This blog takes you through the Scala access modifiers. Members of packages, classes or objects can be labeled with the access modifiers private and protected, and if we are not using either of these two keywords, then access will be assumed as public. These modifiers restrict accesses to the members to certain regions of code. To use an access modifier, you include its keyword in the definition of members of package, class or object as we will see in the following section.

Private Members

A private member is visible only inside the class or object that contains the member definition.

Following is the example code snippet to explain Private member −

Example

class Outer {

   class Inner {

      private def f() { println("f") }

     

      class InnerMost {

         f() // OK

      }

   }

   (new Inner).f() // Error: f is not accessible

}

In Scala, the access (new Inner). f() is illegal because f is declared private in Inner and the access is not from within class Inner. By contrast, the first access to f in class Innermost is OK, because that access is contained in the body of class Inner. Java would permit both accesses because it lets an outer class access private members of its inner classes.

Protected Members

A protected member is only accessible from subclasses of the class in which the member is defined.

Following is the example code snippet to explain protected member −

Example

package p {

   class Super {

      protected def f() { println("f") }

   }

  

   class Sub extends Super {

      f()

   }

  

   class Other {

      (new Super).f() // Error: f is not accessible

   }

}

The access to f in class Sub is OK because f is declared protected in ‘Super’ class and ‘Sub’ class is a subclass of Super. By contrast the access to f in ‘Other’ class is not permitted, because class ‘Other’ does not inherit from class ‘Super’. In Java, the latter access would be still permitted because ‘Other’ class is in the same package as ‘Sub’ class.

Public Members

Unlike private and protected members, it is not required to specify Public keyword for Public members. There is no explicit modifier for public members. Such members can be accessed from anywhere.

Following is the example code snippet to explain public member −

Example

class Outer {

   class Inner {

      def f() { println("f") }

     

      class InnerMost {

         f() // OK

      }

   }

   (new Inner).f() // OK because now f() is public

}

Scope of Protection

Access modifiers in Scala can be augmented with qualifiers. A modifier of the form private[X] or protected[X] means that access is private or protected “up to” X, where X designates some enclosing package, class or singleton object.

Consider the following example −

Example

package society {

   package professional {

      class Executive {

         private[professional] var workDetails = null

         private[society] var friends = null

         private[this] var secrets = null




         def help(another : Executive) {

            println(another.workDetails)

            println(another.secrets) //ERROR

         }

      }

   }

}

Note − the following points from the above example −

Variable workDetails will be accessible to any class within the enclosing package professional.
Variable friends will be accessible to any class within the enclosing package society.
Variable secrets will be accessible only on the implicit object within instance methods (this).

So, this brings us to the end of blog. This Tecklearn ‘Detailed understanding of Scala Access Modifiers’ 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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