即使在使用纱线运行时存在映射器，Map-reduce 作业也会给出 ClassNotFound 异常？

Question

我正在运行一个 hadoop 作业，当我在伪分布式模式下没有纱线的情况下运行它时，它工作正常，但是当使用纱线运行时，它给了我找不到类的异常

16/03/24 01:43:40 INFO mapreduce.Job: Task Id : attempt_1458775953882_0002_m_000003_1, Status : FAILED
Error: java.lang.RuntimeException: java.lang.ClassNotFoundException: Class com.hadoop.keyword.count.ItemMapper not found
    at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2195)
    at org.apache.hadoop.mapreduce.task.JobContextImpl.getMapperClass(JobContextImpl.java:186)
    at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:745)
    at org.apache.hadoop.mapred.MapTask.run(MapTask.java:341)
    at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:164)
    at java.security.AccessController.doPrivileged(Native Method)
    at javax.security.auth.Subject.doAs(Subject.java:415)
    at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1657)
    at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:158)
Caused by: java.lang.ClassNotFoundException: Class com.hadoop.keyword.count.ItemMapper not found
    at org.apache.hadoop.conf.Configuration.getClassByName(Configuration.java:2101)
    at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2193)
    ... 8 more

这是作业的源代码

Configuration conf = new Configuration();
conf.set("keywords", args[2]);

Job job = Job.getInstance(conf, "item count");
job.setJarByClass(ItemImpl.class);
job.setMapperClass(ItemMapper.class);
job.setReducerClass(ItemReducer.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);

这是我正在运行的命令

hadoop jar ~/itemcount.jar /user/rohit/tweets /home/rohit/outputs/23mar-yarn13 vodka,wine,whisky

根据建议编辑代码

package com.hadoop.keyword.count;

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.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.Mapper.Context;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.json.simple.JSONObject;
import org.json.simple.parser.JSONParser;
import org.json.simple.parser.ParseException;

public class ItemImpl {

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

        Configuration conf = new Configuration();
        conf.set("keywords", args[2]);

        Job job = Job.getInstance(conf, "item count");
        job.setJarByClass(ItemImpl.class);
        job.setMapperClass(ItemMapper.class);
        job.setReducerClass(ItemReducer.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);
    }


    public static class ItemMapper extends Mapper<Object, Text, Text, IntWritable> {

        private final static IntWritable one = new IntWritable(1);
        private Text word = new Text();

        JSONParser parser = new JSONParser();

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

            JSONObject tweetObject = null;

            String[] keywords = this.getKeyWords(output);

            try {
                tweetObject = (JSONObject) parser.parse(value.toString());
            } catch (ParseException e) {
                e.printStackTrace();
            }
            if (tweetObject != null) {
                String tweetText = (String) tweetObject.get("text");

                if(tweetText == null){
                    return;
                }

                tweetText = tweetText.toLowerCase();
    /*          StringTokenizer st = new StringTokenizer(tweetText);

                ArrayList<String> tokens = new ArrayList<String>();

                while (st.hasMoreTokens()) {
                    tokens.add(st.nextToken());
                }*/

                for (String keyword : keywords) {
                    keyword = keyword.toLowerCase();
                    if (tweetText.contains(keyword)) {
                        output.write(new Text(keyword), one);
                    }
                }
                output.write(new Text("count"), one);
            }

        }

        String[] getKeyWords(Mapper<Object, Text, Text, IntWritable>.Context context) {

            Configuration conf = (Configuration) context.getConfiguration();
            String param = conf.get("keywords");

            return param.split(",");

        }
    }

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

        @Override
        protected void reduce(Text key, Iterable<IntWritable> values, Context output)
                throws IOException, InterruptedException {

            int wordCount = 0;

            for (IntWritable value : values) {
                wordCount += value.get();
            }

            output.write(key, new IntWritable(wordCount));
        }
    }
}

Answer 1

在完全分布式模式下运行，您的 TaskTracker/NodeManager （运行映射器的东西）在单独的 JVM 中运行，听起来您的类没有进入该 JVM 的类路径。

-libjars <csv,list,of,jars>尝试在作业调用时使用命令行 arg。这将使 Hadoop 将 jar 分发到 TaskTracker JVM 并从该 jar 加载您的类。（请注意，这会将 jar 复制到集群中的每个节点，并使其仅可用于该特定作业。如果您有需要为许多作业调用的公共库，您需要考虑使用Hadoop 分布式缓存。）

您可能还想yarn -jar ...在启动作业时进行尝试hadoop -jar ...，因为这是启动纱线作业的新/首选方式。