#Tuples in Scala

[infernovm]

Scala stabilizes named tuples

Scala 3.7.0, the latest version of the object-oriented, functional language with JVM, native, and JavaScript runtimes, stabilizes named tuples and brings a “crucial” fix that enhances its compatibility with Android. Scala 3.7.0 was announced May 7. Installation instructions can be found at scala-lang.org. In Scala 3.7.0, named tuples, introduced as experimental in Scala 3.5 in August 2024, become…

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Python batch at TCCI

Python is a multi-paradigm programming language: object-oriented programming and structured programming. The syntax (words and structure) is extremely simple to read and follow, most of which can be understood even if you do not know any programming. Python is capable of threading and GPU processing just like any other language.

A few of the basic Python programming fundamentals that data scientists must master include: Data types. Python offers many built-in data types, including floats, integers, and strings. Devs must know the difference between each and when to use them.

Despite the vast range of programming languages, most data analysts choose to work with Python. While some data analysts use other programming languages like Javascript, Scala, and MATLAB; Python remains the popular choice due to its flexibility, scalability, and impressive range of libraries.

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[gaur4vgaur-blog]

Tuples in Scala

The video gives an introduction to tuples in Scala. We will talk about syntax, ways to access elements in tuple and various other useful methods available in tuples.

Tuples are a bunch of values enclosed within parentheses. You can think of tuples as lightweight containers to pass data around. 

Elements in tuple can be of different data types. Hence, it is an excellent alternative to small and simple classes that we used to declare in Java to pass multiple values.

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Scala programming help- Functional programming language help

New Post has been published on https://qualityassignmenthelp.com/scala-programming-help-functional-programming-language-help/

Scala programming help- Functional programming language help

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Scala is a universally acknowledged functional programming language. Like SML and Clojure, Scala is also a functional programming language. Knowing the importance of the Scala language, students all over the globe are getting Scala courses.

Professors assign Scala assignments to hone students’ skills in this language. Unfortunately, some students cannot complete their jobs due to lack of time.

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SO what is Scala

Scala is a remarkably concise, logical, and compelling programming language that results from the combination of both object-oriented and functional languages.

Features like Tuples, Functions, and Macros make Scala a programmers’ friendly language. Running of Scala programs on a java virtual machine makes it even more compatible, versatile, and favorable.

Scala (pronounced as “ska-lah”) is a universally useful programming language planned by Martin Odersky. The plan of Scala began in 2001 at EPFL, Lausanne, Switzerland.

Scala was delivered freely in 2004 on the Java stage. It is intended to be compact and addresses the reactions of Java. Scala source code is arranged to Java byte code, and the following executable code runs on a Java Virtual Machine. The most recent arrival of Scala is 2.12.8.

Prominent features and facts about Scala

Credible Scala programming helpers find themselves responsible for teaching and clarifying the basics of Scala.

Auto-Inference: Scala naturally induces type data. The client provides type data just on the off chance that it is essential.

  Mutable and Immutable Variables: Using Scala programming language is easy and getting Scala programming help from us is fun. Scala permits us to make any factor impermanent or unchanging at the hour of the announcement.

  No Semicolon: Semicolon goes about as a separator in the vast majority of the advanced programming languages(C, C++, Java, and so forth) and is a compulsory character to be composed after each explanation. Notwithstanding, Scala needn’t bother with a semicolon after each proclamation. Newline characters can isolate scala explanations.

  Import articulations: It isn’t essential to compose all import explanations at the program’s start. Bringing in classes in Scala should be possible anytime.

  Features of Scala: Apart from all OOP highlights of Java, Scala has highlights of utilitarian programming dialects like Scheme, Standard ML, and Haskell, including currying, type deduction, permanence, languid assessment, and example coordinating.

  Functions and Procedures: In Scala, capacities and techniques are two distinct substances and are not utilized reciprocally. Ability can restore any sort and contains = sign in its model.

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Top Key benefits Hadoop Services in Toronto Canada

Bilytica # 1 Hadoop Services in Toronto Canada is an open-source programming system created by the Apache Software Foundation. It utilizes programming models to deal with enormous informational indexes. Hadoop is written in Java, and it's based on Hadoop bunches. These bunches are assortments of PCs, or hubs, that cooperate to execute calculations on information. Apache has other programming projects that coordinate with Hadoop, including ones to perform information stockpiling, oversee Hadoop occupations, dissect information, and significantly more.

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Bilytica # 1 Hadoop Services in Toronto Canada

Hadoop biological system

The Hadoop Services in Toronto Canada biological system is a set-up of administrations we can use to work with large information drives. The four fundamental components of the environment include:

MapReduce

Hadoop Distributed File System (HDFS)

One more Resource Negotiator (YARN)

Hadoop Common Let's investigate every one of these administrations.

MapReduce

Hadoop MapReduce is a programming model utilized for conveyed figuring. With this model, we can handle a lot of information in equal on huge groups of ware equipment. With MapReduce, we can utilize Map and Reduce. With Map, we can change over a bunch of information into tuples (key/esteem sets). Decrease accepts the result of Map as information and consolidates the tuples into more modest arrangements of tuples. MapReduce makes it simple to scale information handling to run huge number of machines in a bunch.

During MapReduce occupations, Hadoop sends the errands to their separate servers in the group. At the point when the errands are finished, the groups gather and decrease information into an outcome and send the outcome back to the Hadoop server.

Hadoop Distributed File System (HDFS)

As the name proposes, HDFS is a conveyed record framework. It handles enormous arrangements of information and runs on ware equipment. HDFS assists us with scaling single Hadoop Services in Toronto Canada bunches to different hubs, and it assists us with performing equal handling. The underlying servers, NameNode and DataNode, assist us with actually taking a look at the situation with our bunches. HDFS is intended to be profoundly issue lenient, convenient, and financially savvy.

One more Resource Negotiator (YARN)

Hadoop YARN is a bunch asset the board and occupation planning apparatus. YARN additionally works with the information we store in HDFS, permitting us to perform assignments, for example,

Diagram handling

Intuitive handling

Stream handling

Clump handling

It progressively dispenses assets and timetables application handling. YARN upholds MapReduce, alongside numerous other handling models. It proficiently uses assets and is in reverse viable, implying that it can run on past Hadoop variants with no issues.

Hadoop Common

Hadoop Services in Toronto Canada Common, otherwise called Hadoop Core, gives Java libraries that we can use across all of our Hadoop modules.

Different parts include:

Cassandra is a wide-segment store NoSQL information base administration framework.

Flume totals, gathers, and moves a lot of log information.

Pig is a significant level programming language used to examine huge informational indexes.

HBase is a non-social data set administration framework that sudden spikes in demand for top of HDFS.

Apache Hive is an issue lenient and SQL-like information stockroom programming that handles perusing, composing, and overseeing information.

Lucene is an open-source web crawler programming library written in Java. It gives powerful hunt and ordering highlights.

Mahout: Apache Mahout is an open-source project used to make adaptable AI calculations.

Oozie is a responsibility booking framework used to deal with Hadoop occupations.

MLlib is a versatile AI library with Java, Scala, R, and Python APIs.

Solr is an endeavor search stage based on Lucene.

Sqoop is a CLI application used to move information between social data sets and Hadoop.

Submarine is a cloud-local AI and profound learning stage. It upholds information handling, calculation improvement, ML systems, and containerization endeavors.

Zookeeper is a concentrated server for solid dispersed cloud application coordination.

Advantages of Hadoop

Savvy: 

Traditionally, it costs truckload of cash to store a lot of information. Hadoop tackles this issue, and it likewise stores generally crude information so it tends to be gotten to at whatever point required.

High accessibility: 

The HDFS high accessibility highlight permits us to run at least two repetitive NameNodes in a similar bunch, which takes into consideration a quick failover on the off chance that a machine crashes or on the off chance that there's a disappointment.

Adaptability: 

Storage and handling power can be handily expanded by adding more hubs.

Methodical: 

The HDFS nicely processes all parts and projects.

Adaptability: 

Hadoop Services in Toronto Canada can deal with organized information and unstructured information.

Dynamic people group: 

Hadoop has an enormous client base, so it's not difficult to track down accommodating documentation or help connecting with any issue you experience.

MapReduce: 

MapReduce is strong and can be utilized through Java or Apache Pig. Rich environment: Hadoop has so many sidekick instruments and administrations that effectively coordinate into the stage. These administrations permit us to perform a wide range of assignments connected with our information.

Equal handling: 

Hadoop Services in Toronto Canada proficiently executes equal handling and can even deal with petabytes of information.

Information arranging: 

Changing between various kinds of information arrangements can now and then reason information misfortune, yet designs don't should be changed in Hadoop.

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[seanmonstar]

warp

Over the past several months, I've been working a web framework in Rust. I wanted to make use of the new hyper 0.12 changes, so the framework is just as fast, is asynchronous, and benefits from all the improvements found powering Linkerd. More importantly, I wanted there to be a reason for making a new framework; it couldn't just be yet another framework with the only difference being I've written it. Instead, the way this framework is used is quite different than many that exist. In doing so, it expresses a strong opinion, which might not match your previous experiences, but I believe it manages to do something really special.

I'm super excited to reveal warp, a joint project with @carllerche.

Background

What makes warp different?

I've been working on web servers for years. Before coming to Rust, I did several things in PHP, moved over to Python, and then shifted again to Nodejs. I've tried many frameworks. I found that I often times need to configure predicates, like certain headers required, query parameters needed, etc, and sometimes, I need to configure that a set of routes should be "mounted" at a different path, and possibly want certain predicates there too. I noticed the concept of mounting or sub-routes or sub-resources or whatever the framework calls them didn't feel... natural, at least to me. It frequently felt like a secondary concept, occasionally not having all the power that a standard route does.

I've also been working in Rust for several years now, and what kept using the language was its powerful type system1. The more I wrote Rust, and learned how amazing the "fearless refactoring" is, the more I hated working in dynamic languages (in my case, it was a large Nodejs server), as trying to refactor pieces inevitably would remind us (in production) that our supposedly comprehensive test suite still had holes in it. I wanted app-specific types to save me from shipping bugs.

A few months ago, I found the Finch library in Scala, and shortly after, Akka, both of which instead just treat everything as a sort of function converting from input to output, and from there, you just chain together these different pieces, and they compose and reuse really well. Scala also has a powerful type system, and those frameworks embrace converting information from HTTP messages into app-specific types. I fell in love.

The thing that makes warp special is its Filter system.

Filters

A Filter in warp is essentially a function that can operate on some input, either something from a request, or something from a previous Filter, and returns some output, which could be some app-specific type you wish to pass around, or can be some reply to send back as an HTTP response. That might sound simple, but the exciting part is the combinators that exist on the Filter trait. These allow composing smaller Filters into larger ones, allowing you modularize, and reuse any part of your web server.

Let me show you what I mean. Suppose you need to piece together data from several different places of a request before your have your domain object. Maybe an ID is a path segment, some verification is in a header, and other data is in the body.

let id = warp::path::param(); let verify = warp::header("my-app-header"); let body = warp::body::json();

Each of these is a single Filter. We can combine them together with and, and then map the combined result to get a really natural feeling handler:

let route = id .and(verify) .and(body) .map(|id: u64, ver: MyVerification, body: MyAppThingy| { // ... });

The above route is a new Filter. It has combined the results of the others, and provided their results naturally to the supplied function for map. Additionally, the types are enforced, cause well yea, this is Rust! If you were to change around one of the filters such that it returned a different type, the compiler would let you know that you need to adjust for that change.

This combining of results is smart: it is able to automatically toss results that are nothing (well, unit, so ()), instead of passing worthless unit arguments to your handlers. So if you needed to combine a new Filter into this route that only checks some request values to determine if the request is valid, and otherwise returns nothing, your handler doesn't need to change.

Besides dropping units, did you notice how even though multiple results were combined together, the map closure received each as individual arguments? This greatly improves development, since that means that id.and(verify).and(body) is actually exactly the same as id.and(verify.and(body)), but using just tuples would have changed around the signature of the results. The routing documentation shows more ways this is useful.

This concept powers everything in warp. Once you know you can match a single path segment via warp::path("foo"), then the idea of mounting doesn't need to be something special. You just have your filter chain for a set of endpoints, and simply "and" it with a new path filter. If your "mount" location needs to also gate on headers, or something else, you can just and those Filters as well.

Built-in functionality

As awesome as the Filter system is, if warp didn't provide common web server features, it'd still be annoying to work with. Thus, warp provides a bunch of built-in Filters, allowing you compose the functionality you need to descibe each route or resource or sub-whatever.

Path routing and parameter extraction

Header requirements and extraction

Query string deserialization

JSON and Form bodies

Static Files and Directories

Websockets

Access logging

And others, and more being added.

The docs explains how to use each, and the examples go more in-depth on how to combine them to make actual web servers.

tower-web

A few months ago, there was mention of a web framework, tower-web, that'd be coming soon. The concept behind it is to provide a web framework built around tower's Service trait. That is still coming. warp is being released right now for a couple reasons:

The Filter system is really awesome, as touched on above.

To explore some ideas before solidifying tower and tower-web. We'd like for warp to be able to make use of all the great tower middleware that already exists.

Expect to hear more about it, and how it fits with warp, soon!

warp

This is warp v0.1. It's awesome. It's fast. It's safe. It's correct. There's documentation, and examples, and an issue tracker to file bugs and track progress of new Filters that are coming (CORS almost ready). I want to thank those of you who tried warp out privately and sent feedback in, it was super valuable!

I realize other languages also have nice type systems, but I didn't usually want to pay the cost associated with those languages. Rust just gives me what I want. ↩︎

[felord]

Adv LAB 4- WORKING WITH SCALA COLLECTIONS Solved

Adv LAB 4- WORKING WITH SCALA COLLECTIONS Solved

In this lab you will gain further practice in functional programming in Scala. You will work with functions, maps, lists and tuples. You can use whichever of the tools introduced in Lab 1 you prefer to attempt the following exercises. You should use your lecture notes (part 4&5) and references listed under ‘Further reading’ to help you. Task 1. Mapping and folding The map method of List applies a…

[essidata-blog]

Tuples and operations with Python Tutorial

In This video you will learn everything You Need to know about: Tuples and operations with Python Tutorial and how to use this feature in Python Full Boot Camp presented by www.essidata.com/Academy Don’t Forget to Subscribe and join our Social Network to have access to the most exclusive courses and certificates that will help you building your career on the most wanted skills like web and apps development , big data analysis and machine learning and many framework like python, c#, java, JavaScript, Angular, React, Kotlin, DevOps, aws, azure, Scala … For any comments, information or business Opportunity, please do not hesitate to contact us: Phone / WhatsApp: +351927159955 Email: essiddata@gmail.com https://youtu.be/pwnet2Ph0cs Read the full article

[snortingcode]

Higher Order Functions

[This is part of a series of posts on Scala and Functional Programming: Part 1: Scala Tricks - tupled/untupled

Part 2: Higher Order Functions

Part 3: Cooking yummy Functional code - (partial, curry, compose)!

For a basic introduction to Scala, syntax, etc. you can check out Scala Fast Lane ]]

Unless you’ve been living under a rock, you’ve heard about Higher Order Functions already. You might have even used them in your favorite programming languages. Hopefully, I’ll give some insights here that would cement your understanding of HOFs and motivate you to try a Functional Programming language that does a lot more with HOFs than your favorite programming language can do.

Quite simply, a Higher Order Function is one that takes another function as a parameter or returns another function as a result. But, let’s take a couple of steps back and look at what we mean by a “function”. The best way to understand this is to forget all you know about functions from a programming perspective and think of that old friend, Mathematics.

So, in mathematics, functions define a mapping between a domain and a co-domain. For instance, let’s consider a function that takes two number and returns the product of the two numbers:

scala> def mult(x: Int, y: Int): Int = x * y mult: (x: Int, y: Int)Int

This should be fairly simple to understand but, let me add a quick explanation just to add rigor to the post: the function “mult” takes 2 Integer arguments in a single argument list (yes, we can have multiple argument lists to a function which, as it turns out, is very useful and we’ll explore it in a future post) - x and y, and returns another Integer that is the product of the two - x*y. (Yes, the return type should not be Int but, we’ll use this ‘problem’ to illustrate an important point in a future post - Scala types and pattern matching).

So, this stuff is easy. Running this code in a scala Repl would give you a function object that has a type: (Int, Int) => Int Meaning, it takes two Integers and returns another Integer. Simple stuff so far.

Functions are just that, a relationship that converts one type to another using some defined transformations. Since, these functions are only defining the relationships that exists between two different ‘types’, we have a sense of “purity” when we talk about such functions - they don’t change over time and/or with parameters passed to them. The pure functions are like a Knight with a clear conscience - they always produce the same result for the same set of parameters!

There are 2 intuitions that might help in understanding “Pure Functions”:

Functions are relationships (also called “Morphisms” or simply “arrows” in Category Theory) between two types.

Functions try to define “what things are” not “how to perform a task”.

With that, let’s talk about functions in an imperative language like Java. In a language like Java/Python, etc. the programmer needs to define what to do with each parameter and how to produce the result. Functional Programming takes a different (better!) approach, it defines what things are and how to “make one given the other” and how to compose bigger ideas from smaller ideas.

Since a function, thus defined, is just an abstraction over a Type, we can tinker with it the way we like without disturbing its identity, by composing/decomposing functions together that don’t violate the restrictions that the Types impose on them (It is these restrictions that the functions must honor that give the teeth to these types!)

So, a good way to look at functions is by visualizing the whole code-base as a network where, contrary to a similar analogy in other languages, the functions are the edges of the graph and, the data (Types) flow via these edges from one Type to the other (or same like in our example above) bringing about the necessary transformations. This analogy, I think, is a great way to look at functional code and helps to design bigger, complicated systems.

Let’s look at how a higher order function would fit into this understanding. We know that functions can exist on their own (functions are first-class citizens in a functional programming language) or be part of an Object, in which case they are called methods (There’s a neat trick that converts a method to a function that we’ll talk about in a bit - lifting).

The compose-ability of pure functions (and the way they preserve the type system by definition) is what gives rise to the need (and use) of HOFs. Let’s expand on our tiny example a bit:

Let’s assume that the function mult (Int, Int) => Int is not just multiplying 2 Integers, but these Integers mean something. Let’s just say that this function mult defines the relationship between variables of a term in a linear equation:

def hypothesis(A, X) = ∑ (a*x) , for a in A and x in X

Here, A is a Vector of coefficients and X is a Vector representing some variables. More precisely, let’s define a few Types here to help the intuition we are trying to build:

scala> type A = Int defined type alias A scala> type B = Int defined type alias B scala> def hypothesis(a: Seq[A], x: Seq[X]) = ??? hypothesis: (a: A, b: B)Int

We’ve just defined some type alias. We can now use A, B for Int. Simple.

Our hypothesis(Seq[A], Seq[X]) is now a valid Scala function. We’ve not defined the implementation of this function yet, but, this is valid since function definitions are evaluated when they are called (every time they are called!)

Let’s try to implement this function and see how things go. We’ll start in a (mostly) imperative way and try to improve as we go along:

scala> def hypothesis(a: Seq[A], x: Seq[X]): Int = | (for ((a, x) <- (a zip x)) yield (a * x)).sum

This might look a bit disturbing if you come from an imperative world (which you most likely do) but, try spending a few minutes reading the code and see if it makes any sense to you before reading further.

The function hypothesis takes two Sequences as parameters (Think of Seq for now as a collection with an Iterator and Order between elements), zips them together forming a collection that has pairs (tuples), each of which has the first element from ‘a’ and the second element from ‘b’.

scala> val a = Seq(1, 2, 3, 4) a: Seq[Int] = List(1, 2, 3, 4) scala> val x = Seq(1, 2, 3, 4) x: Seq[Int] = List(1, 2, 3, 4) scala> a zip x res12: Seq[(Int, Int)] = List((1,1), (2,2), (3,3), (4,4))  

Alright, neat!

The construct:

for (e <- elems) yield (doSomething(e))

takes elements from ‘elems’ one at a time as ‘e’ and do something with that (any functional transformation, etc.) This would work well and is fairly easy to read and expressive enough to be understood in a shared code-base. The function ‘sum’ chained at the end would give us the summation that we required.

Let’s say the relationship between A and X changes and is not defined by a simple product but, some other function complexProd. This change would require us to re-write the definition of the hypothesis function and would lead to code duplication and in general would be a nasty thing to do! This is where HOFs would come to our rescue. Let’s see how:

scala> def betterHypo(a: Seq[A], x: Seq[X], f(a: A, x: X): Int): Int =     | (for ((a, x) <- (a zip x)) yield f(a, x)).sum betterHypo: (a: Seq[A], b: Seq[B], f: (Int, Int) => Int)Int

We’ve passed a function, ‘f(a: A, x: X): Int’ to this betterHypo function so that the way we define the relationship between A and X is abstracted out of the function. We can stretch this idea and think of functions that return functions that can later be applied to some parameters in another higher order function.

Just think of functions as an immutable system of arrows from one Type to the other which doesn’t care to say much about the individual elements of the Types alone but, about the Type itself. This is a good intuition to build and a nice parallel emerging from Category Theory.

In the next installment of these set of posts on Scala/FP, I would talk more about Partial Functions, partial application of functions and Currying. Go functional :)

[siva3155]

300+ TOP SCALA Interview Questions and Answers

SCALA Interview Questions for freshers experienced :-

1. What is Scala? Scala is a Java-based Hybrid programming language which is the fusion of both Functional and Object-Oriented Programming Language features. It can integrate itself with Java Virtual Machine and compile the code written. 2. How Scala is both Functional and Object-oriented Programming Language? Scala treats every single value as an Object which even includes Functions. Hence, Scala is the fusion of both Object-oriented and Functional programming features. 3.Write a few Frameworks of Scala Some of the Frameworks supported by Scala are as follows: Akka Framework Spark Framework Play Framework Scalding Framework Neo4j Framework Lift Framework Bowler Framework 4. Explain the types of Variables in Scala? And What is the difference between them? The Variables in Scala are mainly of two types: Mutable Variables We Declare Mutable Variables by using the var keyword. The values in the Mutable Variables support Changes Immutable Variables We declare Immutable Variables using the val keyword. The values in Immutable Variables do not support changes. 5. Explain Streams in Scala. In simple words, we define Stream as a Lazy list which evaluates the elements only when it needs to. This sort of lazy computation enhances the Performance of the program. 6. Mention the Advantages of Scala Some of the major Advantages of Scala are as follows: It is highly Scalable It is highly Testable It is highly Maintainable and Productive It facilitates Concurrent programming It is both Object-Oriented and Functional It has no Boilerplate code Singleton objects are a cleaner solution than Static Scala Arrays use regular Generics Scala has Native Tuples and Concise code 7. Explain the Operators in Scala The following are the Operators in Scala: Arithmetic Operators Relational Operators Logical Operators Bitwise Operators Assignment Operators 8. What is Recursion tail in Scala? ‘Recursion’ is a function that calls itself. For example, a function ‘A’ calls function ‘B’, which calls the function ‘C’. It is a technique used frequently in Functional programming. In order for a Tail recursive, the call back to the function must be the last function to be performed. 9. Explain the use of Tuples in Scala? Scala tuples combine a Finite number of items together so that the programmer can Pass a tuple around as a Whole. Unlike an Array or List, a tuple is Immutable and can hold objects with different Datatypes. 10. How is a Class different from an Object? Class combines the data and its methods whereas an Object is one particular Instance in a class.

SCALA Interview Questions 11. Why do we need App in Scala? App is a helper class that holds the main method and its Members together. The App trait can be used to quickly turn Objects into Executable programs. We can have our classes extend App to render the executable code. object Edureka extends App{ println("Hello World") } 12. What are Higher-order functions? A Higher-order function is a function that does at least one of the following: takes one or more Functions as Arguments, returns a Function as its result. 13. Explain the scope provided for variables in Scala. There are three different scopes depending upon their use. Namely: Fields: Fields are variables declared inside an object and they can be accessed anywhere inside the program depending upon the access modifiers. Fields can be declared using var as well as val. Method Parameters: Method parameters are strictly Immutable. Method parameters are mainly used to Pass values to the methods. These are accessed inside a method, but it is possible to access them from outside the method provided by a Reference. Local Variables: Local variables are declared inside a method and they are accessible only inside the method. They can be accessed if you return them from the method. 14. What is a Closure? Closure is considered as a Function whose return value is Dependent upon the value of one or more variables declared outside the closure function. Course Curriculum Apache Spark and Scala Certification Training Instructor-led SessionsReal-life Case StudiesAssessmentsLifetime Access Example: 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 15. Explain Traits in Scala. A Trait can be defined as a unit which Encapsulates the method and its variables or fields. The following example will help us understand in a better way. trait Printable{ def print() } class A4 extends Printable{ def print(){ println("Hello") } } object MainObject{ def main(args:Array){ var a = new A4() a.print() } } 16. Mention how Scala is different from Java A few scenarios where Scala differs from Java are as follows: All values are treated as Objects. Scala supports Closures Scala Supports Concurrency. It has Type-Inference. Scala can support Nested functions. It has DSL support Traits 17. Explain extend Keyword You can extend a base Scala class and you can design an Inherited class in the same way you do it in Java by using extends keyword, but there are two restrictions: method Overriding requires the override keyword, and only the Primary constructor can pass parameters to the base Constructor. Let us understand by the following example println("How to extend abstract class Parent and define a sub-class of Parent called Child") class Child=(name:String)extends Parent(name){ override def printName:Unit= println(name) } object Child { def apply(name:String):Parent={ new Child(name) } } 18. Explain implicit classes with syntax Implicit classes allow Implicit conversations with the class’s Primary constructor when the class is in scope. Implicit class is a class marked with the “implicit” keyword. This feature was introduced in with Scala 2.10 version. //Syntax: object { implicit class Data type) { def Unit = xyz } } 19. Explain the access Modifiers available in Scala There are mainly three access Modifiers available in Scala. Namely, Private: The Accessibility of a private member is restricted to the Class or the Object in which it declared. The following program will explain this in detail. class Outer { class Inner { private def f() { println("f") } class InnerMost { f() // OK } } (new Inner).f() // Error: f is not accessible } Protected: A protected member is only Accessible from Subclasses of the class in which the member is defined. The following program will explain this in detail. package p class Super { protected def f() { println("f") } } class Sub extends Super { f() } class Other { (new Super).f() // Error: f is not accessible } } Public: 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 class Outer { class Inner { def f() { println("f") } class InnerMost { f() // OK } } (new Inner).f() // OK because now f() is public } 20. What is a Monad in Scala? A Monad is an object that wraps another object. You pass the Monad mini-programs, i.e functions, to perform the data manipulation of the underlying object, instead of manipulating the object directly. Monad chooses how to apply the program to the underlying object. 21. Explain the Scala Anonymous Function. In the Source code, Anonymous functions are called ‘Function literals’ and at run time, function literals are instantiated into objects called Function values. Scala provides a relatively easy Syntax for defining Anonymous functions. //Syntax (z:Int, y:Int)=> z*y Or (_:Int)*(_Int) 22. How do I Append data in a list? In Scala to Append into a List, We have the following methods: use “:+” single value var myList = List.empty myList :+= "a" 23. Why Scala prefers Immutability? Scala prefers Immutability in design and in many cases uses it as default. Immutability can help when dealing with Equality issues or Concurrent programs. 24. Give some examples of Packages in Scala The three important and default Packages in Scala are as follows: Java.lang._ : Java.lang._ package in Java. Provides classes that are fundamental to the design of the Java programming language. Java.io._ : Java.io._ Package used to import every class in Scala for input-output resources. PreDef: Predef provides type aliases for types which are commonly used, such as the immutable collection types Map, Set, and the List constructors 25. Why is an Option used in Scala? Option in Scala is used to Wrap the Missing value. 26. Mention the Identifiers in Scala. There are four types of Scala Identifiers: Alphanumeric identifiers Operator identifiers Mixed identifiers Literal identifiers //Scala program to demonstrate Identifiers in Scala. object Main { //Main method def main(args: Array) { //Valid Identifiers var 'name = "Hari"' var age = 20; var Branch = "Computer Science" println() println() println() } } 27. How do you define a function in Scala? def keyword is used to define the Function in Scala. object add { def addInt( a:Int, b:Int ) : Int = { var sum:Int = 0 sum = a + b return sum } } 28. How is the Scala code compiled? Code is written in Scala IDE or a Scala REPL, Later, the code is converted into a Byte code and transferred to the JVM or Java Virtual Machine for compilation. Big Data Training 29. Explain the functionality of Yield. Yield is used with a loop, Yield produces a value for each iteration. Another way to do is to use map/flatMap and filter with nomads. for (i "#FF0000", "azure" -> "#F0FFFF") 39. Explain Exception Handling in Scala Throw Exception: Throwing an exception looks the same as in Java. You create an exception object and then you throw it with the throw keyword as follows. Throw new IllegalArgumentException Catching an Exception: Scala allows you to try/catch any exception in a single block and then perform pattern matching against it using case blocks. Try the following example program to handle the exception. Example: import java.io.FileReader import java.io.FileNotFoundException import java.io.IOException object Demo { def main(args: Array) { try { val f = new FileReader("input.txt") } catch { case ex: FileNotFoundException ={ println("Missing file exception") } case ex: IOException = { println("IO Exception") } } } } So, with this, we finished some questions on the Intermediate Level. Now, Let us move to the next level of interview questions which happen to be the Advanced Level Interview Questions. 40. Explain Pattern Matching in Scala through an example A Pattern match includes a sequence of alternatives, each starting with the Keyword case. Each alternative includes a Pattern and one or more Expressions, Scala evaluates whenever a pattern matches. An arrow symbol => separates the pattern from the expressions. Try the following example program, which shows how to match against an integer value. object Demo { def main(args: Array) { println(matchTest(3)) } def matchTest(x: Int): String = x match { case 1 = "one" case 2 = "two" case _ = "other" } } 41. Explain Extractors in Scala Course Curriculum Apache Spark and Scala Certification Training Weekday / Weekend Batches An Extractor in Scala is an object that has a method called unapply as one of its members. The purpose of that unapply method is to match the value and take it apart. 42. What is the result of x+y*z and why? Similar to any other programming language, Scala also follows Presidency and Priority tables. According to the tables, Scala Performs the operations as follows. Scala evaluates y*z first. Then adds (y*z) with x 43. What is an Auxiliary constructor We use Auxiliary constructor in Scala for Constructor Overloading. The Auxiliary Constructor must call either previously defined auxiliary constructors or primary constructor in the first line of its body. 44. Explain recursion through a program def factorial_loop(i: BigInt): BigInt = { var result = BigInt(1) for (j- 2 to i.intValue) result *= j result } for (i - 1 to 10) format("%s: %sn", i, factorial_loop(i)) 45. Explain Que with example Queue is a Data Structure similar to Stack except, it follows First In First Out procedure for data processing. In Scala, to work with Queues, you need to import a library called, import scala.collection.mutable.Queue val empty = new Queue SCALA Questions and Answers Pdf Download Read the full article

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Career Opportunities in Python Programming

Prwatech provide Best Python Training Bangalore BTM. Python tops the charts for the foremost promising career choices for techies. Career Opportunities in Python are growing in range across the globe. ordinarily taken as a high-level programming language, Python ensures quicker code readability and brevity, which suggests higher styles with fewer code lines. Also, Python may be a great tool for making dynamic scripts on each little and huge scales.

 Python is a all-purpose language. it's extensively employed in internet development, script writing, testing, likewise as app development and upgrades. additionally, Python offers internet developers a dynamic kind system and automatic memory management that supports multiple programming paradigms. Anybody who becomes an professional in Python has multiple career choices before him, as a developer or as a knowledge somebody.

 In this house, we'll discuss hot career opportunities in Python. we'd additionally throw some fascinating insights on the scope of a Python developer and a programmer in information Analytics. The discussion additionally includes the scope of fresher's and toughened professionals and remote jobs in Python.

 Python as a Career Choice

Companies these days, each in India and US, are on the constant lookout for the qualified and experienced force to fulfill the ever-changing technology wants of consumers. Python, SQL, Java, JavaScript, .NET, PHP, C, C#, C++, ReactJS, AngularJS, Ruby, NodeJS, Go, Android, iOS, and Perl are different types information and on the far side. Knowing over 2 high-level programming languages, together with Python is usually sensible. It provides them a competitive advantage over others.

Scope of a Python Developer

Python, although a more recent entrant within the fray, has gained importance than alternative programming languages and holds a great deal of promise for developers. except for being associate open supply programming language, it's additionally one among the foremost versatile programming languages. developers use it extensively for application development and system development programming. Also, reduced writing effort and higher take a look at performance guarantee higher programming. Hence, python developers are much in demand.

 Hot Career Opportunities as Fresher in Python

Career opportunities as a fresher in Python programming are several. If you have got the proper coaching and right perspective to be told quick, Python includes a heap to supply. Of course, you wish to possess a really robust programming base and problem-solving skills.

 For beginners in Python, students will begin by making variables and loops. Then progress to tuples, lists, and dictionaries. Freshers seeking moneymaking career choices in Python ought to have a sound data of immutable sorts. Capacity to make classifications and instantiate objects, utilizing exemptions and special case handlers, and knowing intelligence among modules, are some of the contrary things that a Python amateur must get it. additionally, as a beginner, a Python coder is additionally expected to grasp the essential variations between Python a pair of and Python three.

 As a fresher, you'll apply for jobs like software engineer, front-end software/web developer, Python developer or programmer, and Devops engineer. Students in their last semester or simply pass-outs may additionally apply for internships in information science, which supplies a gradual begin for a career in Python. Career opportunities in Python as a fresher could begin with a remuneration of 3-5 lakhs once a year, for many corporations. Higher salaries could also be offered by international brands like Accenture or Capgemini.

 Prwatech Institute is specifically designed to help students in developing their skills and learn big data and  hadoop modules - Big Data Hadoop Training Bangalore. We provide the big data hadoop training, python training, apache spark, R programming, scala training and Data Scientist Training Bangalore etc.

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TOC - SCALA Express | 05 Scala Collections - Array, List and Tuple

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Scala programming help- Functional programming language help

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Scala is a remarkably concise, logical, and compelling programming language that results from the combination of both object-oriented and functional languages.

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Scala (pronounced as “ska-lah”) is a universally useful programming language planned by Martin Odersky. The plan of Scala began in 2001 at EPFL, Lausanne, Switzerland.

Scala was delivered freely in 2004 on the Java stage. It is intended to be compact and addresses the reactions of Java. Scala source code is arranged to Java byte code, and the following executable code runs on a Java Virtual Machine. The most recent arrival of Scala is 2.12.8.

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Auto-Inference: Scala naturally induces type data. The client provides type data just on the off chance that it is essential.

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  No Semicolon: Semicolon goes about as a separator in the vast majority of the advanced programming languages(C, C++, Java, and so forth) and is a compulsory character to be composed after each explanation. Notwithstanding, Scala needn’t bother with a semicolon after each proclamation. Newline characters can isolate scala explanations.

  Import articulations: It isn’t essential to compose all import explanations at the program’s start. Bringing in classes in Scala should be possible anytime.

  Features of Scala: Apart from all OOP highlights of Java, Scala has highlights of utilitarian programming dialects like Scheme, Standard ML, and Haskell, including currying, type deduction, permanence, languid assessment, and example coordinating.

  Functions and Procedures: In Scala, capacities and techniques are two distinct substances and are not utilized reciprocally. Ability can restore any sort and contains = sign in its model.

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Key Takeaways The Java language has changed notably over the last 5 years Two major projects that are delivering that change - Valhalla and Amber - are still in flight Java has continued to maintain its core value of backwards compatibility Despite being 25 years old, there is still plenty of life left in the language and platform New technologies, such as Graal, are helping to keep Java at the forefront of programming languages Almost five years ago, I wrote an article outlining some feature ideas from other languages that I felt could be of benefit to Java. A lot has happened since then — at that time, Java 8 was the newly minted release, whereas the most recent version is now Java 14. Let’s take a look at each feature in turn and see what its current status is: whether it has been added to Java, is on the way, or is not currently planned for inclusion. Reified generics My original predictions ruled out reified generics. I did not foresee the ambition that Project Valhalla has for remaking the JVM from the ground up Project Valhalla’s major goals are: aligning JVM memory layout behavior with the cost model of modern hardware; extending generics to allow abstraction over all types, including primitives, values, and even void; and enabling existing libraries, especially the JDK, to compatibly evolve to fully take advantage of these features. Buried within this description is the loaded word "values", which has evolved into the feature that we know today as inline classes. Reified generics and primitive specialization have thus been subsumed into a much larger project that promises to fundamentally change the way that Java is written and executed. Despite the deep low-level changes that are being made, the goals of the project team include minimizing disruption to existing Java applications and providing an easy, opt-in approach for developers who use Valhalla capabilities in their own code. We should note that Valhalla is still very much a work in progress — and there is still no official roadmap for when it might be delivered. Verdict: ON THE WAY (as part of Project Valhalla) Unsigned arithmetic The possibility of supporting this feature has been repeatedly discussed over Java’s history, but there are a number of complexities involved in introducing it. For example, there is the question of how signedness should be represented in the Java type system, and whether it should be visible at JVM bytecode level or not. These issues have not reached any satisfactory consensus and so Java still does not contain unsigned arithmetic — and one notable aspect of Project Valhalla is that it does not include support for unsigned arithmetic. Verdict: NOT BEING CONSIDERED Long indices for arrays Java’s arrays are constrained in size by a simple fact of their design: they take an int as their index. This means that an array is limited to 2**31 elements (remembering that ints are signed), or roughly 2 billion entries. As originally envisaged, the idea of using a long instead of an int would allow developers to create and manipulate much larger arrays. However, since the original "Missing Features" article, the community’s focus in this area has shifted towards providing easy access to large arrays stored off-heap. There are several reasons for this — ease of interop with non-Java libraries (including machine learning and other number-heavy applications) is one important one. However, there are also questions about how useful large, on-heap arrays would even be. Huge, multi-gig arrays would have significant copy costs when moving in and out of the Java heap and could potentially cause serious headaches for the JVM’s garbage collector. For these reasons, large arrays are mainly thought of in the context of off-heap support, and the concept has been incorporated into features under active development within Project Panama. Verdict: ON THE WAY (as part of Project Panama) More expressive import syntax No serious attempts have been made to expand the scope of import syntax or to introduce type aliasing, even at the local (or file-scoped) level. Verdict: NOT BEING CONSIDERED Collection literals Static methods on interfaces were added in Java 9, and the collections were updated to include factory methods for collections. These play the role of collection literals in Java: var ls = List.of(1,2,3); As the factories are playing the role of literals, this change also introduced new implementations of the collection interfaces. These implementations are immutable, because to reuse the existing mutable collections (such as ArrayList) would have violated the programmer expectation that these values should behave as if they were literals. More intrusive solutions, such as directly introducing new literals into the language syntax, were not pursued. Verdict: DELIVERED (as factory methods) Algebraic data types Algebraic data types in Java are in the process of delivery. The feature consists of two major additions to the type system: records and sealed types as well as pattern matching, a substantial new piece of syntax. Java 14 delivers a preview version of two of these aspects — specifically, records, which in Java are essentially named tuples, and the initial components of pattern matching. The new features that make up the introductory pieces of pattern matching are the first form of pattern in Java: the instanceof pattern and a standardized version of switch expressions. The second of these is the scaffolding that will ultimately enable the introduction of general pattern matching, in a similar way to the match expressions with which Scala programmers may be familiar. There are still many more steps to take before this feature is fully delivered — records and patterns are still only in preview. Further JEPs, including JEP 375, which extends the instanceof pattern to allow deconstruction of records, are required to flesh out pattern matching as a whole. As of the arrival of Java 14, key JEPs — including both JEP 375 and JEP 360, which introduces sealed types — are not targeted for any specific Java version. Despite this lack of a concrete roadmap, it is likely that the entirety of the algebraic data types and pattern-matching mechanism could be delivered in standardized form in time for the next LTS release, which is Java 17 in September 2021. Verdict: ON THE WAY (as part of Project Amber) Structural typing Java’s type system has evolved somewhat since Java 8, but in practice there has been no significant move towards general structural typing. For example, when records were being designed, structural typing was explicitly rejected in favor of making records nominal types. This enforces the idea that the name that we give to a type has power and importance, that Java records are defined by more than just the number and types of their components. One minor place where something that resembles structural typing continues to be vaguely visible in Java is in Java’s non-denotable types. These are really just an extension of the example originally discussed in the 2015 piece. In that example, we construct something that looks like a structural type (Object + ) but can only use it within a single expression because there’s no denotable type that we can use as the type of a variable to assign the value to. Since Java 10, the language has had an extended form of type inference that uses var to reduce boilerplate in assignments. We are able to use this capability to extend the scope in which we may call additional methods defined on a type in this way. However, this is limited to the method in which the type inference occurs. The special type that var infers cannot be propogated across method boundaries precisely because it is non-denotable. In reality, these special cases are not true structural typing, and there is no intention of introducing it. The gravitational pull of Java’s design attraction to names and nominal typing is just too strong. Verdict: CONSIDERED BUT REJECTED Dynamic call sites The last five years have seen a large expansion in the use of invokedynamic, although only within the JDK and a small number of technically sophisticated external libraries. It is still the case that "the Java language does not have a keyword or other construct to create general-purpose invokedynamic call sites" just as the original article stated. The suggestion that the Dynalink library could be expanded to take up this role never came to pass, and, in fact, the Nashorn Javascript implementation that produced Dynalink is now itself considered deprecated and could be omitted from Java 15 (although Dynalink itself will remain). Those libraries that do make use of dynamic call sites do so via the MethodHandles API that, while slightly easier to use in 2020, is still out of reach of most Java programmers. The difficulty of finding a balance between flexible dynamic invocation that does not cause too many runtime problems and compelling language-level usage has proved to be too great, at least for the time being. Verdict: NOT BEING CONSIDERED What Did I Miss? The last 5 years have also seen the emergence of a number of projects and trends that I did not predict or address in the original article. The biggest of these are perhaps: Extent and scope of Project Valhalla Project Amber New Release Model for Java Graal and GraalVM Rise of Kotlin To pick out a couple of examples: Although Project Valhalla was started in 2014, it has gained momentum and expanded enormously over the intervening years. It has become the most ambitious and biggest change that Java has ever seen. Valhalla promises to modify every aspect of the Java platform - from the way that memory and values are represented within the VM, through the type system and generics, and up into the library level and language syntax. This project aims to align Java with the current and future state of hardware and to offer performance and other improvements that simply cannot be addressed piecemeal. Instead, Valhalla aims to move the state of the Java platform from its current position (which we can think of as a local maxima) to a place that is much better suited to be the basis of a platform for the coming decades. Original research is always hard to predict, and so it is perhaps unsurprising that the rise of Graal also caught me by surprise. The basic idea is, like many other compelling concepts, very simple once you’ve grasped it. Java’s usual JIT compiler is written in C++ code and executes on special dedicated threads within the JVM. Graal starts from a simple idea: What if, instead, the JIT compiler was written in Java and the compiler threads were actually running a second copy of the Java interpreter? The interpreted-mode JIT compiler would be as capable as the current JIT - so it could compile any class file to machine code. We should expect that it would be slower than the existing C++ compiler, but it would not differ in its behaviour, only in performance. Taking this idea to its logical conclusion, this means that the interpreted-mode JIT could compile the class files that make up the JIT itself. Once it had warmed up, it could replace itself and run at the same performance as the original, native JIT. This intruiging idea turns out to be the starting point for a major class of new technologies, including native compilation of Java (AOT) as well as a new polyglot virtual machine (GraalVM) capable of running many different languages. Conclusions The Java platform has grown in sophistication over the last five years, with many new language and VM features delivered or in flight. Assuming current trends continue, the community is likely to be most interested in the set of standardized features that will be available in Java 17 (due in September 2021). This will be a very different Java than the one that existed in 2014 when we made our original observations, and while some features have been delivered it seems clear that certain others are unlikely ever to arrive and still others have been realized in a very different form. We look forward to seeing what the next five years will bring for the Java language and platform, and especially the aspects that we are unable to predict right now.

http://damianfallon.blogspot.com/2020/04/javas-missing-features-5-years-later.html

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Diving into Haskell with the Haskell Book

A few months back I was motivated to dive back into Haskell and, after surveying the recent landscape, picked up Haskell Programming from First Principles. It's a book that promises a soup-to-nuts approach to Haskell, staring from the mathematical concepts underscoring the language (Lambda calculus) and moving through to a full-blown production-ready project. I've so far gotten a few chapters in, and, while I haven't learned anything new, the authors' approach to the basics -- data types, constructors, etc -- are fairly conversational and comprehensive, and they do a good job of answering the questions that arise naturally while reading. (I'll ignore the likelihood that these same authors are leading into these questions, and answering them, to make me feel better about myself while reading. Why? Because it makes me feel better about myself, that's why. Well done, authors!)

Reading through this book I reflected on my first runthrough of Learn You a Haskell For Great Good when it came out (iirc it was early 2013). My programming career hasn't swerved headllong into functional programming; I'm coding Ruby now instead of the Python I was slinging back then, but these are still pretty apples-to-apples as far as languages go. I liked LYAHFGG a lot -- it's fun and thorough too -- but while set comprehensions and string operations made sense, I wasn't really making the jump from Python to typeclasses. The book that did help me crack into functional programming concepts was Functional Programming in Scala, not LYAHFGG. Here are some possible reflections why:

Haskell syntax is very spare, rather like poetry to Scala's prose; FPIS, by virtue of it's medium, surrounds novel FP concepts with familiar programming syntax (ie extra parens and curlys). This may not matter to everybody, but it seems to have been a bridge I needed.

Pattern matching, a concept at the core of Haskell's type definitions, doesn't really make sense in languages based on duck typing. Python and Ruby, lacking type constructors, can only can go as far offer some degree of tuple destructuring based on positional assignment. Scala and Haskell are solidly in the world of type constructor pattern matching, which is a vital concept on the road to understanding type classes.

Guards, 'where', and 'let' make sense to anybody with a discrete mathematics backrground, and 'expressions' are familiar from languages like Lisp and Scheme, but none of these are that present prevalent in Python. Ruby does have stronger support for expression syntax (ie assigining an entire 'if' or 'begin/rescue' statement to a variable).

Some other random notes:

The quicksort algorithm is the go-to for demonstrating Haskell's declarative power, and the quicksort section in LYAHFGG does a great job showing that. Algorithms in Haskell translate cleanly from general computer sciance. They tend to be composed definitions, not recipes.

I get that (+3) is a partial function adding 3 to whatever is passed in. I understand that it's really (+) 3. But it'll likely never be as clear to me as (\x -> x + 3), because that explicitness makes more sense. Once again, poetry vs prose.

FPIS nailed home the constructor-pattern matching thing in a way that made sense to my Java-related brain.