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!
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.
Scala is becoming the language of choice for many development teams. This talk highlights how Scala excels in the world of multi-core processing and explores how it compares to Java 8.
Video Presentation: http://youtu.be/8vxTowBXJSg
Martin Odersky discusses the past, present, and future of Scala over the past 5 years and next 5 years. Key points include:
- Scala has grown significantly in usage and community over the past 6 years since its first release.
- Scala 2.8 will include improvements like new collections, package objects, named/default parameters, and better tool support.
- Over the next 5 years, Scala will focus on improving concurrency and parallelism through better abstractions, analyses, and static typing support.
This document provides an overview of Scala fundamentals including:
- Scala is a programming language for the JVM that supports both object-oriented and functional paradigms.
- It defines variables, values, lazy values, functions, types, classes, objects, traits, and higher-order functions.
- Classes can extend other classes and traits, allowing for multiple inheritance. Objects are used as singletons.
- Functional concepts like immutability, anonymous functions, and higher-order functions are supported.
Scala is an object-oriented and functional programming language that runs on the Java Virtual Machine. It was created in 2001 by Martin Odersky and aims to integrate features of object-oriented and functional languages. Scala code is compiled to JVM bytecode and supports interoperability with Java libraries and frameworks. Some key features of Scala include support for immutable data structures, pattern matching, traits for mixing composition, and functional programming constructs like functions as first-class values. Scala has gained popularity in industry at companies like LinkedIn, Twitter, and The Guardian.
This document provides an introduction to the Scala programming language. It begins with an overview of Scala's motivation and history. It then covers the basics of Scala including simple data structures, loops, objects, types and generics. More advanced topics such as traits, mixins, implicit conversions and sealed classes are also discussed. The document concludes with references for further reading.
Android is a fast-growing mobile operating system with millions of users worldwide.
Scala is a modern programming language designed to express common programming patterns in a concise, elegant, and type-safe way. Why not combine both of them?
This document provides an overview of the Scala programming language and discusses its usage on Android. Scala is a multi-paradigm language that integrates object-oriented and functional programming. It runs on the Java Virtual Machine and is used by companies like LinkedIn, Twitter, and The Guardian. Scala's features include being object-oriented, functional, statically typed, and expressive while allowing concise code. It supports classes, traits, pattern matching, and lazy evaluation among other things.
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.
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.
This document introduces developing a Scala DSL for Apache Camel. It discusses using Scala techniques like implicit conversion, passing functions as parameters, by-name parameters and currying to build a DSL. It provides examples of building simple routes in the Scala DSL using these techniques and compares them to the Java DSL. It also covers some caveats like interactions between Java and Scala generics and operator precedence. The goal is to introduce basic Scala concepts and syntax that can be used to build a Scala DSL, using Apache Camel as an example.
The document provides an introduction to Scala for Java developers. It outlines key features of Scala including cleaning up Java syntax, going beyond Java with features like case classes, traits, and pattern matching, and its functional focus including support for higher-order functions, the Option type, and for comprehensions. The document also briefly discusses the Scala IDE for Eclipse.
The great attractiveness of purely functional languages is their ability to depart from sequential order of computation. Theoretically, it enables two important features of the compiler:
1) The ability to reorder computation flow, making the program implicitly parallelisable. Modern imperative language compilers, even using careful synchronization of concurrent code, still generate huge chunks of sequential instructions that need to be executed on a single processor core; a purely functional language compilers can dispatch very small chunks to many (hundreds and thousands) of cores, carefully eliminating as many execution path dependencies as possible.
2) As the compiler formalizes different types of side effects, it can detect a whole new class of program errors at compile time, including resource acquisition and releasing problems, concurrent access to shared resources, many types of deadlocks etc. It is not yet a full-fledged program verification, but it is a big step in that direction.
Scala is a semi-imperative language with strong support for functional programming and rich type system. One can isolate the purely functional core of the language which can be put on the firm mathematical foundation of dependent type theories. We argue that it is possible to treat Scala code as it's written by now as an implicit do-notation which can be then reduced to a purely functional core by means of recently introduced Scala macros. The formalism of arrows and applicative contexts can bring Scala to a full glory of an implicitly parallelisable programming language, while still keeping its syntax mostly unchanged.
Here are the answers to your questions:
1. The main differences between a Trait and Abstract Class in Scala are:
- Traits can be mixed in to classes using with, while Abstract Classes can only be extended.
- Traits allow for multiple inheritance as they can be mixed in, while Abstract Classes only allow single inheritance.
- Abstract Classes can have fields and constructor parameters while Traits cannot.
- Abstract Classes can extend other classes, while Traits can only extend other Traits.
2. abstract class Animal {
def isMammal: Boolean
def isFriendly: Boolean = true
def summarize: Unit = {
println("Characteristics of animal:")
}
This document provides an introduction to Apache Spark, including its core components, architecture, and programming model. Some key points:
- Spark uses Resilient Distributed Datasets (RDDs) as its fundamental data structure, which are immutable distributed collections that allow in-memory computing across a cluster.
- RDDs support transformations like map, filter, reduce, and actions like collect that return results. Transformations are lazy while actions trigger computation.
- Spark's execution model involves a driver program that coordinates tasks on worker nodes using an optimized scheduler.
- Spark SQL, MLlib, GraphX, and Spark Streaming extend the core Spark API for structured data, machine learning, graph processing, and stream processing
Slides for my lightning talk at Config Management Camp 2016. See the video here: https://youtu.be/qJ0VNO6z68M
Writing Vagrantfiles is tedious, especially when you’re setting up a multi-VM environment. Typically, people will copy/paste code blocks that define hosts, but that becomes unwieldy. However, a Vagrantfile is “just” Ruby, so can’t we simplify things a bit using the power of the language? Turns out, we can! In this presentation I propose a reusable Vagrantfile that reads the configuration of the environment from a simple YAML file.
See my blog post about this at https://bertvv.github.io/notes-to-self/2015/10/05/one-vagrantfile-to-rule-them-all/
- The document discusses a presentation given by Jongwook Woo on introducing Spark and its uses for big data analysis. It includes information on Woo's background and experience with big data, an overview of Spark and its components like RDDs and task scheduling, and examples of using Spark for different types of data analysis and use cases.
This document provides an overview of a machine learning workshop including tutorials on decision tree classification for flight delays, clustering news articles with k-means clustering, and collaborative filtering for movie recommendations using Spark. The tutorials demonstrate loading and preparing data, training models, evaluating performance, and making predictions or recommendations. They use Spark MLlib and are run in Apache Zeppelin notebooks.
This Hadoop HDFS Tutorial will unravel the complete Hadoop Distributed File System including HDFS Internals, HDFS Architecture, HDFS Commands & HDFS Components - Name Node & Secondary Node. Not only this, even Mapreduce & practical examples of HDFS Applications are showcased in the presentation. At the end, you'll have a strong knowledge regarding Hadoop HDFS Basics.
Session Agenda:
✓ Introduction to BIG Data & Hadoop
✓ HDFS Internals - Name Node & Secondary Node
✓ MapReduce Architecture & Components
✓ MapReduce Dataflows
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What is HDFS? - Introduction to HDFS
The Hadoop Distributed File System provides high-performance access to data across Hadoop clusters. It forms the crux of the entire Hadoop framework.
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What are HDFS Internals?
HDFS Internals are:
1. Name Node – This is the master node from where all data is accessed across various directores. When a data file has to be pulled out & manipulated, it is accessed via the name node.
2. Secondary Node – This is the slave node where all data is stored.
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What is MapReduce? - Introduction to MapReduce
MapReduce is a programming framework for distributed processing of large data-sets via commodity computing clusters. It is based on the principal of parallel data processing, wherein data is broken into smaller blocks rather than processed as a single block. This ensures a faster, secure & scalable solution. Mapreduce commands are based in Java.
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What are HDFS Applications?
1. Data Mining
2. Document Indexing
3. Business Intelligence
4. Predictive Modelling
5. Hypothesis Testing
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Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance.
Email: sales@skillspeed.com
Website: https://www.skillspeed.com
Functional Programming for OO Programmers (part 2)
Code examples demonstrating Functional Programming concepts, with JavaScript and Haskell.
Part 1 can be found here - http://www.slideshare.net/calvinchengx/functional-programming-part01
Source code can be found here - http://github.com/calvinchengx/learnhaskell
Let me know if you spot any errors! Thank you! :-)
The document provides an agenda for a DevOps advanced class on Spark being held in June 2015. The class will cover topics such as RDD fundamentals, Spark runtime architecture, memory and persistence, Spark SQL, PySpark, and Spark Streaming. It will include labs on DevOps 101 and 102. The instructor has over 5 years of experience providing Big Data consulting and training, including over 100 classes taught.
This document provides an overview of Hadoop and related big data technologies. It begins with defining big data and discussing why traditional systems are inadequate. It then introduces Hadoop as a framework for distributed storage and processing of large datasets. The key components of Hadoop - HDFS for storage and MapReduce for processing - are described at a high level. HDFS architecture and read/write operations are outlined. MapReduce paradigm and an example word count job are also summarized. Finally, Hive is introduced as a data warehouse tool built on Hadoop that provides SQL-like queries for large datasets.
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
The document outlines an agenda for a conference on Apache Spark and data science, including sessions on Spark's capabilities and direction, using DataFrames in PySpark, linear regression, text analysis, classification, clustering, and recommendation engines using Spark MLlib. Breakout sessions are scheduled between many of the technical sessions to allow for hands-on work and discussion.
Advanced Data Science on Spark-(Reza Zadeh, Stanford)
The document provides an overview of Spark and its machine learning library MLlib. It discusses how Spark uses resilient distributed datasets (RDDs) to perform distributed computing tasks across clusters in a fault-tolerant manner. It summarizes the key capabilities of MLlib, including its support for common machine learning algorithms and how MLlib can be used together with other Spark components like Spark Streaming, GraphX, and SQL. The document also briefly discusses future directions for MLlib, such as tighter integration with DataFrames and new optimization methods.
This document outlines steps for developing analytic applications using Apache Spark and Python. It covers prerequisites for accessing flight and weather data, deploying a simple data pipe tool to build training, test, and blind datasets, and using an IPython notebook to train predictive models on flight delay data. The agenda includes accessing necessary services on Bluemix, preparing the data, training models in the notebook, evaluating model accuracy, and deploying models.
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
Scala is a powerful language for the JVM that is compatible with JDK 1.5+. It is a functional language maintained by Typesafe along with the community. Scala compiles to JVM bytecode like Java but has features like objects, traits, pattern matching and functional programming. James Gosling, the creator of Java, said that if he were to pick a language other than Java today, it would be Scala.
Scala is a multi-paradigm programming language that runs on the Java Virtual Machine. It is both object-oriented and functional, with support for immutable data and concise syntax. Scala has gained popularity due to its advantages like type safety, concurrency support, and interoperability with Java. However, some of Scala's advanced features can be difficult to read and use for beginners, and tooling support is not as robust as Java. Overall, Scala represents a promising approach that prioritizes simplicity over ease of use.
The document summarizes Scala as a replacement for Java, highlighting its capabilities as both an object-oriented and functional language that compiles to Java bytecode. It provides code examples demonstrating Scala's concise syntax for common tasks like defining classes, filtering collections, string reversal, and exception handling. The document also outlines an agenda for a Scala workshop covering its basic syntax, REPL/IDE setup, functions, pattern matching, OO features, and functional programming concepts.
The document provides an overview of the Scala programming language. It begins with an agenda that outlines topics like differences between Java and Scala, Scala data types, variables, classes, functions, closures, exception handling and collections. It then discusses specific aspects of Scala like verbosity reduction compared to Java, functional programming influences, object-oriented features, interoperability with Java and compilation to Java bytecode. Examples are provided to illustrate Scala concepts like functions, classes, recursion, higher-order functions and exception handling. The document aims to explain Scala and reasons for differences from Java.
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.
The document discusses Scala and why some developers think it could replace Java on the JVM. It provides quotes from several influential developers, including the creator of Java and Groovy, expressing their view that Scala is the best candidate to replace Java in the long run. Specifically, James Gosling says that if he had to pick another language on the JVM besides Java, it would be Scala. Charlie Nutter describes Scala as the current heir apparent to the Java throne and the momentum behind Scala is now unquestionable. James Strachan says that if he had seen the Programming in Scala book in 2003, he may have never created Groovy and that his tip for a long term replacement for Java is
Scala is a general purpose programming language designed to express common programming patterns in a concise, elegant, and type-safe way. It has established itself as one of the main alternative languages on the Java Virtual Machine, being used by companies like Twitter and LinkedIn. Scala fuses functional programming (from which it borrows higher-order functions and closures, generic typing and immutable data structures) and object-oriented programming programming (from which it takes inheritance and encapsulation). It interoperates fully with Java, allowing a smooth transition and access to all existing Java libraries.
Scala’s lightweight syntax makes it easy to extend the language through DSLs. In this talk we are going to have a quick overview of Scala’s main features (closures, higher-order functions, implicits), and collection classes in the standard library. We’ll see how a new concurrency model, such as actors, can be added to the language through a library.
Scala er et Java-relateret, statisk typet programmeringssprog i hastig fremmarch. Sproget kombinerer aspekter fra objekt- og funktionsorienterede sprog og fokuserer på skalerbarhed og effektivitet, både på det kodemæssige og afviklingsmæssige niveau. Syntaksen er elegant og koncis. Samtidig indeholder sproget stærke konstruktioner til understøttelse af parallelle applikationer, der udnytter fremtidens hardwarearkitekturer.
Java som sprog har ikke bevæget sig meget de seneste år. Vi har stadig ikke closures eller funktionelle aspekter som f.eks. C# har haft siden version 3. Er Scala svaret på enhver Javaudviklers bønner eller er sproget kun interessant for tågehoveder som mig, som begynder at synes bedre og bedre om funktionsorientering? Er den store portion syntaktisk sukker, Scala bringer på bordet, bare tomme kalorier?
Scala er et Java-relateret, statisk typet programmeringssprog i hastig fremmarch. Sproget kombinerer aspekter fra objekt- og funktionsorienterede sprog og fokuserer på skalerbarhed og effektivitet, både på det kodemæssige og afviklingsmæssige niveau. Syntaksen er elegant og koncis. Samtidig indeholder sproget stærke konstruktioner til understøttelse af parallelle applikationer, der udnytter fremtidens hardwarearkitekturer.
BCS SPA 2010 - An Introduction to Scala for Java Developers
This document provides an introduction to Scala for Java developers. It outlines how Scala cleans up Java syntax by making semi-colons optional, using == for equals and eq for reference equality, and allowing periods to be omitted. It also describes how Scala goes beyond Java by supporting features like case classes, pattern matching, named and default arguments, and mixin composition with traits. The document provides examples of using these features and explains how Scala seamlessly interoperates with Java.
This document provides a summary of key Scala basics and comparisons to Java, including:
1) It describes type definitions, variables, methods and how they are defined in Scala versus Java. It also covers method invocation syntax differences.
2) It explains classes and constructors, traits versus interfaces, and static methods in Scala versus Java.
3) It provides examples of control structures like if/else, for loops, while loops and exceptions in both languages.
Scala functions provide powerful ways to decompose problems and harness the benefits of functional programming. The presentation introduces core concepts of functional programming using Scala as an example language, highlighting how Scala combines object-oriented and functional programming. It presents benefits like immutability, higher-order functions, and improved concurrency while avoiding past performance issues through modern virtual machines and libraries.
This document discusses design patterns in Scala compared to Java. It covers functional interface, command, builder, iterator, template method, strategy, null object, and decorator patterns. The agenda outlines discussing these patterns in part one covering OO patterns. Each pattern section provides a Java example and how it can be implemented in Scala using functions and other Scala features like case classes.
Softshake 2013: 10 reasons why java developers are jealous of Scala developers
Scala provides many features that simplify programming tasks compared to Java such as type inference, less verbose syntax, simpler definition of classes and collections, closures, pattern matching, implicit conversions, and concise concurrency constructs. However, Scala also has disadvantages like less mature tools compared to Java and lack of binary compatibility between major versions.
This presentation was presented at OSS camp in New Delhi. It deals with the basics of Scala language and how we can use it to build scalable Applications
Javantura v2 - All Together Now - making Groovy and Scala sing together - Din...
Can two JVM languages, none of them Java, work harmoniously, side by side? Should they? In this lecture we’ll see how code written in Groovy and Scala work with Java and how they can be made to work with each other. The lecture is based on the experience of introducing Scala to an actual Groovy project.
This document provides an introduction to Scala concepts and features compared to Java, including how to set up Scala, the Simple Build Tool (SBT), case classes, lazy definitions, imports, objects, pattern matching, collections, higher-order functions, partial functions, currying, implicit conversions, and implicit parameters. Useful Scala resources are also listed.
Linux Enterprise - inleiding cursus, 5 trends in systeembeheer
Dit zijn de slides voor de introductieles van de cursus Enterprise Linux, opleiding ProfBach Toegepaste Informatica, HoGent Bedrijf en Organisatie. Bekijk de opname op http://youtu.be/GGTS37Tsl-M
A Reinforcement Learning Approach for Hybrid Flexible Flowline Scheduling Pro...
Paper presented at MISTA2013, Gent.
In this paper, we present a method based on Learning Automata to solve Hybrid Flexible Flowline Scheduling Problems (HFFSP) with additional constraints like sequence dependent setup times, precedence relations between jobs and machine eligibility. This category of production scheduling problems is noteworthy because it involves several types of constraints that occur in complex real-life production scheduling problems like those in process industry and batch production. In the proposed technique, Learning Automata play a dispersion game to determine the order of jobs to be processed in a way that makespan is minimized, and precedence constraint violations are avoided. Experiments on a set of benchmark problems indicate that this method can yield better results than the ones known until now.
Hoe zoek je informatie op voor een scriptie, welke tools zijn er om die efficient te verzamelen en bij te houden, hoe verwerk je dit tot een literatuurstudie met bijhorende referentielijst?
In deze presentatie die ik gaf aan collega's, licht ik enkele topics toe uit deze boeken:
- Robert C. Martin, Clean Code
- Joshua Bloch, Effective Java
In June 2020, L.L. McKinney, a Black author of young adult novels, began the #publishingpaidme hashtag to create a discussion on how the publishing industry treats Black authors: “what they’re paid. What the marketing is. How the books are treated. How one Black book not reaching its parameters casts a shadow on all Black books and all Black authors, and that’s not the same for our white counterparts.” (Grady 2020) McKinney’s call resulted in an online discussion across 65,000 tweets between authors of all races and the creation of a Google spreadsheet that collected information on over 2,000 titles.
While the conversation was originally meant to discuss the ethical value of book publishing, it became an economic assessment by authors of how publishers treated authors of color and women authors without a full analysis of the data collected. This paper would present the data collected from relevant tweets and the Google database to show not only the range of advances among participating authors split out by their race, gender, sexual orientation and the genre of their work, but also the publishers’ treatment of their titles in terms of deal announcements and pre-pub attention in industry publications. The paper is based on a multi-year project of cleaning and evaluating the collected data to assess what it reveals about the habits and strategies of American publishers in acquiring and promoting titles from a diverse group of authors across the literary, non-fiction, children’s, mystery, romance, and SFF genres.
Odoo allows users to split long shifts into multiple segments directly from the Gantt view.Each segment retains details of the original shift, such as employee assignment, start time, end time, and specific tasks or descriptions.
Lecture_Notes_Unit4_Chapter_8_9_10_RDBMS for the students affiliated by alaga...
Title: Relational Database Management System Concepts(RDBMS)
Description:
Welcome to the comprehensive guide on Relational Database Management System (RDBMS) concepts, tailored for final year B.Sc. Computer Science students affiliated with Alagappa University. This document covers fundamental principles and advanced topics in RDBMS, offering a structured approach to understanding databases in the context of modern computing. PDF content is prepared from the text book Learn Oracle 8I by JOSE A RAMALHO.
Key Topics Covered:
Main Topic : DATA INTEGRITY, CREATING AND MAINTAINING A TABLE AND INDEX
Sub-Topic :
Data Integrity,Types of Integrity, Integrity Constraints, Primary Key, Foreign key, unique key, self referential integrity,
creating and maintain a table, Modifying a table, alter a table, Deleting a table
Create an Index, Alter Index, Drop Index, Function based index, obtaining information about index, Difference between ROWID and ROWNUM
Target Audience:
Final year B.Sc. Computer Science students at Alagappa University seeking a solid foundation in RDBMS principles for academic and practical applications.
About the Author:
Dr. S. Murugan is Associate Professor at Alagappa Government Arts College, Karaikudi. With 23 years of teaching experience in the field of Computer Science, Dr. S. Murugan has a passion for simplifying complex concepts in database management.
Disclaimer:
This document is intended for educational purposes only. The content presented here reflects the author’s understanding in the field of RDBMS as of 2024.
Feedback and Contact Information:
Your feedback is valuable! For any queries or suggestions, please contact muruganjit@agacollege.in
Ardra Nakshatra (आर्द्रा): Understanding its Effects and Remedies
Ardra Nakshatra, the sixth Nakshatra in Vedic astrology, spans from 6°40' to 20° in the Gemini zodiac sign. Governed by Rahu, the north lunar node, Ardra translates to "the moist one" or "the star of sorrow." Symbolized by a teardrop, it represents the transformational power of storms, bringing both destruction and renewal.
About Astro Pathshala
Astro Pathshala is a renowned astrology institute offering comprehensive astrology courses and personalized astrological consultations for over 20 years. Founded by Gurudev Sunil Vashist ji, Astro Pathshala has been a beacon of knowledge and guidance in the field of Vedic astrology. With a team of experienced astrologers, the institute provides in-depth courses that cover various aspects of astrology, including Nakshatras, planetary influences, and remedies. Whether you are a beginner seeking to learn astrology or someone looking for expert astrological advice, Astro Pathshala is dedicated to helping you navigate life's challenges and unlock your full potential through the ancient wisdom of Vedic astrology.
For more information about their courses and consultations, visit Astro Pathshala.
Front desk officers are responsible for taking care of guests and customers. Their work mainly involves interacting with customers and business partners, either in person or through phone calls.
Delegation Inheritance in Odoo 17 and Its Use Cases
There are 3 types of inheritance in odoo Classical, Extension, and Delegation. Delegation inheritance is used to sink other models to our custom model. And there is no change in the views. This slide will discuss delegation inheritance and its use cases in odoo 17.
Now we can take look into how to configure time off types in odoo 17 through this slide. Time-off types are used to grant or request different types of leave. Only then the authorities will have a clear view or a clear understanding of what kind of leave the employee is taking.
Integrated Marketing Communications (IMC)- Concept, Features, Elements, Role of advertising in IMC
Advertising: Concept, Features, Evolution of Advertising, Active Participants, Benefits of advertising to Business firms and consumers.
Classification of advertising: Geographic, Media, Target audience and Functions.
The Jewish Trinity : Sabbath,Shekinah and Sanctuary 4.pdf
we may assume that God created the cosmos to be his great temple, in which he rested after his creative work. Nevertheless, his special revelatory presence did not fill the entire earth yet, since it was his intention that his human vice-regent, whom he installed in the garden sanctuary, would extend worldwide the boundaries of that sanctuary and of God’s presence. Adam, of course, disobeyed this mandate, so that humanity no longer enjoyed God’s presence in the little localized garden. Consequently, the entire earth became infected with sin and idolatry in a way it had not been previously before the fall, while yet in its still imperfect newly created state. Therefore, the various expressions about God being unable to inhabit earthly structures are best understood, at least in part, by realizing that the old order and sanctuary have been tainted with sin and must be cleansed and recreated before God’s Shekinah presence, formerly limited to heaven and the holy of holies, can dwell universally throughout creation
AI Risk Management: ISO/IEC 42001, the EU AI Act, and ISO/IEC 23894
As artificial intelligence continues to evolve, understanding the complexities and regulations regarding AI risk management is more crucial than ever.
Amongst others, the webinar covers:
• ISO/IEC 42001 standard, which provides guidelines for establishing, implementing, maintaining, and continually improving AI management systems within organizations
• insights into the European Union's landmark legislative proposal aimed at regulating AI
• framework and methodologies prescribed by ISO/IEC 23894 for identifying, assessing, and mitigating risks associated with AI systems
Presenters:
Miriama Podskubova - Attorney at Law
Miriama is a seasoned lawyer with over a decade of experience. She specializes in commercial law, focusing on transactions, venture capital investments, IT, digital law, and cybersecurity, areas she was drawn to through her legal practice. Alongside preparing contract and project documentation, she ensures the correct interpretation and application of European legal regulations in these fields. Beyond client projects, she frequently speaks at conferences on cybersecurity, online privacy protection, and the increasingly pertinent topic of AI regulation. As a registered advocate of Slovak bar, certified data privacy professional in the European Union (CIPP/e) and a member of the international association ELA, she helps both tech-focused startups and entrepreneurs, as well as international chains, to properly set up their business operations.
Callum Wright - Founder and Lead Consultant Founder and Lead Consultant
Callum Wright is a seasoned cybersecurity, privacy and AI governance expert. With over a decade of experience, he has dedicated his career to protecting digital assets, ensuring data privacy, and establishing ethical AI governance frameworks. His diverse background includes significant roles in security architecture, AI governance, risk consulting, and privacy management across various industries, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: June 26, 2024
Tags: ISO/IEC 42001, Artificial Intelligence, EU AI Act, ISO/IEC 23894
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Find out more about ISO training and certification services
Training: ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
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Some notes about programming in Scala: it covers Scala syntax and semantics, programming techniques, idioms, patterns. Many Scala features are introduced, from basic to intermediate and advanced. These are not introductory notes, but they assume a working knowledge with some other programming language (Java, C#, C++), object-oriented programming (OOP) concepts, and functional programming (FP) concepts.
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.
Short (45 min) version of my 'Pragmatic Real-World Scala' talk. Discussing patterns and idioms discovered during 1.5 years of building a production system for finance; portfolio management and simulation.
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.
Scala is becoming the language of choice for many development teams. This talk highlights how Scala excels in the world of multi-core processing and explores how it compares to Java 8.
Video Presentation: http://youtu.be/8vxTowBXJSg
Martin Odersky discusses the past, present, and future of Scala over the past 5 years and next 5 years. Key points include:
- Scala has grown significantly in usage and community over the past 6 years since its first release.
- Scala 2.8 will include improvements like new collections, package objects, named/default parameters, and better tool support.
- Over the next 5 years, Scala will focus on improving concurrency and parallelism through better abstractions, analyses, and static typing support.
This document provides an overview of Scala fundamentals including:
- Scala is a programming language for the JVM that supports both object-oriented and functional paradigms.
- It defines variables, values, lazy values, functions, types, classes, objects, traits, and higher-order functions.
- Classes can extend other classes and traits, allowing for multiple inheritance. Objects are used as singletons.
- Functional concepts like immutability, anonymous functions, and higher-order functions are supported.
Scala is an object-oriented and functional programming language that runs on the Java Virtual Machine. It was created in 2001 by Martin Odersky and aims to integrate features of object-oriented and functional languages. Scala code is compiled to JVM bytecode and supports interoperability with Java libraries and frameworks. Some key features of Scala include support for immutable data structures, pattern matching, traits for mixing composition, and functional programming constructs like functions as first-class values. Scala has gained popularity in industry at companies like LinkedIn, Twitter, and The Guardian.
This document provides an introduction to the Scala programming language. It begins with an overview of Scala's motivation and history. It then covers the basics of Scala including simple data structures, loops, objects, types and generics. More advanced topics such as traits, mixins, implicit conversions and sealed classes are also discussed. The document concludes with references for further reading.
Scaladroids: Developing Android Apps with ScalaOstap Andrusiv
Android is a fast-growing mobile operating system with millions of users worldwide.
Scala is a modern programming language designed to express common programming patterns in a concise, elegant, and type-safe way. Why not combine both of them?
This document provides an overview of the Scala programming language and discusses its usage on Android. Scala is a multi-paradigm language that integrates object-oriented and functional programming. It runs on the Java Virtual Machine and is used by companies like LinkedIn, Twitter, and The Guardian. Scala's features include being object-oriented, functional, statically typed, and expressive while allowing concise code. It supports classes, traits, pattern matching, and lazy evaluation among other things.
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.
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.
This document introduces developing a Scala DSL for Apache Camel. It discusses using Scala techniques like implicit conversion, passing functions as parameters, by-name parameters and currying to build a DSL. It provides examples of building simple routes in the Scala DSL using these techniques and compares them to the Java DSL. It also covers some caveats like interactions between Java and Scala generics and operator precedence. The goal is to introduce basic Scala concepts and syntax that can be used to build a Scala DSL, using Apache Camel as an example.
An Introduction to Scala for Java DevelopersMiles Sabin
The document provides an introduction to Scala for Java developers. It outlines key features of Scala including cleaning up Java syntax, going beyond Java with features like case classes, traits, and pattern matching, and its functional focus including support for higher-order functions, the Option type, and for comprehensions. The document also briefly discusses the Scala IDE for Eclipse.
The great attractiveness of purely functional languages is their ability to depart from sequential order of computation. Theoretically, it enables two important features of the compiler:
1) The ability to reorder computation flow, making the program implicitly parallelisable. Modern imperative language compilers, even using careful synchronization of concurrent code, still generate huge chunks of sequential instructions that need to be executed on a single processor core; a purely functional language compilers can dispatch very small chunks to many (hundreds and thousands) of cores, carefully eliminating as many execution path dependencies as possible.
2) As the compiler formalizes different types of side effects, it can detect a whole new class of program errors at compile time, including resource acquisition and releasing problems, concurrent access to shared resources, many types of deadlocks etc. It is not yet a full-fledged program verification, but it is a big step in that direction.
Scala is a semi-imperative language with strong support for functional programming and rich type system. One can isolate the purely functional core of the language which can be put on the firm mathematical foundation of dependent type theories. We argue that it is possible to treat Scala code as it's written by now as an implicit do-notation which can be then reduced to a purely functional core by means of recently introduced Scala macros. The formalism of arrows and applicative contexts can bring Scala to a full glory of an implicitly parallelisable programming language, while still keeping its syntax mostly unchanged.
Here are the answers to your questions:
1. The main differences between a Trait and Abstract Class in Scala are:
- Traits can be mixed in to classes using with, while Abstract Classes can only be extended.
- Traits allow for multiple inheritance as they can be mixed in, while Abstract Classes only allow single inheritance.
- Abstract Classes can have fields and constructor parameters while Traits cannot.
- Abstract Classes can extend other classes, while Traits can only extend other Traits.
2. abstract class Animal {
def isMammal: Boolean
def isFriendly: Boolean = true
def summarize: Unit = {
println("Characteristics of animal:")
}
This document provides an introduction to Apache Spark, including its core components, architecture, and programming model. Some key points:
- Spark uses Resilient Distributed Datasets (RDDs) as its fundamental data structure, which are immutable distributed collections that allow in-memory computing across a cluster.
- RDDs support transformations like map, filter, reduce, and actions like collect that return results. Transformations are lazy while actions trigger computation.
- Spark's execution model involves a driver program that coordinates tasks on worker nodes using an optimized scheduler.
- Spark SQL, MLlib, GraphX, and Spark Streaming extend the core Spark API for structured data, machine learning, graph processing, and stream processing
Slides for my lightning talk at Config Management Camp 2016. See the video here: https://youtu.be/qJ0VNO6z68M
Writing Vagrantfiles is tedious, especially when you’re setting up a multi-VM environment. Typically, people will copy/paste code blocks that define hosts, but that becomes unwieldy. However, a Vagrantfile is “just” Ruby, so can’t we simplify things a bit using the power of the language? Turns out, we can! In this presentation I propose a reusable Vagrantfile that reads the configuration of the environment from a simple YAML file.
See my blog post about this at https://bertvv.github.io/notes-to-self/2015/10/05/one-vagrantfile-to-rule-them-all/
- The document discusses a presentation given by Jongwook Woo on introducing Spark and its uses for big data analysis. It includes information on Woo's background and experience with big data, an overview of Spark and its components like RDDs and task scheduling, and examples of using Spark for different types of data analysis and use cases.
This document provides an overview of a machine learning workshop including tutorials on decision tree classification for flight delays, clustering news articles with k-means clustering, and collaborative filtering for movie recommendations using Spark. The tutorials demonstrate loading and preparing data, training models, evaluating performance, and making predictions or recommendations. They use Spark MLlib and are run in Apache Zeppelin notebooks.
This Hadoop HDFS Tutorial will unravel the complete Hadoop Distributed File System including HDFS Internals, HDFS Architecture, HDFS Commands & HDFS Components - Name Node & Secondary Node. Not only this, even Mapreduce & practical examples of HDFS Applications are showcased in the presentation. At the end, you'll have a strong knowledge regarding Hadoop HDFS Basics.
Session Agenda:
✓ Introduction to BIG Data & Hadoop
✓ HDFS Internals - Name Node & Secondary Node
✓ MapReduce Architecture & Components
✓ MapReduce Dataflows
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What is HDFS? - Introduction to HDFS
The Hadoop Distributed File System provides high-performance access to data across Hadoop clusters. It forms the crux of the entire Hadoop framework.
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What are HDFS Internals?
HDFS Internals are:
1. Name Node – This is the master node from where all data is accessed across various directores. When a data file has to be pulled out & manipulated, it is accessed via the name node.
2. Secondary Node – This is the slave node where all data is stored.
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What is MapReduce? - Introduction to MapReduce
MapReduce is a programming framework for distributed processing of large data-sets via commodity computing clusters. It is based on the principal of parallel data processing, wherein data is broken into smaller blocks rather than processed as a single block. This ensures a faster, secure & scalable solution. Mapreduce commands are based in Java.
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What are HDFS Applications?
1. Data Mining
2. Document Indexing
3. Business Intelligence
4. Predictive Modelling
5. Hypothesis Testing
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Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance.
Email: sales@skillspeed.com
Website: https://www.skillspeed.com
Functional Programming for OO Programmers (part 2)Calvin Cheng
Code examples demonstrating Functional Programming concepts, with JavaScript and Haskell.
Part 1 can be found here - http://www.slideshare.net/calvinchengx/functional-programming-part01
Source code can be found here - http://github.com/calvinchengx/learnhaskell
Let me know if you spot any errors! Thank you! :-)
The document provides an agenda for a DevOps advanced class on Spark being held in June 2015. The class will cover topics such as RDD fundamentals, Spark runtime architecture, memory and persistence, Spark SQL, PySpark, and Spark Streaming. It will include labs on DevOps 101 and 102. The instructor has over 5 years of experience providing Big Data consulting and training, including over 100 classes taught.
This document provides an overview of Hadoop and related big data technologies. It begins with defining big data and discussing why traditional systems are inadequate. It then introduces Hadoop as a framework for distributed storage and processing of large datasets. The key components of Hadoop - HDFS for storage and MapReduce for processing - are described at a high level. HDFS architecture and read/write operations are outlined. MapReduce paradigm and an example word count job are also summarized. Finally, Hive is introduced as a data warehouse tool built on Hadoop that provides SQL-like queries for large datasets.
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
The document outlines an agenda for a conference on Apache Spark and data science, including sessions on Spark's capabilities and direction, using DataFrames in PySpark, linear regression, text analysis, classification, clustering, and recommendation engines using Spark MLlib. Breakout sessions are scheduled between many of the technical sessions to allow for hands-on work and discussion.
Advanced Data Science on Spark-(Reza Zadeh, Stanford)Spark Summit
The document provides an overview of Spark and its machine learning library MLlib. It discusses how Spark uses resilient distributed datasets (RDDs) to perform distributed computing tasks across clusters in a fault-tolerant manner. It summarizes the key capabilities of MLlib, including its support for common machine learning algorithms and how MLlib can be used together with other Spark components like Spark Streaming, GraphX, and SQL. The document also briefly discusses future directions for MLlib, such as tighter integration with DataFrames and new optimization methods.
This document outlines steps for developing analytic applications using Apache Spark and Python. It covers prerequisites for accessing flight and weather data, deploying a simple data pipe tool to build training, test, and blind datasets, and using an IPython notebook to train predictive models on flight delay data. The agenda includes accessing necessary services on Bluemix, preparing the data, training models in the notebook, evaluating model accuracy, and deploying models.
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
Scala is a powerful language for the JVM that is compatible with JDK 1.5+. It is a functional language maintained by Typesafe along with the community. Scala compiles to JVM bytecode like Java but has features like objects, traits, pattern matching and functional programming. James Gosling, the creator of Java, said that if he were to pick a language other than Java today, it would be Scala.
Scala is a multi-paradigm programming language that runs on the Java Virtual Machine. It is both object-oriented and functional, with support for immutable data and concise syntax. Scala has gained popularity due to its advantages like type safety, concurrency support, and interoperability with Java. However, some of Scala's advanced features can be difficult to read and use for beginners, and tooling support is not as robust as Java. Overall, Scala represents a promising approach that prioritizes simplicity over ease of use.
The document summarizes Scala as a replacement for Java, highlighting its capabilities as both an object-oriented and functional language that compiles to Java bytecode. It provides code examples demonstrating Scala's concise syntax for common tasks like defining classes, filtering collections, string reversal, and exception handling. The document also outlines an agenda for a Scala workshop covering its basic syntax, REPL/IDE setup, functions, pattern matching, OO features, and functional programming concepts.
The document provides an overview of the Scala programming language. It begins with an agenda that outlines topics like differences between Java and Scala, Scala data types, variables, classes, functions, closures, exception handling and collections. It then discusses specific aspects of Scala like verbosity reduction compared to Java, functional programming influences, object-oriented features, interoperability with Java and compilation to Java bytecode. Examples are provided to illustrate Scala concepts like functions, classes, recursion, higher-order functions and exception handling. The document aims to explain Scala and reasons for differences from Java.
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.
The document discusses Scala and why some developers think it could replace Java on the JVM. It provides quotes from several influential developers, including the creator of Java and Groovy, expressing their view that Scala is the best candidate to replace Java in the long run. Specifically, James Gosling says that if he had to pick another language on the JVM besides Java, it would be Scala. Charlie Nutter describes Scala as the current heir apparent to the Java throne and the momentum behind Scala is now unquestionable. James Strachan says that if he had seen the Programming in Scala book in 2003, he may have never created Groovy and that his tip for a long term replacement for Java is
Scala is a general purpose programming language designed to express common programming patterns in a concise, elegant, and type-safe way. It has established itself as one of the main alternative languages on the Java Virtual Machine, being used by companies like Twitter and LinkedIn. Scala fuses functional programming (from which it borrows higher-order functions and closures, generic typing and immutable data structures) and object-oriented programming programming (from which it takes inheritance and encapsulation). It interoperates fully with Java, allowing a smooth transition and access to all existing Java libraries.
Scala’s lightweight syntax makes it easy to extend the language through DSLs. In this talk we are going to have a quick overview of Scala’s main features (closures, higher-order functions, implicits), and collection classes in the standard library. We’ll see how a new concurrency model, such as actors, can be added to the language through a library.
Scala er et Java-relateret, statisk typet programmeringssprog i hastig fremmarch. Sproget kombinerer aspekter fra objekt- og funktionsorienterede sprog og fokuserer på skalerbarhed og effektivitet, både på det kodemæssige og afviklingsmæssige niveau. Syntaksen er elegant og koncis. Samtidig indeholder sproget stærke konstruktioner til understøttelse af parallelle applikationer, der udnytter fremtidens hardwarearkitekturer.
Java som sprog har ikke bevæget sig meget de seneste år. Vi har stadig ikke closures eller funktionelle aspekter som f.eks. C# har haft siden version 3. Er Scala svaret på enhver Javaudviklers bønner eller er sproget kun interessant for tågehoveder som mig, som begynder at synes bedre og bedre om funktionsorientering? Er den store portion syntaktisk sukker, Scala bringer på bordet, bare tomme kalorier?
Scala er et Java-relateret, statisk typet programmeringssprog i hastig fremmarch. Sproget kombinerer aspekter fra objekt- og funktionsorienterede sprog og fokuserer på skalerbarhed og effektivitet, både på det kodemæssige og afviklingsmæssige niveau. Syntaksen er elegant og koncis. Samtidig indeholder sproget stærke konstruktioner til understøttelse af parallelle applikationer, der udnytter fremtidens hardwarearkitekturer.
BCS SPA 2010 - An Introduction to Scala for Java DevelopersMiles Sabin
This document provides an introduction to Scala for Java developers. It outlines how Scala cleans up Java syntax by making semi-colons optional, using == for equals and eq for reference equality, and allowing periods to be omitted. It also describes how Scala goes beyond Java by supporting features like case classes, pattern matching, named and default arguments, and mixin composition with traits. The document provides examples of using these features and explains how Scala seamlessly interoperates with Java.
This document provides a summary of key Scala basics and comparisons to Java, including:
1) It describes type definitions, variables, methods and how they are defined in Scala versus Java. It also covers method invocation syntax differences.
2) It explains classes and constructors, traits versus interfaces, and static methods in Scala versus Java.
3) It provides examples of control structures like if/else, for loops, while loops and exceptions in both languages.
Qcon2011 functions rockpresentation_scalaMichael Stal
Scala functions provide powerful ways to decompose problems and harness the benefits of functional programming. The presentation introduces core concepts of functional programming using Scala as an example language, highlighting how Scala combines object-oriented and functional programming. It presents benefits like immutability, higher-order functions, and improved concurrency while avoiding past performance issues through modern virtual machines and libraries.
This document discusses design patterns in Scala compared to Java. It covers functional interface, command, builder, iterator, template method, strategy, null object, and decorator patterns. The agenda outlines discussing these patterns in part one covering OO patterns. Each pattern section provides a Java example and how it can be implemented in Scala using functions and other Scala features like case classes.
Softshake 2013: 10 reasons why java developers are jealous of Scala developersMatthew Farwell
Scala provides many features that simplify programming tasks compared to Java such as type inference, less verbose syntax, simpler definition of classes and collections, closures, pattern matching, implicit conversions, and concise concurrency constructs. However, Scala also has disadvantages like less mature tools compared to Java and lack of binary compatibility between major versions.
This presentation was presented at OSS camp in New Delhi. It deals with the basics of Scala language and how we can use it to build scalable Applications
Can two JVM languages, none of them Java, work harmoniously, side by side? Should they? In this lecture we’ll see how code written in Groovy and Scala work with Java and how they can be made to work with each other. The lecture is based on the experience of introducing Scala to an actual Groovy project.
This document provides an introduction to Scala concepts and features compared to Java, including how to set up Scala, the Simple Build Tool (SBT), case classes, lazy definitions, imports, objects, pattern matching, collections, higher-order functions, partial functions, currying, implicit conversions, and implicit parameters. Useful Scala resources are also listed.
Linux Enterprise - inleiding cursus, 5 trends in systeembeheerBert Van Vreckem
Dit zijn de slides voor de introductieles van de cursus Enterprise Linux, opleiding ProfBach Toegepaste Informatica, HoGent Bedrijf en Organisatie. Bekijk de opname op http://youtu.be/GGTS37Tsl-M
A Reinforcement Learning Approach for Hybrid Flexible Flowline Scheduling Pro...Bert Van Vreckem
Paper presented at MISTA2013, Gent.
In this paper, we present a method based on Learning Automata to solve Hybrid Flexible Flowline Scheduling Problems (HFFSP) with additional constraints like sequence dependent setup times, precedence relations between jobs and machine eligibility. This category of production scheduling problems is noteworthy because it involves several types of constraints that occur in complex real-life production scheduling problems like those in process industry and batch production. In the proposed technique, Learning Automata play a dispersion game to determine the order of jobs to be processed in a way that makespan is minimized, and precedence constraint violations are avoided. Experiments on a set of benchmark problems indicate that this method can yield better results than the ones known until now.
Hoe zoek je informatie op voor een scriptie, welke tools zijn er om die efficient te verzamelen en bij te houden, hoe verwerk je dit tot een literatuurstudie met bijhorende referentielijst?
In deze presentatie die ik gaf aan collega's, licht ik enkele topics toe uit deze boeken:
- Robert C. Martin, Clean Code
- Joshua Bloch, Effective Java
Beyond the Advance Presentation for By the Book 9John Rodzvilla
In June 2020, L.L. McKinney, a Black author of young adult novels, began the #publishingpaidme hashtag to create a discussion on how the publishing industry treats Black authors: “what they’re paid. What the marketing is. How the books are treated. How one Black book not reaching its parameters casts a shadow on all Black books and all Black authors, and that’s not the same for our white counterparts.” (Grady 2020) McKinney’s call resulted in an online discussion across 65,000 tweets between authors of all races and the creation of a Google spreadsheet that collected information on over 2,000 titles.
While the conversation was originally meant to discuss the ethical value of book publishing, it became an economic assessment by authors of how publishers treated authors of color and women authors without a full analysis of the data collected. This paper would present the data collected from relevant tweets and the Google database to show not only the range of advances among participating authors split out by their race, gender, sexual orientation and the genre of their work, but also the publishers’ treatment of their titles in terms of deal announcements and pre-pub attention in industry publications. The paper is based on a multi-year project of cleaning and evaluating the collected data to assess what it reveals about the habits and strategies of American publishers in acquiring and promoting titles from a diverse group of authors across the literary, non-fiction, children’s, mystery, romance, and SFF genres.
Split Shifts From Gantt View in the Odoo 17Celine George
Odoo allows users to split long shifts into multiple segments directly from the Gantt view.Each segment retains details of the original shift, such as employee assignment, start time, end time, and specific tasks or descriptions.
Lecture_Notes_Unit4_Chapter_8_9_10_RDBMS for the students affiliated by alaga...Murugan Solaiyappan
Title: Relational Database Management System Concepts(RDBMS)
Description:
Welcome to the comprehensive guide on Relational Database Management System (RDBMS) concepts, tailored for final year B.Sc. Computer Science students affiliated with Alagappa University. This document covers fundamental principles and advanced topics in RDBMS, offering a structured approach to understanding databases in the context of modern computing. PDF content is prepared from the text book Learn Oracle 8I by JOSE A RAMALHO.
Key Topics Covered:
Main Topic : DATA INTEGRITY, CREATING AND MAINTAINING A TABLE AND INDEX
Sub-Topic :
Data Integrity,Types of Integrity, Integrity Constraints, Primary Key, Foreign key, unique key, self referential integrity,
creating and maintain a table, Modifying a table, alter a table, Deleting a table
Create an Index, Alter Index, Drop Index, Function based index, obtaining information about index, Difference between ROWID and ROWNUM
Target Audience:
Final year B.Sc. Computer Science students at Alagappa University seeking a solid foundation in RDBMS principles for academic and practical applications.
About the Author:
Dr. S. Murugan is Associate Professor at Alagappa Government Arts College, Karaikudi. With 23 years of teaching experience in the field of Computer Science, Dr. S. Murugan has a passion for simplifying complex concepts in database management.
Disclaimer:
This document is intended for educational purposes only. The content presented here reflects the author’s understanding in the field of RDBMS as of 2024.
Feedback and Contact Information:
Your feedback is valuable! For any queries or suggestions, please contact muruganjit@agacollege.in
Ardra Nakshatra (आर्द्रा): Understanding its Effects and RemediesAstro Pathshala
Ardra Nakshatra, the sixth Nakshatra in Vedic astrology, spans from 6°40' to 20° in the Gemini zodiac sign. Governed by Rahu, the north lunar node, Ardra translates to "the moist one" or "the star of sorrow." Symbolized by a teardrop, it represents the transformational power of storms, bringing both destruction and renewal.
About Astro Pathshala
Astro Pathshala is a renowned astrology institute offering comprehensive astrology courses and personalized astrological consultations for over 20 years. Founded by Gurudev Sunil Vashist ji, Astro Pathshala has been a beacon of knowledge and guidance in the field of Vedic astrology. With a team of experienced astrologers, the institute provides in-depth courses that cover various aspects of astrology, including Nakshatras, planetary influences, and remedies. Whether you are a beginner seeking to learn astrology or someone looking for expert astrological advice, Astro Pathshala is dedicated to helping you navigate life's challenges and unlock your full potential through the ancient wisdom of Vedic astrology.
For more information about their courses and consultations, visit Astro Pathshala.
Front Desk Management in the Odoo 17 ERPCeline George
Front desk officers are responsible for taking care of guests and customers. Their work mainly involves interacting with customers and business partners, either in person or through phone calls.
Delegation Inheritance in Odoo 17 and Its Use CasesCeline George
There are 3 types of inheritance in odoo Classical, Extension, and Delegation. Delegation inheritance is used to sink other models to our custom model. And there is no change in the views. This slide will discuss delegation inheritance and its use cases in odoo 17.
How to Configure Time Off Types in Odoo 17Celine George
Now we can take look into how to configure time off types in odoo 17 through this slide. Time-off types are used to grant or request different types of leave. Only then the authorities will have a clear view or a clear understanding of what kind of leave the employee is taking.
Integrated Marketing Communications (IMC)- Concept, Features, Elements, Role of advertising in IMC
Advertising: Concept, Features, Evolution of Advertising, Active Participants, Benefits of advertising to Business firms and consumers.
Classification of advertising: Geographic, Media, Target audience and Functions.
The Jewish Trinity : Sabbath,Shekinah and Sanctuary 4.pdfJackieSparrow3
we may assume that God created the cosmos to be his great temple, in which he rested after his creative work. Nevertheless, his special revelatory presence did not fill the entire earth yet, since it was his intention that his human vice-regent, whom he installed in the garden sanctuary, would extend worldwide the boundaries of that sanctuary and of God’s presence. Adam, of course, disobeyed this mandate, so that humanity no longer enjoyed God’s presence in the little localized garden. Consequently, the entire earth became infected with sin and idolatry in a way it had not been previously before the fall, while yet in its still imperfect newly created state. Therefore, the various expressions about God being unable to inhabit earthly structures are best understood, at least in part, by realizing that the old order and sanctuary have been tainted with sin and must be cleansed and recreated before God’s Shekinah presence, formerly limited to heaven and the holy of holies, can dwell universally throughout creation
AI Risk Management: ISO/IEC 42001, the EU AI Act, and ISO/IEC 23894PECB
As artificial intelligence continues to evolve, understanding the complexities and regulations regarding AI risk management is more crucial than ever.
Amongst others, the webinar covers:
• ISO/IEC 42001 standard, which provides guidelines for establishing, implementing, maintaining, and continually improving AI management systems within organizations
• insights into the European Union's landmark legislative proposal aimed at regulating AI
• framework and methodologies prescribed by ISO/IEC 23894 for identifying, assessing, and mitigating risks associated with AI systems
Presenters:
Miriama Podskubova - Attorney at Law
Miriama is a seasoned lawyer with over a decade of experience. She specializes in commercial law, focusing on transactions, venture capital investments, IT, digital law, and cybersecurity, areas she was drawn to through her legal practice. Alongside preparing contract and project documentation, she ensures the correct interpretation and application of European legal regulations in these fields. Beyond client projects, she frequently speaks at conferences on cybersecurity, online privacy protection, and the increasingly pertinent topic of AI regulation. As a registered advocate of Slovak bar, certified data privacy professional in the European Union (CIPP/e) and a member of the international association ELA, she helps both tech-focused startups and entrepreneurs, as well as international chains, to properly set up their business operations.
Callum Wright - Founder and Lead Consultant Founder and Lead Consultant
Callum Wright is a seasoned cybersecurity, privacy and AI governance expert. With over a decade of experience, he has dedicated his career to protecting digital assets, ensuring data privacy, and establishing ethical AI governance frameworks. His diverse background includes significant roles in security architecture, AI governance, risk consulting, and privacy management across various industries, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: June 26, 2024
Tags: ISO/IEC 42001, Artificial Intelligence, EU AI Act, ISO/IEC 23894
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1. Scala workshop
Created by
Fredrik Vraalsen (fredrik@vraalsen.no)
and
Alf Kristian Støyle (alf.kristian@gmail.com)
Adapted
Bert Van Vreckem (bert.vanvreckem@hogent.be)
2. If I were to pick a language to use
today other than Java, it would be
Scala.
James Gosling
3. Scala, it must be stated, is the current heir
apparent to the Java throne. No other language
on the JVM seems as capable of being a
"replacement for Java" as Scala, and the
momentum behind Scala is now unquestionable.
Charlies Olivier Nutter - JRuby lead
4. Though my tip though for the long term
replacement of javac is Scala. I'm very
impressed with it! I can honestly say if
someone had shown me the Programming
in Scala book by by Martin Odersky, Lex
Spoon & Bill Venners back in 2003 I'd
probably have never created Groovy.
James Strachen
5. public class Person {
private int age;
private String name;
public Person(int age, String name) {
this.age = age;
this.name = name;
}
public int getAge() {
return this.age;
}
public void setAge(int age) {
this.age = age;
}
public String getName() {
return this.name;
}
public void setName(String name) {
this.name = name;
}
}
class Person(var age: Int,
var name: String)
6. List<Person> persons = ...
List<Person> adults = new LinkedList<Person>();
List<Person> kids = new LinkedList<Person>();
for (Person person : persons) {
if (person.getAge() < 18) {
kids.add(person);
} else {
adults.add(person);
}
}
val (kids, adults) =
persons.partition(_.age < 18)
7. String s = "!em esreveR";
System.out.println(s.reverse());
val s = "!em esreveR"
println(s.reverse)
=> Reverse me!
16. Variables
Scala: Java:
val s = "Hello World" public final String s =
"Hello World";
var i = 1
public int i = 1;
private var j = 3
private int j = 3;
18. Classes and constructors
Scala: Java:
class Person(val name: public class Person {
String) private final String
name;
public Person(String
name) {
this.name = name;
}
public String getName() {
return name;
}
}
19. Traits (= Interface + Mixin)
Scala: Java:
trait Shape { interface Shape {
def area: Double public double area();
} }
class Circle extends Object public class Circle extends
with Shape Object implements Shape
20. No “static” in Scala
Scala: Java:
object PersonUtil { public class PersonUtil {
val ageLimit = 18 public static final int
AGE_LIMIT = 18;
def countPersons( public static int
persons: List[Person]) = countPersons(
... List<Person> persons)
} {
...
}
}
23. Varargs
def foo(values: String*){ } public void foo(String... values){ }
foo("bar", "baz") foo("bar", "baz");
val arr = Array("bar", "baz") String[] arr =
new String[]{"bar", "baz"}
foo(arr: _*) foo(arr);
24. (Almost) everything is an expression
val res = if (foo) x else y
val res = for (i <- 1 to 10) yield i
// List(1, ..., 10)
val res = try { x } catch { ...; y }
finally { } // x or y
30. REPL: Read-Eval-Print Loop
● Command line shell for on-the-fly execution of
Scala statements
●
$ cd ${SCALA_HOME}/bin
$ scala
● Windows, e.g. C:Program FilesScala 2.8.0
● Linux, e.g. /opt/scala or /usr/local/scala
31. IDE
● They are all !#$&§? compared to what you are
used to with Java support
● Netbeans (very good) but bad in other areas...
● IntelliJ IDEA (pretty good) but slow compilation,
bonus community edition is free (with Scala)
● Eclipse (not all that good) but very fast when
working
32. Netbeans 6.9.x installation
● Download plugins
http://sf.net/projects/erlybird/files/nb-scala/6.9v1.1.0/
● In NetBeans "Tools" | "Plugins",
● click on "Downloaded" tab title,
● click on "Add Plugins..." button,
● choose the directory where the Scala plugins are unzipped,
● select all listed *.nbm files, following the instructions.
● Set ${SCALA_HOME} or %SCALA_HOME% environment variable
● Edit ${NETBEANS_HOME}/etc/netbeans.conf
● Add "-J-Dscala.home=/opt/scala" to
netbeans_default_options
● More info: http://wiki.netbeans.org/Scala
33. Tasks (20 min)
● Run REPL
● Windows: %scala_home%/bin/scala
● Linux: scala or /opt/scala/bin/scala
● Execute a few statements
● Copy & unpack
'HeliumPublicCursussenNavorming
ScalaScalaTraining.zip'
● into 'DocumentenNetbeansProjects' (Windows)
● Into '/home/student/NetbeansProjects' (Linux)
34. Tasks (20 min)
● Run unit test in IDE (JUnit 4)
● Open the 'ScalaTraining' project in NetBeans
● Try to compile and test the project (should work)
● scalaexamples.intro.MyFirstTest
– Create a failing test
– Make it run
● Make the two classes scalaexamples/intro/HelloWorld.scala
print “Hello world”. What is the difference?
● Remove comments from @Test for the methods in
scalaexamples/intro/CreateStuffTest.scala. Make tests pass.
36. First class functions
val even = Function[Int, Boolean] {
def apply(i: Int) = i % 2 == 0
}
val even: (Int => Boolean) = (i: Int) => i % 2 == 0
val even = (i: Int) => i % 2 == 0
even.apply(42) // true
even(13) // false
37. First class functions
val numbers = List(1, 2, 3, 4, 5)
numbers.filter(even) // List(2, 4)
numbers.filter((i: Int) => i > 2) // List(3, 4, 5)
numbers.filter(i => i > 2) // List(3, 4, 5)
numbers.filter(_ > 2) // List(3, 4, 5)
38. Collections
numbers.filter(i => i > 2) // List(3, 4, 5)
numbers.find(i => i > 2) // Some(3)
numbers.exists(i => i > 2) // true
numbers.forall(i => i > 2) // false
numbers.map(i => i * 2) // List(2, 4, 6, 8, 10)
numbers.foldLeft(0) { (a, b) => a + b } // 15
39. Closures
val people = List(Person(“Alf”), Person(“Fredrik”))
var name = “Fredrik”
val nameFilter = (p: Person) => p.name == name
people.filter(nameFilter) // Person(“Fredrik”)
name = “Alf”
people.filter(nameFilter) // Person(“Alf”)
40. Tasks (30 min)
● Open the 'first-class-functions' project
● Tests in package
scalaexamples.firstclassfunctions
● Add @Test to one and one method
● Follow instructions in the code
● Make the tests pass
42. myObject match {
case 1 => println("First was hit")
case 2 => println("Second was Hit")
case _ => println("Unknown")
}
43. myObject match {
case i: Int => println("Found an int")
case s: String => println("Found a String")
case _ => println("Unknown")
}
44. myObject match {
case i: Int => println("Found an int")
case s: String => println("Found a String")
case other => println("Unknown " + other)
}
45. myObject match {
case i: Int if i == 1 => println("Found an int")
case s: String => println("Found a String")
case other => println("Unknown " + other)
}
46. val res = myObject match {
case i: Int if i == 1 => "Found an int"
case s: String => "Found a String"
case other => "Unknown " + other
}
47. val res = myObject match {
case (first, second) => second
case (first, second, third) => third
}
48. val mathedElement = list match {
case List(firstElement, lastElement) => lastElement
case List(firstElement, _ *) => firstElement
case _ => "failed"
}
50. public static Integer getSecondOr0(List<Integer> list) {
if (list != null && list.size() >= 2) {
return list.get(1);
} else {
return 0;
}
}
def second_or_0(list:List[Int]) = list match {
case List(_, x, _*) => x
case _ => 0
}
51. Case classes
● Class types that can be used in pattern
matching
● Generated into your class:
● equals
● hashCode
● toString
● getters (and optionally setters)
● ++
52. abstract class Person(name: String)
case class Man(name: String) extends Person(name)
case class Woman(name: String, children: List[Person])
extends Person(name)
53. p match {
case Man(name) => println("Man with name " + name)
case Woman(name, children) => println("Woman with name "
+ name + " and with " +
children.size + " children")
}
54. val regex = """(d+)(w+)""".r
val myString = ...
val res: String = myString match {
case regex(digits, word) => digits
case _ => "None"
}
55. val regex = """(d+)(w+)""".r
val myString = ...
val res: Option[String] = myString match {
case regex(digit, word) => Some(digit)
case _ => None
}
56. The Option type, never again
NullPointerException
● Option has two possible values
● Some(value)
● None
val someOption: Option[String] = Some("value")
val noOption: Option[String] = None
57. def getValue(s: Any): Option[String]
getValue(object) match {
case Some(value) => println(value)
case None => println("Nothing")
}
val result = getValue(object).getOrElse("Nothing")
58. Tasks (30 min)
● Open the 'pattern-matching' project
● Tests in package
scalaexamples.patternmatching
● Add @Test to one and one method
● Follow instructions in the code
● Make the tests pass
60. Annotations – not marker interfaces
@serializable class Person
@SerialVersionUID(1) class Person
@cloneable class Person
@remote class Service
61. object
● object is a “singleton” class
● Call may look like static method calls in Java
● Can inherit from other classes and traits
● Can be passed as a reference
object MyObject {
def foo = "bar"
}
var baz = MyObject.foo
val personObject = MyObject
baz = personObject.foo
62. Companion object
● Can read the companion class' private fields
● Has to be in the same source file
class Person(private val age: Int)
object Person {
def getPersonAge(p: Person) = p.age
}
val personInstance = new Person(30)
val age = Person.getPersonAge(personInstance)
63. Magical apply
class Person private(val age: Int)
object Person {
def apply(age: Int) = new Person(age)
}
var personInstance = Person.apply(30)
personInstance = Person(30)
64. Not built in, clever use of apply
val myList = List(1, 2, 3)
val mySet = Set(1, 2, 3)
val myMap = Map(1 -> "one", 2 -> "two")
65. Constructors
● Always one primary constructor
● Parameters are automatically instance variables
● Class “body” is the primary constructors content
● Auxiliary constructors MUST call or chain to primary
constructor
class MyClass(myString: String, myInt: Int)
val myOtherInt = 10
println("In main body")
}
72. The Ordered trait
class Person(private val age: Int)
extends Ordered[Person] {
def compare(other: Person) =
this.age - other.age
}
val person1 = new Person(21)
val person2 = new Person(31)
person1 < person2 // true
person1 <= person2 // true
person1 >= person2 // false
73. “Dynamic mixins”
class Person(age: Int) {
override def toString = "my age is " + age
}
trait MyTrait {
override def toString = "I am sure " +
super.toString
}
val person = new Person(30) with MyTrait
println(person)
=> I am sure my age is 30
74. is-a vs has-a
● Inheritance as usual
● Easier to implement has-a
● Inheritance vs composition
75. Tasks (30 min)
● Open 'oo-traits' project
● Tests under scalaexamples
● companionobject
● inheritance
● traits
● Add @Test to one and one method
● Follow instructions in code
● Make tests pass
78. Functional programming
● Purity
● Mathematical functions have no side effects
● f(x) = 2x + 3
● y = sin(x)
● Mathematical functions always give same result
for same input
● Only immutable objects (and object graphs)
79. In practice
● All fields must be immutable
● All methods must return something
● No side-effects from method calls
●
println("...") is also a side effect!
80. List
● head :: tail
● Prepends to the head of the list
● Other operations (filter, map, remove,
partition...) returns a new List instance
85. Refactoring imperative style code
● Side effect: printing table
● Functional style: return String
● Harder to test
● How to test result of println()?
● While loop & vars
● Functional style: val, for expressions, helper
functions
● Helper functions can be tested separately
86. Functional style multiplication table
def makeRowSeq(row: Int) =
for (col <- 1 to 10) yield {
val prod = (row * col).toString
val padding = " " * (4 - prod.length)
padding + prod
}
def makeRow(row: Int) = makeRowSeq(row).mkString
def multiTable = {
val tableSeq =
for (row <- 1 to 10)
yield makeRow(row)
tableSeq.mkString("n")
}
87. A closer look at Collections
● Overview of collection traits
http://www.decodified.com/scala/collections-api.xml
89. Iterable
● Abstract method iterator
● Default implementation of foreach:
def foreach[U](f:Elem => U): Unit = {
val it = iterator
while (it.hasNext) f(it.next())
}
● Subclasses may override
● Some concrete methods
● Subcollections, "zippers", comparison
90. Seq, IndexedSeq, LinearSeq
● Sequence:
● iterable that has a length,
● elements have fixed index position, starting with 0
● Operations:
● Indexing (apply), search, addition, update, sorting,
reversal, comparison, set operations
● IndexedSeq, LinearSeq
● No new operations, but different performance
– LinearSeq: efficient head, tail
– IndexedSeq: efficient apply, length
91. Sets, SortedSet
● Set = iterable that contain no duplicates
● Operations for tests, addition, removal, set
operations
● SortedSet
● Iterator/foreach visit elements in given ordering
● Default implementation: binary tree
92. Maps
● Map = collection of pairs of keys and values
e.g. Map("x" -> 24, "y" -> 25, "z" -> 26)
● Operations for lookup, addition/update, removal,
subcollections, transformations
97. Higher order functions
Short summary of first class functions:
val even: (Int => Boolean) = (i: Int) => i % 2 == 0
Same type definition:
def test(numbers: List[Int], f: Int => Boolean) = ...
Call:
test(List(1, 2, 3), (i: Int) => i % 2 == 0)
98. Higher order functions
def test(numbers: List[Int],
f: Int => Boolean) =
numbers.map(tall => f(tall))
// List[Boolean]
99. Higher order functions
Functions with several parameters must list them
in parenthesis:
def test(l: List[String],
f: (Int, String) => Boolean)
100. call-by-value vs. call-by-name
● by-value: expressions are evaluated before
being passed to the function
● by-name: expressions evaluated inside function
● nice when computationally expensive
● possible to create nice APIs
105. def using[T <: { def close() }, A]
(closeable: T)
(f: T => A) = {
try {
f(closeable)
} finally {
if (closeable != null) {
try {
closeable.close()
}
catch {
case e: Exception =>
// Do something clever!?
}
}
}
}
106. Tasks (30 min)
● Open 'higher-order-functions' project
● Tests in scalaexamples.higherorderfunctions
● Add @Test to one and one method
● Implement missing functions in PersonFilter
and so on.
● Follow instructions in code
● Make tests pass