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Jingdong book

Spark最佳实践

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Jingdong book

Spark最佳实践

Name: Spark最佳实践
Brand: Jingdong book
SKU: 3118116391
Price: 44.38 USD
Availability: OutOfStock

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Editer Recommend

1.UCloud季昕华、Databricks连城，阿里胡熠，棒米科技武泽胜，腾讯肖磊\靳志辉\徐羽等联袂推荐！
2.腾讯专家首次分享Spark实践，作者均为腾讯一线员工，拥有丰富的实战经验
3.基于真实数据，用案例分析全面解读大数据应用设计
4.腾讯杰出研发者的Spark实践，揭秘广告和推荐系统核心技术

Content Description

本书是Spark实战指南，全书共分8章。前4章介绍Spark的部署、工作机制和内核，后4章分别通过实战项目介绍SparkSQL、SparkStreaming、SparkGraphX和SparkMLib功能模块。此外，本书详细介绍了常见的实战问题，比如大数据环境下的配置设置、程序调优等。本书附带的一键安装脚本，更能为初学者提供很大帮助。

Author Description

陈欢，腾讯杰出程序员，15年编码经验，曾任职网络安全、互联网金融等部门，亲手从零建设了财付通业务的Spark集群，并使之同时支持SQL、实时计算、机器学习等多种数据计算场景。他目前就职于腾讯社交与效果广告部，从事大数据分析工作。

林世飞，腾讯杰出研究员，2005年加入腾讯，先后在无线产品、安全中心、搜索平台、开放平台、社交与效果广告部等部门从事开发和团队管理工作。他对网络安全、搜索引擎、数据挖掘、机器学习有一定了解，热衷知识传播和分享，曾获腾讯学院2009年年度杰出讲师。目前，他就职于社交与效果广告部，负责广告系统相关的研发工作。

Catalogue

第1 章 Spark 与大数据 1
1．1大数据的发展及现状1
1．1．1大数据时代所面临的问题1
1．1．2谷歌的大数据解决方案2
1．1．3Hadoop 生态系统3
1．2Spark 应时而生4
1．2．1Spark 的起源4
1．2．2Spark 的特点5
1．2．3Spark 的未来发展6
第2章 Spark 基础8
2．1Spark 本地单机模式体验8
2．1．1安装虚拟机8
2．1．2安装JDK19
2．1．3下载Spark 预编译包21
2．1．4本地体验Spark22
2．2高可用Spark 分布式集群部署25
2．2．1集群总览26
2．2．2集群机器的型号选择28
2．2．3初始化集群机器环境29
2．2．4部署ZooKeeper 集群33
2．2．5编译Spark35
2．2．6部署Spark Standalone 集群37
2．2．7高可用Hadoop 集群40
2．2．8让Spark 运行在YARN 上40
2．2．9一键部署高可用Hadoop +
Spark集群42
2．3Spark 编程指南43
2．3．1交互式编程43
2．3．2RDD 创建44
2．3．3RDD 操作47
2．3．4使用其他语言开发Spark 程序54
2．4打包和提交54
2．4．1编译、链接、打包54
2．4．2提交56
第3章 Spark 工作机制58
3．1调度管理58
3．1．1集群概述及名词解释58
3．1．2Spark 程序之间的调度60
3．1．3Spark 程序内部的调度63
3．2内存管理65
3．2．1RDD 持久化65
3．2．2共享变量66
3．3容错机制67
3．3．1容错体系概述67
3．3．2Master 节点失效68
3．3．3Slave 节点失效69
3．4监控管理69
3．4．1Web 界面69
3．4．2REST API72
3．4．3Metrics 指标体系73
3．4．4其他监控工具73
3．5Spark 程序配置管理73
3．5．1Spark 程序配置加载过程74
3．5．2环境变量配置74
3．5．3Spark 属性项配置74
3．5．4查看当前的配置76
3．5．5配置Spark 日志76
第4章 Spark 内核讲解77
4．1Spark 核心数据结构RDD77
4．1．1RDD 的定义78
4．1．2RDD 的Transformation80
4．1．3RDD 的Action82
4．1．4Shuffle83
4．2SparkContext84
4．2．1SparkConf 配置84
4．2．2初始化过程85
4．2．3其他功能接口87
4．3DAG 调度87
4．3．1DAGScheduler87
4．3．2TaskScheduler90
第5章 Spark SQL 与数据仓库92
5．1Spark SQL 基础93
5．1．1分布式SQL 引擎93
5．1．2支持的SQL 语法97
5．1．3支持的数据类型98
5．1．4DataFrame99
5．1．5DataFrame 数据源103
5．1．6性能调优104
5．2Spark SQL 原理和运行机制104
5．2．1Spark SQL 整体架构105
5．2．2Catalyst 执行优化器105
5．3应用场景：基于淘宝数据建立电商
数据仓库110
5．3．1电商数据仓库场景111
5．3．2数据准备和表设计111
5．3．3用Spark SQL 来完成日常运营
数据分析115
5．3．4Spark SQL 在大规模数据下的
性能表现120
第6章 Spark 流式计算122
6．1Spark Streaming 基础知识123
6．1．1入门简单示例123
6．1．2基本概念124
6．1．3高级操作129
6．2深入理解Spark Streaming132
6．2．1DStream 的两类操作132
6．2．2容错处理134
6．2．3性能调优136
6．2．4与Storm 的对比137
6．3应用场景：一个类似百度统计的流式
实时系统139
6．3．1Web log 实时统计场景139
6．3．2日志实时采集140
6．3．3流式分析系统实现140
第7章 Spark 图计算149
7．1什么是图计算149
7．1．1图的基本概念149
7．1．2图计算的应用150
7．2Spark GraphX 简介151
7．2．1GraphX 实现151
7．2．2GraphX 常用API 介绍152
7．3应用场景：基于新浪微博数据的社交
网络分析153
7．3．1社交网络分析的主要应用153
7．3．2社区发现算法简介154
7．3．3用GraphX 实现Louvain
算法156
7．3．4小试牛刀：谁是你的闺蜜162
7．3．5真实的场景：新浪微博关系
分析164
第8章 Spark MLlib169
8．1机器学习简介169
8．1．1什么是机器学习169
8．1．2机器学习示例171
8．1．3机器学习的基本方法172
8．1．4机器学习的常见技巧173
8．1．5机器学习参考资料174
8．2MLlib 库简介174
8．2．1基础数据类型174
8．2．2主要的库175
8．2．3附带的示例程序176
8．3应用场景：搜索广告点击率
预估系统178
8．3．1应用场景178
8．3．2逻辑回归179
8．3．3学习算法181
8．3．4模型评估184
8．3．5数据准备186
8．3．6模型训练187
8．3．7模型调优195
附录Scala 语言参考197

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