Distributed processing has a strong theoretical foundation, but many day-to-day practitioners make limited use of the advantages this theory can give them. The result includes unreliable systems with obscure and intermittent failures, that can cost time, money and in extreme cases, lives. Reliable construction of distributed and concurrent systems must incorporate theory in practice. This book provides a concise presentation of the theory closely linked to the practical realization of these concepts. This highly practical presentation contains all the elements needed for a complete development of a distributed system. The book includes examples from C, Java and Eiffel, and sample code is available online.

A practical guide to help you tackle different real-time data processing and analytics problems using the best tools for each scenario About This Book Learn about the various challenges in real-time data processing and use the right tools to overcome them This book covers popular tools and frameworks such as Spark, Flink, and Apache Storm to solve all your distributed processing problems A practical guide filled with examples, tips, and tricks to help you perform efficient Big Data processing in real-time Who This Book Is For If you are a Java developer who would like to be equipped with all the tools required to devise an end-to-end practical solution on real-time data streaming, then this book is for you. Basic knowledge of real-time processing would be helpful, and knowing the fundamentals of Maven, Shell, and Eclipse would be great. What You Will Learn Get an introduction to the established real-time stack Understand the key integration of all the components Get a thorough understanding of the basic building blocks for real-time solution designing Garnish the search and visualization aspects for your real-time solution Get conceptually and practically acquainted with real-time analytics Be well equipped to apply the knowledge and create your own solutions In Detail With the rise of Big Data, there is an increasing need to process large amounts of data continuously, with a shorter turnaround time. Real-time data processing involves continuous input, processing and output of data, with the condition that the time required for processing is as short as possible. This book covers the majority of the existing and evolving open source technology stack for real-time processing and analytics. You will get to know about all the real-time solution aspects, from the source to the presentation to persistence. Through this practical book, you'll be equipped with a clear understanding of how to solve challenges on your own. We'll cover topics such as how to set up components, basic executions, integrations, advanced use cases, alerts, and monitoring. You'll be exposed to the popular tools used in real-time processing today such as Apache Spark, Apache Flink, and Storm. Finally, you will put your knowledge to practical use by implementing all of the techniques in the form of a practical, real-world use case. By the end of this book, you will have a solid understanding of all the aspects of real-time data processing and analytics, and will know how to deploy the solutions in production environments in the best possible manner. Style and Approach In this practical guide to real-time analytics, each chapter begins with a basic high-level concept of the topic, followed by a practical, hands-on implementation of each concept, where you can see the working and execution of it. The book is written in a DIY style, with plenty of practical use cases, well-explained code examples, and relevant screenshots and diagrams.

An introduction to fundamental theories of concurrent computation and associated programming languages for developing distributed and mobile computing systems. Starting from the premise that understanding the foundations of concurrent programming is key to developing distributed computing systems, this book first presents the fundamental theories of concurrent computing and then introduces the programming languages that help develop distributed computing systems at a high level of abstraction. The major theories of concurrent computation—including the π-calculus, the actor model, the join calculus, and mobile ambients—are explained with a focus on how they help design and reason about distributed and mobile computing systems. The book then presents programming languages that follow the theoretical models already described, including Pict, SALSA, and JoCaml. The parallel structure of the chapters in both part one (theory) and part two (practice) enable the reader not only to compare the different theories but also to see clearly how a programming language supports a theoretical model. The book is unique in bridging the gap between the theory and the practice of programming distributed computing systems. It can be used as a textbook for graduate and advanced undergraduate students in computer science or as a reference for researchers in the area of programming technology for distributed computing. By presenting theory first, the book allows readers to focus on the essential components of concurrency, distribution, and mobility without getting bogged down in syntactic details of specific programming languages. Once the theory is understood, the practical part of implementing a system in an actual programming language becomes much easier.

Mit der Verfügbarkeit verteilter Systeme wächst der Bedarf an einer fundamentalen Diskussion dieses Gebiets. Hier ist sie! Abgedeckt werden die grundlegenden Konzepte wie Zeit, Zustand, Gleichzeitigkeit, Reihenfolge, Kenntnis, Fehler und Übereinstimmung. Die Betonung liegt auf der Entwicklung allgemeiner Mechanismen, die auf eine Vielzahl von Problemen angewendet werden können. Sorgfältig ausgewählte Beispiele (Taktgeber, Sperren, Kameras, Sensoren, Controller, Slicer und Syncronizer) dienen gleichzeitig der Vertiefung theoretischer Aspekte und deren Umsetzung in die Praxis. Alle vorgestellten Algorithmen werden mit durchschaubaren, induktionsbasierten Verfahren bewiesen.

Doreen Galli uses her considerable academic and professional experience to bring together the worlds of theory and practice providing leading edge solutions to tomorrow's challenges. "Distributed Operating Systems: Concepts and Practice" offers a good balance of real world examples and the underlying theory of distributed computing. The flexible design makes it usable for students, practitioners and corporate training. This book describes in detail each major aspect of distributed operating systems from a conceptual and practical viewpoint. The operating systems of Amoeba, Clouds, and Chorus(TM) (the base technology for JavaOS(TM)) are utilized as examples throughout the text; while the technologies of Windows 2000(TM), CORBA(TM), DCOM(TM), NFS, LDAP, X.500, Kerberos, RSA(TM), DES, SSH, and NTP demonstrate real life solutions. A simple client/server application is included in the appendix to demonstrate key distributed computing programming concepts. This book proves invaluable as a course text or as a reference book for those who wish to update and enhance their knowledge base. A Companion Website provides supplemental information. A broad range of distributed computing issues and concepts: Kernels, IPC, memory management, object-based operating systems, distributed file systems (with NFS and X.500), transaction management, process management, distributed synchronization, and distributed security A major case study of Windows 2000 to demonstrate a real life commercial solution Detail Boxes contain in-depth examples such as complex algorithms Project-oriented exercises providing hands-on-experience Relevant sources including 'core' Web and ftp sites, as well as research papers Easy reference with complete list of acronyms and glossary to aid readability

A tutorial leading the aspiring Go developer to full mastery of Golang's distributed features. Key Features This book provides enough concurrency theory to give you a contextual understanding of Go concurrency It gives weight to synchronous and asynchronous data streams in Golang web applications It makes Goroutines and Channels completely familiar and natural to Go developers Book Description Distributed Computing with Go gives developers with a good idea how basic Go development works the tools to fulfill the true potential of Golang development in a world of concurrent web and cloud applications. Nikhil starts out by setting up a professional Go development environment. Then you’ll learn the basic concepts and practices of Golang concurrent and parallel development. You’ll find out in the new few chapters how to balance resources and data with REST and standard web approaches while keeping concurrency in mind. Most Go applications these days will run in a data center or on the cloud, which is a condition upon which the next chapter depends. There, you’ll expand your skills considerably by writing a distributed document indexing system during the next two chapters. This system has to balance a large corpus of documents with considerable analytical demands. Another use case is the way in which a web application written in Go can be consciously redesigned to take distributed features into account. The chapter is rather interesting for Go developers who have to migrate existing Go applications to computationally and memory-intensive environments. The final chapter relates to the rather onerous task of testing parallel and distributed applications, something that is not usually taught in standard computer science curricula. What you will learn Gain proficiency with concurrency and parallelism in Go Learn how to test your application using Go's standard library Learn industry best practices with technologies such as REST, OpenAPI, Docker, and so on Design and build a distributed search engine Learn strategies on how to design a system for web scale Who this book is for This book is for developers who are familiar with the Golang syntax and have a good idea of how basic Go development works. It would be advantageous if you have been through a web application product cycle, although it’s not necessary.

A comprehensive guide to distributed algorithms that emphasizes examples and exercises rather than mathematical argumentation.