Question

我正在尝试编写一个可以从youtube数据集中分析一些信息的工作。我相信我已经从驱动程序类中的地图中正确设置了输出键，但我仍然得到上面的错误我发布了代码这里有例外，

Mapper

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);
}

}

Reducer类

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));
}

}

驱动程序类

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);

}

}

我得到的例外

java.io.IOException：键入map中的键不匹配：expected org.apache.hadoop.io.Text，收到org.apache.hadoop.io.LongWritable 在 org.apache.hadoop.mapred.MapTask $ MapOutputBuffer.collect（MapTask.java:1069）在 org.apache.hadoop.mapred.MapTask $ NewOutputCollector.write（MapTask.java:712）在 org.apache.hadoop.mapreduce.task.TaskInputOutputContextImpl.write（TaskInputOutputContextImpl.java:89）在 org.apache.hadoop.mapreduce.lib.map.WrappedMapper $ Context.write（WrappedMapper.java:112）在org.apache.hadoop.mapreduce.Mapper.map（Mapper.java:124）at org.apache.hadoop.mapreduce.Mapper.run（Mapper.java:145）at at org.apache.hadoop.mapred.MapTask.runNewMapper（MapTask.java:784）at at org.apache.hadoop.mapred.MapTask.run（MapTask.java:341）at at org.apache.hadoop.mapred.YarnChild $ 2.run（YarnChild.java:168）at 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）在org.apache.hadoop.mapred.YarnChild.main（YarnChild.java:163）

我在驱动程序类中设置了输出键

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

但为什么我仍然会收到错误？请帮忙，我是mapreduce的新手

Answer 1

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

发生的事情是映射器实际上没有处理任何数据。它没有输入mapper()方法（因为它正在寻找map()方法），因此保持地图阶段不变，这意味着地图输出键仍为LongWritable。

顺便说一下，

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

非常危险。假设所有输入数据至少包含3个\t个字符，这是冒险的。处理大量数据时，几乎总会有一些不符合您期望的格式，并且您不希望整个作业在发生这种情况时死亡。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);
    }
}

Answer 2

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);

    }

}

如何解决预期的org.apache.hadoop.io.Text，在mapreduce作业中收到org.apache.hadoop.io.LongWritable

2 个答案: