apache hadoop 2.2中org.apache.hadoop.mapreduce导入的问题

2ledvvac  于 2021-06-03  发布在  Hadoop
关注(0)|答案(4)|浏览(312)

我最近安装了新的hadoop2.2。我以前写过一个简单的单词计数mapreduce程序,它可以在cdh4上轻松地工作。但现在,我对所有这些都有问题 org.apache.hadoop.mapreduce 进口。有人能告诉我到底要导出哪个jar来修复这些导入吗?代码如下,以防有人需要指出我需要做的更改,以确保它在hadoop2.2中运行。

import java.io.IOException;
import java.lang.InterruptedException;
import java.util.regex.Matcher;
import java.util.regex.Pattern;

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;

public class MapRWordCount {
    private final static IntWritable ONE = new IntWritable(1);
    private final static Pattern WORD = Pattern.compile("\\w+");

    public static class WordCountMapper 
            extends Mapper<LongWritable, Text, Text, IntWritable> {
        private final Text word = new Text();

        @Override
        public void map(LongWritable key, Text value, Context context) 
                throws IOException, InterruptedException {

            String valueString = value.toString();
            Matcher matcher = WORD.matcher(valueString);
            while (matcher.find()) {
                word.set(matcher.group().toLowerCase());
                context.write(word, ONE);
            }
        }
    }

    public static class WordCountReducer 
            extends Reducer<Text, IntWritable, Text, IntWritable> {
        private final IntWritable totalCount = new IntWritable();

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

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

        if (args.length != 2) {
            System.err.println("Usage: MapRWordCount <input_path> <output_path>");
            System.exit(-1);
        }

        Job job = new Job();
        job.setJarByClass(MapRWordCount.class);
        job.setJobName("MapReduce Word Count");

        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        job.setMapperClass(WordCountMapper.class);
        job.setCombinerClass(WordCountReducer.class);
        job.setReducerClass(WordCountReducer.class);

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

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

}
brjng4g3

brjng4g31#

使用此链接可以找到所需的jar文件
下载它们,右击你的项目进入构建路径>配置构建路径>添加外部jar

bksxznpy

bksxznpy2#

在maven中,我必须将以下内容添加到pom.xml中,然后以干净的方式构建,以便能够在java中找到mapper和reducer类:

<dependency>
    <groupId>org.apache.hadoop</groupId>
    <artifactId>hadoop-common</artifactId>
    <version>2.2.0</version>
</dependency>
<dependency>
    <groupId>org.apache.hadoop</groupId>
    <artifactId>hadoop-mapreduce-client-core</artifactId>
    <version>2.2.0</version>
</dependency>

现在,请不要抛出以下错误:

import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
bfrts1fy

bfrts1fy3#

我在以下地点找到了jar:

$HADOOP_HOME/share/hadoop/common/hadoop-common-2.2.0.jar
$HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-client-core-2.2.0.jar
rpppsulh

rpppsulh4#

如果您只是在hadoop2.2中寻找适当jar的位置,那么请查看下面的 share/hadoop/{common,hdfs,mapreduce} . 你会发现文件以 -2.2.0.jar 这很可能就是你要找的。
这应该与cdh4中的相同,除非您安装了与hadoop1.x结构匹配的“mr1”版本。

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