This document discusses ReactiveX and RxJS. ReactiveX is a library for reactive programming that originated from the Volta project in 2007. It has implementations in many languages including RxJS for JavaScript. RxJS uses Observables to handle asynchronous data streams from sources like user input, animations, and network events. Observables are lazy push collections that can be composed using operators. RxJS is used by Netflix to handle streaming large amounts of asynchronous data.
Reactive programming uses observable streams to provide an API for asynchronous programming. Observables represent lazy push-based collections of multiple values over time. They address issues with callbacks such as callback hell and lack of error handling. Observables are cold (replayed on subscribe) or hot (shared between subscribers). Common methods include just(), of(), fromArray(), fromPromise(), fromEvent(), and fromCallback() to create observables from different sources. Operators like map(), flatMap(), and switchMap() transform streams, while retryWhen() and retry() handle errors.
This document introduces Functional Reactive Programming (FRP) using RxJS. FRP is a programming paradigm for reactive programming with asynchronous data streams. RxJS is a library for FRP in JavaScript that allows programming with asynchronous data streams from various sources like files, web services, and user events. The document provides an overview of RxJS, demonstrates its usage, and discusses why FRP is useful for creating cleaner and more maintainable code when dealing with asynchronous processes and events.
Introductory presentation for the Clash of Technologies: RxJS vs RxJava event organized by SoftServe @ betahouse (17.01.2015). Comparison document with questions & answers available here: https://docs.google.com/document/d/1VhuXJUcILsMSP4_6pCCXBP0X5lEVTsmLivKHcUkFvFY/edit#.
This document provides an overview of Kotlin backend development with a focus on GraphQL and REST APIs. Key points include:
- The author has over 10 years of experience with functional reactive full stack development using Kotlin.
- GraphQL is introduced as an API format developed by Facebook that is strongly typed, self-documenting, and allows clients to specify the data they need in one request.
- Frameworks like Apollo and additional libraries can expand GraphQL's capabilities by adding features like caching, monitoring, and schema stitching.
- The author focuses on using Apollo for its support across platforms like Kotlin, JavaScript, iOS, and Android. Reasons for choosing Apollo include its wide backend support
This document discusses migrating from a GraphQL API built with Graphene and Django to a REST API using MMT. It begins by explaining GraphQL and Graphene, noting that Graphene is a library for building GraphQL APIs and Graphene-Django allows reusing Django code. The document then considers some limitations of the GraphQL approach including permissions, debugging, and performance issues. It concludes by suggesting how to simplify the data flow and architecture when moving to MMT for the API.
This document discusses using Spring Boot and Kotlin together. It outlines some benefits of Kotlin like less verbosity and null safety compared to Java. It also discusses some common pitfalls when using JPA with Kotlin like needing no-arg constructors. Finally, it provides examples of where the speaker has used Spring Boot and Kotlin together like for streaming and CRUD applications.
This document compares Java and Scala for web development using the Playframework 2.5.x. It provides an overview of Scala and the Playframework, including how to set up a Play application with Scala. It demonstrates building a sample web application for a speaker database using Play and Scala, including setting up the project structure, models, database integration, JSON handling, testing, and deployment. The document aims to demonstrate how to develop a web application from start to finish using the Playframework and Scala.
This document discusses ReactiveX and RxJS. ReactiveX is a library for reactive programming that originated from the Volta project in 2007. It has implementations in many languages including RxJS for JavaScript. RxJS uses Observables to handle asynchronous data streams from sources like user input, animations, and network events. Observables are lazy push collections that can be composed using operators. RxJS is used by Netflix to handle streaming large amounts of asynchronous data.
Reactive programming uses observable streams to provide an API for asynchronous programming. Observables represent lazy push-based collections of multiple values over time. They address issues with callbacks such as callback hell and lack of error handling. Observables are cold (replayed on subscribe) or hot (shared between subscribers). Common methods include just(), of(), fromArray(), fromPromise(), fromEvent(), and fromCallback() to create observables from different sources. Operators like map(), flatMap(), and switchMap() transform streams, while retryWhen() and retry() handle errors.
This document introduces Functional Reactive Programming (FRP) using RxJS. FRP is a programming paradigm for reactive programming with asynchronous data streams. RxJS is a library for FRP in JavaScript that allows programming with asynchronous data streams from various sources like files, web services, and user events. The document provides an overview of RxJS, demonstrates its usage, and discusses why FRP is useful for creating cleaner and more maintainable code when dealing with asynchronous processes and events.
Introductory presentation for the Clash of Technologies: RxJS vs RxJava event organized by SoftServe @ betahouse (17.01.2015). Comparison document with questions & answers available here: https://docs.google.com/document/d/1VhuXJUcILsMSP4_6pCCXBP0X5lEVTsmLivKHcUkFvFY/edit#.
This document provides an overview of Kotlin backend development with a focus on GraphQL and REST APIs. Key points include:
- The author has over 10 years of experience with functional reactive full stack development using Kotlin.
- GraphQL is introduced as an API format developed by Facebook that is strongly typed, self-documenting, and allows clients to specify the data they need in one request.
- Frameworks like Apollo and additional libraries can expand GraphQL's capabilities by adding features like caching, monitoring, and schema stitching.
- The author focuses on using Apollo for its support across platforms like Kotlin, JavaScript, iOS, and Android. Reasons for choosing Apollo include its wide backend support
This document discusses migrating from a GraphQL API built with Graphene and Django to a REST API using MMT. It begins by explaining GraphQL and Graphene, noting that Graphene is a library for building GraphQL APIs and Graphene-Django allows reusing Django code. The document then considers some limitations of the GraphQL approach including permissions, debugging, and performance issues. It concludes by suggesting how to simplify the data flow and architecture when moving to MMT for the API.
This document discusses using Spring Boot and Kotlin together. It outlines some benefits of Kotlin like less verbosity and null safety compared to Java. It also discusses some common pitfalls when using JPA with Kotlin like needing no-arg constructors. Finally, it provides examples of where the speaker has used Spring Boot and Kotlin together like for streaming and CRUD applications.
This document compares Java and Scala for web development using the Playframework 2.5.x. It provides an overview of Scala and the Playframework, including how to set up a Play application with Scala. It demonstrates building a sample web application for a speaker database using Play and Scala, including setting up the project structure, models, database integration, JSON handling, testing, and deployment. The document aims to demonstrate how to develop a web application from start to finish using the Playframework and Scala.
Implementing GraphQL API in Elixir – Victor DeryaginElixir Club
The document discusses Teachers Pay Teachers' experience implementing a GraphQL API in Elixir. Some key points:
- TpT has used Elixir and GraphQL for around 2 years to power their API, handling 2000 requests per second and significant backend work.
- Their tech stack includes Phoenix, Absinthe, Ecto, React/Apollo Client. Challenges included access control, evolving schemas, batching queries, and distributed tracing.
- Absinthe provided features like middleware, introspection and separation of schemas. Elixir tools like OTP processes helped with scalability and robustness.
- Overall they are happy with Elixir and GraphQL but note ongoing effort is
The word "Reactive" can be confusing. As the founder of the Reactive Amsterdam meetup, I can tell there are two main topics here: Functional Reactive Programming (here with reference to Android) and "Reactive" in the sense of the Reactive Manifesto.
River Trail: A Path to Parallelism in JavaScriptRoberto Falconi
River Trail enables new web usages with positive impact on performances using high-level parallel patterns, bounds checked array accesses, automatic heap management and familiar JavaScript libraries.
This document compares the performance of for loops, iterators, and Java 8 streams for processing collections of objects. It describes benchmark tests performed on different collection sizes ranging from 10 to 1,000,000 objects. The tests focused on common collection operations like finding the youngest/highest paid object, filtering by a property, and grouping by a property. The results showed that streams were generally faster than for loops for larger collections (>1000 objects) but slower for smaller collections. Iterators had the best performance overall. The document concludes that while streams offer a functional programming style, traditional for loops may still be better for smaller collections or certain object types due to stream overhead.
All About GRAND Stack: GraphQL, React, Apollo, and Neo4j (Mark Needham) - Gre...GreeceJS
In this presentation, we explore application development using the GRAND stack (GraphQL, React, Apollo, Neo4j) for building web applications backed by a graph database. We will review the components to build a simple web application, including how to build a React component, an introduction to JSX, an overview of GraphQL and why it is a game-changer for front-end development. We'll learn how to model, store, and query data in the Neo4j graph database using GraphQL to power our web application.
Usually we hear a lot about “great success” however we learn a lot more from mistakes which help to gain experience and skills.
In this talk I'm going to share challenges team faced with on a project which started with simple requirement to implement a replacement for an old core business system in a like-for-like fashion with new technologies. That led to explosion of different issues and challenges during implementation and eventually to stabilization and to a long awaited chance to go-live.
Can someone tell me quickly, what typescript is? Yes, here it is. If you know JavaSctipt, this is a quick intro to jump into typescript as fast as possible.
1. The document discusses the process of productionalizing a financial analytics application built on Spark over multiple iterations. It started with data scientists using Python and data engineers porting code to Scala RDDs. They then moved to using DataFrames and deployed on EMR.
2. Issues with code quality and testing led to adding ScalaTest, PR reviews, and daily Jenkins builds. Architectural challenges were addressed by moving to Databricks Cloud which provided notebooks, jobs, and throwaway clusters.
3. Future work includes using Spark SQL windows and Dataset API for stronger typing and schema support. The iterations improved the code, testing, deployment, and use of latest Spark features.
This document discusses how to perform similar image search using OpenCV. It explains that images are first preprocessed through grayscale conversion, blurring, and histogram equalization. Interesting image features are then extracted using techniques like Hessian, FAST, and Laplacian of Gaussian detectors. These features are matched between the input image and a feature database using fast lookup and FLANN. OpenCV is recommended for its implementation of computer vision algorithms and hardware acceleration support, though it can have issues with compilation and unstable APIs.
Python is popular amongst data scientists and engineers for data processing tasks. The big data ecosystem has traditionally been rather JVM centric. Often Java (or Scala) are the only viable option to implement data processing pipelines. That sometimes poses an adoption barrier for organizations that have already invested in other language ecosystems. The Apache Beam project provides a unified programming model for data processing and its ongoing portability effort aims to enable multiple language SDKs (currently Java, Python and Go) on a common set of runners. The combination of Python streaming on the Apache Flink runner is one example. Let’s take a look how the Flink runner translates the Beam model into the native DataStream (or DataSet) API, how the runner is changing to support portable pipelines, how Python user code execution is coordinated with gRPC based services and how a sample pipeline runs on Flink.
This document discusses the design alternatives for a medical record system project at a hospital. It presents a Morph chart evaluating options for the database, programming language, operating system, and web language. Three feasible solutions are considered: a program, mobile app, and web app. A numerical evaluation matrix assigns scores in areas like expandability, stability, and accessibility. Based on the highest total score, the final proposed solution is a web app developed with Ruby on Rails using a MySQL database.
This document discusses functional programming concepts in Ruby, including lambda calculus, LISP, anonymous functions, higher order functions, immutability, and pure functions. While Ruby is not a purely functional language, it incorporates some functional traits like anonymous functions, higher order functions that accept or return functions, and immutable strings. The document also briefly mentions advantages of functional programming like readability, modularity, concurrency and testability, as well as some disadvantages and related functional languages.
Adopting language server for apache camel feedback from a java/Eclipse plugi...Aurélien Pupier
Adopting language server for apache camel feedback from a java/eclipse plugin developer perspective
https://github.com/camel-tooling/camel-language-server
- Fluent Decorator
- In, Out, In, Out, Shake It All About
- The Lonely Override
- Monad
- Functional Reactive?
你可以在以下找到中文說明:
http://www.codedata.com.tw/social-coding/java8-patterns/
This document summarizes the migration of a location services application from Ruby to Scala at Whitepages. It discusses key areas of interest over the software development lifecycle. For prototyping, Ruby was faster due to its dynamic typing. However, for production, Scala was better suited due to its static typing catching errors, built-in support for concurrency using Futures, and better performance in terms of throughput, latency and hardware utilization. Both languages are object-oriented, readable and composable, but Scala integrates better with the JVM and has advantages for maintainability and integration.
RESTful Machine Learning with Flask and TensorFlow Serving - Carlo MazzaferroPyData
Those of us who use TensorFlow often focus on building the model that's most predictive, not the one that's most deployable. So how to put that hard work to work? In this talk, we'll walk through a strategy for taking your machine learning models from Jupyter Notebook into production and beyond.
My talk from SICS Data Science Day, describing FlinkML, the Machine Learning library for Apache Flink.
I talk about our approach to large-scale machine learning and how we utilize state-of-the-art algorithms to ensure FlinkML is a truly scalable library.
You can watch a video of the talk here: https://youtu.be/k29qoCm4c_k
Implementing GraphQL API in Elixir – Victor DeryaginElixir Club
The document discusses Teachers Pay Teachers' experience implementing a GraphQL API in Elixir. Some key points:
- TpT has used Elixir and GraphQL for around 2 years to power their API, handling 2000 requests per second and significant backend work.
- Their tech stack includes Phoenix, Absinthe, Ecto, React/Apollo Client. Challenges included access control, evolving schemas, batching queries, and distributed tracing.
- Absinthe provided features like middleware, introspection and separation of schemas. Elixir tools like OTP processes helped with scalability and robustness.
- Overall they are happy with Elixir and GraphQL but note ongoing effort is
The word "Reactive" can be confusing. As the founder of the Reactive Amsterdam meetup, I can tell there are two main topics here: Functional Reactive Programming (here with reference to Android) and "Reactive" in the sense of the Reactive Manifesto.
River Trail: A Path to Parallelism in JavaScriptRoberto Falconi
River Trail enables new web usages with positive impact on performances using high-level parallel patterns, bounds checked array accesses, automatic heap management and familiar JavaScript libraries.
This document compares the performance of for loops, iterators, and Java 8 streams for processing collections of objects. It describes benchmark tests performed on different collection sizes ranging from 10 to 1,000,000 objects. The tests focused on common collection operations like finding the youngest/highest paid object, filtering by a property, and grouping by a property. The results showed that streams were generally faster than for loops for larger collections (>1000 objects) but slower for smaller collections. Iterators had the best performance overall. The document concludes that while streams offer a functional programming style, traditional for loops may still be better for smaller collections or certain object types due to stream overhead.
All About GRAND Stack: GraphQL, React, Apollo, and Neo4j (Mark Needham) - Gre...GreeceJS
In this presentation, we explore application development using the GRAND stack (GraphQL, React, Apollo, Neo4j) for building web applications backed by a graph database. We will review the components to build a simple web application, including how to build a React component, an introduction to JSX, an overview of GraphQL and why it is a game-changer for front-end development. We'll learn how to model, store, and query data in the Neo4j graph database using GraphQL to power our web application.
Usually we hear a lot about “great success” however we learn a lot more from mistakes which help to gain experience and skills.
In this talk I'm going to share challenges team faced with on a project which started with simple requirement to implement a replacement for an old core business system in a like-for-like fashion with new technologies. That led to explosion of different issues and challenges during implementation and eventually to stabilization and to a long awaited chance to go-live.
Can someone tell me quickly, what typescript is? Yes, here it is. If you know JavaSctipt, this is a quick intro to jump into typescript as fast as possible.
1. The document discusses the process of productionalizing a financial analytics application built on Spark over multiple iterations. It started with data scientists using Python and data engineers porting code to Scala RDDs. They then moved to using DataFrames and deployed on EMR.
2. Issues with code quality and testing led to adding ScalaTest, PR reviews, and daily Jenkins builds. Architectural challenges were addressed by moving to Databricks Cloud which provided notebooks, jobs, and throwaway clusters.
3. Future work includes using Spark SQL windows and Dataset API for stronger typing and schema support. The iterations improved the code, testing, deployment, and use of latest Spark features.
This document discusses how to perform similar image search using OpenCV. It explains that images are first preprocessed through grayscale conversion, blurring, and histogram equalization. Interesting image features are then extracted using techniques like Hessian, FAST, and Laplacian of Gaussian detectors. These features are matched between the input image and a feature database using fast lookup and FLANN. OpenCV is recommended for its implementation of computer vision algorithms and hardware acceleration support, though it can have issues with compilation and unstable APIs.
Python is popular amongst data scientists and engineers for data processing tasks. The big data ecosystem has traditionally been rather JVM centric. Often Java (or Scala) are the only viable option to implement data processing pipelines. That sometimes poses an adoption barrier for organizations that have already invested in other language ecosystems. The Apache Beam project provides a unified programming model for data processing and its ongoing portability effort aims to enable multiple language SDKs (currently Java, Python and Go) on a common set of runners. The combination of Python streaming on the Apache Flink runner is one example. Let’s take a look how the Flink runner translates the Beam model into the native DataStream (or DataSet) API, how the runner is changing to support portable pipelines, how Python user code execution is coordinated with gRPC based services and how a sample pipeline runs on Flink.
This document discusses the design alternatives for a medical record system project at a hospital. It presents a Morph chart evaluating options for the database, programming language, operating system, and web language. Three feasible solutions are considered: a program, mobile app, and web app. A numerical evaluation matrix assigns scores in areas like expandability, stability, and accessibility. Based on the highest total score, the final proposed solution is a web app developed with Ruby on Rails using a MySQL database.
This document discusses functional programming concepts in Ruby, including lambda calculus, LISP, anonymous functions, higher order functions, immutability, and pure functions. While Ruby is not a purely functional language, it incorporates some functional traits like anonymous functions, higher order functions that accept or return functions, and immutable strings. The document also briefly mentions advantages of functional programming like readability, modularity, concurrency and testability, as well as some disadvantages and related functional languages.
Adopting language server for apache camel feedback from a java/Eclipse plugi...Aurélien Pupier
Adopting language server for apache camel feedback from a java/eclipse plugin developer perspective
https://github.com/camel-tooling/camel-language-server
- Fluent Decorator
- In, Out, In, Out, Shake It All About
- The Lonely Override
- Monad
- Functional Reactive?
你可以在以下找到中文說明:
http://www.codedata.com.tw/social-coding/java8-patterns/
This document summarizes the migration of a location services application from Ruby to Scala at Whitepages. It discusses key areas of interest over the software development lifecycle. For prototyping, Ruby was faster due to its dynamic typing. However, for production, Scala was better suited due to its static typing catching errors, built-in support for concurrency using Futures, and better performance in terms of throughput, latency and hardware utilization. Both languages are object-oriented, readable and composable, but Scala integrates better with the JVM and has advantages for maintainability and integration.
RESTful Machine Learning with Flask and TensorFlow Serving - Carlo MazzaferroPyData
Those of us who use TensorFlow often focus on building the model that's most predictive, not the one that's most deployable. So how to put that hard work to work? In this talk, we'll walk through a strategy for taking your machine learning models from Jupyter Notebook into production and beyond.
My talk from SICS Data Science Day, describing FlinkML, the Machine Learning library for Apache Flink.
I talk about our approach to large-scale machine learning and how we utilize state-of-the-art algorithms to ensure FlinkML is a truly scalable library.
You can watch a video of the talk here: https://youtu.be/k29qoCm4c_k
RxSwift is a library for reactive programming in Swift that allows asynchronous data streams to be represented as Observables. It provides components like Observables, Observers, and Schedulers that allow asynchronous and event-based programs to be written in a functional, non-blocking manner. RxSwift uses operators to filter, transform, and combine Observable data streams and includes extensions for UI bindings and almost any other type of object.
Slides from my JAX London 2016 talk, discussing how the new features affect library design. Follows on from the Java SE 8 Best Practices talk - http://www.slideshare.net/scolebourne/java-se-8-best-practices-53975908
Discover how GraphQL can help you building and evolve your business APIs.
GraphQL is a query language for APIs developed internally by Facebook. It allows the developers to ask for exactly what they need and nothing more, making it easier to evolve APIs over time and create powerful developer tools around an API.
Presented by Helder Vasconcelos and Luis Gonçalves on 21st May 2019 @ Reactor Innovation Hub.
Powered by https://taikai.network
Reactive programming using rx java & akka actors - pdx-scala - june 2014Thomas Lockney
Reactive programming is an event-driven paradigm focused on data flow and treating code as a set of behaviors in response to events. RxJava is a library that uses observable sequences to compose asynchronous and event-based programs in a reactive style. It provides operators like map, flatMap, and filter to transform streams of events. Akka implements the actor model with actors that react to immutable messages in isolation through message passing. Both RxJava and Akka support reactive principles like scalability, resilience and responsiveness but require additional work.
Getting Started with Spring for GraphQLVMware Tanzu
WaffleCorp is a major provider of breakfast products available direct to consumer or through our strategic partnerships. The current implementation of the e-commerce platform is a monolithic Spring MVC application that serves data through a collection of REST APIs.
Currently, the only provider of the REST API is our e-commerce web application. We've been tasked with opening up our APIs to our new iOS and Android apps, partner microservices, and IoT applications.
The issue we ran into is that a REST API is not a one-size-fits-all approach. We need a more flexible solution to meet the requirements of all of our client applications. This is a perfect use case for the speed and flexibility of GraphQL.
GraphQL is a query language for APIs and a runtime for fulfilling those queries with your existing data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools.
In this session, you’ll learn what GraphQL is and why you should consider it in your next project. You’ll learn how to use GraphQL in your Spring Boot applications by leveraging the Spring for GraphQL project. By the end of this session, you’ll understand how to stand up a GraphQL endpoint and request the data you need, and nothing more.
My slides from the Big Data Applications meetup on 27th of July, talking about FlinkML. Also some things about open-source ML development and an illustation of interactive Flink machine learning with Apache Zeppelin.
This document discusses reactive systems and programming. It begins with an introduction to reactive systems and programming, explaining the difference between the two. It then discusses why reactive systems are useful, covering topics like efficient resource utilization. The document goes on to explain key concepts like observables, backpressure, and reactive libraries. It provides examples of reactive programming with Spring Reactor and reactive data access with Couchbase. Overall, the document provides a high-level overview of reactive systems and programming concepts.
Anatomy of Data Frame API : A deep dive into Spark Data Frame APIdatamantra
In this presentation, we discuss about internals of spark data frame API. All the code discussed in this presentation available at https://github.com/phatak-dev/anatomy_of_spark_dataframe_api
Introduction to React Native from Mobile Warsaw
This is a short presentation of concepts of React Native mobile application Framework.
It's an introductory talk for Application developers.
These are the slides to the webinar about Custom Pregel algorithms in ArangoDB https://youtu.be/DWJ-nWUxsO8. It provides a brief introduction to the capabilities and use cases for Pregel.
This document provides information about parameterization and lambda expressions in Java. It discusses how parameterization allows functions to be configured through parameters. Lambda expressions are described as anonymous functions that can be passed as arguments or stored in variables. The document outlines the benefits of streams in Java 8 for processing data in a declarative way through composable and parallelizable operations like filter, map, and reduce. It compares streams to collections and explains intermediate and terminal stream operations.
The document discusses reactive programming and how it can help solve issues with asynchronous programming. Reactive programming uses observable data streams and asynchronous operations to make asynchronous code more readable and maintainable. It introduces reactive programming concepts like observables, operators, and schedulers. Examples are given of how reactive extensions like RxJS can simplify asynchronous tasks like autocomplete and handling click events. The benefits of building reactive systems and microservices using a reactive approach are also covered.
This document discusses the history and characteristics of various web API technologies, including SOAP, REST, OData, GraphQL, Falcor, and JSON API. It argues that while GraphQL follows REST architectural constraints like uniform interface and hypermedia, REST itself is not a standard. The best technology depends on the specific needs and requirements of each project and team.
This document summarizes new enhancements to the Java Streams API in Java 9, including the addition of takeWhile, dropWhile, ofNullable methods as well as performance improvements to the iterate method. It provides examples of how each new method works and why they are useful, such as takeWhile and dropWhile being potentially more efficient than filter in some cases. It also shows performance test results indicating that streams in Java 9 are faster than in Java 8. In addition, background information is given on streams, monads, and existing stream methods from Java 8 like filter, map, and collect.
This document discusses Java 8 streams and how they are implemented under the hood. Streams use a pipeline concept where the output of one unit becomes the input of the next. Streams are built on functional interfaces and lambdas introduced in Java 8. Spliterators play an important role in enabling parallel processing of streams by splitting data sources into multiple chunks that can be processed independently in parallel. The reference pipeline implementation uses a linked list of processing stages to represent the stream operations and evaluates whether operations can be parallelized to take advantage of multiple threads or cores.
A compiler is a computer program which helps you transform source code written in a high-level language into low-level machine language.
Correctness, speed of compilation, preserve the correct the meaning of the code are some important features of compiler design.
Compilers are divided into three parts 1) Single Pass Compilers 2)Two Pass Compilers, and 3) Multipass Compilers.
The “compiler” was word first used in the early 1950s by Grace Murray Hopper.
Steps for Language processing system are: Preprocessor, Interpreter, Assembler, Linker/Loader.
Important compiler construction tools are 1) Scanner generators, 2)Syntax-3) directed translation engines, 4) Parser generators, 5) Automatic code generators.
The main task of the compiler is to verify the entire program, so there are no syntax or semantic errors.
Reactive Programming on Android - RxAndroid - RxJavaAli Muzaffar
Introduction to RxJava for reactive programming and how to use RxAndroid to do reactive programming on Android.
There is a sample android app to go with the slides that has all the source shown in the project.
This document provides an introduction to Apache Flink and its streaming capabilities. It discusses stream processing as an abstraction and how Flink uses streams as a core abstraction. It compares Flink streaming to Spark streaming and outlines some of Flink's streaming features like windows, triggers, and how it handles event time.
Sunspot is a popular ruby library providing access to Apache Solr, the renounced text search engine. In these slides, we go display how you can use this gem in your app.
This document provides an overview of Docker for web developers. It defines containers and Docker, discusses the benefits of Docker like faster deployment and portability. It explains key Docker concepts like images, containers, Dockerfile for building images, Docker platform, and commands for managing images and containers. The document also describes what happens behind the scenes when a container is run, and how to install and use Docker on Linux, Windows and Mac.
Vue.js is a progressive JavaScript framework for building user interfaces. It uses declarative templates and components to render the view layer. The core library focuses only on the view layer and is lightweight, approachable for beginners knowing HTML, CSS, and JavaScript, and versatile enough to build complex applications. It works by compiling templates into virtual DOM render functions for efficient updates. Key features include conditional rendering, list rendering, templates with interpolations and directives to bind data to the DOM, and components to build reusable code. Larger applications can use a central store to manage global state.
There and Back Again - A Tale of Programming LanguagesBADR
A programming language is defined by its syntax, semantics, paradigms, typing system, translation scheme, and memory management. There are many programming languages because each has a different focus, like being imperative, functional, procedural, or object-oriented. Languages get translated via compilation, interpretation, or a hybrid approach, and are typed statically or dynamically. Popular examples include C, Java, Lisp, and Smalltalk.
Take Pride in Your Code - Test-Driven DevelopmentBADR
TDD is one of the best practices a developer would pick throughout his professional career. In this slide-deck, we shed a light on enriching your code with the goodies of TDD.
This document provides an overview of different database types including relational, NoSQL, document, key-value, graph, and column family databases. It discusses the history and drivers behind the development of NoSQL databases, as well as concepts like horizontal scaling, the CAP theorem, and eventual consistency. Specific databases are also summarized, including MongoDB, Redis, Neo4j, and HBase.
Explicit Semantic Analysis (ESA) is a technique for semantic analysis of text that represents text in a "concept space" derived from Wikipedia articles, rather than just keywords. It maps keywords to "explicit concepts" represented by Wikipedia articles to capture more meaning. ESA represents a text as a weighted vector of the concepts it is related to. This captures more semantics than bag-of-words and allows ESA to understand related concepts not explicitly mentioned. ESA has been shown to accurately assess semantic relatedness in applications.
- Git stores snapshots of files and file trees in commits rather than tracking differences between file versions. This allows it to be very efficient and perform well on large projects.
- Git is highly local - all operations can be performed offline and it stores its data locally rather than relying on remote servers like many other version control systems.
- Git has strong integrity checks built-in to ensure data integrity and detect unintended changes to files.
An algorithm is a set of steps to accomplish a task. Common algorithms include sorting, searching, and graph algorithms. Algorithms are described based on their correctness, resource usage, and asymptotic time complexity. Understanding algorithms helps improve coding skills and can aid career opportunities that involve algorithmic problem solving. Key algorithms were briefly outlined, including breadth-first search on graphs and using bipartite graph checks to verify assumptions about bug gender interactions.
This document provides an overview of Android, including its history, architecture, and core application components. It describes how Andy Rubin created Android as an open-source mobile platform and sold it to Google. It outlines Android's architecture with layers including the Linux kernel, libraries, Android runtime, and applications. It also explains the four main application components - intents, activities, services, and broadcast receivers - and how they allow communication between apps and components.
This document provides an overview of Apache Hadoop, including its history, architecture, and key components. Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity servers. It allows for the distributed processing of large data sets across clusters of computers using a simple programming model. The document outlines Hadoop's origins from Google's paper on MapReduce and the GFS file system. It describes Hadoop's core components - the Hadoop Distributed File System (HDFS) for storage and MapReduce for distributed processing. Use cases for Hadoop including log analysis, search, and analytics are also mentioned.
This document discusses different object-oriented programming principles for implementing a change request to allow ducks to fly. It first covers using inheritance to add a fly behavior to ducks. It then discusses using interfaces to define a fly behavior that can be implemented by different objects. A key principle is to separate the aspects that vary from what stays the same and to code to interfaces rather than implementations.
The Perks and Perils of the Singleton Design PatternBADR
Singleton design pattern is on the most famous and used design patterns. It's also a source of endless debates between software engineers! In this session, we discuss the perks and perils of the singleton design patters.
Preparing Non - Technical Founders for Engaging a Tech AgencyISH Technologies
Preparing non-technical founders before engaging a tech agency is crucial for the success of their projects. It starts with clearly defining their vision and goals, conducting thorough market research, and gaining a basic understanding of relevant technologies. Setting realistic expectations and preparing a detailed project brief are essential steps. Founders should select a tech agency with a proven track record and establish clear communication channels. Additionally, addressing legal and contractual considerations and planning for post-launch support are vital to ensure a smooth and successful collaboration. This preparation empowers non-technical founders to effectively communicate their needs and work seamlessly with their chosen tech agency.Visit our site to get more details about this. Contact us today www.ishtechnologies.com.au
Malibou Pitch Deck For Its €3M Seed Roundsjcobrien
French start-up Malibou raised a €3 million Seed Round to develop its payroll and human resources
management platform for VSEs and SMEs. The financing round was led by investors Breega, Y Combinator, and FCVC.
What to do when you have a perfect model for your software but you are constrained by an imperfect business model?
This talk explores the challenges of bringing modelling rigour to the business and strategy levels, and talking to your non-technical counterparts in the process.
The Key to Digital Success_ A Comprehensive Guide to Continuous Testing Integ...kalichargn70th171
In today's business landscape, digital integration is ubiquitous, demanding swift innovation as a necessity rather than a luxury. In a fiercely competitive market with heightened customer expectations, the timely launch of flawless digital products is crucial for both acquisition and retention—any delay risks ceding market share to competitors.
Measures in SQL (SIGMOD 2024, Santiago, Chile)Julian Hyde
SQL has attained widespread adoption, but Business Intelligence tools still use their own higher level languages based upon a multidimensional paradigm. Composable calculations are what is missing from SQL, and we propose a new kind of column, called a measure, that attaches a calculation to a table. Like regular tables, tables with measures are composable and closed when used in queries.
SQL-with-measures has the power, conciseness and reusability of multidimensional languages but retains SQL semantics. Measure invocations can be expanded in place to simple, clear SQL.
To define the evaluation semantics for measures, we introduce context-sensitive expressions (a way to evaluate multidimensional expressions that is consistent with existing SQL semantics), a concept called evaluation context, and several operations for setting and modifying the evaluation context.
A talk at SIGMOD, June 9–15, 2024, Santiago, Chile
Authors: Julian Hyde (Google) and John Fremlin (Google)
https://doi.org/10.1145/3626246.3653374
Most important New features of Oracle 23c for DBAs and Developers. You can get more idea from my youtube channel video from https://youtu.be/XvL5WtaC20A
8 Best Automated Android App Testing Tool and Framework in 2024.pdfkalichargn70th171
Regarding mobile operating systems, two major players dominate our thoughts: Android and iPhone. With Android leading the market, software development companies are focused on delivering apps compatible with this OS. Ensuring an app's functionality across various Android devices, OS versions, and hardware specifications is critical, making Android app testing essential.
Hand Rolled Applicative User ValidationCode KataPhilip Schwarz
Could you use a simple piece of Scala validation code (granted, a very simplistic one too!) that you can rewrite, now and again, to refresh your basic understanding of Applicative operators <*>, <*, *>?
The goal is not to write perfect code showcasing validation, but rather, to provide a small, rough-and ready exercise to reinforce your muscle-memory.
Despite its grandiose-sounding title, this deck consists of just three slides showing the Scala 3 code to be rewritten whenever the details of the operators begin to fade away.
The code is my rough and ready translation of a Haskell user-validation program found in a book called Finding Success (and Failure) in Haskell - Fall in love with applicative functors.
Microservice Teams - How the cloud changes the way we workSven Peters
A lot of technical challenges and complexity come with building a cloud-native and distributed architecture. The way we develop backend software has fundamentally changed in the last ten years. Managing a microservices architecture demands a lot of us to ensure observability and operational resiliency. But did you also change the way you run your development teams?
Sven will talk about Atlassian’s journey from a monolith to a multi-tenanted architecture and how it affected the way the engineering teams work. You will learn how we shifted to service ownership, moved to more autonomous teams (and its challenges), and established platform and enablement teams.
7. Introduction
● It is a declarative programming paradigm, which means programming is
done with expressions.
● Functional code, the output value of a function depends only on the
arguments that are input to the function.
32. Introduction
● Defines a “one-to-many” dependency between objects so that when one
object changes state, all its dependents are notified and updated.
● Subject: the object which will frequently change its state and upon
which other objects depend.
● Observer: the object which depends on a subject and updates according
to its subject’s state.
35. Agenda
● Functional Programming (java 8)
○ Getting Started
○ Examples
● Observer Pattern
○ Introduction
○ Real life example
○ UML
● ReactiveX
○ Introduction
○ Getting Started
○ Example
○ Operators
36. ReactiveX
ReactiveX is a combination of the best ideas from
the Observer pattern, the Iterator pattern, and functional programming
37. Introduction
● Reactive programming is a programming paradigm
oriented around data flows and propagation of change.
● Let:
a = b + c
what about if b or c has been changed?!
38. Introduction
● Functional Reactive programming (FRP) is a style of
programming based on two keys ideas: continuous
time-varying behaviors, and event based reactively.
● Popularized by Erik Meijer when he created the Rx
library for .Net while at Microsoft.
40. What Are Reactive Extensions?
● It’s a library that allows you to use FRP at many
programming languages.
● Allows you to represent asynchronous data streams
with “Observables”.
● And Parametrize the concurrency in those
asynchronous data streams using “Schedulers”
41. Rx Family
● C#: Rx.NET
● Javascript: RxJS
● RxJava (Java, Scala, Groovy, Clojure, Kotlin).
● Ruby: Rx.rb
● Python: RxPY
● More at http://reactivex.io/
42. Why Functional Reactive Programming?
● Writing concurrent programs correctly is difficult.
● You can transform & compose asynchronous operations.
● High-level abstraction.
● Standard error handling.
44. Observables and Observer
● In ReactiveX an Observer subscribes to an Observable.
● Then That Observer reacts to whatever item or sequence of items the
Observable emits.
● This patterns facilitate concurrent operations because it does not need
to block while waiting for the observable.
45. Observer onNext, onCompleted, onError
An Observer implements a subset of the following methods:
● onNext: We call this method whenever the Observable emits an item.
● onError: Call this method to indicate the the observable has failed and
will not make further calls to onNext or onCompleted.
● onCompleted: Call this method after you have called onNext for the final
time and if we have not encountered any errors.
47. RxJava
● It’s a JVM implementation of Reactive Extensions.
● Started at Netflix but now Open Sourced.
● Supports Java 6+ & Android 2.3+.
● Java 8 lambda support.