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[网络/IT/软硬件] 《Designing Data-Intensive Applications》[Martin Kleppmann][azw3][无损5分]

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    artour 发表于 2021-7-11 18:40:19 | 显示全部楼层 |阅读模式
    本帖最后由 artour 于 2021-7-11 18:42 编辑

    数据密集型应用系统设计(英文原版)

    豆瓣评分
    9.7



    作者: Martin Kleppmann
    出版社: O'Reilly Media
    副标题: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
    出版年: 2017-4-2
    页数: 614
    定价: USD 44.99
    装帧: Paperback
    ISBN: 9781449373320



    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


    内容简介(仅供参考)

    第一部分,主要讨论有关增强数据密集型应用系统所需的若干基本原则。首先开篇第1章即瞄准目标:可靠性、可扩展性与可维护性,如何认识这些问题以及如何达成目标。第2章我们比较了多种不同的数据模型和查询语言,讨论各自的适用场景。接下来第3章主要针对存储引擎,即数据库是如何安排磁盘结构从而提高检索效率。第4章转向数据编码(序列化)方面,包括常见模式的演化历程。

    第二部分,我们将从单机的数据存储转向跨机器的分布式系统,这是扩展性的重要一步,但随之而来的是各种挑战。所以将依次讨论数据远程复制(第5章)、数据分区(第6章)以及事务(第7章)。接下来的第8章包括分布式系统的更多细节,以及分布式环境如何达成一致性与共识(第9章)。

    第三部分,主要针对产生派生数据的系统,所谓派生数据主要指在异构系统中,如果无法用一个数据源来解决所有问题,那么一种自然的方式就是集成多个不同的数据库、缓存模块以及索引模块等。首先第10章以批处理开始来处理派生数据,紧接着第11章采用流式处理。第12章总结之前介绍的多种技术,并分析讨论未来构建可靠、可扩展和可维护应用系统可能的新方向或方法。









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