Relentlessly practical, the book delivers insight, not documentation, to teach developers what they need to know to build the next generation of SOAs.After explaining the advantages of service-orientation for application design and teaching the basics of how to develop SOAs using WCF, the book shows how you can take advantage of built-in features such as service hosting, instance management, asynchronous calls, synchronization, reliability, transaction management, disconnected queued calls and security to build best in class applications. "Programming WCF Services" focuses on the rationale behind particular design decisions, often shedding light on poorly-documented and little-understood aspects of SOA development. Developers and architects will learn not only the "how" of WCF programming, but also relevant design guidelines, best practices, and pitfalls. Original techniques and utilities provided by the author throughout the book go well beyond anything that can be found in conventional sources.Based on experience and insight gained while taking part in the strategic design of WCF and working with the team that implemented it, "Programming WCF Services" provides experienced working professionals with the definitive work on WCF. Not only will this book make you a WCF expert, it will make you a better software engineer. It's the Rosetta Stone of WCF.

    The books also provides a companion web site that provides the toold and materials necessary to build the hardware and software.

    You'll learn how to confidently and securely build and deploy OAuth on both the client and server sides. Foreword by Ian Glazer.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the TechnologyThink of OAuth 2 as the web version of a valet key. It is an HTTP-based security protocol that allows users of a service to enable applications to use that service on their behalf without handing over full control. And OAuth is used everywhere, from Facebook and Google, to startups and cloud services.About the BookOAuth 2 in Action teaches you practical use and deployment of OAuth 2 from the perspectives of a client, an authorization server, and a resource server. You'll begin with an overview of OAuth and its components and interactions. Next, you'll get hands-on and build an OAuth client, an authorization server, and a protected resource. Then you'll dig into tokens, dynamic client registration, and more advanced topics. By the end, you'll be able to confidently and securely build and deploy OAuth on both the client and server sides.What's InsideCovers OAuth 2 protocol and designAuthorization with OAuth 2OpenID Connect and User-Managed AccessImplementation risksJOSE, introspection, revocation, and registrationProtecting and accessing REST APIsAbout the ReaderReaders need basic programming skills and knowledge of HTTP and JSON.About the AuthorJustin Richer is a systems architect and software engineer. Antonio Sanso is a security software engineer and a security researcher. Both authors contribute to open standards and open source.Table of ContentsPart 1 - First stepsWhat is OAuth 2.0 and why should you care?The OAuth dance Part 2 - Building an OAuth 2 environmentBuilding a simple OAuth clientBuilding a simple OAuth protected resourceBuilding a simple OAuth authorization serverOAuth 2.0 in the real world Part 3 - OAuth 2 implementation and vulnerabilitiesCommon client vulnerabilitiesCommon protected resources vulnerabilitiesCommon authorization server vulnerabilitiesCommon OAuth token vulnerabilities Part 4 - Taking OAuth furtherOAuth tokensDynamic client registrationUser authentication with OAuth 2.0Protocols and profiles using OAuth 2.0Beyond bearer tokensSummary and conclusions

    Building on the successful top-down approach of previous editions, this fourth edition continues with an early emphasis on application-layer paradigms and application programming interfaces, encouraging a hands-on experience with protocols and networking concepts.

    This book contains the fundamental laws of software development, the primary pieces of understanding that make the difference between a mid-level/junior programmer and the high-level senior software engineer. The book exists to help all programmers understand the process of writing software, on a very fundamental level that can be applied to any programming language or project, from here into eternity. Code Simplicity is also written in such a way that even non-technical managers of software teams can gain an understanding of what the “right way” and the “wrong way” is (and why they are right and wrong) when it comes to software design. The focus of the book is primarily on “software design,” the process of creating a plan for a software project and making technical decisions about the pattern and structure of a system.

    Readers will progress from building a basic physics engine to creating intelligent moving objects and complex systems, setting the foundation for further experiments in generative design. Subjects covered include forces, trigonometry, fractals, cellular automata, self-organization, and genetic algorithms. The book's examples are written in Processing, an open-source language and development environment built on top of the Java programming language. On the book's website (http://www.natureofcode.com), the examples run in the browser via Processing's JavaScript mode.

    But with so many distributed databases and tools available today, it’s often difficult to understand what each one offers and how they differ. With this practical guide, Alex Petrov guides developers through the concepts behind modern database and storage engine internals.Throughout the book, you’ll explore relevant material gleaned from numerous books, papers, blog posts, and the source code of several open source databases. These resources are listed at the end of parts one and two. You’ll discover that the most significant distinctions among many modern databases reside in subsystems that determine how storage is organized and how data is distributed.This book examines:Storage engines: Explore storage classification and taxonomy, and dive into B-Tree-based and immutable log structured storage engines, with differences and use-cases for eachDistributed systems: Learn step-by-step how nodes and processes connect and build complex communication patterns, from UDP to reliable consensus protocolsDatabase clusters: Discover how to achieve consistent models for replicated data

    Instead of hard-coding dependencies, such as specifying a database driver, you inject a list of services that a component may need. The services are then connected by a third party. This technique enables you to better manage future changes and other complexity in your software.About this BookDependency Injection in .NET introduces DI and provides a practical guide for applying it in .NET applications. The book presents the core patterns in plain C#, so you'll fully understand how DI works. Then you'll learn to integrate DI with standard Microsoft technologies like ASP.NET MVC, and to use DI frameworks like StructureMap, Castle Windsor, and Unity. By the end of the book, you'll be comfortable applying this powerful technique in your everyday .NET development.This book is written for C# developers. No previous experience with DI or DI frameworks is required. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. Winner of 2013 Jolt Awards: The Best Books—one of five notable books every serious programmer should read.What's InsideMany C#-based examplesA catalog of DI patterns and anti-patternsUsing both Microsoft and open source DI frameworksTabel of ContentsPART 1 PUTTING DEPENDENCY INJECTION ON THE MAPA Dependency Injection tasting menuA comprehensive exampleDI ContainersPART 2 DI CATALOGDI patternsDI anti-patternsDI refactoringsPART 3 DIY DIObject CompositionObject LifetimeInterceptionPART 4 DI CONTAINERSCastle WindsorStructureMapSpring.NETAutofacUnityMEF

    The study of type systems--and of programming languages from a type-theoretic perspective--has important applications in software engineering, language design, high-performance compilers, and security.This text provides a comprehensive introduction both to type systems in computer science and to the basic theory of programming languages. The approach is pragmatic and operational; each new concept is motivated by programming examples and the more theoretical sections are driven by the needs of implementations. Each chapter is accompanied by numerous exercises and solutions, as well as a running implementation, available via the Web. Dependencies between chapters are explicitly identified, allowing readers to choose a variety of paths through the material.The core topics include the untyped lambda-calculus, simple type systems, type reconstruction, universal and existential polymorphism, subtyping, bounded quantification, recursive types, kinds, and type operators. Extended case studies develop a variety of approaches to modeling the features of object-oriented languages.

    It’s time we stand face-to-digital-face with the true powers and limitations of the algorithms that already automate important decisions in healthcare, transportation, crime, and commerce. Hello World is indispensable preparation for the moral quandaries of a world run by code, and with the unfailingly entertaining Hannah Fry as our guide, we’ll be discussing these issues long after the last page is turned.

    Not only will you learn about the enterprise class features in the 9.2 release, you’ll also discover that PostgeSQL is more than just a database system—it’s also an impressive application platform.With numerous examples throughout this book, you’ll learn how to achieve tasks that are difficult or impossible in other databases. If you’re an existing PostgreSQL user, you’ll pick up gems you may have missed along the way.Learn basic administration tasks, such as role management, database creation, backup, and restoreApply the psql command-line utility and the pgAdmin graphical administration toolExplore PostgreSQL tables, constraints, and indexesLearn powerful SQL constructs not generally found in other databasesUse several different languages to write database functionsTune your queries to run as fast as your hardware will allowQuery external and variegated data sources with Foreign Data WrappersLearn how to replicate data, using built-in replication features

    Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.Each chapter will cover a different technique in a spreadsheet so you can follow along: - Mathematical optimization, including non-linear programming and genetic algorithms- Clustering via k-means, spherical k-means, and graph modularity- Data mining in graphs, such as outlier detection- Supervised AI through logistic regression, ensemble models, and bag-of-words models- Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation- Moving from spreadsheets into the R programming languageYou get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.

    Updated for Python 3, this expanded edition shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. By exploring the fundamental theory behind design choices, High Performance Python helps you gain a deeper understanding of Python's implementation.How do you take advantage of multicore architectures or clusters? Or build a system that scales up and down without losing reliability? Experienced Python programmers will learn concrete solutions to many issues, along with war stories from companies that use high-performance Python for social media analytics, productionized machine learning, and more.Get a better grasp of NumPy, Cython, and profilersLearn how Python abstracts the underlying computer architectureUse profiling to find bottlenecks in CPU time and memory usageWrite efficient programs by choosing appropriate data structuresSpeed up matrix and vector computationsUse tools to compile Python down to machine codeManage multiple I/O and computational operations concurrentlyConvert multiprocessing code to run on local or remote clustersDeploy code faster using tools like Docker

    And it’s still short, profusely illustrated…and best of all–fun to read.If you’ve read it before, you’ll rediscover what made Don’t Make Me Think so essential to Web designers and developers around the world. If you’ve never read it, you’ll see why so many people have said it should be required reading for anyone working on Web sites.

    It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.