使用MapReduce并行构建Lucene索引

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：

<pre name="code" class="java">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); 
}




}
</pre>
控制台生成的信息如下：
<pre name="code" class="java">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
</pre>
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。