark partitions
File Based partitioning of RDDs
Concept of HDFS and data locality
Technique of parallel operations
Comparing coalesce and Repartition and RDD actions

Machine Learning using Spark MLlib

Why Machine Learning
What is Machine Learning
Applications of Machine Learning
Face Detection: USE CASE
Machine Learning Techniques
Introduction to MLlib
Features of MLlib and MLlib Tools
Various ML algorithms supported by MLlib

Integrating Apache Flume and Apache Kafka

Why Kafka, what is Kafka and Kafka architecture
Kafka workflow and Configuring Kafka cluster
Basic operations and Kafka monitoring tools
Integrating Apache Flume and Apache Kafka

Apache Spark Streaming

Why Streaming is Necessary
What is Spark Streaming
Spark Streaming Features
Spark Streaming Workflow
Streaming Context and DStreams
Transformations on DStreams
Describe Windowed Operators and Why it is Useful
Important Windowed Operators
Slice, Window and ReduceByWindow Operators
Stateful Operators

Improving Spark Performance

Learning about accumulators
The common performance issues and troubleshooting the performance problems

DataFrames and Spark SQL

Need for Spark SQL
What is Spark SQL
Spark SQL Architecture
SQL Context in Spark SQL
User Defined Functions
Data Frames and Datasets
Interoperating with RDDs
JSON and Parquet File Formats
Loading Data through Different Sources

Scheduling and Partitioning in Apache Spark

Concept of Scheduling and Partitioning in Spark
Hash partition and range partition
Scheduling applications
Static partitioning and dynamic sharing
Concept of Fair scheduling
Map partition with index and Zip
High Availability
Single-node Recovery with Local File System and High Order Functions

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