Question

我有一个简单的hadoop应用程序，它获取一个CSV文件，然后用“，”分割条目，然后计算第一个项目。

以下是我的代码。

package com.bluedolphin;

import java.io.IOException;
import java.util.Iterator;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
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.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reporter;
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.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class MyJob extends Configured implements Tool {
    private final static LongWritable one = new LongWritable(1);


    public static class MapClass extends Mapper<Object, Text, Text, LongWritable> {
        private Text word = new Text();
        public void map(Object key, 
                    Text value, 
                    OutputCollector<Text, LongWritable> output,
                    Reporter reporter) throws IOException, InterruptedException {
            String[] citation = value.toString().split(",");
            word.set(citation[0]);
            output.collect(word, one);
        }
    }

    public static class Reduce extends Reducer<Text, LongWritable, Text, LongWritable> {
        public void reduce(
                Text key, 
                Iterator<LongWritable> values, 
                OutputCollector<Text, LongWritable> output,
                Reporter reporter) throws IOException, InterruptedException {
            int sum = 0;

            while (values.hasNext()) {
                sum += values.next().get();
            }
            output.collect(key, new LongWritable(sum));
        }
    }
    public static class Combiner extends Reducer<Text, IntWritable, Text, LongWritable> {
        public void reduce(
                Text key, 
                Iterator<LongWritable> values, 
                OutputCollector<Text, LongWritable> output,
                Reporter reporter) throws IOException, InterruptedException {
            int sum = 0;

            while (values.hasNext()) {
                sum += values.next().get();
            }
            output.collect(key, new LongWritable(sum));

        }
    }

    public int run(String[] args) throws Exception {
        Configuration conf = getConf();

        Job job = new Job(conf, "MyJob");
        job.setJarByClass(MyJob.class);

        Path in = new Path(args[0]);
        Path out = new Path(args[1]);

        FileInputFormat.setInputPaths(job, in);
        FileOutputFormat.setOutputPath(job, out);

        job.setMapperClass(MapClass.class);
    //  job.setCombinerClass(Combiner.class);
        job.setReducerClass(Reduce.class);
    //  job.setInputFormatClass(KeyValueInputFormat.class);
        job.setInputFormatClass(TextInputFormat.class);
    //  job.setOutputFormatClass(KeyValueOutputFormat.class);

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

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

    public static void main(String args[]) throws Exception {
        int res = ToolRunner.run(new Configuration(), new MyJob(), args);
        System.exit(res);
    }
}

这是错误：

11/12/16 22:16:58 INFO mapred.JobClient: Task Id : attempt_201112161948_0005_m_000000_0, Status : FAILED
java.io.IOException: Type mismatch in key from map: expected org.apache.hadoop.io.Text, recieved org.apache.hadoop.io.LongWritable
    at org.apache.hadoop.mapred.MapTask$MapOutputBuffer.collect(MapTask.java:1013)
    at org.apache.hadoop.mapred.MapTask$NewOutputCollector.write(MapTask.java:690)
    at org.apache.hadoop.mapreduce.TaskInputOutputContext.write(TaskInputOutputContext.java:80)
    at org.apache.hadoop.mapreduce.Mapper.map(Mapper.java:124)
    at org.apache.hadoop.mapreduce.Mapper.run(Mapper.java:144)
    at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:763)
    at org.apache.hadoop.mapred.MapTask.run(MapTask.java:369)
    at org.apache.hadoop.mapred.Child$4.run(Child.java:259)
    at java.security.AccessController.doPrivileged(Native Method)
    at javax.security.auth.Subject.doAs(Subject.java:416)
    at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1059)
    at org.apache.hadoop.mapred.Child.main(Child.java:253)

Answer 1

要在代码中修复一些事情

旧的（o.a.h.mapred）和新的API（o.a.h.mapreduce）不兼容，因此不应混用。

import org.apache.hadoop.mapred.OutputCollector;  
import org.apache.hadoop.mapred.Reporter;  
import org.apache.hadoop.mapreduce.Job;  
import org.apache.hadoop.mapreduce.Mapper;  
import org.apache.hadoop.mapreduce.Reducer;

确保映射器/缩减器的输入/输出的类型为o.a.h.io.Writable。 Mapper的输入键是Object，使其成为LongWritable。
看起来Combiner和Reducer的功能是一样的，所以你不需要重复它。

job.setCombinerClass(Reducer.class);

此外，您可以使用WordCount示例，您的要求与WordCount示例之间没有太大区别。

Answer 2

一般说明，如果我们有Mapper<K1,V1, K2,V2>和Reducer<K2,V2, K3,V3>，最好在（在工作中）声明以下内容

JobConf conf = new JobConf(MyJob.class);
...
conf.setMapOutputKeyClass(K2.class);
conf.setMapOutputValueClass(V2.class);

您可以看到另一个示例here。

Answer 3

旧API（o.a.h.mapred）和新API（o.a.h.mapreduce）不兼容，因此不应混用。

import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;

您应该尝试使用Context替换Map和Reduce函数签名中的OutputCollector和Reporter。 map（K1 key，V1 val，Context context）和output.collect（k，v）with context.write（k，v）

供参考使用此链接以及有关迁移到新API的更多详细信息 http://www.slideshare.net/sh1mmer/upgrading-to-the-new-map-reduce-api#

键入map中的键不匹配：expected .. Text，received ... LongWritable

3 个答案: