操作系统:Win7 64位

Hadoop:2.7.4

中文分词工具包IKAnalyzer: 5.1.0

开发工具:Intellij IDEA 2017 Community

 

准备中文分词工具包

项目需要引入中文分词工具包IKAnalyzer,故第一步是对中文分词工具包的打包并安装到本地库,在这过程中参考研究了以下文章及博客,非常感谢:

http://blog.csdn.net/zhu_tianwei/article/details/46607421

http://blog.csdn.net/cyxlzzs/article/details/7999212

http://blog.csdn.net/cyxlzzs/article/details/8000385

https://my.oschina.net/twosnail/blog/370744

1:下载中文分词工具包,源代码地址: https://github.com/linvar/IKAnalyzer

2:下载的源代码工程的pom.xml文件有点小问题,字典文件不能打包进jar,后面在运行时会报错误,需要修改一下

增加 properties节点:

<properties>
   <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
   <jdk.version>1.8</jdk.version>
</properties>

增加dependency节点,加入lucene-analyzers-common库:

<dependency>  
          <groupId>org.apache.lucene</groupId>  
          <artifactId>lucene-analyzers-common</artifactId>
          <version>5.1.0</version>  
</dependency> 

修改build节点,加入resources及maven-jar-plugin:

<build>
   <resources>
      <resource>
         <directory>src/main/java</directory>
         <includes>
            <include>**/*.dic</include>
         </includes>
      </resource>
   </resources>
   <plugins>
      <plugin>
         <groupId>org.apache.maven.plugins</groupId>
         <artifactId>maven-compiler-plugin</artifactId>
         <version>3.1</version>
         <configuration>
            <source>${jdk.version}</source>
            <target>${jdk.version}</target>
         </configuration>
      </plugin>
      <plugin>
         <groupId>org.apache.maven.plugins</groupId>
         <artifactId>maven-jar-plugin</artifactId>
         <version>2.4</version>
         <configuration>
            <archive>
               <manifest>
                  <addClasspath>true</addClasspath>
                  <classpathPrefix>lib/</classpathPrefix>
               </manifest>
            </archive>
            <!--过滤掉不希望包含在jar中的文件 -->
            <excludes>
               <exclude>${project.basedir}/xml/*</exclude>
            </excludes>
         </configuration>
      </plugin>
   </plugins>
</build>

完成修改后,可以打包安装到本地库了,使用mvn install 命令,可以在本地库中看到

测试项目:本地hadoop环境使用IDEA创建mapreduce项目及调试

中文词频统计及排序:

1. 创建maven工程hdfstest,将前面中文分词工具包的配置文件拷贝到放在resources目录内,结构如下:

           测试项目:本地hadoop环境使用IDEA创建mapreduce项目及调试

在分词扩展字典 ext.dic中保存的是需要分词的中文短语,在src同级目录下创建input目录,用于保存本地的测试输入文件,在resources目录下需要添加日志配置文件log4j.properties,否则会有如下所示警告信息,无法在窗口输出mapreduce内容,

 测试项目:本地hadoop环境使用IDEA创建mapreduce项目及调试

将以下行添加到log4j.properties配置文件后,在Idea中调试时,可以在底部Console窗口中输出调试及mapreduce信息:

log4j.rootLogger=INFO, stdout
log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.Target=System.out
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern=%d{ISO8601} %-5p %c{1} - %m%n

测试项目:本地hadoop环境使用IDEA创建mapreduce项目及调试

 

2. 修改pom.xml 配置文件,引入分词工具包及hadoop库

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>hadoop.mapreduce</groupId>
    <artifactId>hdfstest</artifactId>
    <version>1.0</version>

    <repositories>
        <repository>
            <id>apache</id>
            <url>http://maven.apache.org</url>
        </repository>
    </repositories>

    <dependencies>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-common</artifactId>
            <version>2.7.4</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-hdfs</artifactId>
            <version>2.7.4</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-mapreduce-client-core</artifactId>
            <version>2.7.4</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-mapreduce-client-jobclient</artifactId>
            <version>2.7.4</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-mapreduce-client-common</artifactId>
            <version>2.7.4</version>
        </dependency>
        <dependency>
            <groupId>org.wltea.analyzer</groupId>
            <artifactId>IKAnalyzer</artifactId>
            <version>5.1.0</version>
        </dependency>
    </dependencies>

    <build>
        <resources>
            <resource>
                <directory>src/main/resources</directory>
                <includes>
                    <include>**/*</include>
                </includes>
            </resource>
        </resources>
        <plugins>
            <plugin>  
                <artifactId>maven-dependency-plugin</artifactId>  
                <executions>  
                    <execution>  
                        <id>copy-dependencies</id>  
                        <phase>prepare-package</phase>  
                        <goals>  
                            <goal>copy-dependencies</goal>  
                        </goals>  
                        <configuration>  
                            <!-- ${project.build.directory}为Maven内置变量,缺省为target -->   
                            <outputDirectory>${project.build.directory}/classes/lib</outputDirectory>
                            <!-- 表示是否不包含间接依赖的包  -->  
                            <excludeTransitive>false</excludeTransitive>  
                            <!-- 表示复制的jar文件去掉版本信息 -->   
                            <stripVersion>true</stripVersion>  
                        </configuration>  
                    </execution>  
                </executions>  
            </plugin> 
        </plugins>
    </build>
</project>

3. 添加java工程代码 ChineseWordSplit

  • 引入hadoop及中文分词包:
package examples;

import java.io.IOException;
import java.io.StringReader;

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.Text;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat;
import org.apache.hadoop.mapreduce.lib.map.InverseMapper;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat;
import org.wltea.analyzer.core.IKSegmenter;
import org.wltea.analyzer.core.Lexeme;

 

  • 在ChineseWordSplit类中添加一个内部mapper类:TokenizerMapper, 从hadoop的Mapper类继承,实现中文分词的功能
    public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> {
        private final static IntWritable one = new IntWritable(1);
        private Text word = new Text();

        public void map(Object key, Text value, Mapper<Object, Text, Text, IntWritable>.Context context)
                throws IOException, InterruptedException
        {
            StringReader input = new StringReader(value.toString());
            IKSegmenter ikSeg = new IKSegmenter(input, true);
            for (Lexeme lexeme = ikSeg.next(); lexeme != null; lexeme = ikSeg.next()) {
                this.word.set(lexeme.getLexemeText());
                context.write(this.word, one);
            }
        }
    }
  • 在ChineseWordSplit类中添加一个内部Reducer类:IntSumReducer,从hadoop的Reducer类继承
    public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
        private IntWritable result = new IntWritable();

        public void reduce(Text key, Iterable<IntWritable> values,
                           Reducer<Text, IntWritable, Text, IntWritable>.Context context)
                throws IOException, InterruptedException
        {
            int sum = 0;
            for (IntWritable val : values) {
                sum += val.get();
            }
            this.result.set(sum);
            context.write(key, this.result);
        }
    }
  • 创建主程序入口main:在类ChineseWordSplit中添加main函数
    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        
        //下述3行配置文件用于提交job到本地mapreduce运行,此时无法调试map及reduce函数
        //conf.set("mapreduce.framework.name", "yarn");
        //conf.set("yarn.resourcemanager.hostname", "localhost");
        //conf.set("mapreduce.job.jar", "D:\\temp\\hadooptest\\hdfstest\\target\\hdfstest-1.0.jar");

        String inputFile = args[0];
        Path outDir = new Path(args[1]);

        // 临时目录,保存第一个job的结果,用于第二个job的输入
        Path tempDir = new Path(args[2] + System.currentTimeMillis());

        // first job
        System.out.println("start task...");
        Job job = Job.getInstance(conf, "word count");
        job.setJarByClass(ChineseWordSplit.class);
        job.setMapperClass(TokenizerMapper.class);
        job.setCombinerClass(IntSumReducer.class);
        job.setReducerClass(IntSumReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        FileInputFormat.addInputPath(job, new Path(inputFile));
        FileOutputFormat.setOutputPath(job, tempDir);


        //second job, 第一个job的输出作为第二个job的输入
        job.setOutputFormatClass(SequenceFileOutputFormat.class);
        if (job.waitForCompletion(true)) {
            System.out.println("start sort...");
            Job sortJob = Job.getInstance(conf, "word sort");
            sortJob.setJarByClass(ChineseWordSplit.class);
             /*InverseMapper由hadoop库提供,作用是实现map()之后的数据对的key和value交换*/
            sortJob.setMapperClass(InverseMapper.class);
            sortJob.setInputFormatClass(SequenceFileInputFormat.class);

            // 反转map键值,计算词频并降序
            sortJob.setMapOutputKeyClass(IntWritable.class);
            sortJob.setMapOutputValueClass(Text.class);
            sortJob.setSortComparatorClass(IntWritableDecreasingComparator.class);
            sortJob.setNumReduceTasks(1); //设定reduce数量,输出一个文件

            sortJob.setOutputKeyClass(IntWritable.class);
            sortJob.setOutputValueClass(Text.class);

            // 输入及输出
            FileInputFormat.addInputPath(sortJob, tempDir);
            FileSystem fileSystem = outDir.getFileSystem(conf);
            if (fileSystem.exists(outDir)) {
                fileSystem.delete(outDir, true);
            }
            FileOutputFormat.setOutputPath(sortJob, outDir);

            if (sortJob.waitForCompletion(true)) {
                System.out.println("finish job");
                System.exit(0);
            }
        }
    }
  • 添加降序比较类:在类ChineseWordSplit中添加降序比较类,在main函数中,串联了2个mapreduce job,第一个job使用中文分词工具将中文分词并统计,结果放在中间目录tempDir中,第二个job以前一个job为输入,将K-V反转,然后作降序排列,使用hadoop自带的InverseMapper类作为Mapper类,没有Reducer类,并需要一个排序比较类
    private static class IntWritableDecreasingComparator extends IntWritable.Comparator {
        public int compare(WritableComparable a, WritableComparable b) {
            return -super.compare(a, b);
        }

        public int compare(byte[] b1, int s1, int l1, byte[] b2, int s2, int l2) {
            return -super.compare(b1, s1, l1, b2, s2, l2);
        }
    }

4. 运行:

将项目打包为jar文件,保存到mapreduce目录:D:\Application\hadoop-2.7.4\share\hadoop\mapreduce,进入到bin目录,执行下面命令,3个参数分别表示文件输入,输出及中间目录

hadoop jar /D:\Application\hadoop-2.7.4\share\hadoop\mapreduce\hdfstest-1.0.jar examples/ChineseWordSplit hdfs://localhost:9000/input/people.txt hdfs://localhost:9000/output hdfs://localhost:9000/tmp

在浏览器中查看运行状态,可以看到有2个job:“word count”,“word sort”, 第二个job完成后,可以在hdfs输出目录看到文件

测试项目:本地hadoop环境使用IDEA创建mapreduce项目及调试

测试项目:本地hadoop环境使用IDEA创建mapreduce项目及调试

5:调试

  • 方法一:本机MapReduce调试,以本地目录为输入输出

进入菜单 Run->Edit Configurations,添加Application,”WordSplit_local”,如下所示,此时可以直接在Idea中点击运行或调试按钮,不需要启动hadoop mapreduce

测试项目:本地hadoop环境使用IDEA创建mapreduce项目及调试

方法二:本机MapReduce调试,以本地hdfs目录为输入及输出

和上面类似,创建一个新的Application,只需修改Program arguments项, 配置为hdfs的文件目录,但运行或调试前,需要启动本地hadoop,在hadoop sbin命令行执行start-all.cmd 命令,这样可以访问并输出到hdfs中

测试项目:本地hadoop环境使用IDEA创建mapreduce项目及调试

在Mapper类中打上断点,调试时可以进入到map函数,如下图所示(特别注意,要在类中IntWritable行打上断点,我在调试时,如果不打上断点,无法进入到map函数)

测试项目:本地hadoop环境使用IDEA创建mapreduce项目及调试

上述2个方法,是无法在浏览器中看到mapreduce job 状态的,只能调试map及reduce,并在输出目录查看运行结果,在控制台中可以看到,job 地址是:Job - The url to track the job: http://localhost:8080/,如果想提交到本地的mapreduce运行,请使用下面第3个方法

  • 方法三:本地提交MapReduce,以hdfs目录为输入及输出

如果想在mapreduce中查看job的状态,可以添加如下代码,在代码中需要制定运行的jar包地址,此时,点击运行按钮,可以在mapreduce中看到job状态

        Configuration conf = new Configuration();

        //下述3行配置文件用于提交job到本地mapreduce运行,此时无法调试map及reduce函数
        conf.set("mapreduce.framework.name", "yarn");
        conf.set("yarn.resourcemanager.hostname", "localhost");
        conf.set("mapreduce.job.jar", "D:\\temp\\hadooptest\\hdfstest\\target\\hdfstest-1.0.jar");

测试项目:本地hadoop环境使用IDEA创建mapreduce项目及调试

 

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