Hadoop MapReduce v2参考手册 第2版（影印版）

定　　价：64 元

• 作者：（美）冈纳拉森　著，
• 出版时间：2016/1/1
• ISBN：9787564160890
• 出 版 社：东南大学出版社

• 中图法分类：TP274 
• 页码：304
• 纸张：胶版纸
• 版次：1
• 开本：16开

内容简介

　　《Hadoop MapReduce V2参考手册(第2版)(影印版)(英文版)》开篇介绍了Hadoop YARN、MapReduce、HDFs以及其他Hadoop生态系统组件的安装。在《Hadoop MapReduce V2参考手册(第2版)(影印版)(英文版)》的指引下，你很快就会学习到很多激动人心的主题，例如MapReduce模式，使用Hadoop处理分析、归类、在线销售、推荐、数据索引及搜索。你还会学习到如何使用包括Hive、HBase、Pig、Mahout、Nutch～BGi raph在内的Hadoop生态系统项目以及如何在云环境下进行部署。

目 录

Preface
Chapter 1：Getting Started with Hadooo v2
IntrOductiOn
Setting up Hadoop v2 on your local machine
Writing a WordCount MapReduce application，bundling it
and running it using the Hadoop local mode
Adding a combiner step to the WordCount MapReduce program
Setting up HDFS
Setting up Hadoop YARN in a distributed cluster environment
using Hadoop v2
Setting up Hadoop ecosystem in a distributed cluster environment
using a Hadoop distribution
HDFS command—line file operations
Running the WordCount program in a distributed cluster environment
Benchmarking HDFS using DFSIO
Benchmarking Hadoop MapReduce using TeraSort
Chapter 2：Cloud Deployments—Using Hadoop YARN on
Cloud Environments
Introduction
Running Hadoop MapReduce v2 computations using Amazon
Elastic MapReduce
Saving money using Amazon EC2 Spot Instances to execute EMR job flows
Executing a Pig script using EMR
Executing a Hive script using EMR
Creating an Amazon EMR job flow using the AWS Command Line Interface
Deploying an Apache HBase cluster on Amazon EC2 using EMR
Using EMR bootstrap actions to configure VMs for the Amazon EMR jobs
Using Apache Whirr to deploy an Apache Hadoop cluster in a
cloud environment
Chapter 3：Hadoop Essentials—C0nfigurations，Unit Tests，and Other APIs
Introduction
Optimizing Hadoop YARN and MapReduce cOnfiguratiOns for
cluster deployments
Shared user Hadoop clusters——using Fair and Capacity schedulers
Setting classpath precedence to user—provided JARs
Speculative execution of straggling tasks
Unit testing Hadoop MapReduce applications using MRUnit
Integration testing Hadoop MapReduce applications using
MiniYarnCluster
Adding a new DataNode
Decommissioning DataNodes
Using multiple disks／volumes and limiting HDFS disk usage
Setting the HDFS block size
Setting the file replication factor
Using the HDFs Java API
Chapter 4：Develooin～ComDlex Hadooo MaoReduce Aoolications
IntrOductiOn
Choosing appropriate Hadoop data types
Implementing a custom Hadoop Writable data type
Implementing a custom Hadoop key type
Emitting data of different value types from a Mapper
Choosing a suitable Hadoop InputFormat for your input data format
Adding support for new input data formats——implementing
a custom InputFormat
Formatting the results of MapReduce computations——using
Hadoop OutputFormats
Writing multiple outputs from a MapReduce computation
Hadoop intermediate data partitioning
Secondary sorting——sorting Reduce input values
BrOadcasting and distributing shared resources to tasks in a
MapReduce job—Hadoop DistributedCache
Using Hadoop with legacy applications——Hadoop streaming
Adding dependencies between MapReduce jobs
Hadoop counters to report custom metrics
Chapter5：Analvtics
Introduction
Simple analytics using MapReduce
Performing GROUP BY using MapReduce
Calculating frequency distributions and sorting using MapReduce
Plotting the Hadoop MapReduce results using gnuplot
Calculating histograms using MapReduce
Calculating Scatter plots using MapReduce
Parsing a complex dataset with Hadoop
Joining two datasets using MapReduce
Chapter6：Hadooo Ecosystem—Apache Hive
Introduction
Getting started with Apache Hive
Creating databases and tables using Hive CLI
Simple SQL—style data querying using Apache Hive
Creating and populating Hive tables and views using Hive query results
Utilizing different storage formats in Hive.storing table data
using ORC files
Using Hive built—in functions
Hive batch mode—using a query file
Performing a join with Hive
Creating partitioned Hive tables
Writing Hive User·defined Functions（UDF）
HCatalog—·performing Java MapReduce computations on
data mapped to Hive tables
HCatalog——writing data to Hive tables from Java
MapReduce computations
Chapter7：HadooD Ecosystem II—Pig.HBase.Mahout.and Sannn
Introduction
Getting started with Apache Pig
Joining two datasets using Pig
Accessing a Hive table data in Pig using HCatalog
Getting started with Apache HBase
Data random access using Java client APIs
Running MapReduce jobs on HBase
Using Hive to insert data into HBase tables
Getting started with Apache Mahout
Running K—means with Mahout
Importing data to HDFS from a relational database using Apache Sqoop
Exporting data from HDFs to a relational database using Apache Sqoop
Tahie OrContencs
Chapter8：Searching and Indexine
Introduction
Generating an inverted index using Hadoop MapReduce
Intradomain web crawling using Apache Nutch
Indexing and searching web documents using Apache Solr
Configuring Apache HBase as the backend data store for Apache Nutch
Whole web crawling with Apache Nutch using a HadooP／HBase cluster
Elasticsearch for indexing and searching
Generating the in—links graph for crawled web pages
Chapter 9：CIassmcatiOns。Recommendations，and Findineg RelationshipS
Introduction
Performing content—based recommendations
Classification using the naive Bayes classifier
Assigning advertisements to keywords using the Adwords
balance algorithm
Chapter 10：Mass Text Data processing
Introduction
Data preprocessing using Hadoop streaming and Python
De—duplicating data using Hadoop streaming
Loading large datasets to an Apache HBase data store—importtsv
and bulkload
Creating TF and TF—IDF vectors for the text data
Clustering text data using Apache Mahout
Topic discovery using Latent Dirichlet Allocation（LDA）
Document classification using Mahout Naive Bayes Classifier
Index