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.

     At any given moment, somewhere in the world someone struggles with the same software design problems you have. You know you don't want to reinvent the wheel (or worse, a flat tire), so you look to Design Patterns--the lessons learned by those who've faced the same problems. With Design Patterns, you get to take advantage of the best practices and experience of others, so that you can spend your time on...something else. Something more challenging. Something more complex. Something more fun. You want to learn about the patterns that matter--why to use them, when to use them, how to use them (and when NOT to use them). But you don't just want to see how patterns look in a book, you want to know how they look "in the wild". In their native environment. In other words, in real world applications. You also want to learn how patterns are used in the Java API, and how to exploit Java's built-in pattern support in your own code. You want to learn the real OO design principles and why everything your boss told you about inheritance might be wrong (and what to do instead). You want to learn how those principles will help the next time you're up a creek without a design pattern. Most importantly, you want to learn the "secret language" of Design Patterns so that you can hold your own with your co-worker (and impress cocktail party guests) when he casually mentions his stunningly clever use of Command, Facade, Proxy, and Factory in between sips of a martini. You'll easily counter with your deep understanding of why Singleton isn't as simple as it sounds, how the Factory is so often misunderstood, or on the real relationship between Decorator, Facade and Adapter. With Head First Design Patterns, you'll avoid the embarrassment of thinking Decorator is something from the "Trading Spaces" show. Best of all, in a way that won't put you to sleep! We think your time is too important (and too short) to spend it struggling with academic texts. If you've read a Head First book, you know what to expect--a visually rich format designed for the way your brain works. Using the latest research in neurobiology, cognitive science, and learning theory, Head First Design Patterns will load patterns into your brain in a way that sticks. In a way that lets you put them to work immediately. In a way that makes you better at solving software design problems, and better at speaking the language of patterns with others on your team.

    If you're a reasonably proficient programmer who can think logically, you have all the background you'll need. Stepanov and Rose introduce the relevant abstract algebra and number theory with exceptional clarity. They carefully explain the problems mathematicians first needed to solve, and then show how these mathematical solutions translate to generic programming and the creation of more effective and elegant code. To demonstrate the crucial role these mathematical principles play in many modern applications, the authors show how to use these results and generalized algorithms to implement a real-world public-key cryptosystem. As you read this book, you'll master the thought processes necessary for effective programming and learn how to generalize narrowly conceived algorithms to widen their usefulness without losing efficiency. You'll also gain deep insight into the value of mathematics to programming--insight that will prove invaluable no matter what programming languages and paradigms you use. You will learn aboutHow to generalize a four thousand-year-old algorithm, demonstrating indispensable lessons about clarity and efficiencyAncient paradoxes, beautiful theorems, and the productive tension between continuous and discreteA simple algorithm for finding greatest common divisor (GCD) and modern abstractions that build on itPowerful mathematical approaches to abstractionHow abstract algebra provides the idea at the heart of generic programmingAxioms, proofs, theories, and models: using mathematical techniques to organize knowledge about your algorithms and data structuresSurprising subtleties of simple programming tasks and what you can learn from themHow practical implementations can exploit theoretical knowledge

    So, why does conventional wisdom insist that software engineers focus primarily on the design and development of large-scale computing systems?In this collection of essays and articles, key members of Google's Site Reliability Team explain how and why their commitment to the entire lifecycle has enabled the company to successfully build, deploy, monitor, and maintain some of the largest software systems in the world. You'll learn the principles and practices that enable Google engineers to make systems more scalable, reliable, and efficient--lessons directly applicable to your organization.This book is divided into four sections: Introduction--Learn what site reliability engineering is and why it differs from conventional IT industry practicesPrinciples--Examine the patterns, behaviors, and areas of concern that influence the work of a site reliability engineer (SRE)Practices--Understand the theory and practice of an SRE's day-to-day work: building and operating large distributed computing systemsManagement--Explore Google's best practices for training, communication, and meetings that your organization can use

    It presents a learning journey, not definitive guidance. It describes the experiences of a development team with no prior CQRS proficiency in building, deploying (to Windows Azure), and maintaining a sample real-world, complex, enterprise system to showcase various CQRS and ES concepts, challenges, and techniques.The development team did not work in isolation; we actively sought input from industry experts and from a wide group of advisors to ensure that the guidance is both detailed and practical.The CQRS pattern and event sourcing are not mere simplistic solutions to the problems associated with large-scale, distributed systems. By providing you with both a working application and written guidance, we expect you’ll be well prepared to embark on your own CQRS journey.

    With this hands-on guide, you'll learn how to write effective, idiomatic Python code by leveraging its best and possibly most neglected features. Author Luciano Ramalho takes you through Python's core language features and libraries, and shows you how to make your code shorter, faster, and more readable at the same time.Many experienced programmers try to bend Python to fit patterns they learned from other languages, and never discover Python features outside of their experience. With this book, those Python programmers will thoroughly learn how to become proficient in Python 3.This book covers:Python data model: understand how special methods are the key to the consistent behavior of objectsData structures: take full advantage of built-in types, and understand the text vs bytes duality in the Unicode ageFunctions as objects: view Python functions as first-class objects, and understand how this affects popular design patternsObject-oriented idioms: build classes by learning about references, mutability, interfaces, operator overloading, and multiple inheritanceControl flow: leverage context managers, generators, coroutines, and concurrency with the concurrent.futures and asyncio packagesMetaprogramming: understand how properties, attribute descriptors, class decorators, and metaclasses work"

    But while most of us today can work a computer--albeit with the help of the ever-present computer software manual--we know little about what goes on inside the box and virtually nothing about software designor the world of computer programming. In Patterns of Software, the respected software pioneer and computer scientist, Richard Gabriel, gives us an informative inside look at the world of software design and computer programming and the business that surrounds them. In this wide-ranging volume, Gabriel discusses such topics as whatmakes a successful programming language, how the rest of the world looks at and responds to the work of computer scientists, how he first became involved in computer programming and software development, what makes a successful software business, and why his own company, Lucid, failed in 1994, tenyears after its inception. Perhaps the most interesting and enlightening section of the book is Gabriel's detailed look at what he believes are the lessons that can be learned from architect Christopher Alexander, whose books--including the seminal A Pattern Language--have had a profound influence on the computer programmingcommunity. Gabriel illuminates some of Alexander's key insights--the quality without a name, pattern languages, habitability, piecemeal growth--and reveals how these influential architectural ideas apply equally well to the construction of a computer program. Gabriel explains the concept ofhabitability, for example, by comparing a program to a New England farmhouse and the surrounding structures which slowly grow and are modified according to the needs and desires of the people who live and work on the farm. Programs live and grow, and their inhabitants--the programmers--need to workwith that program the way the farmer works with the homestead. Although computer scientists and software entrepreneurs will get much out of this book, the essays are accessible to everyone and will intrigue anyone curious about Silicon Valley, computer programming, or the world of high technology.

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

    Did you design your system to survivef a sudden rush of visitors from Digg or Slashdot? Or an influx of real world customers from 100 different countries? Are you ready for a world filled with flakey networks, tangled databases, and impatient users?If you're a developer and don't want to be on call for 3AM for the rest of your life, this book will help.In Release It!, Michael T. Nygard shows you how to design and architect your application for the harsh realities it will face. You'll learn how to design your application for maximum uptime, performance, and return on investment.Mike explains that many problems with systems today start with the design.

    Though it may not seem this way for those who have been in the field for most of their careers, in the overall scheme of professions, software builders are relative "newbies." In the short history of the software field, a lot of facts have been identified, and a lot of fallacies promulgated. Those facts and fallacies are what this book is about. There's a problem with those facts-and, as you might imagine, those fallacies. Many of these fundamentally important facts are learned by a software engineer, but over the short lifespan of the software field, all too many of them have been forgotten. While reading Facts and Fallacies of Software Engineering , you may experience moments of "Oh, yes, I had forgotten that," alongside some "Is that really true?" thoughts. The author of this book doesn't shy away from controversy. In fact, each of the facts and fallacies is accompanied by a discussion of whatever controversy envelops it. You may find yourself agreeing with a lot of the facts and fallacies, yet emotionally disturbed by a few of them! Whether you agree or disagree, you will learn why the author has been called "the premier curmudgeon of software practice." These facts and fallacies are fundamental to the software building field-forget or neglect them at your peril!

    Aiming to tell the dark secrets of computer arithmetic, this title is suitable for library developers, compiler writers, and lovers of elegant hacks.

    This is necessary because, in the words of Michael Polanyi, "we can know more than we can tell." Our design choices are not the result of an ineluctable chain of logic; they come from a deeper place, one which is visceral and inarticulate.Polanyi calls this "tacit knowledge", a thing which we only understand as part of something else. When we speak, we do not focus on making sounds, we focus on our words. We understand the muscular act of speech, but would struggle to explain it.To write software, we must learn where to draw boundaries. Good software is built through effective indirection. We seem to have decided that this skill can only be learned through practice; it cannot be taught, except by example. Our decisions may improve with time, but not our ability to explain them. It's true that the study of these questions cannot yield a closed-form solution for judging software design. We can make our software simple, but we cannot do the same to its problem domain, its users, or the physical world. Our tacit knowledge of this environment will always inform our designs.This doesn't mean that we can simply ignore our design process. Polanyi tells us that tacit knowledge only suffices until we fail, and the software industry is awash with failure. Our designs may never be provably correct, but we can give voice to the intuition that shaped them. Our process may always be visceral, but it doesn't have to be inarticulate.And so this book does not offer knowledge, it offers clarity. It is aimed at readers who know Clojure, but struggle to articulate the rationale of their designs to themselves and others. Readers who use other languages, but have a passing familiarity with Clojure, may also find this book useful.

    However, data structures for these languages do not always translate well to functional languages such as Standard ML, Haskell, or Scheme. This book describes data structures from the point of view of functional languages, with examples, and presents design techniques that allow programmers to develop their own functional data structures. The author includes both classical data structures, such as red-black trees and binomial queues, and a host of new data structures developed exclusively for functional languages. All source code is given in Standard ML and Haskell, and most of the programs are easily adaptable to other functional languages. This handy reference for professional programmers working with functional languages can also be used as a tutorial or for self-study.

    Python is considered easy to learn, but there's no quicker way to mastery of the language than learning from an expert teacher. This edition of "Learning Python" puts you in the hands of two expert teachers, Mark Lutz and David Ascher, whose friendly, well-structured prose has guided many a programmer to proficiency with the language. "Learning Python," Second Edition, offers programmers a comprehensive learning tool for Python and object-oriented programming. Thoroughly updated for the numerous language and class presentation changes that have taken place since the release of the first edition in 1999, this guide introduces the basic elements of the latest release of Python 2.3 and covers new features, such as list comprehensions, nested scopes, and iterators/generators. Beyond language features, this edition of "Learning Python" also includes new context for less-experienced programmers, including fresh overviews of object-oriented programming and dynamic typing, new discussions of program launch and configuration options, new coverage of documentation sources, and more. There are also new use cases throughout to make the application of language features more concrete. The first part of "Learning Python" gives programmers all the information they'll need to understand and construct programs in the Python language, including types, operators, statements, classes, functions, modules and exceptions. The authors then present more advanced material, showing how Python performs common tasks by offering real applications and the libraries available for those applications. Each chapter ends with a series of exercises that will test your Python skills and measure your understanding."Learning Python," Second Edition is a self-paced book that allows readers to focus on the core Python language in depth. As you work through the book, you'll gain a deep and complete understanding of the Python language that will help you to understand the larger application-level examples that you'll encounter on your own. If you're interested in learning Python--and want to do so quickly and efficiently--then "Learning Python," Second Edition is your best choice.

    While it's safe to say that relational databases aren't going anywhere, we can also say that the ecosystem around data is never going to be the same.Of all the new tools and solutions, for me, Redis has been the most exciting. Why? First because it's unbelievably easy to learn. Hours is the right unit to use when talking about length of time it takes to get comfortable with Redis. Secondly, it solves a specific set of problems while at the same time being quite generic. What exactly does that mean? Redis doesn't try to be all things to all data. As you get to know Redis, it'll become increasingly evident what does and what does not belong in it. And when it does, as a developer, it's a great experience.While you can build a complete system using Redis only, I think most people will find that it supplements their more generic data solution - whether that be a traditional relational database, a document-oriented system, or something else. It's the kind of solution you use to implement specific features. In that way, it's similar to an indexing engine. You wouldn't build your entire application on Lucene. But when you need good search, it's a much better experience - for both you and your users. Of course, the similarities between Redis and indexing engines end there.The goal of this book is to build the foundation you'll need to master Redis. We'll focus on learning Redis' five data structures and look at various data modeling approaches. We'll also touch on some key administrative details and debugging techniques.LicenseThe Little Redis Book is licensed under the Attribution-NonCommercial 3.0 Unported license. You should not have paid for this book.You are free to copy, distribute, modify or display the book. However, I ask that you always attribute the book to me, Karl Seguin, and do not use it for commercial purposes.You can see the full text of the license at:http://creativecommons.org/licenses/b...About The AuthorKarl Seguin is a developer with experience across various fields and technologies. He's an active contributor to Open-Source Software projects, a technical writer and an occasional speaker. He's written various articles, as well as a few tools, about Redis. Redis powers the ranking and statistics of his free service for casual game developers: mogade.com.Karl wrote The Little MongoDB Book, the free and popular book about MongoDB.His blog can be found at http://openmymind.net and he tweets via @karlseguinWith Thanks ToA special thanks to Perry Neal for lending me his eyes, mind and passion. You provided me with invaluable help. Thank you.Latest VersionThe latest source of this book is available at: http://github.com/karlseguin/the-litt...