IDEA 本地远程执行MapReduce(HA集群)程序找不到自定义Mapper与Reduce类

本文通过IDEA本地执行MR程序的main函数 ,而不是打包成Jar手工放到服务器上运行,发现以下错误提示:No job jar file set,然后在HDFS的/tmp下也没发现有该项目的Jar包,可以推测是任务提交给yarn后,本地并没有将项目打包成Jar提交给ResourceManager,导致找不到Mapper与Reducer类。

所以总体思路就是将项目借助Maven打包成Jar,然后通过添加mapreduce.job.jar的xml配置指定该Jar包在本地的存储路径。注意不能是绝对路径,必须是相对路径(项目文件为根路径),否则还是无法提交成功,但是No job jar file set的提示消失了,却依旧找不到类。

 

package com.atguigu.mapreduce.wordcount;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
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;

import java.io.IOException;

public class WordCountDriver {

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

        private IntWritable outV = new IntWritable();

        @Override
        protected void reduce(Text key, Iterable<IntWritable> values, Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException {
            int sum = 0;
            for (IntWritable value : values) {
                sum += value.get();
            }
            outV.set(sum);
            context.write(key, outV);
        }
    }

    public static class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> {

        private Text outK = new Text();
        private IntWritable outV = new IntWritable(1);

        @Override
        protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, IntWritable>.Context context) throws IOException, InterruptedException {

            // 1.获取一行
            String line = value.toString();

            // 2.切割
            String[] words = line.split(" ");

            for (String word : words) {
                // 封装outK
                outK.set(word);
                //写出
                context.write(outK, outV);
            }
        }
    }


    public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException {

        // 1 获取job
        Configuration conf = new Configuration();
        conf.set("mapreduce.app-submission.cross-platform", "true");
        conf.set("mapreduce.job.jar","test.jar");

        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输出的KV类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);

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

        // 6 设置输入路径和输出路径
//        FileInputFormat.setInputPaths(job, new Path("D:\\BaiduNetdiskDownload\\11_input\\inputword"));
//        FileOutputFormat.setOutputPath(job, new Path("D:\\hadoop\\output\\output1"));
        FileInputFormat.setInputPaths(job, new Path("/input/word.txt"));
        FileOutputFormat.setOutputPath(job, new Path("/output"));

        // 7 提交job
        boolean result = job.waitForCompletion(true);

        System.exit(result ? 0 : 1);

    }

}

 点击package按钮进行打包

 

 

 

 

好的,我可以为您介绍一个基于MapReduce的云计算用户搜索关键词统计Java程序。这个程序包括MapperReducer和主函数。以下是示例代码: ```java 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 SearchKeywordCount { // Mapper 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); } } } // Reducer 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); } } // 主函数 public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); Job job = Job.getInstance(conf, "search keyword count"); job.setJarByClass(SearchKeywordCount.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); } } ``` 这个程序的工作原理如下: 1. Mapper(TokenizerMapper)读取输入数据,将每行文本拆分为单词,并为每个单词输出(key, value)对,其中key是单词,value是1。 2. Reducer(IntSumReducer)接收相同key的所有value,对它们进行求和,得到每个单词的总出现次数。 3. 主函数设置MapReduce作业,指定MapperReducer、Combiner,以及输入输出路径。 4. 运行该程序时,需要指定输入和输出路径作为参数。 这个程序可以用于统计大量文本数据中各个搜索关键词的出现次数。输入数据可以是用户搜索日志,输出结果将显示每个关键词被搜索的次数。
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