Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords?
In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications.
Peer under the hood of the systems you already use, and learn how to use and operate them more effectively
Make informed decisions by identifying the strengths and weaknesses of different tools
Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity
Understand the distributed systems research upon which modern databases are built
Peek behind the scenes of major online services, and learn from their architectures

This text identifies, examines, and illustrates fundamental concepts in computer system design that are common across operating systems, networks, database systems, distributed systems, programming languages, software engineering, security, fault tolerance, and architecture. Through carefully analyzed case studies from each of these disciplines, it demonstrates how to apply these concepts to tackle practical system design problems.
To support the focus on design, the text identifies and explains abstractions that have proven successful in practice such as, remote procedure call, client/service organization, file systems, data integrity, consistency, and authenticated messages. Most computer systems are built using a handful of such abstractions. The text describes how these abstractions are implemented, demonstrates how they are used in different systems, and prepares the reader to apply them in future designs.
Features:

Concepts of computer system design guided by fundamental principles.
Cross-cutting approach that identifies abstractions common to networking, operating systems, transaction systems, distributed systems, architecture, and software engineering.
Case studies that make the abstractions real: naming (DNS and the URL); file systems (the UNIX file system); clients and services (NFS); virtualization (virtual machines); scheduling (disk arms); security (TLS).
Numerous pseudocode fragments that provide concrete examples of abstract concepts.
Extensive support. The authors and MIT OpenCourseWare provide on-line, free of charge, open educational resources, including additional chapters, course syllabi, board layouts and slides, lecture videos, and an archive of lecture schedules, class assignments, and design projects.

Streaming data is a big deal in big data these days. As more and more businesses seek to tame the massive unbounded data sets that pervade our world, streaming systems have finally reached a level of maturity sufficient for mainstream adoption. With this practical guide, data engineers, data scientists, and developers will learn how to work with streaming data in a conceptual and platform-agnostic way.
Expanded from Tyler Akidau’s popular blog posts "Streaming 101" and "Streaming 102", this book takes you from an introductory level to a nuanced understanding of the what, where, when, and how of processing real-time data streams. You’ll also dive deep into watermarks and exactly-once processing with co-authors Slava Chernyak and Reuven Lax.
You’ll explore:
How streaming and batch data processing patterns compare
The core principles and concepts behind robust out-of-order data processing
How watermarks track progress and completeness in infinite datasets
How exactly-once data processing techniques ensure correctness
How the concepts of streams and tables form the foundations of both batch and streaming data processing
The practical motivations behind a powerful persistent state mechanism, driven by a real-world example
How time-varying relations provide a link between stream processing and the world of SQL and relational algebra

在当今整个中国社会都处于互联网转型的浪潮中，不管是政府职能单位、业务规模庞大的央企，还是面临最激烈竞争的零售行业都处于一个重要的转折点，这个转折对企业业务模式带来了冲击，当然也给企业的信息中心部门带来了挑战：如何构建IT系统架构更好地满足互联网时代下企业业务发展的需要。阿里巴巴的共享服务理念以及企业级互联网架构建设的思路，给这些企业带来了不少新的思路，这也是我最终决定写这本书的最主要原因。本书从阿里巴巴启动中台战略说起，详细阐述了共享服务理念给企业业务发展带来的业务价值。接着会分享阿里巴巴在建设共享服务体系时如何进行技术框架的选择，哪些重要的技术平台支撑起了共享服务体系，这也是迄今为止对阿里巴巴集团中间件体系对外最全面系统的介绍。除了技术层面之外，本书还分享了阿里巴巴内部的一些经验和实践，如组织的架构和体制如何更好地支持共享服务体系的持续发展。

每个应用程序都会产生数据，包括日志消息、度量指标、用户活动记录、响应消息等。如何移动数据，几乎变得与数据本身一样重要。如果你是架构师、开发者或者产品工程师，同时也是Apache Kafka新手，那么这本实践指南将会帮助你成为流式平台上处理实时数据的专家。
本书由出身于LinkedIn的Kafka核心作者和一线技术人员共同执笔，详细介绍了如何部署Kafka集群、开发可靠的基于事件驱动的微服务，以及基于Kafka平台构建可伸缩的流式应用程序。通过详尽示例，你将会了解到Kafka的设计原则、可靠性保证、关键API，以及复制协议、控制器和存储层等架构细节。
● 了解发布和订阅消息模型以及该模型如何被应用在大数据生态系统中
● 学习使用Kafka生产者和消费者来生成消息和读取消息
● 了解Kafka保证可靠性数据传递的模式和场景需求
● 使用Kafka构建数据管道和应用程序的最佳实践
● 在生产环境中管理Kafka，包括监控、调优和维护
● 了解Kafka的关键度量指标
● 探索Kafka如何成为流式处理利器

Have you ever wanted to learn more about Databases but did not know where to start? This is a book just for you.
We can treat databases and other infrastructure components as black boxes, but it doesn’t have to be that way. Sometimes we have to take a closer look at what’s going on because of performance issues. Sometimes databases misbehave, and we need to find out what exactly is going on. Some of us want to work in infrastructure and develop databases. This book’s main intention is to introduce you to the cornerstone concepts and help you understand how databases work.
The book consists of two parts: Storage Engines and Distributed Systems since that’s where most of the differences between the vast majority of databases is coming from.

In modern computing a program is usually distributed among several processes. The fundamental challenge when developing reliable and secure distributed programs is to support the cooperation of processes required to execute a common task, even when some of these processes fail. Failures may range from crashes to adversarial attacks by malicious processes.
Cachin, Guerraoui, and Rodrigues present an introductory description of fundamental distributed programming abstractions together with algorithms to implement them in distributed systems, where processes are subject to crashes and malicious attacks. The authors follow an incremental approach by first introducing basic abstractions in simple distributed environments, before moving to more sophisticated abstractions and more challenging environments. Each core chapter is devoted to one topic, covering reliable broadcast, shared memory, consensus, and extensions of consensus. For every topic, many exercises and their solutions enhance the understanding
This book represents the second edition of "Introduction to Reliable Distributed Programming". Its scope has been extended to include security against malicious actions by non-cooperating processes. This important domain has become widely known under the name "Byzantine fault-tolerance".

Developing reliable, scalable distributed systems today is often more black art than science. Building these systems is complicated and, because few formally established patterns are available for designing them, most of these systems end up looking very unique. This practical guide shows you how to use existing software design patterns for designing and building reliable distributed applications.Although patterns such as those developed more than 20 years ago by the Gang of Four were largely restricted to running on single machines, author Brendan Burns—a Partner Architect in Microsoft Azure—demonstrates how you can reuse several of them in modern distributed applications.Systems engineers and application developers will learn how these patterns provide a common language and framework for dramatically increasing the quality of your system.

Kubernetes in Action, Second Edition teaches you to use Kubernetes to deploy container-based distributed applications. You'll start with an overview of how Docker containers work with Kubernetes and move quickly to building your first cluster. You'll gradually expand your initial application, adding features and deepening your knowledge of Kubernetes architecture and operation. In this revised and expanded second edition, you’ll take a deep dive into the structure of a Kubernetes-based application and discover how to manage a Kubernetes cluster in production. As you navigate this comprehensive guide, you'll also appreciate thorough coverage of high-value topics like monitoring, tuning, and scaling.
what's inside
Up and running with Kubernetes
Deploying containers across a cluster
Securing clusters
Updating applications with zero downtime

The fourth edition of this classic textbook provides major updates. This edition has completely new chapters on Big Data Platforms (distributed storage systems, MapReduce, Spark, data stream processing, graph analytics) and on NoSQL, NewSQL and polystore systems. It also includes an updated web data management chapter that includes RDF and semantic web discussion, an integrated database integration chapter focusing both on schema integration and querying over these systems. The peer-to-peer computing chapter has been updated with a discussion of blockchains. The chapters that describe classical distributed and parallel database technology have all been updated.
The new edition covers the breadth and depth of the field from a modern viewpoint. Graduate students, as well as senior undergraduate students studying computer science and other related fields will use this book as a primary textbook. Researchers working in computer science will also find this textbook useful.
This textbook has a companion web site that includes background information on relational database fundamentals, query processing, transaction management, and computer networks for those who might need this background. The web site also includes all the figures and presentation slides as well as solutions to exercises (restricted to instructors).

从移动电话到互联网，我们的生活越来越依赖于以无缝和透明的方式将计算机和其他设备链接在一起的分布式系统。本书全面介绍分布式系统的设计原理和实践及其最新进展，并使用大量最新的实例研究来阐明分布式系统的设计与开发方法。
本书前几版已被爱丁堡大学、伊利诺伊大学、卡内基-梅隆大学、南加州大学、得克萨斯A&M大学、多伦多大学、罗切斯特理工学院、北京大学等众多名校选用为教材。第5版在上一版的基础上，新增了三章内容，分别介绍间接通信、分布式对象和组件、分布式系统设计（以Google为例）。
本书网站www.cdk5.net为学生和教师提供了丰富的学习资源和教学资源（源代码、参考文献、教学幻灯片、勘误等）。

Get ready to unlock the power of your data. With the fourth edition of this comprehensive guide, you’ll learn how to build and maintain reliable, scalable, distributed systems with Apache Hadoop. This book is ideal for programmers looking to analyze datasets of any size, and for administrators who want to set up and run Hadoop clusters.
Using Hadoop 2 exclusively, author Tom White presents new chapters on YARN and several Hadoop-related projects such as Parquet, Flume, Crunch, and Spark. You’ll learn about recent changes to Hadoop, and explore new case studies on Hadoop’s role in healthcare systems and genomics data processing.
Learn fundamental components such as MapReduce, HDFS, and YARN
Explore MapReduce in depth, including steps for developing applications with it
Set up and maintain a Hadoop cluster running HDFS and MapReduce on YARN
Learn two data formats: Avro for data serialization and Parquet for nested data
Use data ingestion tools such as Flume (for streaming data) and Sqoop (for bulk data transfer)
Understand how high-level data processing tools like Pig, Hive, Crunch, and Spark work with Hadoop
Learn the HBase distributed database and the ZooKeeper distributed configuration service

Distributed Systems: An Algorithmic Approach, Second Edition provides a balanced and straightforward treatment of the underlying theory and practical applications of distributed computing. As in the previous version, the language is kept as unobscured as possible―clarity is given priority over mathematical formalism. This easily digestible text:
Features significant updates that mirror the phenomenal growth of distributed systems
Explores new topics related to peer-to-peer and social networks
Includes fresh exercises, examples, and case studies
Supplying a solid understanding of the key principles of distributed computing and their relationship to real-world applications, Distributed Systems: An Algorithmic Approach, Second Edition makes both an ideal textbook and a handy professional reference.

I wanted a text that would bring together the ideas behind many of the more recent distributed systems - systems such as Amazon's Dynamo, Google's BigTable and MapReduce, Apache's Hadoop and so on.
In this text I've tried to provide a more accessible introduction to distributed systems. To me, that means two things: introducing the key concepts that you will need in order to have a good time reading more serious texts, and providing a narrative that covers things in enough detail that you get a gist of what's going on without getting stuck on details. It's 2013, you've got the Internet, and you can selectively read more about the topics you find most interesting.

《高可用架构（第1卷）》由数十位一线架构师的实践与经验凝结而成，选材兼顾技术性、前瞻性与专业深度。各技术焦点，均由极具代表性的领域专家或实践先行者撰文深度剖析，共同组成“高可用”的全局视野与领先高度，内容包括精华案例、分布式原理、电商架构等热门专题，及云计算、容器、运维、大数据、安全等重点方向。不仅架构师可以从中受益，其他IT、互联网技术从业者同样可以得到提升。

Microservices in Go teaches you how to handle advanced server side development in Go; it uses microservices to accomplish this, but is relevant to all server-side developers. The author gives you tips on how to deploy Go apps to popular cloud platforms like Amazon, Google, and Digital ocean. The book has a lot of meaty chapters, including:
Queuing architectures such as Kafka, and how to practically use them
Influxdb, and how to monitor production systems with metrics
How to use Docker to make deployments simpler
Using ETCD/Consul to do service discovery and automated failover of mysql/redis
Building mobile clients on IOS and Android

随着信息技术的广泛应用和快速发展，云计算作为一种新兴的商业计算模型日益受到人们的广泛关注。本书是一本完整讲述云计算与分布式系统基本理论及其应用的教材。书中从现代分布式模型概述开始，介绍了并行、分布式与云计算系统的设计原理、系统体系结构和创新应用，并通过开源应用和商业应用例子，阐述了如何为科研、电子商务、社会网络和超级计算等创建高性能、可扩展的、可靠的系统。
本书特色
 全面覆盖现代分布式计算技术，包括集群、网格、面向服务的体系结构、大规模并行处理器、对等网络和云计算。
 提供的案例研究来自主流分布式计算供应商，如亚马逊、微软、谷歌等。
 解释如何利用虚拟化来促进管理、调试、迁移和灾难恢复。
 专为本科生或研究生的分布式系统课程而设计——每章后都配有习题和进一步阅读建议，并为教师提供配套的PPT等教辅资源。