Dive into ØMQ (aka ZeroMQ), the smart socket library that gives you fast, easy, message-based concurrency for your applications. With this quick-paced guide, you’ll learn hands-on how to use this scalable, lightweight, and highly flexible networking tool for exchanging messages among clusters, the cloud, and other multi-system environments. ØMQ maintainer Pieter Hintjens takes you on a tour of real-world applications, using extended examples in C to help you work with ØMQ’s API, sockets, and patterns. Learn how to use specific ØMQ programming techniques, build multithreaded applications, and create your own messaging architectures. You’ll discover how ØMQ works with several programming languages and most operating systems—with little or no cost. Learn ØMQ’s main patterns: request-reply, publish-subscribe, and pipeline Work with ØMQ sockets and patterns by building several small applications Explore advanced uses of ØMQ’s request-reply pattern through working examples Build reliable request-reply patterns that keep working when code or hardware fails Extend ØMQ’s core pub-sub patterns for performance, reliability, state distribution, and monitoring Learn techniques for building a distributed architecture with ØMQ Discover what’s required to build a general-purpose framework for distributed applications
Annotation With 'Introducing Python', Bill Lubanovic brings years of knowledge as a programmer, system administrator and author to a book of impressive depth that's fun to read and simple enough for non-programmers to use. Along with providing a strong foundation in the language itself, Lubanovic shows you how to use Python for a range of applications in business, science and the arts, drawing on the rich collection of open source packages developed by Python fans.
The five-volume set LNCS 9786-9790 constitutes the refereed proceedingsof the 16th International Conference on Computational Science and ItsApplications, ICCSA 2016, held in Beijing, China, in July 2016. The 239 revised full papers and 14 short papers presented at 33 workshops were carefully reviewed and selected from 849 submissions. They are organized in five thematical tracks: computational methods, algorithms and scientific applications; high performance computing and networks; geometric modeling, graphics and visualization; advanced and emerging applications; and information systems and technologies.
The whole planet is getting connected and building vast new communities. Billions of us are online, all the time. This online world thinks faster, and thinks differently. Smart, fast, and creative, our new communities are a very real challenge to old power and old money. And old money -- after its War on Drugs and War on Terror -- is now launching its War on the Internet. What is going on, and where will this lead us? Pieter Hintjens -- author, programmer, and activist -- tells all in this vast story of Culture & Empire: Digital Revolution.
"Even connecting a few programs across a few sockets is plain nasty when you start to handle real life situations. Trillions? The cost would be unimaginable. Connecting computers is so difficult that software and services to do this is a multi-billion dollar business. So today we're still connecting applications using raw UDP and TCP, proprietary protocols, HTTP, Websockets. It remains painful, slow, hard to scale, and essentially centralized. To fix the world, we needed to do two things. One, to solve the general problem of "how to connect any code to any code, anywhere." Two, to wrap that up in the simplest possible building blocks that people could understand and use easily. It sounds ridiculously simple. And maybe it is. That's kind of the whole point." If you are a programmer and you aim to build large systems, in any language, then Code Connected is essential reading. Code Connected Volume 1 takes you through learning ZeroMQ, step-by-step, with over 80 examples. You will learn the basics, the API, the different socket types and how they work, reliability, and a host of patterns you can use in your applications. This is the Professional Edition for C/C++.
This book is for Python programmers with an intermediate background and an interest in design patterns implemented in idiomatic Python. Programmers of other languages who are interested in Python can also benefit from this book, but it would be better if they first read some introductory materials that explain how things are done in Python.
We are living in the dawn of what has been termed as the "Fourth Industrial Revolution," which is marked through the emergence of "cyber-physical systems" where software interfaces seamlessly over networks with physical systems, such as sensors, smartphones, vehicles, power grids or buildings, to create a new world of Internet of Things (IoT). Data and information are fuel of this new age where powerful analytics algorithms burn this fuel to generate decisions that are expected to create a smarter and more efficient world for all of us to live in. This new area of technology has been defined as Big Data Science and Analytics, and the industrial and academic communities are realizing this as a competitive technology that can generate significant new wealth and opportunity. Big data is defined as collections of datasets whose volume, velocity or variety is so large that it is difficult to store, manage, process and analyze the data using traditional databases and data processing tools. Big data science and analytics deals with collection, storage, processing and analysis of massive-scale data. Industry surveys, by Gartner and e-Skills, for instance, predict that there will be over 2 million job openings for engineers and scientists trained in the area of data science and analytics alone, and that the job market is in this area is growing at a 150 percent year-over-year growth rate. We have written this textbook, as part of our expanding "A Hands-On Approach"(TM) series, to meet this need at colleges and universities, and also for big data service providers who may be interested in offering a broader perspective of this emerging field to accompany their customer and developer training programs. The typical reader is expected to have completed a couple of courses in programming using traditional high-level languages at the college-level, and is either a senior or a beginning graduate student in one of the science, technology, engineering or mathematics (STEM) fields. An accompanying website for this book contains additional support for instruction and learning (www.big-data-analytics-book.com) The book is organized into three main parts, comprising a total of twelve chapters. Part I provides an introduction to big data, applications of big data, and big data science and analytics patterns and architectures. A novel data science and analytics application system design methodology is proposed and its realization through use of open-source big data frameworks is described. This methodology describes big data analytics applications as realization of the proposed Alpha, Beta, Gamma and Delta models, that comprise tools and frameworks for collecting and ingesting data from various sources into the big data analytics infrastructure, distributed filesystems and non-relational (NoSQL) databases for data storage, and processing frameworks for batch and real-time analytics. This new methodology forms the pedagogical foundation of this book. Part II introduces the reader to various tools and frameworks for big data analytics, and the architectural and programming aspects of these frameworks, with examples in Python. We describe Publish-Subscribe messaging frameworks (Kafka & Kinesis), Source-Sink connectors (Flume), Database Connectors (Sqoop), Messaging Queues (RabbitMQ, ZeroMQ, RestMQ, Amazon SQS) and custom REST, WebSocket and MQTT-based connectors. The reader is introduced to data storage, batch and real-time analysis, and interactive querying frameworks including HDFS, Hadoop, MapReduce, YARN, Pig, Oozie, Spark, Solr, HBase, Storm, Spark Streaming, Spark SQL, Hive, Amazon Redshift and Google BigQuery. Also described are serving databases (MySQL, Amazon DynamoDB, Cassandra, MongoDB) and the Django Python web framework. Part III introduces the reader to various machine learning algorithms with examples using the Spark MLlib and H2O frameworks, and visualizations using frameworks such as Lightning, Pygal and Seaborn.