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Jingdong book

Spark快速大数据分析

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Jingdong book

Spark快速大数据分析

Name: Spark快速大数据分析
Brand: Jingdong book
SKU: 3118211221
Price: 34.65 USD
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Spark开发者出品！
《Spark快速大数据分析》是一本为Spark初学者准备的书，它没有过多深入实现细节，而是更多关注上层用户的具体用法。不过，本书绝不仅仅限于Spark的用法，它对Spark的核心概念和基本原理也有较为全面的介绍，让读者能够知其然且知其所以然。
本书介绍了开源集群计算系统Apache Spark，它可以加速数据分析的实现和运行。利用Spark，你可以用Python、Java以及Scala的简易API来快速操控大规模数据集。
本书由Spark开发者编写，可以让数据科学家和工程师即刻上手。你能学到如何使用简短的代码实现复杂的并行作业，还能了解从简单的批处理作业到流处理以及机器学习等应用。

Content Description

《Spark快速大数据分析》由 Spark 开发者及核心成员共同打造，讲解了网络大数据时代应运而生的、能高效迅捷地分析处理数据的工具——Spark，它带领读者快速掌握用 Spark 收集、计算、简化和保存海量数据的方法，学会交互、迭代和增量式分析，解决分区、数据本地化和自定义序列化等问题。

Author Description

Holden Karau，是Databricks的软件开发工程师，活跃于开源社区。她还著有《Spark快速数据处理》。

Andy Konwinski，是Databricks联合创始人，Apache Spark项目技术专家，还是Apache Mesos项目的联合发起人。

Patrick Wendell，是Databricks联合创始人，也是Apache Spark项目技术专家。他还负责维护Spark核心引擎的几个子系统。

Matei Zaharia，是Databricks的CTO，同时也是Apache Spark项目发起人以及Apache基金会副主席。

Comments

“Spark是构建大数据应用流行的框架，而如果有人要我推荐一些指南书籍，《Spark快速大数据分析》无疑会排在首位。”
——Ben Lorica，O'Reilly首席数据科学家

Catalogue

推荐序xi
译者序xiv
序xvi
前言xvii
第1章Spark数据分析导论1
1．1Spark是什么1
1．2一个大一统的软件栈2
1．2．1Spark Core2
1．2．2Spark SQL3
1．2．3Spark Streaming3
1．2．4MLlib3
1．2．5GraphX3
1．2．6集群管理器4
1．3Spark的用户和用途4
1．3．1数据科学任务4
1．3．2数据处理应用5
1．4Spark简史5
1．5Spark的版本和发布6
1．6Spark的存储层次6
第2章Spark下载与入门7
2．1下载Spark7
2．2Spark中Python和Scala的shell9
2．3Spark 核心概念简介12
2．4独立应用14
2．4．1初始化SparkContext15
2．4．2构建独立应用16
2．5总结19
第3章RDD编程21
3．1RDD基础21
3．2创建RDD23
3．3RDD操作24
3．3．1转化操作24
3．3．2行动操作26
3．3．3惰性求值27
3．4向Spark传递函数27
3．4．1Python27
3．4．2Scala28
3．4．3Java29
3．5常见的转化操作和行动操作30
3．5．1基本RDD30
3．5．2在不同RDD类型间转换37
3．6持久化( 缓存)39
3．7总结40
第4章键值对操作41
4．1动机41
4．2创建Pair RDD42
4．3Pair RDD的转化操作42
4．3．1聚合操作45
4．3．2数据分组49
4．3．3连接50
4．3．4数据排序51
4．4Pair RDD的行动操作52
4．5数据分区（进阶）52
4．5．1获取RDD的分区方式55
4．5．2从分区中获益的操作56
4．5．3影响分区方式的操作57
4．5．4示例：PageRank57
4．5．5自定义分区方式59
4．6总结61
第5章数据读取与保存63
5．1动机63
5．2文件格式64
5．2．1文本文件64
5．2．2JSON66
5．2．3逗号分隔值与制表符分隔值68
5．2．4SequenceFile71
5．2．5对象文件73
5．2．6Hadoop输入输出格式73
5．2．7文件压缩77
5．3文件系统78
5．3．1本地/“常规”文件系统78
5．3．2Amazon S378
5．3．3HDFS79
5．4Spark SQL中的结构化数据79
5．4．1Apache Hive80
5．4．2JSON80
5．5数据库81
5．5．1Java数据库连接81
5．5．2Cassandra82
5．5．3HBase84
5．5．4Elasticsearch85
5．6总结86
第6章Spark编程进阶87
6．1简介87
6．2累加器88
6．2．1累加器与容错性90
6．2．2自定义累加器91
6．3广播变量91
6．4基于分区进行操作94
6．5与外部程序间的管道96
6．6数值RDD 的操作99
6．7总结100
第7章在集群上运行Spark101
7．1简介101
7．2Spark运行时架构101
7．2．1驱动器节点102
7．2．2执行器节点103
7．2．3集群管理器103
7．2．4启动一个程序104
7．2．5小结104
7．3使用spark-submit 部署应用105
7．4打包代码与依赖107
7．4．1使用Maven构建的用Java编写的Spark应用108
7．4．2使用sbt构建的用Scala编写的Spark应用109
7．4．3依赖冲突 111
7．5Spark应用内与应用间调度111
7．6集群管理器112
7．6．1独立集群管理器112
7．6．2Hadoop YARN115
7．6．3Apache Mesos116
7．6．4Amazon EC2117
7．7选择合适的集群管理器120
7．8总结121
第8章Spark调优与调试123
8．1使用SparkConf配置Spark123
8．2Spark执行的组成部分：作业、任务和步骤127
8．3查找信息131
8．3．1Spark网页用户界面131
8．3．2驱动器进程和执行器进程的日志134
8．4关键性能考量135
8．4．1并行度135
8．4．2序列化格式136
8．4．3内存管理137
8．4．4硬件供给138
8．5总结139
第9章Spark SQL141
9．1连接Spark SQL142
9．2在应用中使用Spark SQL144
9．2．1初始化Spark SQL144
9．2．2基本查询示例145
9．2．3SchemaRDD146
9．2．4缓存148
9．3读取和存储数据149
9．3．1Apache Hive149
9．3．2Parquet150
9．3．3JSON150
9．3．4基于RDD152
9．4JDBC/ODBC服务器153
9．4．1使用Beeline155
9．4．2长生命周期的表与查询156
9．5用户自定义函数156
9．5．1Spark SQL UDF156
9．5．2Hive UDF157
9．6Spark SQL性能158
9．7总结159
第10章Spark Streaming161
10．1一个简单的例子162
10．2架构与抽象164
10．3转化操作167
10．3．1无状态转化操作167
10．3．2有状态转化操作169
10．4输出操作173
10．5输入源175
10．5．1核心数据源175
10．5．2附加数据源176
10．5．3多数据源与集群规模179
10．624/7不间断运行180
10．6．1检查点机制180
10．6．2驱动器程序容错181
10．6．3工作节点容错182
10．6．4接收器容错182
10．6．5处理保证183
10．7Streaming用户界面183
10．8性能考量184
10．8．1批次和窗口大小184
10．8．2并行度184
10．8．3垃圾回收和内存使用185
10．9总结185
第11章基于MLlib的机器学习187
11．1概述187
11．2系统要求188
11．3机器学习基础189
11．4数据类型192
11．5算法194
11．5．1特征提取194
11．5．2统计196
11．5．3分类与回归197
11．5．4聚类202
11．5．5协同过滤与推荐203
11．5．6降维204
11．5．7模型评估206
11．6一些提示与性能考量206
11．6．1准备特征206
11．6．2配置算法207
11．6．3缓存RDD以重复使用207
11．6．4识别稀疏程度207
11．6．5并行度207
11．7流水线API208
11．8总结209
作者简介210
封面介绍210

Book Abstract

Spark简史
Spark 是由一个强大而活跃的开源社区开发和维护的，社区中的开发者们来自许许多多不同的机构。如果你或者你所在的机构是第一次尝试使用 Spark，也许你会对 Spark 这个项目的历史感兴趣。Spark 是于 2009 年作为一个研究项目在加州大学伯克利分校 RAD 实验室（AMPLab 的前身）诞生。实验室中的一些研究人员曾经用过 Hadoop MapReduce。他们发现 MapReduce 在迭代计算和交互计算的任务上表现得效率低下。因此，Spark 从一开始就是为交互式查询和迭代算法设计的，同时还支持内存式存储和高效的容错机制。
2009 年，关于 Spark 的研究论文在学术会议上发表，同年 Spark 项目正式诞生。其后不久，相比于 MapReduce，Spark 在某些任务上已经获得了 10 ～ 20 倍的性能提升。
Spark 最早的一部分用户来自加州伯克利分校的其他研究小组，其中比较著名的有 Mobile Millennium。作为机器学习领域的研究项目，他们利用 Spark 来监控并预测旧金山湾区的交通拥堵情况。仅仅过了短短的一段时间，许多外部机构也开始使用 Spark。如今，有超过 50 个机构将自己添加到了使用 Spark 的机构列表页面。在 Spark 社区如火如荼的社区活动 Spark Meetups和 Spark 峰会中，许多机构也向大家积极分享他们特有的 Spark 应用场景。除了加州大学伯克利分校，对 Spark 作出贡献的主要机构还有 Databricks、雅虎以及英特尔。
2011 年，AMPLab 开始基于 Spark 开发更高层的组件，比如 Shark（Spark 上的 Hive）1 和 Spark Streaming。这些组件和其他一些组件一起被称为伯克利数据分析工具栈（BDAS，https://amplab.cs.berkeley.edu/software/）。
1Shark 已经被 Spark SQL 所取代。
Spark 最早在 2010 年 3 月开源，并且在 2013 年 6 月交给了 Apache 基金会，现在已经成了 Apache 开源基金会的顶级项目。
Spark的版本和发布
自其出现以来，Spark 就一直是一个非常活跃的项目，Spark 社区也一直保持着非常繁荣的态势。随着版本号的不断更迭，Spark 的贡献者也与日俱增。Spark 1.0 吸引了 100 多个开源程序员参与开发。尽管项目活跃度在飞速地提升，Spark 社区依然保持着常规的发布新版本的节奏。2014 年 5 月，Spark 1.0 正式发布，而本书则主要关注 Spark 1.1.0 以及后续的版本。不过，大多数概念在老版本的 Spark 中依然适用，而大多数示例也能运行在老版本的 Spark 上。
Spark的存储层次
Spark 不仅可以将任何 Hadoop 分布式文件系统（HDFS）上的文件读取为分布式数据集，也可以支持其他支持 Hadoop 接口的系统，比如本地文件、亚马逊 S3、Cassandra、Hive、HBase 等。我们需要弄清楚的是，Hadoop 并非 Spark 的必要条件，Spark 支持任何实现了 Hadoop 接口的存储系统。Spark 支持的 Hadoop 输入格式包括文本文件、SequenceFile、Avro、Parquet 等。我们会在第 5 章讨论读取和存储时详细介绍如何与这些数据源进行交互。
……

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