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本书以大数据处理引擎Spark的稳定版本1.6.x为基础，从应用案例、原理、源码、流程、调 优等多个角度剖析Spark上的实时计算框架Spark Streaming。在勾勒出Spark Streaming架构轮廓的 基础上，从基本源码开始进行剖析，由浅入深地引导已具有Spark和Spark Streaming基础技术知识 的读者进行Spark Streaming的进阶学习，理解Spark Streaming的原理和运行机制，为流数据处理 的决策和应用提供了技术参考；结合Spark Streaming的深入应用的需要，对Spark Streaming的性 能调优进行了分析，也对Spark Streaming功能的改造和扩展提供了指导。 本书适合大数据领域CTO、架构师、高级软件工程师，尤其是Spark领域已有Spark Streaming 基础知识的从业人员阅读，也可供需要深入学习Spark、Spark Streaming的高校研究生和高年级本 科生参考。

 

资料目录：

第1章 Spark Streaming应用概述······1

 

1.1 Spark Streaming应用案例 ·······2

 

1.2　Spark Streaming应用剖析 ·····13

 

第2章 Spark Streaming基本原理····15

 

2.1　Spark Core简介 ··················16

 

2.2 Spark Streaming设计思想 ·····26

 

2.3 Spark Streaming整体架构 ·····30

 

2.4 编程接口 ·························33

 

第3章 Spark Streaming运行流程详解·············39

 

3.1 从StreamingContext的初始化到启动 ··········40

 

3.2 数据接收 ·························54

 

3.3 数据处理 ·························91

 

3.4 数据清理 ························115

 

3.5 容错机制 ························127

 

3.5.1 容错原理 ·························128

 

3.5.2 Driver容错机制 ·················152

 

3.5.3 Executor容错机制 ··············161

 

3.6 No Receiver方式 ···············167

 

3.7 输出不重复 ·····················175

 

3.8 消费速率的动态控制 ·········176

 

3.9 状态操作 ························189

3.10 窗口操作 ·······················212

3.11 页面展示 ·······················216

3.12 Spark Streaming应用程序的停止··········227

第4章Spark Streaming 性能调优机制···········237

 

4.1 并行度解析 ·····················238

4.1.1 数据接收的并行度 ·············238

4.1.2 数据处理的并行度 ·············240

4.2 内存······························240

4.3 序列化 ···························240

4.4 Batch Interval ···················241

4.5 Task ·······························242

4.6 JVM GC ·························242

第5章Spark 2.0中的流计算··········245

 

5.1 连续应用程序 ··················246

5.2 无边界表unbounded table ····248

5.3 增量输出模式 ··················249

5.4 API简化 ··························250

5.5 其他改进 ························250