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rawRegexSplitter 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jarProperties 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pagePipe 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 instancesSubAssembly 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sinkFlow 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();}}