我正在尝试TotalOrderPartitionerHadoop。这样做时,我得到以下错误。错误声明-“错误的密钥类”
驱动程序代码-
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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.partition.InputSampler;
import org.apache.hadoop.mapreduce.lib.partition.TotalOrderPartitioner;
public class WordCountJobTotalSort {
public static void main (String args[]) throws Exception
{
if (args.length < 2 )
{
System.out.println("Plz provide I/p and O/p directory ");
System.exit(-1);
}
Job job = new Job();
job.setJarByClass(WordCountJobTotalSort.class);
job.setJobName("WordCountJobTotalSort");
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.setInputFormatClass(SequenceFileInputFormat.class);
job.setMapperClass(WordMapper.class);
job.setPartitionerClass(TotalOrderPartitioner.class);
job.setReducerClass(WordReducer.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
job.setNumReduceTasks(2);
TotalOrderPartitioner.setPartitionFile(job.getConfiguration(), new Path("/tmp/partition.lst"));
InputSampler.writePartitionFile(job, new InputSampler.RandomSampler<IntWritable, Text>(1,2,2));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
Map程序代码-
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
public class WordMapper extends Mapper <LongWritable,Text,Text, IntWritable >
{
public void map(IntWritable mkey, Text value,Context context)
throws IOException, InterruptedException {
String s = value.toString();
for (String word : s.split(" "))
{
if (word.length() > 0 ){
context.write(new Text(word), new IntWritable(1));
}
}
}
}
减速机代码-
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class WordReducer extends Reducer <Text, IntWritable, Text, IntWritable> {
public void reduce(Text rkey, Iterable<IntWritable> values ,Context context )
throws IOException, InterruptedException {
int count=0;
for (IntWritable value : values){
count = count + value.get();
}
context.write(rkey, new IntWritable(count));
}
}
错误-
[cloudera@localhost workspace]$ hadoop jar WordCountJobTotalSort.jar WordCountJobTotalSort file_seq/part-m-00000 file_out
15/05/18 00:45:13 INFO input.FileInputFormat: Total input paths to process : 1
15/05/18 00:45:13 INFO partition.InputSampler: Using 2 samples
15/05/18 00:45:13 INFO zlib.ZlibFactory: Successfully loaded & initialized native-zlib library
15/05/18 00:45:13 INFO compress.CodecPool: Got brand-new compressor [.deflate]
Exception in thread "main" java.io.IOException: wrong key class: org.apache.hadoop.io.LongWritable is not class org.apache.hadoop.io.Text
at org.apache.hadoop.io.SequenceFile$RecordCompressWriter.append(SequenceFile.java:1340)
at org.apache.hadoop.mapreduce.lib.partition.InputSampler.writePartitionFile(InputSampler.java:336)
at WordCountJobTotalSort.main(WordCountJobTotalSort.java:47)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)
at java.lang.reflect.Method.invoke(Method.java:597)
at org.apache.hadoop.util.RunJar.main(RunJar.java:208)
输入文件-
[cloudera@localhost 工作区]$hadoop fs-文本文件\u seq/part-m-00000
0你好你好
12如何
20是
26你的
36工作
3条答案
按热度按时间sxpgvts31#
注解这两行并执行hadoop作业
好的,如果它不工作,那么在注解这两行之后,您必须设置输入和输出格式类
nkkqxpd92#
在我的例子中,我得到了同样的错误键类错误,因为我使用的是带有自定义可写的combiner。当我评论combiner时,它工作得很好。
8nuwlpux3#
inputsampler在Map阶段(在shuffle和reduce之前)执行采样,采样通过Map器的输入键完成。我们需要确保Map器的输入和输出键是相同的;否则mr框架将找不到合适的bucket将输出键、值对放入采样空间。
在这种情况下,输入键是可长写的,因此inputsampler将基于所有可长写键的子集创建一个分区。但是输出键是text,因此mr框架将无法从分区中的with中找到合适的bucket。
我们可以通过引入准备阶段来解决这个问题。