hadoop入门，跑出第一个WordCount

2023-08-01 16:31:22

1.环境准备

下载：http://mirror.bit.edu.cn/apache/hadoop/common/hadoop-2.7.2/hadoop-2.7.2.tar.gz

解压：解压后，修改etc/hadoop/hadoop-env.sh 中JAVA_HOME, 我的java_home(可以通过cat /etc/profile)是/user/java/latest

2.Hadoop Single_Node Cluster

参考官方文档：http://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-common/SingleCluster.html

3.WordCount示例

a.maven 配置(pom.xml)

<?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>my.hadoopstudy</groupId>

  <artifactId>hadoopstudy</artifactId>

  <packaging>jar</packaging>

  <version>1.0-SNAPSHOT</version>

  <name>hadoopstudy</name>

  <url>http://maven.apache.org</url>

  <dependencies>

       <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hadoop-common</artifactId>

            <version>2.5.1</version>

        </dependency>

        <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hadoop-hdfs</artifactId>

            <version>2.5.1</version>

        </dependency>

        <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hadoop-client</artifactId>

            <version>2.5.1</version>

        </dependency>

        <dependency>

            <groupId>jdk.tools</groupId>

            <artifactId>jdk.tools</artifactId>

            <version>1.8.0_65</version>

            <scope>system</scope>

            <systemPath>${JAVA_HOME}/lib/tools.jar</systemPath>

        </dependency>

    <dependency>

      <groupId>junit</groupId>

      <artifactId>junit</artifactId>

      <version>3.8.1</version>

      <scope>test</scope>

    </dependency>

  </dependencies>

</project>

b.Mapper代码：

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, Context context) throws IOException, InterruptedException {

            StringTokenizer itr = new StringTokenizer(value.toString());

            while (itr.hasMoreTokens()) {

                word.set(itr.nextToken());

                context.write(word, one);

            }

        }

    }

c.Reducer代码：

public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {

        private IntWritable result = new IntWritable();

        public void reduce(Text key, Iterable<IntWritable> values, Context context)

                throws IOException, InterruptedException {

            int sum = 0;

            for (IntWritable val : values) {

                sum += val.get();

            }

            result.set(sum);

            context.write(key, result);

        }

    }

d.整个java代码如下:

package my.hadoopstudy.mapreduce;

import java.io.IOException;

import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.Text;

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.output.FileOutputFormat;

public class WordCount {

    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, Context context) throws IOException, InterruptedException {

            StringTokenizer itr = new StringTokenizer(value.toString());

            while (itr.hasMoreTokens()) {

                word.set(itr.nextToken());

                context.write(word, one);

            }

        }

    }

    public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {

        private IntWritable result = new IntWritable();

        public void reduce(Text key, Iterable<IntWritable> values, Context context)

                throws IOException, InterruptedException {

            int sum = 0;

            for (IntWritable val : values) {

                sum += val.get();

            }

            result.set(sum);

            context.write(key, result);

        }

    }

    public static void main(String[] args) throws Exception {

        Configuration conf = new Configuration();

        Job job = Job.getInstance(conf, "word count");

        job.setJarByClass(WordCount.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(args[0]));

        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        System.exit(job.waitForCompletion(true) ? 0 : 1);

    }

}

e.打包：在项目目录下 mvn package，将target中jar包放到hadoop目录下自己建的study目录

f.运行：bin/hadoop jar study/hadoopstudy-1.0-SNAPSHOT.jar my.hadoopstudy.mapreduce.WordCount /user/wangke/wordcount/input /user/wangke/wordcount/output

4.遇到的问题及解决方式：

a.JAVA_HOME一定要记得修改

b.要按照2中官方文档修改相关的xml文件配置

c.第二次按照官方pseudo-distributed,报错如下：hadoop-there-are-0-datanodes-running-and-no-nodes-are-excluded-in-this-operation

解决方式： sbin/stop_all.sh --> 删除current文件(rm -r /tmp/hadoop-admin/dfs/data/current) ,然后重新按照pseudo-distributed就没问题了

d.在pseudo-distributed下跑jar时，connecting to resourcemanager一直连不上，，retry。这是因为没有启动yarn（本来以为这个local跑的时候，根本不需要yarn，但是只有启动yarn，才能打开8032resourceManager端口）

解决方式：修改yarn-site.xml配置：

<configuration>

<!-- Site specific YARN configuration properties -->

    <property>

        <name>yarn.nodemanager.aux-services</name>

        <value>mapreduce_shuffle</value>

    </property>

    <property>

        <name>yarn.resourcemanager.address</name>

        <value>127.0.0.1:8032</value>

    </property>

    <property>

        <name>yarn.resourcemanager.scheduler.address</name>

        <value>127.0.0.1:8030</value>

    </property>

    <property>

        <name>yarn.resourcemanager.resource-tracker.address</name>

        <value>127.0.0.1:8031</value>

    </property>

</configuration>

sbin/yarn-start.sh 发现执行成功，bin/hdfs dfs -cat /user/wangke/wordcount/output/part-r-00000 查看结果没问题

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