This document provides an overview of Scala and compares it to Java in 3 sentences:
Scala is a multi-paradigm programming language that runs on the Java Virtual Machine and interoperates seamlessly with Java, combining object-oriented and functional programming in one concise, high-level language. Scala improves on Java with features like closures, pattern matching, traits, and immutable data while maintaining a familiar syntax; it compiles to Java bytecode and allows Java and Scala code to interoperate. Scala aims to be a better language for writing concurrent, parallel and distributed applications than Java with its support for actors, futures and software transactional memory.
This document summarizes a talk given about Nokia's migration to Scala for its Places API. The key points are:
1) Nokia migrated its Places API codebase to Scala to take advantage of Scala's features like its powerful type system, immutable data structures, and functional programming capabilities.
2) The migration was done gradually over time while continuing to develop new features. They discovered many benefits of Scala along the way like improved test readability and JSON parsing capabilities.
3) Nokia uses Scala features like case classes, options, and functions to model data and add type safety to its codebase. This uncovered bugs that would have been hard to find in Java.
Выступление в рамках спецкурса "Немейнстримовые технологии разработки", читаемого в НГУ. http://bit.ly/mainstreamless
Аудио дорожка работает, но нужно иметь некоторое терпение, так как грузится она не моментально.
This document discusses Scala features for parallelism, concurrency, and reactive programming. Some key points include:
- Scala supports parallel collections that can perform operations like map, reduce, and filter in parallel.
- Futures represent asynchronous computations whose results are not yet known. They allow non-blocking operations.
- Actors are units of concurrency that communicate asynchronously by message passing. They encapsulate state and behavior.
- Akka is a toolkit for building highly concurrent, distributed, and fault-tolerant event-driven applications using actors. It implements the actor model in Scala.
This document provides an overview of the Scala programming language. Some key points:
- Scala is a statically typed language that runs on the JVM and supports both object-oriented and functional programming.
- Scala code examples demonstrate features like traits, implicit conversions, pattern matching, immutable data structures, and functional-style programming with maps, options, and pattern matching.
- Scala is used to build scalable and concurrent applications with libraries like Akka for actors and STM for software transactional memory. Popular web frameworks built on Scala include Play, Lift and Scalatra.
The document introduces Scala and provides an overview of Scala basics including variables, functions, objects, classes, traits, pattern matching, for-comprehensions and more. It also discusses Scala's capabilities for generic programming, lazy evaluation, and integration with Java. Examples are provided throughout to demonstrate Scala concepts.
Slides from my talk at the Junction (Jan 24, 2013)
Single-core performance has hit a ceiling, and building web-scale multi-core applications using imperative programming models is nightmarishly difficult. Parallel programming creates a new set of challenges, best practices and design patterns. Scala is designed to enable building scalable systems, elegantly blending functional and object oriented paradigms into an expressive and concise language, while retaining interoperability with Java. Scala is the fastest growing JVM programming language, being rapidly adopted by leading companies such as Twitter, LinkedIn and FourSquare.
This presentation provides a comprehensive overview of the language, which managed to increase type safety while feeling more dynamic, being more concise and improving readability at the same time. We will see how Scala simplifies real life problems by empowering the developer with powerful functional programming primitives, without giving up on the object oriented paradigm. The overview includes tools for multi-core programming in Scala, the type system, collection framework and domain-specific languages. We’ll explore the power of compile-time meta-programming, which is made possible by the newly released Scala 2.10, and get a glimpse into what to expect from 2.11 in 2014.
We will also see how Scala helps overcome the inherent limitations of Java, such as type erasure, array covariance and boxing overhead.
Multiple examples emphasize how Scala pushes the JVM harder than any other mainstream language through the infinite number of boilerplate busters, increased type safety and productivity boosters from a Java developer’s perspective.
Slides for my recent presentation at the CASE meetup, May 21st. Discusses functional programming features in Scala. Goes from basic FP features like higher-order functions all the way through to monads.
Scala: Object-Oriented Meets Functional, by Iulian Dragos3Pillar Global
A presentation from Iulian Dragos of Typesafe that gives an overview of the Scala programming language. The presentation was given at a Functional Angle conference in Timisoara, Romania sponsored by 3Pillar. Iulian Dragos has been working on Scala since 2004. He currently works for Typesafe, a start-up that was co-founded by Scala’s creator, Martin Odersky.
This document summarizes a talk given about Nokia's migration to Scala for its Places API. The key points are:
1) Nokia migrated its Places API codebase to Scala to take advantage of Scala's features like its powerful type system, immutable data structures, and functional programming capabilities.
2) The migration was done gradually over time while continuing to develop new features. They discovered many benefits of Scala along the way like improved test readability and JSON parsing capabilities.
3) Nokia uses Scala features like case classes, options, and functions to model data and add type safety to its codebase. This uncovered bugs that would have been hard to find in Java.
Выступление в рамках спецкурса "Немейнстримовые технологии разработки", читаемого в НГУ. http://bit.ly/mainstreamless
Аудио дорожка работает, но нужно иметь некоторое терпение, так как грузится она не моментально.
This document discusses Scala features for parallelism, concurrency, and reactive programming. Some key points include:
- Scala supports parallel collections that can perform operations like map, reduce, and filter in parallel.
- Futures represent asynchronous computations whose results are not yet known. They allow non-blocking operations.
- Actors are units of concurrency that communicate asynchronously by message passing. They encapsulate state and behavior.
- Akka is a toolkit for building highly concurrent, distributed, and fault-tolerant event-driven applications using actors. It implements the actor model in Scala.
This document provides an overview of the Scala programming language. Some key points:
- Scala is a statically typed language that runs on the JVM and supports both object-oriented and functional programming.
- Scala code examples demonstrate features like traits, implicit conversions, pattern matching, immutable data structures, and functional-style programming with maps, options, and pattern matching.
- Scala is used to build scalable and concurrent applications with libraries like Akka for actors and STM for software transactional memory. Popular web frameworks built on Scala include Play, Lift and Scalatra.
The document introduces Scala and provides an overview of Scala basics including variables, functions, objects, classes, traits, pattern matching, for-comprehensions and more. It also discusses Scala's capabilities for generic programming, lazy evaluation, and integration with Java. Examples are provided throughout to demonstrate Scala concepts.
Slides from my talk at the Junction (Jan 24, 2013)
Single-core performance has hit a ceiling, and building web-scale multi-core applications using imperative programming models is nightmarishly difficult. Parallel programming creates a new set of challenges, best practices and design patterns. Scala is designed to enable building scalable systems, elegantly blending functional and object oriented paradigms into an expressive and concise language, while retaining interoperability with Java. Scala is the fastest growing JVM programming language, being rapidly adopted by leading companies such as Twitter, LinkedIn and FourSquare.
This presentation provides a comprehensive overview of the language, which managed to increase type safety while feeling more dynamic, being more concise and improving readability at the same time. We will see how Scala simplifies real life problems by empowering the developer with powerful functional programming primitives, without giving up on the object oriented paradigm. The overview includes tools for multi-core programming in Scala, the type system, collection framework and domain-specific languages. We’ll explore the power of compile-time meta-programming, which is made possible by the newly released Scala 2.10, and get a glimpse into what to expect from 2.11 in 2014.
We will also see how Scala helps overcome the inherent limitations of Java, such as type erasure, array covariance and boxing overhead.
Multiple examples emphasize how Scala pushes the JVM harder than any other mainstream language through the infinite number of boilerplate busters, increased type safety and productivity boosters from a Java developer’s perspective.
Slides for my recent presentation at the CASE meetup, May 21st. Discusses functional programming features in Scala. Goes from basic FP features like higher-order functions all the way through to monads.
Scala: Object-Oriented Meets Functional, by Iulian Dragos3Pillar Global
A presentation from Iulian Dragos of Typesafe that gives an overview of the Scala programming language. The presentation was given at a Functional Angle conference in Timisoara, Romania sponsored by 3Pillar. Iulian Dragos has been working on Scala since 2004. He currently works for Typesafe, a start-up that was co-founded by Scala’s creator, Martin Odersky.
This document provides an introduction to Scala for Java developers. It discusses that Scala is a hybrid object-oriented and functional language that runs on the JVM and interoperates well with Java. It highlights several features of Scala that allow for more concise code compared to Java, such as type inference, expressions instead of statements, higher-order functions, and case classes.
This document summarizes some of the key differences between Scala and Java syntax. It covers topics like type definitions, variables, methods, classes, traits, collections, exceptions, control flow, and packages. Overall, the document shows that Scala code is more concise and expressive than equivalent Java code for many common programming constructs.
The document provides an overview of higher-kinds, typeclasses, type-level encodings, and continuations in Scala. It discusses how kind systems classify types, how typeclasses allow abstraction over types, and examples of encoding data structures like linked lists at the type level. It also introduces delimited continuations in Scala and how they can be used to simulate control flow constructs like break statements.
This document provides an introduction to the Scala programming language. It discusses what Scala is, how to get started, basic concepts like mutability and functions, and Scala features like classes, traits, pattern matching, and collections. Scala combines object-oriented and functional programming. It runs on the Java Virtual Machine and is compatible with Java. The document provides code examples to demonstrate Scala concepts and features.
This document provides an overview of Scalaz and functional programming concepts like Functor, Applicative, and Monad as they relate to the Option type in Scalaz. It discusses Option syntax, constructing Options, working with Options using fold, map, and other methods, and defines Option as an instance of Functor, Applicative, and Monoid. It also briefly touches on concepts like context bounds and alternative monoids for Option.
This document summarizes key features of the Scala programming language. Some key points include:
- Scala runs on the Java Virtual Machine (JVM) and allows for type inference, immutable values, functional programming patterns like pattern matching, and object-oriented features like traits for inheritance.
- Scala favors immutable values over mutable variables for scalability. Features like actors allow for concurrency without shared mutable state.
- Scala code can be compiled to JavaScript using Scala.js, allowing full-stack development in a single language.
- Traits provide a powerful way to do multiple inheritance by combining traits and classes at runtime.
This document summarizes a talk on using the Scalaz library. It introduces key Scalaz concepts like typeclasses, monoids, and validation. Typeclasses allow uniform implementation of common patterns across types. Monoids represent structures with an associative binary operation and an identity. Validation provides an applicative way to represent failure without exceptions. The document gives examples of using these concepts to represent positions, filters, and compose validations.
Kotlin Basics - Apalon Kotlin Sprint Part 2Kirill Rozov
This document provides an overview of Kotlin basics including:
- Basic data types like Int, String, Boolean
- Collections like List, Set, Map
- Variables, functions, control flow
- Classes, properties, constructors
- Inheritance, interfaces
- Additional features like lambdas, extensions, coroutines
It aims to introduce fundamental Kotlin concepts and syntax in a concise manner.
The document is a slide presentation on Scala that provides an introduction to the language in 90 minutes or less. It covers Scala basics like being object oriented and functional, static typing, compilation to JVM bytecode, and interoperability with Java. It also discusses Scala tools, its use in open source projects and industry, recommended books, and jobs involving Scala. Code examples are provided to demonstrate Hello World programs, variables, methods, conditionals, sequences, and closures in Scala.
I used these slides for a Scala workshop that I gave. They are based on these: http://www.scala-lang.org/node/4454. Thanks to Alf Kristian Støyle and Fredrik Vraalsen for sharing!
Scala is an object-oriented and functional programming language that runs on the Java Virtual Machine. It was designed by Martin Odersky and developed at EPFL in Switzerland. Scala combines object-oriented and functional programming principles, including support for immutable data structures, pattern matching, and closures. It interoperates seamlessly with existing Java code and libraries.
Slides of a talk I gave at work on Scala. It is geared toward Java developers. Some of the examples are in my company's domain, which is analyzing energy usage (i.e. a "read" is an electric meter read).
Scala is a multi-paradigm language that runs on the JVM and interoperates with Java code and libraries. It combines object-oriented and functional programming by allowing functions to be treated as objects and supports features like traits, pattern matching, and immutable data structures. The Scala compiler infers types and generates boilerplate code like getters/setters, making development more productive compared to Java. While Scala has a learning curve, it allows a more concise and scalable language for building applications.
"The joy of Scala" - Maxim Novak / Wix
Around eight years ago I started my journey as a developer. Since then, I've played around with many languages and thought that C# offers the best developer productivity. After joining Wix two years ago, I was exposed to the amazing world of Scala and Functional Programming and never looked back.
In Scala the code is much more concise, less ceremonious, immutable by default, combines functional with object oriented, seamlessly interoperates with Java, and many software engineering patterns are already baked into the language. Most importantly - Scala is FUN! By the end of the session you too will, hopefully, convert to Scala and never look back.
Recording of the lecture (Hebrew) - https://youtu.be/TcnYTwff2xU
This is brief presentation on the Scala programming language. It is aimed at Java developers who are curious about Scala. It was given at a San Francisco Java User Group in January 2009.
The Scala programming language has been gaining momentum recently as an alternative (and some might say successor) to Java on the JVM. This talk will start with an introduction to basic Scala syntax and concepts, then delve into some of Scala's more interesting and unique features. At the end we'll show a brief example of how Scala is used by the Lift web framework to simplify dynamic web apps.
This document provides a dictionary of scala programming concepts including definitions of common jargon like ADT, typeclasses, extension methods, and call-by semantics. It defines ADT as algebraic data types, which were introduced in languages like Algol 60 and ML, and pattern matching which allows decomposing ADT values. Typeclasses are defined as rules for types like equality, with instances providing implementations. Extension methods and implicit conversions add methods to types via implicit parameters. Call-by-name and call-by-need semantics are discussed in relation to lazy evaluation.
Watch video (in Hebrew): http://parleys.com/play/53f7a9cce4b06208c7b7ca1e
Type classes are a fundamental feature of Scala, which allows you to layer new functionality on top of existing types externally, i.e. without modifying or recompiling existing code. When combined with implicits, this is a truly remarkable tool that enables many of the advanced features offered by the Scala library ecosystem. In this talk we'll go back to basics: how type classes are defined and encoded, and cover several prominent use cases.
A talk given at the Underscore meetup on 19 August, 2014.
(How) can we benefit from adopting scala?Tomasz Wrobel
Scala offers benefits from adopting it such as increased productivity through concise and expressive code, static typing with type inference, support for both object-oriented and functional programming paradigms, and interoperability with Java. Switching from Java to Scala involves some changes like using val for immutable variables and var for mutable, but overall the syntax is quite similar which eases the transition.
Scala is a programming language that runs on the Java Virtual Machine (JVM) and is designed to express common programming patterns in a concise, elegant, and type-safe way. Some key points about Scala include that it is functional and object-oriented, statically typed, and allows seamless integration with Java. Scala code is often more concise than equivalent Java code through features like pattern matching and immutable data structures. Functional programming principles like pure functions, recursion, and avoiding side effects are emphasized in Scala.
This document provides an introduction to Scala for Java developers. It discusses that Scala is a hybrid object-oriented and functional language that runs on the JVM and interoperates well with Java. It highlights several features of Scala that allow for more concise code compared to Java, such as type inference, expressions instead of statements, higher-order functions, and case classes.
This document summarizes some of the key differences between Scala and Java syntax. It covers topics like type definitions, variables, methods, classes, traits, collections, exceptions, control flow, and packages. Overall, the document shows that Scala code is more concise and expressive than equivalent Java code for many common programming constructs.
The document provides an overview of higher-kinds, typeclasses, type-level encodings, and continuations in Scala. It discusses how kind systems classify types, how typeclasses allow abstraction over types, and examples of encoding data structures like linked lists at the type level. It also introduces delimited continuations in Scala and how they can be used to simulate control flow constructs like break statements.
This document provides an introduction to the Scala programming language. It discusses what Scala is, how to get started, basic concepts like mutability and functions, and Scala features like classes, traits, pattern matching, and collections. Scala combines object-oriented and functional programming. It runs on the Java Virtual Machine and is compatible with Java. The document provides code examples to demonstrate Scala concepts and features.
This document provides an overview of Scalaz and functional programming concepts like Functor, Applicative, and Monad as they relate to the Option type in Scalaz. It discusses Option syntax, constructing Options, working with Options using fold, map, and other methods, and defines Option as an instance of Functor, Applicative, and Monoid. It also briefly touches on concepts like context bounds and alternative monoids for Option.
This document summarizes key features of the Scala programming language. Some key points include:
- Scala runs on the Java Virtual Machine (JVM) and allows for type inference, immutable values, functional programming patterns like pattern matching, and object-oriented features like traits for inheritance.
- Scala favors immutable values over mutable variables for scalability. Features like actors allow for concurrency without shared mutable state.
- Scala code can be compiled to JavaScript using Scala.js, allowing full-stack development in a single language.
- Traits provide a powerful way to do multiple inheritance by combining traits and classes at runtime.
This document summarizes a talk on using the Scalaz library. It introduces key Scalaz concepts like typeclasses, monoids, and validation. Typeclasses allow uniform implementation of common patterns across types. Monoids represent structures with an associative binary operation and an identity. Validation provides an applicative way to represent failure without exceptions. The document gives examples of using these concepts to represent positions, filters, and compose validations.
Kotlin Basics - Apalon Kotlin Sprint Part 2Kirill Rozov
This document provides an overview of Kotlin basics including:
- Basic data types like Int, String, Boolean
- Collections like List, Set, Map
- Variables, functions, control flow
- Classes, properties, constructors
- Inheritance, interfaces
- Additional features like lambdas, extensions, coroutines
It aims to introduce fundamental Kotlin concepts and syntax in a concise manner.
The document is a slide presentation on Scala that provides an introduction to the language in 90 minutes or less. It covers Scala basics like being object oriented and functional, static typing, compilation to JVM bytecode, and interoperability with Java. It also discusses Scala tools, its use in open source projects and industry, recommended books, and jobs involving Scala. Code examples are provided to demonstrate Hello World programs, variables, methods, conditionals, sequences, and closures in Scala.
I used these slides for a Scala workshop that I gave. They are based on these: http://www.scala-lang.org/node/4454. Thanks to Alf Kristian Støyle and Fredrik Vraalsen for sharing!
Scala is an object-oriented and functional programming language that runs on the Java Virtual Machine. It was designed by Martin Odersky and developed at EPFL in Switzerland. Scala combines object-oriented and functional programming principles, including support for immutable data structures, pattern matching, and closures. It interoperates seamlessly with existing Java code and libraries.
Slides of a talk I gave at work on Scala. It is geared toward Java developers. Some of the examples are in my company's domain, which is analyzing energy usage (i.e. a "read" is an electric meter read).
Scala is a multi-paradigm language that runs on the JVM and interoperates with Java code and libraries. It combines object-oriented and functional programming by allowing functions to be treated as objects and supports features like traits, pattern matching, and immutable data structures. The Scala compiler infers types and generates boilerplate code like getters/setters, making development more productive compared to Java. While Scala has a learning curve, it allows a more concise and scalable language for building applications.
"The joy of Scala" - Maxim Novak / Wix
Around eight years ago I started my journey as a developer. Since then, I've played around with many languages and thought that C# offers the best developer productivity. After joining Wix two years ago, I was exposed to the amazing world of Scala and Functional Programming and never looked back.
In Scala the code is much more concise, less ceremonious, immutable by default, combines functional with object oriented, seamlessly interoperates with Java, and many software engineering patterns are already baked into the language. Most importantly - Scala is FUN! By the end of the session you too will, hopefully, convert to Scala and never look back.
Recording of the lecture (Hebrew) - https://youtu.be/TcnYTwff2xU
This is brief presentation on the Scala programming language. It is aimed at Java developers who are curious about Scala. It was given at a San Francisco Java User Group in January 2009.
The Scala programming language has been gaining momentum recently as an alternative (and some might say successor) to Java on the JVM. This talk will start with an introduction to basic Scala syntax and concepts, then delve into some of Scala's more interesting and unique features. At the end we'll show a brief example of how Scala is used by the Lift web framework to simplify dynamic web apps.
This document provides a dictionary of scala programming concepts including definitions of common jargon like ADT, typeclasses, extension methods, and call-by semantics. It defines ADT as algebraic data types, which were introduced in languages like Algol 60 and ML, and pattern matching which allows decomposing ADT values. Typeclasses are defined as rules for types like equality, with instances providing implementations. Extension methods and implicit conversions add methods to types via implicit parameters. Call-by-name and call-by-need semantics are discussed in relation to lazy evaluation.
Watch video (in Hebrew): http://parleys.com/play/53f7a9cce4b06208c7b7ca1e
Type classes are a fundamental feature of Scala, which allows you to layer new functionality on top of existing types externally, i.e. without modifying or recompiling existing code. When combined with implicits, this is a truly remarkable tool that enables many of the advanced features offered by the Scala library ecosystem. In this talk we'll go back to basics: how type classes are defined and encoded, and cover several prominent use cases.
A talk given at the Underscore meetup on 19 August, 2014.
(How) can we benefit from adopting scala?Tomasz Wrobel
Scala offers benefits from adopting it such as increased productivity through concise and expressive code, static typing with type inference, support for both object-oriented and functional programming paradigms, and interoperability with Java. Switching from Java to Scala involves some changes like using val for immutable variables and var for mutable, but overall the syntax is quite similar which eases the transition.
Scala is a programming language that runs on the Java Virtual Machine (JVM) and is designed to express common programming patterns in a concise, elegant, and type-safe way. Some key points about Scala include that it is functional and object-oriented, statically typed, and allows seamless integration with Java. Scala code is often more concise than equivalent Java code through features like pattern matching and immutable data structures. Functional programming principles like pure functions, recursion, and avoiding side effects are emphasized in Scala.
Scala for Java Developers provides an overview of Scala for Java developers. It discusses:
- The goals of understanding what Scala is, learning more about it, and installing Scala.
- An introduction to Scala including what it is, its history from 1995 to 2013, and whether it is a good fit for certain uses based on its strengths like functional programming and weaknesses like syntax.
- How to get started with Scala including required and optional software and plugins.
- Key Scala features like objects, classes, traits, pattern matching, and collections.
The document discusses Scala programming concepts including object-oriented programming, functional programming, collections, pattern matching, and more. It provides code examples of defining objects and classes, functions, for expressions, match expressions, case classes, traits, generics, and collections like List and Map.
This document introduces Scala and its key features. Scala is a JVM language that combines object-oriented and functional programming. It allows for concise code through features like type inference, method declaration simplification, and pattern matching. Scala supports both object-oriented concepts like classes and traits as well as functional programming with immutable data structures and higher-order functions. It also builds in support for concurrency through its Actor model. In summary, Scala aims to make code more concise while enabling functional and concurrent programming on the JVM.
Type classes 101 - classification beyond inheritanceAlexey Raga
Inheritance and interfaces implementation are often used in languages like Java in order to express "Is-a" and "Can-do" capabilities. In Scala we can do better by separating these concerns using the concept of type classes.
Feel of Kotlin (Berlin JUG 16 Apr 2015)intelliyole
Kotlin is a statically typed programming language that targets the JVM, Android and web. It has been in development since 2010 by JetBrains. Kotlin code is concise, expressive and safe to develop with. It is interoperable with Java code and has equivalent performance, with a smooth learning curve that maintains existing Java investments. JetBrains uses Kotlin in their own products and tools. The document provides examples of Kotlin features like classes, traits, null safety, extension functions, operator overloading, and HTML builders.
This document provides a recap of concepts covered on day 1 of a Scala training, including:
- How variables, methods, classes, tuples, and the Option type work differently in Scala compared to Java.
- Key Scala features like first class functions, closures, pattern matching, and traits.
- An overview of the schedule for day 1 which includes higher order functions, implicit conversions, XML support, and building a quiz game task.
Scala is a multi-paradigm programming language that runs on the Java Virtual Machine. It integrates features of object-oriented and functional programming languages. Some key features of Scala include: supporting both object-oriented and functional programming, providing improvements over Java in areas like syntax, generics, and collections, and introducing new features like pattern matching, traits, and implicit conversions.
This document summarizes Jeffrey Zhao's background and areas of expertise. It includes:
1. Jeffrey Zhao is a software engineer from China who blogs at http://blog.zhaojie.me/. His areas of expertise include JavaScript, F#, Scala, C#, Python, .NET and Mono.
2. The document provides code examples in different programming languages for common algorithms and data structures like sorting and grouping, demonstrating Jeffrey's proficiency in languages like C#, F#, Ruby, Java, and JavaScript.
3. It introduces Jscex, a JavaScript library created by Jeffrey that allows writing asynchronous code in a synchronous style using JavaScript computation expressions. Jscex compiles asynchronous code into
Scala can be used for both object-oriented programming and functional programming. It supports features like functions as first-class objects, closures, patterns, and immutable data structures. Scala can be used to build domain specific languages through its support for features like functions, pattern matching, and implicit conversions. Some examples of Scala DSLs include SimpleModeler for modeling and g3/g4 for Android development. Scala also supports concepts from functional programming like functors, monads, and other type classes to build abstractions.
This document summarizes key similarities and differences between Scala and Java types, variables, methods, classes, collections, control structures, and other language features. Some of the main points covered include:
- Type definitions, variables, and methods are defined similarly but with different syntax in Scala vs Java
- Classes and traits in Scala are like classes and interfaces in Java
- Scala avoids static methods and instead uses singleton objects
- Control structures like if/else, for loops, and exceptions work similarly
- Scala supports features like tuples, pattern matching, and expression-oriented programming that have no direct equivalent in Java
This document provides an introduction to Scala. It discusses:
- Who the author is and their background with Scala and Spark
- Why Scala is a scalable language that runs on the JVM and supports object oriented and functional programming
- How to install Scala and use the Scala interpreter
- Basic Scala syntax like defining values and variables, type inference, strings, tuples, objects, importing classes
- Common functions and operations like map, reduce, anonymous functions, pattern matching
- Code samples for RDD relations and SparkPi
- Tips for using Scala in practice including SBT and good IDEs like IntelliJ
This document provides an overview of the Scala programming language. Some key points:
- Scala runs on the Java Virtual Machine and was created by Martin Odersky at EPFL.
- It has been around since 2003 and the current stable release is 2.7.7. Release 2.8 beta 1 is due out soon.
- Scala combines object-oriented and functional programming. It has features like pattern matching, actors, XML literals, and more that differ from Java. Everything in Scala is an object.
Some parts of our applications don't need to be asynchronous or interact with the outside world: it's enough that they are stateful, possibly with the ability to handle failure, context, and logging. Although you can use ZIO 2 or monad transformers for this task, both come with drawbacks. In this presentation, Jorge Vásquez will introduce you to ZPure, a data type from ZIO Prelude, which lets you scale back on the power of ZIO 2, but with the same high-performance, type-inference, and ergonomics you expect from ZIO 2 libraries.
Scala has a static, strong, and Turing complete type system that can infer types. It supports object-oriented programming with named types like Dog and functional programming with parameterized types like List[Int]. New types can be defined through classes, traits, case classes, and type members. Types are more general than classes and include structural types. Variance controls subtype relationships for parameterized types. Type bounds and existential types provide additional type safety. Higher kinded types allow types to be parameterized over other types.
pragmaticrealworldscalajfokus2009-1233251076441384-2.pdfHiroshi Ono
The document discusses Scala and functional programming concepts. It provides examples of building a chat application in 30 lines of code using Lift, defining messages as case classes, and implementing a chat server and comet component. It then summarizes that Scala is a pragmatically-oriented, statically typed language that runs on the JVM and provides a unique blend of object-oriented and functional programming. Traits allow for code reuse and multiple class inheritances. Functional programming concepts like immutable data structures, higher-order functions, and for-comprehensions are discussed.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
2. • JVM
123.toByte
"1".toInt
true.toString
• val compare = (x: Int, y: Int) => x > y
compare(1, 2) // result: Boolean = false
• Java sta&c
( ) Scala object
object Dog {
val whatever = "dog" // static field in Java
}
class Dog {
def callWhatever = Dog.whatever
}
3. • val compare = (x: Int, y: Int) => x > y
list sortWith compare
• class AComparator {
def compare(x: Int, y: Int) = x > y
}
list sortWith (new AComparator).compare
• object annonymous extends scala.Function2[Int, Int, Boolean] {
override def apply(x: Int, y: Int) = x > y
}
list sortWith annonymous
• sta&c object
( )
4. (1)
• 1.+(1)
”.” ”()”
1 + 1
1.>(0)
1 > 0
(1 > 0).&(2 > 1)
(1 > 0) & 2 > 1
stack.push(10)
stack push 10
stack.pop
stack pop
• stack push {
{...}
val a = 1
val b = 2
a + b
}
• def !@#%^&*-<=>?|~:/ = println("noop")
def √(x: Double) = Math.sqrt(x)
val Π = Math.Pi
val r = √(9*Π)
• ‘<’, ‘>’ ’[’ ‘]’
5. (2)
• for
for (i <- List(1, 2)) {
println(i)
}
List(1, 2) foreach {i => println(i)}
for (i <- List(1, 2)) yield {
i + 10
}
List(1, 2) map {i => i + 10}
• // synchronized is function call instead of keyword
def check = synchronized {
// isInstanceOf is function call instead of keyword
100.isInstanceOf[String]
}
• stack.pop.asInstanceOf[Int] // (Integer) stack.pop() in Java
6. • return
val r1 = { // return 3
val a = 1
val b = 2
a + b
}
val r2 = if (true) 1 else 2
val r3 = // return (): Unit
for (i <- List(1, 2)) {
println(i)
}
val r4 = // return List(11, 12)
for (i <- List(1, 2)) yield {
i + 10
}
val r5 = // return java.io.File
try {
val f = new File("afile")
f
} catch {
case ex: IOException => null
}
11. (2)
-
• (Invariant): C[T], C is invariant on T
Tsub Tsup T C[Tsub] C[Tsup] C[T]
• (Covariant): C[+T], C is covariant on T
Tsub T C[Tsub] C[T]
• (Contrava&ant): C[‐T], C is contravariant on T
Tsup T C[Tsup] C[T]
Java ?
Scala +/-
12. (2)
class Animal {}
class Dog extends Animal {}
class Fox extends Animal {}
• Animal Animal
class Xian<T> {} // Java class Xian[+T] //
void isAnimalXian(Xian<? extends Animal> in) def isAnimalXian(in: Xian[Animal])
isAnimalXian(new Xian<Dog>()) isAnimalXian(new Xian[Dog])
isAnimalXian(new Xian<Fox>()) isAnimalXian(new Xian[Fox])
• Dog Dog
class Xian<T> {} // Java class Xian[-T] //
void isDogXian(Xian<? super Dog> in) def isDogXian(in: Xian[Dog])
isDogXian(new Xian[Animal]) isDogXian(new Xian[Animal])
13. vs
•
trait Pair[K, V] { class PairImpl extends Pair[Dog, Fox] {
def get(key: K): V def get(key: Dog): Fox
def set(key: K, value: V) def put(key: Dog, value: Fox)
} }
=>
• + +
trait Pair { class PairImpl extends Pair {
type K // deferred type type K = Dog
type V // deferred type type V = Fox
def get(key: K): V def get(key: K): V
def set(key: K, value: V) def set(key: K, value: V)
} }
=>
• => /
• => type StrToList = HashMap[String, List[String]]
14. Trait Structural Type
• Trait: + =>
• Structural Type:
Type
trait Subject {
type Observer = {def update(subject: Subject)} // type member
private var observers = List[Observer]() // field
def addObserver(observer: Observer) = observers ::= observer
def notifyObservers = observers foreach (_.update(this))
}
class Price(var value: Double) {def setValue(v: Double) = value = v}
Java Interface
Trait
val observablePrice = new Price(10) with Subject {
override def setValue(v: Double) = {
super.setValue(v); notifyObservers
}
}
observablePrice.addObserver(new AnyRef { update(Subject) Observer
def update(subject: Subject) {
println("price changed")
}
})
15. Trait Self Type
•
• Class Trait: OilPrice extends Price with Subject with Market with ....
•
• (Self Type)
trait Subject {self: Price =>
type Observer = {def update(subject: Price)} // type member
private var observers = List[Observer]() // field
this Subject
def addObserver(observer: Observer) = observers ::= observer
def notifyObservers = observers foreach (_.update(this))
}
observablePrice.addObserver(new AnyRef { // only need a update(Any) method
def update(subject: Price) {
println("price changed to " + subject.value)
}
})
• Self Type:
• Trait
• class trait
• self Trait super this self self
• Trait
•
• class trait ( Class )
16. apply
• apply <instance>(args) <instance>.apply(args)
• apply Class
class Price(var value: Int) {
def apply() = value // ‘def apply = value’, without ’()’ is bad idea
}
object Price {
def apply(v: Int) = new Price(v)
}
val p = Price(10)
p // Price@565f0e7d
p() // 10
Price(10)() // 10
• apply Scala
val list = List(1, 2, 3) // List.apply[A](xs: A*)
val array = Array("a", "b", "c") // Array.apply[T](xs: T*)
val map = Map("a" -> 1,
"b" -> 2,
"c" -> 3) // MapFactory.apply[A, B](elems: (A, B)*)
array(0) // array.apply(index): "a"
map("a") // map.apply(key): 1
17. • Java Switch ( Java7) Scala
val str = "abcdef"
str.toSeq match {
case Seq('a', 'b', rest@_*) => println(rest) // cdef
case _ => println("no match")
}
var any: Any = _
any match {
case "scala" | "java" => "string"
case i: Int if i > 10 => "int > 10"
case `str` => "abcdef"
case _: String => "string type"
case hd :: tail => "List"
case _ => "other"
}
case class Address(coutry: String, city: String, street: String, no: Int)
case class Person(name: String, age: Int, address: Address)
val p = Person("Wang wu", 12, Address("China", "Beijing", "Chang'an", 10))
p match {
case Person(name, age, Address(coutry, city, street, no)) =>
println(name + "|" + coutry + "|" + city + "|" + street + "|" + no)
}
18. Scala
• Existential type ( Java )
• Implicit type * ( )
• val, var, lazy val ( )
• Partial function * ( )
• Primary constructor ( apply)
• Accessor properties (Why getter, setter? )
• XML Process ( )
* DSL
19. (DSL )
• Actors
class Ping(count: Int, pong: Actor) extends Actor {
def act() {
var pingsLeft = count - 1
pong ! Ping
while (true) {
receive { {...}
case Pong =>
if (pingsLeft % 1000 == 0)
println("Pong”)
if (pingsLeft > 0) {
pong ! Ping
pingsLeft -= 1
} else {
pong ! Stop
exit()
}}}}}
• Bill Venners scalatest
class StackSpec extends FlatSpec with ShouldMatchers {
"A Stack" should "pop values in last-in-first-out order" in {
val stack = new Stack[Int]
stack.push(1)
stack.push(2)
stack.pop() should equal (2) should
stack.pop() should equal (1)
}
it should "throw NoSuchElementException if an empty stack is popped" in {
val emptyStack = new Stack[String]
evaluating { emptyStack.pop() } should produce [NoSuchElementException]
}
}