Want to impress your coworkers and write the fastest, most efficient, stable code you ever have? Don't reinvent the wheel. Reuse decades of research into battle-tested, highly optimized, and proven techniques available on any Unix system.This book will teach you what you need to know so that you can write your own servers, debug your entire stack when things go awry, and understand how things are working under the hood.http://www.jstorimer.com/products/wor...

    As scale and demand increase, so does Complexity. Fortunately, scalability and simplicity are not mutually exclusive—rather than using some trendy technology, a different approach is needed. Big data systems use many machines working in parallel to store and process data, which introduces fundamental challenges unfamiliar to most developers.Big Data shows how to build these systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy to understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides readers through the theory of big data systems, how to use them in practice, and how to deploy and operate them once they're built.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.

    The text converts all programming code into the C++ language.

    By describing the overall characteristics of the architecture, these patterns not only guide designers and developers on how to design components, but also determine the ways in which those components should interact.This O’Reilly report takes a deep dive into many common software architecture patterns. Each pattern includes a full explanation of how it works, explains the pattern’s benefits and considerations, and describes the circumstances and conditions it was designed to address. The report also includes an analysis and scorecard for each pattern based on several architecture and software development quality attributes.Patterns include: - Layered architecture - Event-driven architecture - Microkernel architecture - Microservices architecture - Space-based architectureIn addition to these specific patterns, you’ll also learn about the Architecture by Implication anti-pattern and the causes and effects of not using architecture patterns.Mark Richards is an experienced software architect with significant experience and expertise in application, integration, and enterprise architecture. Active in the software industry since 1983, he is the author/presenter of several O’Reilly books and videos, including Software Architecture Fundamentals; Enterprise Messaging, Java Message Service, 2nd Edition; and 97 Things Every Software Architect Should Know.

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

    Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.

    Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

    Distributed Systems provides students of computer science and engineering with the skills they will need to design and maintain software for distributed applications. It will also be invaluable to software engineers and systems designers wishing to understand new and future developments in the field. From mobile phones to the Internet, our lives depend increasingly on distributed systems linking computers and other devices together in a seamless and transparent way. The fifth edition of this best-selling text continues to provide a comprehensive source of material on the principles and practice of distributed computer systems and the exciting new developments based on them, using a wealth of modern case studies to illustrate their design and development. The depth of coverage will enable readers to evaluate existing distributed systems and design new ones.

    The authors present updated coverage of compilers based on research and techniques that have been developed in the field over the past few years. The book provides a thorough introduction to compiler design and covers topics such as context-free grammars, fine state machines, and syntax-directed translation.

    This guide also helps you understand the many data-mining techniques in use today.Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates

    It's an essential read for anyone dealing with scaling an online business."--Chris Lalonde, VP, Technical Operations and Infrastructure Architecture, Bullhorn "Abbott and Fisher again tackle the difficult problem of scalability in their unique and practical manner. Distilling the challenges of operating a fast-growing presence on the Internet into 50 easy-to understand rules, the authors provide a modern cookbook of scalability recipes that guide the reader through the difficulties of fast growth."--Geoffrey Weber, Vice President, Internet Operations, Shutterfly "Abbott and Fisher have distilled years of wisdom into a set of cogent principles to avoid many nonobvious mistakes."--Jonathan Heiliger, VP, Technical Operations, Facebook "In "The Art of Scalability," the AKF team taught us that scale is not just a technology challenge. Scale is obtained only through a combination of people, process, "and "technology. With "Scalability Rules," Martin Abbott and Michael Fisher fill our scalability toolbox with easily implemented and time-tested rules that once applied will enable massive scale."--Jerome Labat, VP, Product Development IT, Intuit "When I joined Etsy, I partnered with Mike and Marty to hit the ground running in my new role, and it was one of the best investments of time I have made in my career. The indispensable advice from my experience working with Mike and Marty is fully captured here in this book. Whether you're taking on a role as a technology leader in a new company or you simply want to make great technology decisions, "Scalability Rules "will be the go-to resource on your bookshelf."--Chad Dickerson, CTO, Etsy ""Scalability Rules "provides an essential set of practical tools and concepts anyone can use when designing, upgrading, or inheriting a technology platform. It's very easy to focus on an immediate problem and overlook issues that will appear in the future. This book ensures strategic design principles are applied to everyday challenges."--Robert Guild, Director and Senior Architect, Financial Services "An insightful, practical guide to designing and building scalable systems. A must-read for both product-building and operations teams, this book offers concise and crisp insights gained from years of practical experience of AKF principals. With the complexity of modern systems, scalability considerations should be an integral part of the architecture and implementation process. Scaling systems for hypergrowth requires an agile, iterative approach that is closely aligned with product features; this book shows you how."--Nanda Kishore, Chief Technology Officer, ShareThis "For organizations looking to scale technology, people, and processes rapidly or effectively, the twin pairing of "Scalability Rules "and "The Art of Scalability "are unbeatable. The rules-driven approach in "Scalability Rules "makes this not only an easy reference companion, but also allows organizations to tailor the Abbott and Fisher approach to their specific needs both immediately and in the future!"--Jeremy Wright, CEO, BNOTIONS.ca and Founder, b5media 50 Powerful, Easy-to-Use Rules for Supporting Hypergrowth in Any Environment "Scalability Rules" is the easy-to-use scalability primer and reference for every architect, developer, web professional, and manager. Authors Martin L. Abbott and Michael T. Fisher have helped scale more than 200 hypergrowth Internet sites through their consulting practice. Now, drawing on their unsurpassed experience, they present 50 clear, proven scalability rules-and practical guidance for applying them. Abbott and Fisher transform scalability from a "black art" to a set of realistic, technology-agnostic best practices for supporting hypergrowth in nearly any environment, including both frontend and backend systems. For architects, they offer powerful new insights for creating and evaluating designs. For developers, they share specific techniques for handling everything from databases to state. For managers, they provide invaluable help in goal-setting, decision-making, and interacting with technical teams. Whatever your role, you'll find practical risk/benefit guidance for setting priorities-and getting maximum "bang for the buck." - Simplifying architectures and avoiding "over-engineering"- Scaling via cloning, replication, separating functionality, and splitting data sets- Scaling out, not up- Getting more out of databases without compromising scalability- Avoiding unnecessary redirects and redundant double-checking- Using caches and content delivery networks more aggressively, without introducing unacceptable complexity- Designing for fault tolerance, graceful failure, and easy rollback- Striving for statelessness when you can; efficiently handling state when you must- Effectively utilizing asynchronous communication- Learning quickly from mistakes, and much more

    The title says it is the hard way to learn to writecode but it’s actually not. It’s the “hard” way only in that it’s the way people used to teach things. In this book youwill do something incredibly simple that all programmers actually do to learn a language: 1. Go through each exercise. 2. Type in each sample exactly. 3. Make it run.That’s it. This will be very difficult at first, but stick with it. If you go through this book, and do each exercise for1-2 hours a night, then you’ll have a good foundation for moving on to another book. You might not really learn“programming” from this book, but you will learn the foundation skills you need to start learning the language.This book’s job is to teach you the three most basic essential skills that a beginning programmer needs to know:Reading And Writing, Attention To Detail, Spotting Differences.

    Whether you’re an intermediate-level Python programmer or a student of computational modeling, you’ll delve into examples of complex systems through a series of exercises, case studies, and easy-to-understand explanations.You’ll work with graphs, algorithm analysis, scale-free networks, and cellular automata, using advanced features that make Python such a powerful language. Ideal as a text for courses on Python programming and algorithms, Think Complexity will also help self-learners gain valuable experience with topics and ideas they might not encounter otherwise.Work with NumPy arrays and SciPy methods, basic signal processing and Fast Fourier Transform, and hash tablesStudy abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machinesGet starter code and solutions to help you re-implement and extend original experiments in complexityExplore the philosophy of science, including the nature of scientific laws, theory choice, realism and instrumentalism, and other topicsExamine case studies of complex systems submitted by students and readers

    We have put thousands of hours into the fourth edition of the book, writing and revising its material to include significant improvements and new material based on feedback from previous editions.

    It guides you through the creation and deployment of a simple webapp, showing how Docker can be used at all stages, including development, testing and deployment.Other topics in this book include using Docker to provide a microservices architecture, how to best do service discovery, and how to bundle applications using Docker. You'll also get an overview of the large ecosystem that has sprung up around Docker, including the various PaaS offerings and configuration tools.