如何在 mapreduce 作业中解决 expected org.apache.hadoop.io.Text, received org.apache.hadoop.io.LongWritable

Question

我正在尝试编写一个可以分析来自 youtube 数据的一些信息的作业 set.I 相信我已经正确设置了 driver class 中地图的输出键，但是我仍然收到上述错误，我在这里发布代码和异常，

映射器

public class YouTubeDataMapper extends Mapper<LongWritable,Text,Text,IntWritable>{

private static final IntWritable one = new IntWritable(1); 
private Text category = new Text(); 
public void mapper(LongWritable key,Text value,Context context) throws IOException, InterruptedException{
    String str[] = value.toString().split("\t");
    category.set(str[3]);
    context.write(category, one);
}

}

减速器class

public class YouTubeDataReducer extends Reducer<Text,IntWritable,Text,IntWritable>{

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

}

Driver Class

public class YouTubeDataDriver {

public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();

    @SuppressWarnings("deprecation")
    Job job = new Job(conf, "categories");
    job.setJarByClass(YouTubeDataDriver.class);

    job.setMapOutputKeyClass(Text.class);
    job.setMapOutputValueClass(IntWritable.class);
    // job.setNumReduceTasks(0);
    job.setOutputKeyClass(Text.class);// Here i have set the output keys
    job.setOutputValueClass(IntWritable.class);

    job.setMapperClass(YouTubeDataMapper.class);
    job.setReducerClass(YouTubeDataReducer.class);

    job.setInputFormatClass(TextInputFormat.class);
    job.setOutputFormatClass(TextOutputFormat.class);

    FileInputFormat.addInputPath(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));
    Path out = new Path(args[1]);
    out.getFileSystem(conf).delete(out);
    job.waitForCompletion(true);

}

}

我得到的异常

java.io.IOException: Type mismatch in key from map: expected org.apache.hadoop.io.Text, received org.apache.hadoop.io.LongWritable at org.apache.hadoop.mapred.MapTask$MapOutputBuffer.collect(MapTask.java:1069) at org.apache.hadoop.mapred.MapTask$NewOutputCollector.write(MapTask.java:712) at org.apache.hadoop.mapreduce.task.TaskInputOutputContextImpl.write(TaskInputOutputContextImpl.java:89) at org.apache.hadoop.mapreduce.lib.map.WrappedMapper$Context.write(WrappedMapper.java:112) at org.apache.hadoop.mapreduce.Mapper.map(Mapper.java:124) at org.apache.hadoop.mapreduce.Mapper.run(Mapper.java:145) at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:784) at org.apache.hadoop.mapred.MapTask.run(MapTask.java:341) at org.apache.hadoop.mapred.YarnChild.run(YarnChild.java:168) at java.security.AccessController.doPrivileged(Native Method) at javax.security.auth.Subject.doAs(Subject.java:422) at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1642) at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:163)

我已经在 driver class

中设置了输出键

    job.setOutputKeyClass(Text.class);// Here i have set the output keys
    job.setOutputValueClass(IntWritable.class);

但为什么我仍然收到错误消息？请帮助，我是 mapreduce 的新手

Answer 1

下面的代码（用对象更新 LongWritable）对我有用 -

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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

public class YouTubeDataDriver {

    public static class YouTubeDataMapper
            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 YouTubeDataReducer
            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();

        @SuppressWarnings("deprecation")
        Job job = new Job(conf, "categories");
        job.setJarByClass(YouTubeDataDriver.class);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);
        // job.setNumReduceTasks(0);
        job.setOutputKeyClass(Text.class);// Here i have set the output keys
        job.setOutputValueClass(IntWritable.class);

        job.setMapperClass(YouTubeDataMapper.class);
        job.setReducerClass(YouTubeDataReducer.class);

        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);

        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        Path out = new Path(args[1]);
        out.getFileSystem(conf).delete(out);
        job.waitForCompletion(true);

    }

}

Answer 2

将 mapper() 方法重命名为 map()（参见 official docs）。

发生的事情是映射器实际上没有处理任何数据。它没有进入 mapper() 方法（因为它正在寻找 map() 方法），因此保持 map 阶段不变，这意味着 map 输出键仍然是 LongWritable.

顺便说一句，

String str[] = value.toString().split("\t");
category.set(str[3]);

非常危险。假设您的所有输入数据都至少包含 3 \t 个字符是有风险的。在处理大量数据时，几乎总会有一些数据不是您期望的格式，并且您不希望在这种情况发生时您的整个工作就此结束。考虑做类似的事情：

String valueStr = value.toString();
if (valueStr != null) {
    String str[] = valueStr.split("\t");
    if (str[] != null && str.size > 3) {
        category.set(str[3]);
        context.write(category, one);
    }
}

如何在 mapreduce 作业中解决 expected org.apache.hadoop.io.Text, received org.apache.hadoop.io.LongWritable

How to solve expected org.apache.hadoop.io.Text, received org.apache.hadoop.io.LongWritable in mapreduce job

java

hadoop

mapreduce