Scala’s exceptions work like exceptions in many other languages like Java. Instead of returning a value in the normal way, a method can terminate by throwing an exception. However, Scala doesn’t actually have checked exceptions.

When you want to handle exceptions, you use a try{…}catch{…} block like you would in Java except that the catch block uses matching to identify and handle the exceptions.

Throwing Exceptions

Throwing an exception looks the equivalent as in Java. You create an exception object and then you throw it with the throw keyword as follows.

throw new IllegalArgumentException

Catching Exceptions

Scala allows you to try/catch any exception in a single block and then perform pattern matching against it using case blocks. Try the subsequent example program to handle exception.

Example

import java.io.FileReader

import java.io.FileNotFoundException

import java.io.IOException




object Demo {

   def main(args: Array[String]) {

      try {

         val f = new FileReader("input.txt")

      } catch {

         case ex: FileNotFoundException =>{

            println("Missing file exception")

         }

        

         case ex: IOException => {

            println("IO Exception")

         }

      }

   }

}

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

Missing file exception

The behavior of this try-catch expression is the equivalent as in other languages with exceptions. The body is executed, and if it throws an exception, each catch clause is tried in turn.

The finally Clause

You can wrap an expression with a finally clause if you want to cause some code to execute no matter how the expression terminates. Try the subsequent program.

Example

import java.io.FileReader

import java.io.FileNotFoundException

import java.io.IOException




object Demo {

   def main(args: Array[String]) {

      try {

         val f = new FileReader("input.txt")

      } catch {

         case ex: FileNotFoundException => {

            println("Missing file exception")

         }

        

         case ex: IOException => {

            println("IO Exception")

         }

      } finally {

         println("Exiting finally...")

      }

   }

}

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

Missing file exception

Exiting finally…

So, this brings us to the end of blog. This Tecklearn ‘How to perform Exception Handling in Scala 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:

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.

398