cascading--wordcount

在eclipse下运行wordcount,使用cascading封装

准备：centos系统，jdk,hadoop,eclipse,cascading的lib包，官网可下载，自带cascading封装的wordcount源码，以及爬虫数据data目录，这些均可以在官网下载

我是在cascading官网把材料下载好后，在eclipse中运行，可以得到测试数据

难点：cascading的版本与官网自带的wordcount实例可能不匹配，这需要自己自行修改，我的cascading版本不是在官网下载的

给出我的运行结果图：

代码如下：完整版

package com.zjf.cascading.example;

/*
 * WordCount example
 * zjf-pc
 * Copyright (c) 2007-2012 Concurrent, Inc. All Rights Reserved.
 * Project and contact information: http://www.concurrentinc.com/
 */

import java.util.Map;
import java.util.Properties;

import cascading.cascade.Cascade;
import cascading.cascade.CascadeConnector;
import cascading.cascade.Cascades;
import cascading.flow.Flow;
import cascading.flow.FlowConnector;
import cascading.operation.Identity;
import cascading.operation.aggregator.Count;
import cascading.operation.regex.RegexFilter;
import cascading.operation.regex.RegexGenerator;
import cascading.operation.regex.RegexReplace;
import cascading.operation.regex.RegexSplitter;
import cascading.operation.xml.TagSoupParser;
import cascading.operation.xml.XPathGenerator;
import cascading.operation.xml.XPathOperation;
import cascading.pipe.Each;
import cascading.pipe.Every;
import cascading.pipe.GroupBy;
import cascading.pipe.Pipe;
import cascading.pipe.SubAssembly;
import cascading.scheme.SequenceFile;
import cascading.scheme.TextLine;
import cascading.tap.Tap;
import cascading.tap.Hfs;
import cascading.tap.Lfs;
import cascading.tuple.Fields;

public class WordCount
  {
  @SuppressWarnings("serial")
private static class ImportCrawlDataAssembly extends SubAssembly
    {
    public ImportCrawlDataAssembly( String name )
      {
      //拆分文本行到url和raw
      RegexSplitter regexSplitter = new RegexSplitter( new Fields( "url", "raw" ) );
      Pipe importPipe = new Each( name, new Fields( "line" ), regexSplitter );
      //删除所有pdf文档
      importPipe = new Each( importPipe, new Fields( "url" ), new RegexFilter( ".*\\.pdf$", true ) );
      //把":n1"替换为"\n",丢弃无用的字段
      RegexReplace regexReplace = new RegexReplace( new Fields( "page" ), ":nl:", "\n" );
      importPipe = new Each( importPipe, new Fields( "raw" ), regexReplace, new Fields( "url", "page" ) );
      //此句强制调用
      setTails( importPipe );
      }
    }

  @SuppressWarnings("serial")
private static class WordCountSplitAssembly extends SubAssembly
    {
    public WordCountSplitAssembly( String sourceName, String sinkUrlName, String sinkWordName )
      {
      //创建一个新的组件,计算所有页面中字数,和一个页面中的字数
      Pipe pipe = new Pipe(sourceName);
     //利用TagSoup将HTML转成XHTML，只保留"url"和"xml"去掉其它多余的
      pipe = new Each( pipe, new Fields( "page" ), new TagSoupParser( new Fields( "xml" ) ), new Fields( "url", "xml" ) );
      //对"xml"字段运用XPath(XML Path Language)表达式,提取"body"元素
      XPathGenerator bodyExtractor = new XPathGenerator( new Fields( "body" ), XPathOperation.NAMESPACE_XHTML, "//xhtml:body" );
      pipe = new Each( pipe, new Fields( "xml" ), bodyExtractor, new Fields( "url", "body" ) );
      //运用另一个XPath表达式删除所有元素,只保留文本节点,删除在"script"元素中的文本节点
      String elementXPath = "//text()[ name(parent::node()) != ‘script‘]";
      XPathGenerator elementRemover = new XPathGenerator( new Fields( "words" ), XPathOperation.NAMESPACE_XHTML, elementXPath );
      pipe = new Each( pipe, new Fields( "body" ), elementRemover, new Fields( "url", "words" ) );
      //用正则表达式将文档打乱成一个个独立的单词,和填充每个单词(新元组)到当前流使用"url"和"word"字段
      RegexGenerator wordGenerator = new RegexGenerator( new Fields( "word" ), "(?<!\\pL)(?=\\pL)[^ ]*(?<=\\pL)(?!\\pL)" );
      pipe = new Each( pipe, new Fields( "words" ), wordGenerator, new Fields( "url", "word" ) );
      //按"url"分组
      Pipe urlCountPipe = new GroupBy( sinkUrlName, pipe, new Fields( "url", "word" ) );
      urlCountPipe = new Every( urlCountPipe, new Fields( "url", "word" ), new Count(), new Fields( "url", "word", "count" ) );
      //按"word"分组
      Pipe wordCountPipe = new GroupBy( sinkWordName, pipe, new Fields( "word" ) );
      wordCountPipe = new Every( wordCountPipe, new Fields( "word" ), new Count(), new Fields( "word", "count" ) );
      //此句强制调用
      setTails( urlCountPipe, wordCountPipe );
      }
    }

  public static void main( String[] args )
    {
      //设置当前工作jar
     Properties properties = new Properties();
     FlowConnector.setApplicationJarClass(properties, WordCount.class);
     FlowConnector flowConnector = new FlowConnector(properties);
     /**
      * 在运行设置的参数里设置如下代码：
      * 右击Main.java，选择run as>run confugrations>java application>Main>Agruments->Program arguments框内写入如下代码
      * data/url+page.200.txt output local
      * 分析:
      * args[0]代表data/url+page.200.txt，它位于当前应用所在的目录下面，且路径必须是本地文件系统里的路径
      * 我的所在目录是/home/hadoop/app/workspace/HadoopApplication001/data/url+page.200.txt
      * 且该路径需要自己创建，url+page.200.txt文件也必须要有,可以在官网下下载
      *
      * args[1]代表output文件夹,第二个参数，它位于分布式文件系统hdfs中
      * 我的路径是：hdfs://s104:9000/user/hadoop/output，该路径需要自己创建
      * 在程序运行成功后，output目录下会自动生成三个文件夹pages,urls,words
      * 里面分别包含所有的page,所有的url,所有的word
      *
      * args[2]代表local,第三个参数，它位于本地文件系统中
      * 我的所在目录是/home/hadoop/app/workspace/HadoopApplication001/local
      * 该文件夹不需要自己创建，在程序运行成功后会自动生成在我的上述目录中，
      * 且在该local文件夹下会自动生成两个文件夹urls和words,里面分别是url个数和word个数
      */
      String inputPath = args[ 0 ];
      String pagesPath = args[ 1 ] + "/pages/";
      String urlsPath = args[ 1 ] + "/urls/";
      String wordsPath = args[ 1 ] + "/words/";
      String localUrlsPath = args[ 2 ] + "/urls/";
      String localWordsPath = args[ 2 ] + "/words/";

    // import a text file with crawled pages from the local filesystem into a Hadoop distributed filesystem
    // the imported file will be a native Hadoop sequence file with the fields "page" and "url"
    // note this examples stores crawl pages as a tabbed file, with the first field being the "url"
    // and the second being the "raw" document that had all new line chars ("\n") converted to the text ":nl:".

    //初始化Pipe管道处理爬虫数据装配,返回字段url和page
    Pipe importPipe = new ImportCrawlDataAssembly( "import pipe" );

     //创建tap实例
    Tap localPagesSource = new Lfs( new TextLine(), inputPath );
    Tap importedPages = new Hfs( new SequenceFile( new Fields( "url", "page" ) ), pagesPath );

    //链接pipe装配到tap实例
    Flow importPagesFlow = flowConnector.connect( "import pages", localPagesSource, importedPages, importPipe );

    //拆分之前定义的wordcount管道到新的两个管道url和word
    // these pipes could be retrieved via the getTails() method and added to new pipe instances
    SubAssembly wordCountPipe = new WordCountSplitAssembly( "wordcount pipe", "url pipe", "word pipe" );

    //创建hadoop SequenceFile文件存储计数后的结果
    Tap sinkUrl = new Hfs( new SequenceFile( new Fields( "url", "word", "count" ) ), urlsPath );
    Tap sinkWord = new Hfs( new SequenceFile( new Fields( "word", "count" ) ), wordsPath );

    //绑定多个pipe和tap,此处指定的是pipe名称
    Map<String, Tap> sinks = Cascades.tapsMap( new String[]{"url pipe", "word pipe"}, Tap.taps( sinkUrl, sinkWord ) );
    //wordCountPipe指的是一个装配
    Flow count = flowConnector.connect( importedPages, sinks, wordCountPipe );

   //创建一个装配,导出hadoop sequenceFile 到本地文本文件
    Pipe exportPipe = new Each( "export pipe", new Identity() );
    Tap localSinkUrl = new Lfs( new TextLine(), localUrlsPath );
    Tap localSinkWord = new Lfs( new TextLine(), localWordsPath );

   // 使用上面的装配来连接两个sink
    Flow exportFromUrl = flowConnector.connect( "export url", sinkUrl, localSinkUrl, exportPipe );
    Flow exportFromWord = flowConnector.connect( "export word", sinkWord, localSinkWord, exportPipe );

    ////装载flow,顺序随意,并执行
    Cascade cascade = new CascadeConnector().connect( importPagesFlow, count, exportFromUrl, exportFromWord );
    cascade.complete();
    }
  }