使用MapReduce并行构建Lucene索引

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Lucene(神界篇)

mapreduce hadoop lucene solr

散仙前几篇博客上，已经写了单机程序使用使用hadoop的构建lucene索引，本篇呢，我们里看下如何使用MapReduce来构建索引，代码如下：

package com.mapreduceindex;

import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import java.util.Random;

import org.apache.commons.io.output.NullWriter;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.lucene.analysis.Analyzer;
import org.apache.lucene.document.Document;
import org.apache.lucene.document.Field.Store;
import org.apache.lucene.document.StringField;
import org.apache.lucene.document.TextField;
import org.apache.lucene.index.IndexWriter;
import org.apache.lucene.index.IndexWriterConfig;
import org.apache.lucene.util.Version;
import org.apache.solr.store.hdfs.HdfsDirectory;
import org.mortbay.log.Log;
import org.wltea.analyzer.lucene.IKAnalyzer;

import com.qin.wordcount.MyWordCount;

/**
 * 
 * 使用MapReduce构建索引
 * @author qindongliang
 * 大数据技术交流群: 376932160
 *  搜索技术一号群：  324714439
 *  搜索技术一号群：  206247899
 * Hadoop版本2.2.0
 * Lucene版本4.8.0
 *   Solr版本4.8.0
 * 
 * **/
public class BuildIndexMapReduce {

	/**
	 * 获取一个IndexWriter
	 * @param outDir 索引的输出目录
	 * @return IndexWriter 获取一个IndexWriter
	 * */
	public static IndexWriter  getIndexWriter(String outDir) throws Exception{
		Analyzer  analyzer=new IKAnalyzer(true);//IK分词
 		IndexWriterConfig    config=new IndexWriterConfig(Version.LUCENE_48, analyzer);
 		Configuration conf=new Configuration();
 		conf.set("fs.defaultFS","hdfs://192.168.46.32:9000/");//HDFS目录
 		Path path=new Path("hdfs://192.168.46.32:9000/qin/"+outDir);//索引目录
 		HdfsDirectory directory=new HdfsDirectory(path, conf);
 		long heapSize = Runtime.getRuntime().totalMemory()/ 1024L / 1024L;//总内存
		long heapMaxSize = Runtime.getRuntime().maxMemory()/ 1024L / 1024L;//使用的最大内存
		config.setRAMBufferSizeMB(((heapMaxSize-heapSize)*0.7));//空闲内存的70%作为合并因子
 		IndexWriter writer=new IndexWriter(directory, config);//
 		return writer;
		
	}
	
	/**
	 * 索引的工具类
	 * 
	 * **/
	public static class LuceneDocumentUtil{
		public static Document getDoc(String filed,String value){
			    Document d=new Document();
			    //模拟载入schemal文件，根据solr的scheml文件来灵活的坐一些索引，
				d.add(new TextField("content", value, Store.YES));
			return d;
		}
		
	}
	/**
	 * @author qindongliang
	 *
	 */
	private static class BuildIndexMapper extends Mapper<LongWritable, Text, NullWritable, NullWritable> {
		
		IndexWriter iw;
		List<Document> documenst=new ArrayList<>();
		
		
	@Override
	protected void setup(Context context)throws IOException, InterruptedException {
	    Random rd=new Random();
		int i=rd.nextInt(99999999);//此处的索引目录名可以使用UUID来使它唯一
		try{
		iw=getIndexWriter(i+"");//初始化IndexWriter
		}catch(Exception e){
			e.printStackTrace();
		}
		
		
	 
	}
		
	
	@Override
		protected void map(LongWritable key, Text value,Context context)
				throws IOException, InterruptedException {
		Log.info("  记录的日志信息： "+value.toString());
		String values[]=value.toString().split("\1");//此处读入被索引的文件每一行
		String fieldName=values[0];
		String fieldValue=values[1];
		Document d=LuceneDocumentUtil.getDoc(fieldName, fieldValue);
		if(d==null){
			return;
		}
		documenst.add(d);
		if(documenst.size()>5000){//使用批处理提交
			iw.addDocuments(documenst);
			documenst.clear();
		}
		
		// context.write(null, null);
		}
	/***
	 * 在Map结束时，做一些事，提交索引
	 * 
	 * */
		@Override
		protected void cleanup(Context context)throws IOException, InterruptedException {
			if(documenst.size()>0){
				iw.addDocuments(documenst);
			}
			if(iw!=null){
			iw.close(true);//关闭至合并完成
			}
			
		}
	}
public static void main(String[] args)throws Exception {
	
	Configuration conf=new Configuration();
	
    conf.set("mapreduce.job.jar", "myjob.jar");
	conf.set("fs.defaultFS","hdfs://192.168.46.32:9000");
	conf.set("mapreduce.framework.name", "yarn");  
	conf.set("yarn.resourcemanager.address", "192.168.46.32:8032"); 
	/**Job任务**/
   //Job job=new Job(conf, "testwordcount");//废弃此API
   Job job=Job.getInstance(conf, "build index ");
	job.setJarByClass(BuildIndexMapReduce.class);
 	System.out.println("模式：  "+conf.get("yarn.resourcemanager.address"));;
	// job.setCombinerClass(PCombine.class);
	 job.setNumReduceTasks(0);//设置为3
	 job.setMapperClass(BuildIndexMapper.class);
	 job.setInputFormatClass(TextInputFormat.class);
	 job.setOutputFormatClass(TextOutputFormat.class);

 
	
	 job.setMapOutputKeyClass(NullWritable.class);
	 job.setMapOutputValueClass(NullWritable.class);
 

		String path="hdfs://192.168.46.32:9000/qin/output";
		FileSystem fs=FileSystem.get(conf);
		Path p=new Path(path);
		if(fs.exists(p)){
			fs.delete(p, true);
			System.out.println("输出路径存在，已删除！");
		}
	FileInputFormat.setInputPaths(job, "hdfs://192.168.46.32:9000/qin/indexinput");
	FileOutputFormat.setOutputPath(job,p );
	System.exit(job.waitForCompletion(true) ? 0 : 1);  
}

	
	
	
}

控制台生成的信息如下：

模式：  192.168.46.32:8032
INFO - RMProxy.createRMProxy(56) | Connecting to ResourceManager at /192.168.46.32:8032
WARN - JobSubmitter.copyAndConfigureFiles(149) | Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
INFO - FileInputFormat.listStatus(287) | Total input paths to process : 3
INFO - JobSubmitter.submitJobInternal(394) | number of splits:3
INFO - Configuration.warnOnceIfDeprecated(840) | user.name is deprecated. Instead, use mapreduce.job.user.name
INFO - Configuration.warnOnceIfDeprecated(840) | mapred.jar is deprecated. Instead, use mapreduce.job.jar
INFO - Configuration.warnOnceIfDeprecated(840) | fs.default.name is deprecated. Instead, use fs.defaultFS
INFO - Configuration.warnOnceIfDeprecated(840) | mapred.reduce.tasks is deprecated. Instead, use mapreduce.job.reduces
INFO - Configuration.warnOnceIfDeprecated(840) | mapred.mapoutput.value.class is deprecated. Instead, use mapreduce.map.output.value.class
INFO - Configuration.warnOnceIfDeprecated(840) | mapreduce.map.class is deprecated. Instead, use mapreduce.job.map.class
INFO - Configuration.warnOnceIfDeprecated(840) | mapred.job.name is deprecated. Instead, use mapreduce.job.name
INFO - Configuration.warnOnceIfDeprecated(840) | mapreduce.inputformat.class is deprecated. Instead, use mapreduce.job.inputformat.class
INFO - Configuration.warnOnceIfDeprecated(840) | mapred.input.dir is deprecated. Instead, use mapreduce.input.fileinputformat.inputdir
INFO - Configuration.warnOnceIfDeprecated(840) | mapred.output.dir is deprecated. Instead, use mapreduce.output.fileoutputformat.outputdir
INFO - Configuration.warnOnceIfDeprecated(840) | mapreduce.outputformat.class is deprecated. Instead, use mapreduce.job.outputformat.class
INFO - Configuration.warnOnceIfDeprecated(840) | mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps
INFO - Configuration.warnOnceIfDeprecated(840) | mapred.mapoutput.key.class is deprecated. Instead, use mapreduce.map.output.key.class
INFO - Configuration.warnOnceIfDeprecated(840) | mapred.working.dir is deprecated. Instead, use mapreduce.job.working.dir
INFO - JobSubmitter.printTokens(477) | Submitting tokens for job: job_1407866786826_0001
INFO - YarnClientImpl.submitApplication(174) | Submitted application application_1407866786826_0001 to ResourceManager at /192.168.46.32:8032
INFO - Job.submit(1272) | The url to track the job: http://h1:8088/proxy/application_1407866786826_0001/
INFO - Job.monitorAndPrintJob(1317) | Running job: job_1407866786826_0001
INFO - Job.monitorAndPrintJob(1338) | Job job_1407866786826_0001 running in uber mode : false
INFO - Job.monitorAndPrintJob(1345) |  map 0% reduce 0%
INFO - Job.monitorAndPrintJob(1345) |  map 33% reduce 0%
INFO - Job.monitorAndPrintJob(1345) |  map 100% reduce 0%
INFO - Job.monitorAndPrintJob(1356) | Job job_1407866786826_0001 completed successfully
INFO - Job.monitorAndPrintJob(1363) | Counters: 27
	File System Counters
		FILE: Number of bytes read=0
		FILE: Number of bytes written=238179
		FILE: Number of read operations=0
		FILE: Number of large read operations=0
		FILE: Number of write operations=0
		HDFS: Number of bytes read=67091
		HDFS: Number of bytes written=9708
		HDFS: Number of read operations=147
		HDFS: Number of large read operations=0
		HDFS: Number of write operations=75
	Job Counters 
		Launched map tasks=3
		Data-local map tasks=3
		Total time spent by all maps in occupied slots (ms)=81736
		Total time spent by all reduces in occupied slots (ms)=0
	Map-Reduce Framework
		Map input records=166
		Map output records=0
		Input split bytes=326
		Spilled Records=0
		Failed Shuffles=0
		Merged Map outputs=0
		GC time elapsed (ms)=11308
		CPU time spent (ms)=9200
		Physical memory (bytes) snapshot=469209088
		Virtual memory (bytes) snapshot=2544439296
		Total committed heap usage (bytes)=245399552
	File Input Format Counters 
		Bytes Read=62970
	File Output Format Counters 
		Bytes Written=0

本次，散仙测试的使用的数据源有3个文件，当然散仙在这里是小文件，在实际生产中，尽量避免有小文件存放在HDFS上，应该提前合并小文件为大文文件，散仙用了3个测试文件，所以会起了3个map进程，最后生成的索引，有3份，如果需要，我们还可以用生成的多份索引使用一个reduce作业，来完成合并。