Functional Programming in Kotlin is a serious tutorial for programmers looking to learn FP and apply it to the everyday business of coding. Based on the bestselling Functional Programming in Scala, this book guides intermediate Java and Kotlin programmers from basic techniques to advanced topics in a logical, concise, and clear progression. In it, you'll find concrete examples and exercises that open up the world of functional programming. The book will deliver practical mastery of FP using Kotlin and a valuable perspective on program design that you can apply to other languages.
what's inside
Functional programming techniques for real-world applications
Write combinator libraries
Identify common structures and idioms in functional design
Code for simplicity, modularity, and fewer bugs

Distributed systems have become more fine-grained in the past 10 years, shifting from code-heavy monolithic applications to smaller, self-contained microservices. But developing these systems brings its own set of headaches. With lots of examples and practical advice, the second edition of this practical book takes a holistic view of the topics that system architects and administrators must consider when building, managing, and evolving microservice architectures.
Microservice technologies are moving quickly, and this revised edition gets you up to date with a new chapter on serverless and cloud-native applications, expanded coverage of user interfaces, more hands-on code examples, and other additions throughout the book.
Author Sam Newman provides you with a firm grounding in the concepts while diving into current solutions for modeling, integrating, testing, deploying, and monitoring your own autonomous services. You’ll follow a fictional company throughout the book to learn how building a microservice architecture affects a single domain.

Can a system ever truly be considered reliable if it isn't fundamentally secure? In two previous O'Reilly books, experts from Google showed how reliability is fundamental to service design. Site Reliability Engineering and The Site Reliability Workbook demonstrated how and why a commitment to the entire service lifecycle enables organizations to successfully build, deploy, monitor, and maintain software systems.
Security is also crucial to the design and operation of scalable systems in production, as it plays an important part in product quality, performance, reliability, and availability. In a world where most products are connected to the internet, and with cloud technologies and machine learning becoming more prevalent, enabling security by default is increasingly important.
This book shares best practices to help an organization of any size design scalable and reliable systems that are fundamentally secure. It also offers insights into how teams across an organization can collaborate on security and reliability.
Specifically, you'll learn about:
Design Strategies
Implementation & Operations
Preparing for the Worst: Detection & Response
Scaling Security and the Organization

Get a comprehensive understanding of gRPC fundamentals through real-world examples. With this practical guide, you’ll learn how this high-performance interprocess communication protocol is capable of connecting polyglot services in microservices architecture, while providing a rich framework for defining service contracts and data types.
Complete with hands-on examples written in Go, Java, Node, and Python, this book also covers the essential techniques and best practices to use gRPC in production systems. Authors Kasun Indrasiri and Danesh Kuruppu discuss the importance of gRPC in the context of microservices development.

Learn how to design, develop, and deploy smart contracts on the Ethereum blockchain. With this practical book, software developers with a high-level understanding of the blockchain will use the Solidity programming language to write decentralized applications (dApps) using the web3.js library.
Smart contracts have allowed technologists to create a new breed of software systems that have ushered in the next revolution in human organization. Kevin Solorio, Randall Kanna, and David Hoover teach you the fundamentals of Solidity, as well as key topics such as unit testing, deployment, and preventing security vulnerabilities. You'll also learn how to leverage the built-in public/private key cryptography in the Ethereum protocol.
Learn the fundamentals of how the Ethereum blockchain works
Setup a local Ethereum client using Parity
Develop and test secure smart contracts using Solidity and Vyper
Learn how to deploy smart contracts
Interact with your smart contract using Web3 (JavaScript)
Work with hands-on examples and walkthroughs of common scenarios

Most applications today are distributed in some fashion. Monitoring the health and performance of these distributed architectures requires a new approach. Enter distributed tracing, a method of profiling and monitoring applications—especially those that use microservice architectures. There’s just one problem: distributed tracing can be hard. But it doesn’t have to be.
With this practical guide, you’ll learn what distributed tracing is and how to use it to understand the performance and operation of your software. Key players at LightStep walk you through instrumenting your code for tracing, collecting the data that your instrumentation produces, and turning it into useful, operational insights. If you want to start implementing distributed tracing, this book tells you what you need to know.
You’ll learn:
The pieces of a distributed tracing deployment: Instrumentation, data collection, and delivering value
Best practices for instrumentation (the methods for generating trace data from your service)
How to deal with or avoid overhead, costs, and sampling
How to work with spans (the building blocks of request-based distributed traces) and choose span characteristics that lead to valuable traces
Where distributed tracing is headed in the future

This is the Scala edition of Category Theory for Programmers by Bartosz Milewski. This book contains code snippets in both Haskell and Scala.

统计学习方法即机器学习方法，是计算机及其应用领域的一门重要学科。本书分为监督学 习和无监督学习两篇，全面系统地介绍了统计学习的主要方法。包括感知机、k 近邻法、朴素贝叶斯法、决策树、逻辑斯谛回归与最大熵模型、支持向量机、提升方法、EM 算法、隐马尔可夫模型和条件随机场，以及聚类方法、奇异值分解、主成分分析、潜在语义分析、概率潜在语义分析、马尔可夫链蒙特卡罗法、潜在狄利克雷分配和 PageRank 算法等。除有关统计学习、监督学习和无监督学习的概论和总结的四章外，每章介绍一种方法。叙述力求从具体问题或实例入手， 由浅入深，阐明思路，给出必要的数学推导，便于读者掌握统计学习方法的实质，学会运用。 为满足读者进一步学习的需要，书中还介绍了一些相关研究，给出了少量习题，列出了主要参考文献。 本书是统计机器学习及相关课程的教学参考书，适用于高等院校文本数据挖掘、信息检索及自然语言处理等专业的大学生、研究生，也可供从事计算机应用相关专业的研发人员参考。

The awesome Linux zines you love, now as a fancy box set!
Ever wished you could spy on your computer with a handy incantation or bewitch your programs to debug themselves--now you can by becoming a Linux wizard! Okay, reading these zines won't actually make you a wizard, but you'll sure feel like one after you learn some neat Linux tricks.
With this collected edition of Julia Evans's wildly popular Linux zines, you'll get super awesome repackaged versions of the zines like:
* Networking, ACK! to learn how cat pics teleport from a server to your laptop through the magic of networking
* So You Want to be a Wizard, which teaches you how to become a certified programming wizard
* Bite Size Linux to explore the nitty gritty of how signals, sockets, and virtual memory work.
* Let's Learn tcpdump! to dump all your network traffic right into your CLI
* Linux debugging tools you'll love, so you can get the scoop on things like opensnoop, netcat, and netstat
* Spying on Your Programs with strace lets you be a secret agent so you can see what programs your computer is (or isn't!) running
* Profiling & Tracing with perf teaches you how much CPU your functions are using
Through Julia's insatiable curiosity for all things technical, you'll view programming in a way you never have before--now on fancier paper!

The latest edition of a popular text and reference on database research, with substantial new material and revision; covers classical literature and recent hot topics.
Lessons from database research have been applied in academic fields ranging from bioinformatics to next-generation Internet architecture and in industrial uses including Web-based e-commerce and search engines. The core ideas in the field have become increasingly influential. This text provides both students and professionals with a grounding in database research and a technical context for understanding recent innovations in the field. The readings included treat the most important issues in the database area―the basic material for any DBMS professional. This fourth edition has been substantially updated and revised, with 21 of the 48 papers new to the edition, four of them published for the first time. Many of the sections have been newly organized, and each section includes a new or substantially revised introduction that discusses the context, motivation, and controversies in a particular area, placing it in the broader perspective of database research. Two introductory articles, never before published, provide an organized, current introduction to basic knowledge of the field; one discusses the history of data models and query languages and the other offers an architectural overview of a database system. The remaining articles range from the classical literature on database research to treatments of current hot topics, including a paper on search engine architecture and a paper on application servers, both written expressly for this edition. The result is a collection of papers that are seminal and also accessible to a reader who has a basic familiarity with database systems.

Database System Concepts by Silberschatz, Korth and Sudarshan is now in its 7th edition and is one of the cornerstone texts of database education. It presents the fundamental concepts of database management in an intuitive manner geared toward allowing students to begin working with databases as quickly as possible.
The text is designed for a first course in databases at the junior/senior undergraduate level or the first year graduate level. It also contains additional material that can be used as supplements or as introductory material for an advanced course. Because the authors present concepts as intuitive descriptions, a familiarity with basic data structures, computer organization, and a high-level programming language are the only prerequisites. Important theoretical results are covered, but formal proofs are omitted. In place of proofs, figures and examples are used to suggest why a result is true.

Microservices have become the backbone of the largest web companies in existence today, including Netflix, Amazon, and Google. These companies run large distributed systems that span multiple datacenters and geographic regions, and this usually involves distributed schedulers, service discovery, load balancing, traffic shaping, monitoring, and logging solutions.
This practical book provides several common design patterns for building a platform around microservices at scale. Developers, software architects, infrastructure engineers, and site reliability engineers will learn about schedulers, fault tolerant remote procedure calls, distributed tracing, traffic shaping, and the role of chaos engineering in building resilient systems. You’ll be able to design a platform that can power microservices that span multiple datacenters, regions, and provide a high degree of availability on public and hybrid clouds.
You’ll explore:
The landscape of internet scale services
Guiding principles, practices, and core infrastructure components for building and scaling services on top of SOA/microservice architecture
Practices, tools, and techniques for operating large-scale microservices and distributed systems on dynamic and elastic infrastructure
Patterns and techniques for making internet services highly available and reliable, including multi-geography architectures

Understand how to apply distributed tracing to microservices-based architectures
Key Features
A thorough conceptual introduction to distributed tracing
An exploration of the most important open standards in the space
A how-to guide for code instrumentation and operating a tracing infrastructure
Book Description
Mastering Distributed Tracing will equip you to operate and enhance your own tracing infrastructure. Through practical exercises and code examples, you will learn how end-to-end tracing can be used as a powerful application performance management and comprehension tool.
The rise of Internet-scale companies, like Google and Amazon, ushered in a new era of distributed systems operating on thousands of nodes across multiple data centers. Microservices increased that complexity, often exponentially. It is harder to debug these systems, track down failures, detect bottlenecks, or even simply understand what is going on. Distributed tracing focuses on solving these problems for complex distributed systems. Today, tracing standards have developed and we have much faster systems, making instrumentation less intrusive and data more valuable.
Yuri Shkuro, the creator of Jaeger, a popular open-source distributed tracing system, delivers end-to-end coverage of the field in Mastering Distributed Tracing. Review the history and theoretical foundations of tracing; solve the data gathering problem through code instrumentation, with open standards like OpenTracing, W3C Trace Context, and OpenCensus; and discuss the benefits and applications of a distributed tracing infrastructure for understanding, and profiling, complex systems.
What you will learn
How to get started with using a distributed tracing system
How to get the most value out of end-to-end tracing
Learn about open standards in the space
Learn about code instrumentation and operating a tracing infrastructure
Learn where distributed tracing fits into microservices as a core function
Who this book is for
Any developer interested in testing large systems will find this book very revealing and in places, surprising. Every microservice architect and developer should have an insight into distributed tracing, and the book will help them on their way. System administrators with some development skills will also benefit. No particular programming language skills are required, although an ability to read Java, while non-essential, will help with the core chapters.

This book provides you with the chance to study how 26 experienced programmers think when they are building something new. The programs you will read about in this book were all written from scratch to solve difficult problems. A web server, a pedometer, a Python interpreter, a web-based spreadsheet, and many more applications are written, in 500 lines of code or less, and described by their creators so that you can learn from their insights and their mistakes.

Modern distributed applications run in environments that may include thousands of processors, web and mobile clients, hybrid cloud deployments, and petabytes of data. The traditional patterns and practices for enterprise application development simply can't deliver the millisecond response times and near-perfect reliability these systems require. Reactive applications meet these demands by employing a loosely-coupled system of independent, isolated components that communicate via asynchronous message passing. These systems are highly responsive to changes in load, extremely fault tolerant through self-healing, and designed to elastically scale. For developers working in JVM-based systems, the Typesafe stack, anchored by the Java and Scala programming languages and the Akka toolkit and runtime, gives developers the tools to build a production-ready reactive application platform.

When data-driven applications fail, identifying the cause is both challenging and time-consuming—especially as data pipelines become more and more complex. Hunting for the root cause of application failure from messy, raw, and distributed logs is difficult for performance experts and a nightmare for data operations teams. This report examines DataOps processes and tools that enable you to manage modern data pipelines efficiently.
Author Ted Malaska describes a data operations framework and shows you the importance of testing and monitoring to plan, rebuild, automate, and then manage robust data pipelines—whether it’s in the cloud, on premises, or in a hybrid configuration. You’ll also learn ways to apply performance monitoring software and AI to your data pipelines in order to keep your applications running reliably.
You’ll learn:
How performance management software can reduce the risk of running modern data applications
Methods for applying AI to provide insights, recommendations, and automation to operationalize big data systems and data applications
How to plan, migrate, and operate big data workloads and data pipelines in the cloud and in hybrid deployment models

Whether you’re a software engineer aspiring to enter the world of artificial intelligence, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where do I begin? This step-by-step guide teaches you how to build practical applications using deep neural networks for the cloud and mobile using a hands-on approach.
Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people can use in the real world. Train, optimize, and deploy computer vision models with Keras, TensorFlow, CoreML, TensorFlow Lite, and MLKit, rapidly taking your system from zero to production quality.
Develop AI applications for the desktop, cloud, smartphones, browser, and smart robots using Raspberry Pi, Jetson Nano, and Google Coral
Perform Object Classification, Detection, Segmentation in real-time
Learn by building examples such as Silicon Valley’s "Not Hotdog" app, image search engines, and Snapchat filters
Train an autonomous car in a video game environment and then build a real mini version
Use transfer learning to train models in minutes
Generate photos from sketches in your browser with Generative Adversarial Networks (GANs with pix2pix), and Body Pose Estimation (PoseNet)
Discover 50+ practical tips for data collection, model interoperability, debugging, avoiding bias, and scaling to millions of users

Perform fast interactive SQL analytics against different data sources using the Presto distributed SQL query engine. With this practical book, you’ll learn how to conduct analytics on data where it lives, including Hive, Cassandra, relational databases, and proprietary data stores. Matt Fuller from Starburst Data and Presto cocreator Martin Traverso show analysts how to manage, use, and even develop with Presto.
Initially developed by Facebook, open source Presto is now used by Netflix, Airbnb, LinkedIn, Twitter, Uber, and many other companies. You’ll learn how a single Presto query can combine data from multiple sources to allow for analytics across your entire organization.
This book will help you:
Get started using Presto
Explore Presto architectural concepts
Learn best practices and tuning
Use Presto with various business intelligence and SQL analytical tools
Query data from different data sources, including query federation
Learn how to use Presto on Amazon Web Services, Microsoft Azure, and Google Cloud Platform

Want to build an application that uses natural language text, but aren’t sure where to start or what tools to use? This practical book gets you started with natural language processing from the basics to powerful modern techniques. Data scientists will learn how to build enterprise-quality NLP applications using deep learning and the Apache Spark distributed processing framework.
This guide includes concrete examples, practical and theoretical explanations, and hands-on exercises for NLP on Spark. You’ll understand why these techniques work from machine learning, linguistic, and practical points of view.
This book shows you how to:
Process text in a distributed environment using Spark-NLP, a production-ready library for NLP built on Spark
Create, tune, and deploy your own word embeddings
Adapt your NLP applications to multiple languages
Use text in machine learning and deep learning

For web developers and other programmers interested in using JavaScript, this bestselling book provides the most comprehensive JavaScript reference section on the market. The seventh edition represents a significant update, with new material for ECMAScript 2017 (ES8), and new chapters on language-specific features. JavaScript: The Definitive Guide is ideal for experienced programmers who want to learn the programming language of the web, and for current JavaScript programmers who want to master it.