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编写第一个hadoop3.3.6的mapreduce程序

article 2024/9/19 9:06:12

hadoop还是用的上个伪分布环境。
hadoop安装在龙蜥anolis8.9上，开发是在windows下。
1、windows下首先要下载hadoop的包，hadoop-3.3.6.tar.gz，比如我的解压到d:\java\hadoop-3.3.6中。
配置环境：HADOOP_HOME，内容为：D:\java\hadoop-3.3.6
2、到项目https://github.com/cdarlint/winutils/tree/master/hadoop-3.3.6/bin中，下载：
hadoop.dll、winutils.exe
放到d:\java\hadoop-3.3.6\bin中。
同时将路径D:\java\hadoop-3.3.6\bin放到PATH中。
1、创建maven工程。
pom.xml文件内容：

<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 https://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>
    <groupId>com.rainpet</groupId>
    <artifactId>MapReduceDemo</artifactId>
    <version>0.0.1-SNAPSHOT</version>

    <name>hadoop-test</name>
    <url>http://maven.apache.org</url>

    <properties>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <hadoop.version>3.3.6</hadoop.version>
        <!-- Logger -->
        <lg4j2.version>2.12.1</lg4j2.version>
    </properties>

    <dependencies>
        <dependency>
            <!-- 客户端版本，最好和集群版本一致 -->
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>3.3.6</version>
        </dependency>
        <dependency>
            <groupId>junit</groupId>
            <artifactId>junit</artifactId>
            <version>4.12</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-hdfs-client</artifactId>
            <version>3.3.6</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-yarn-client</artifactId>
            <version>3.3.6</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-common</artifactId>
            <version>3.3.6</version>
        </dependency>
        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-log4j12</artifactId>
            <version>1.7.36</version>
        </dependency>
    </dependencies>

    <build>
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-shade-plugin</artifactId>
                <version>3.2.4</version>
                <executions>
                    <execution>
                        <phase>package</phase>
                        <goals>
                            <goal>shade</goal>
                        </goals>
                        <configuration>
                            <transformers>
                                <transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
                                    <mainClass>com.rainpet.MapReduceDemo.WordCountDriver</mainClass>
                                </transformer>
                            </transformers>
                        </configuration>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>
</project>

2、创建包及文件：com.rainpet.MapReduceDemo.WordCountDriver.java
内容为：

package com.rainpet.MapReduceDemo;

import java.io.IOException;
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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

/*
 * WordCount案例Driver代码
 * 1、获取job
 * 2、设置jar包路径
 * 3、关联Mapper和Reducer
 * 4、设置Map输出kv类型
 * 5、设置最终输出kv类型
 * 6、设置输入路径
 * 7、设置输出路径
 * 8、提交任务
 */
public class WordCountDriver {
    public static void main(String[] args) throws ClassNotFoundException, IOException, InterruptedException {

        // 1、获取job
        Configuration conf = new Configuration();
        conf.set("fs.defaultFS", "hdfs://master:8020");
        //conf.set("fs.hdfs.impl", "org.apache.hadoop.hdfs.DistributedFileSystem");
        Job job = Job.getInstance(conf);

        // 2、设置jar包路径
        job.setJarByClass(WordCountDriver.class);

        // 3、关联Mapper和Reducer
        job.setMapperClass(WordCountMapper.class);
        job.setReducerClass(WordCountReducer.class);

        // 4、设置Map输出kv类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);

        // 5、设置最终输出kv类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        // 6、设置输入地址
        FileInputFormat.setInputPaths(job, new Path(args[0]));

        // 7、设置输出路径
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        // 8、提交任务
        boolean result = job.waitForCompletion(true);

        System.exit(result ? 0 : 1);
    }
}

3、创建文件：
com.rainpet.MapReduceDemo.WordCountMapper.java
内容为：

package com.rainpet.MapReduceDemo;

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

/*
 * WordCount案例Map阶段代码
 * KEYIN, Map阶段输入K的类型:LongWritable
 * VALUEIN, Map阶段输入V的类型:Text
 * KEYOUT, Map阶段输出K的类型:Text
 * VALUEOUT,Map阶段输出K的类型:IntWritable
 */
// Map阶段继承Mapper类
public class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> {

    // 定义输出kv对的数据类型
    Text outK = new Text();
    IntWritable outV = new IntWritable(1);

    @Override
    protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, IntWritable>.Context context)
            throws IOException, InterruptedException {
        // TODO Auto-generated method stub
        // 将输入的数据转换成String类型
        String line = value.toString();
        // 将数据按空格切分
        String[] words = line.split(" ");
        for (String word : words) {
            outK.set(word);
            context.write(outK, outV);
        }
    }

}

4、创建包及文件：
com.rainpet.MapReduceDemo.WordCountReducer.java
内容为：

package com.rainpet.MapReduceDemo;

import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

/*
 * WordCount案例Reduce阶段代码
 * KEYIN, Reduce阶段输入K的类型:Text
 * VALUEIN, Reduce阶段输入V的类型:IntWritable
 * KEYOUT, Reduce阶段输出K的类型:Text
 * VALUEOUT,Reduce阶段输出K的类型:IntWritable
 */
// Reduce阶段继承Reducer类
public class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> {

    // 定义Reduce阶段输出值的类型
    IntWritable outV = new IntWritable();

    @Override
    protected void reduce(Text key, Iterable<IntWritable> values,
                          Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException {
        // TODO Auto-generated method stub
        // 统计单词的个数
        int sum = 0;
        for (IntWritable value : values) {
            sum += value.get();
        }
        outV.set(sum);
        context.write(key, outV);
    }

}

5、修改工程的jdk兼容性为jdk1.8
java编译器字节码的版本也为1.8。
6、编译，打包，得到文件：
MapReduceDemo-0.0.1-SNAPSHOT.jar
7、上传到hadoop服务器
执行命令：
hadoop jar MapReduceDemo-0.0.1-SNAPSHOT.jar /user/input /user/output2
正常情况就可以得到结果了。
数据也是使用了上次使用的一个数据文件：/user/input/1.txt

在这里插入图片描述
8、查看结果
hdfs dfs -cat /user/output2/*
9、输出文件删除
hdfs dfs -rm -R /user/output2
10、windows下运行的话，可能会遇到这个问题：
Permission denied: user=administrator, access=WRITE
方法是：在系统的环境变量里面添加HADOOP_USER_NAME=hadoop
这个问题原文：
https://blog.csdn.net/zhangjunli/article/details/106321516