Scala, short for Scalable Language, is a hybrid functional programming language. It was created by Martin Odersky. Scala smoothly integrates the features of object-oriented and functional languages. Scala is compiled to run on the Java Virtual Machine. Many existing companies, who depend on Java for business-critical applications, are turning to Scala to boost their development productivity, applications scalability and overall reliability.

Here we have presented a few points that makes Scala the first choice of application developers.

Scala is object-oriented

Scala is a pure object-oriented language in the sense that every value is an object. Types and behavior of objects are described by classes and traits which will be explained in subsequent chapters.

Classes are extended by subclassing and a flexible mixin-based composition mechanism as a clean replacement for multiple inheritance.

Scala is functional

Scala is also a functional language in the sense that every function is a value and every value is an object so ultimately every function is an object.

Scala provides a lightweight syntax for defining anonymous functions, it supports higher-order functions, it allows functions to be nested, and supports currying. These concepts will be explained in subsequent chapters.

Scala is statically typed

Scala, unlike some of the other statically typed languages (C, Pascal, Rust, etc.), does not expect you to provide redundant type information. You don’t have to specify a type in most cases, and you certainly don’t have to repeat it.

Scala runs on the JVM

Scala is compiled into Java Byte Code which is executed by the Java Virtual Machine (JVM). This means that Scala and Java have a common runtime platform. You can easily move from Java to Scala.

The Scala compiler compiles your Scala code into Java Byte Code, which can then be executed by the ‘scala‘ command. The ‘scala‘ command is similar to the java command, in that it executes your compiled Scala code.

Scala can Execute Java Code

Scala enables you to use all the classes of the Java SDK and also your own custom Java classes, or your favorite Java open source projects.

Scala can do Concurrent & Synchronize processing

Scala allows you to express general programming patterns in an effective way. It reduces the number of lines and helps the programmer to code in a type-safe way. It allows you to write codes in an immutable manner, which makes it easy to apply concurrency and parallelism (Synchronize).

Scala vs Java

Scala has a set of features that completely differ from Java. Some of these are −

All types are objects
Type inference
Nested Functions
Functions are objects
Domain specific language (DSL) support
Traits
Closures
Concurrency support inspired by Erlang

Scala Web Frameworks

Scala is being employed everywhere and necessaryly in enterprise web applications. You can check a few of the most popular Scala web frameworks −

Scala – Environment Setup

Scala can be installed on any UNIX flavored or Windows based system. Before you start installing Scala on your machine, you must have Java 1.8 or greater installed on your computer.

Follow the steps given below to install Scala.

Step 1: Verify Your Java Installation

First of all, you need to have Java Software Development Kit (SDK) installed on your system. To verify this, execute any of the subsequent two commands depending on the platform you are working on.

If the Java installation has been done properly, then it will display the current version and specification of your Java installation. A sample output is given in the subsequent table.

Platform

Command

Sample Output

Windows

Open Command Console and type −

\>java –version

Java version “1.8.0_31”

Java (TM) SE Run Time

Environment (build 1.8.0_31-b31)

Java Hotspot (TM) 64-bit Server

VM (build 25.31-b07, mixed mode)

Linux

Open Command terminal and type −

$java –version

Java version “1.8.0_31”

Open JDK Runtime Environment (rhel-2.8.10.4.el6_4-x86_64)

Open JDK 64-Bit Server VM (build 25.31-b07, mixed mode)

We assume that the readers of this tutorial have Java SDK version 1.8.0_31 installed on their system.

In case you do not have Java SDK, download its current version from http://www.oracle.com/technetwork/java/javase/downloads/index.html and install it.

Step 2: Set Your Java Environment

Set the environment variable JAVA_HOME to point to the base directory location where Java is installed on your machine. For example,

Sr.No

Platform & Description

Windows

Set JAVA_HOME to C:\ProgramFiles\java\jdk1.7.0_60

Linux

Export JAVA_HOME=/usr/local/java-current

Append the full path of Java compiler location to the System Path.

Sr.No

Platform & Description

Windows

Append the String “C:\Program Files\Java\jdk1.7.0_60\bin” to the end of the system variable PATH.

Linux

Export PATH=$PATH:$JAVA_HOME/bin/

Execute the command java -version from the command prompt as explained above.

Step 3: Install Scala

You can download Scala from http://www.scala-lang.org/downloads. At the time of writing this tutorial, I downloaded ‘scala-2.11.5-installer.jar’. Make sure you have admin privilege to proceed. Now, execute the subsequent command at the command prompt −

Platform

Command & Output

Description

Windows

\>java –jar scala-2.11.5-installer.jar\>

This command will display an installation wizard, which will guide you to install Scala on your windows machine. During installation, it will ask for license agreement, simply accept it and further it will ask a path where Scala will be installed. I selected default given path “C:\Program Files\Scala”, you can select a suitable path as per your convenience.

Linux

Command −

$java –jar scala-2.9.0.1-installer.jar

Output −

Welcome to the installation of Scala 2.9.0.1!

The homepage is at − http://Scala-lang.org/

press 1 to continue, 2 to quit, 3 to redisplay

1…………………………………………

[ Starting to unpack ]

[ Processing package: Software Package Installation (1/1) ]

[ Unpacking finished ]

[ Console installation done ]

During installation, it will ask for license agreement, to accept it type 1 and it will ask a path where Scala will be installed. I entered /usr/local/share, you can select a suitable path as per your convenience.

Finally, open a new command prompt and type Scala -version and press Enter. You should see the subsequent −

Platform	Command	Output
Windows	\>scala -version	Scala code runner version 2.11.5 — Copyright 2002-2013, LAMP/EPFL
Linux	$scala -version	Scala code runner version 2.9.0.1 – Copyright 2002-2013, LAMP/EPFL

So, this brings us to the end of blog. This Tecklearn ‘Overview of Scala Programming Language and How to install Scala on your system’ 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:

https://www.tecklearn.com/course/apache-spark-and-scala-certification/

Apache Spark and Scala Training

About the Course

Tecklearn Spark training lets you master real-time data processing using Spark streaming, Spark SQL, Spark RDD and Spark Machine Learning libraries (Spark MLlib). This Spark certification training helps you master the essential skills of the Apache Spark open-source framework and Scala programming language, including Spark Streaming, Spark SQL, machine learning programming, GraphX programming, and Shell Scripting Spark. You will also understand the role of Spark in overcoming the limitations of MapReduce. Upon completion of this online training, you will hold a solid understanding and hands-on experience with Apache Spark.

Why Should you take Apache Spark and Scala Training?

The average salary for Apache Spark developer ranges from approximately $93,486 per year for Developer to $128,313 per year for Data Engineer. – Indeed.com
Wells Fargo, Microsoft, Capital One, Apple, JPMorgan Chase & many other MNC’s worldwide use Apache Spark across industries.
Global Spark market revenue will grow to $4.2 billion by 2022 with a CAGR of 67% Marketanalysis.com

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

Got a question for us? Please mention it in the comments section and we will get back to you.

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