我正在使用以下代码执行reduce side join
/*
* HadoopMapper.java
*
* Created on Apr 8, 2012, 5:39:51 PM
*/
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
// import org.apache.commons.logging.Log;
// import org.apache.commons.logging.LogFactory;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.TextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.contrib.utils.join.*;
/**
*
* @author
*/
public class DataJoin extends Configured implements Tool
{
public static class MapClass extends DataJoinMapperBase
{
protected Text generateInputTag(String inputFile)
{
String datasource = inputFile.split("-")[0];
return new Text(datasource);
}
protected Text generateGroupKey(TaggedMapOutput aRecord)
{
String line = ((Text) aRecord.getData()).toString();
String[] tokens = line.split(",");
String groupKey = tokens[0];
return new Text(groupKey);
}
protected TaggedMapOutput generateTaggedMapOutput(Object value)
{
TaggedWritable retv = new TaggedWritable((Text) value);
retv.setTag(this.inputTag);
return retv;
}
}
public static class Reduce extends DataJoinReducerBase
{
protected TaggedMapOutput combine(Object[] tags, Object[] values)
{
if (tags.length < 2) return null;
String joinedStr = "";
for (int i=0; i<values.length; i++)
{
if (i > 0) joinedStr += ",";
TaggedWritable tw = (TaggedWritable) values[i];
String line = ((Text) tw.getData()).toString();
String[] tokens = line.split(",", 2);
joinedStr += tokens[1];
}
TaggedWritable retv = new TaggedWritable(new Text(joinedStr));
retv.setTag((Text) tags[0]);
return retv;
}
}
public static class TaggedWritable extends TaggedMapOutput
{
private Writable data;
public TaggedWritable(Writable data)
{
this.tag = new Text("");
this.data = data;
}
public Writable getData()
{
return data;
}
public void write(DataOutput out) throws IOException
{
this.tag.write(out);
this.data.write(out);
}
public void readFields(DataInput in) throws IOException
{
this.tag.readFields(in);
this.data.readFields(in);
}
}
public int run(String[] args) throws Exception
{
Configuration conf = getConf();
JobConf job = new JobConf(conf, DataJoin.class);
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if (otherArgs.length != 2)
{
System.err.println("Usage: wordcount <in> <out>");
System.exit(2);
}
Path in = new Path(args[0]);
Path out = new Path(args[1]);
FileInputFormat.setInputPaths(job, in);
FileOutputFormat.setOutputPath(job, out);
job.setJobName("DataJoin");
job.setMapperClass(MapClass.class);
job.setReducerClass(Reduce.class);
job.setInputFormat(TextInputFormat.class);
job.setOutputFormat(TextOutputFormat.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(TaggedWritable.class);
job.set("mapred.textoutputformat.separator", ",");
JobClient.runJob(job);
return 0;
}
public static void main(String[] args) throws Exception
{
int res = ToolRunner.run(new Configuration(),
new DataJoin(),
args);
System.exit(res);
}
}
我能够编译我的代码。当我在hadoop中运行时，我在组合器中遇到以下错误
12/04/17 19:59:29 INFO mapred.JobClient: map 100% reduce 27%
12/04/17 19:59:38 INFO mapred.JobClient: map 100% reduce 30%
12/04/17 19:59:47 INFO mapred.JobClient: map 100% reduce 33%
12/04/17 20:00:23 INFO mapred.JobClient: Task Id : attempt_201204061316_0018_r_000000_2, Status : FAILED
java.lang.RuntimeException: java.lang.NoSuchMethodException: DataJoin$TaggedWritable.<init>()
at org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:115)
at org.apache.hadoop.io.serializer.WritableSerialization$WritableDeserializer.deserialize(WritableSerialization.java:62)
at org.apache.hadoop.io.serializer.WritableSerialization$WritableDeserializer.deserialize(WritableSerialization.java:40)
at org.apache.hadoop.mapred.Task$ValuesIterator.readNextValue(Task.java:1136)
at org.apache.hadoop.mapred.Task$ValuesIterator.next(Task.java:1076)
at org.apache.hadoop.mapred.ReduceTask$ReduceValuesIterator.moveToNext(ReduceTask.java:246)
at org.apache.hadoop.mapred.ReduceTask$ReduceValuesIterator.next(ReduceTask.java:242)
at org.apache.hadoop.contrib.utils.join.DataJoinReducerBase.regroup(DataJoinReducerBase.java:106)
我用来运行hadoop的命令是 / hadoop / core / bin / hadoop jar /export/scratch/lopez/Join/DataJoin.jar DataJoin / export / scratch / user / lopez / Join / export / scratch / user / lopez / Join_Output
并且DataJoin.jar文件中包含DataJoin $ TaggedWritable
我检查了一些论坛，发现由于非静态类可能会发生错误。我的程序没有非静态类！
有人可以帮助我吗
谢谢你克里斯我编辑你说的。我更新了我的代码以获取两个文件。但我得到相同的错误信息
我收到同样的消息 INFO mapred.FileInputFormat：要处理的总输入路径：2
错误是
Status : FAILED
java.lang.ArrayIndexOutOfBoundsException: 1
at DataJoin$Reduce.combine(DataJoin.java:69)
at org.apache.hadoop.contrib.utils.join.DataJoinReducerBase.joinAndCollect(DataJoinReducerBase.java:205)
at org.apache.hadoop.contrib.utils.join.DataJoinReducerBase.joinAndCollect(DataJoinReducerBase.java:214)
at org.apache.hadoop.contrib.utils.join.DataJoinReducerBase.joinAndCollect(DataJoinReducerBase.java:214)
at org.apache.hadoop.contrib.utils.join.DataJoinReducerBase.joinAndCollect(DataJoinReducerBase.java:181)
at org.apache.hadoop.contrib.utils.join.DataJoinReducerBase.reduce(DataJoinReducerBase.java:135)
at org.apache.hadoop.mapred.ReduceTask.runOldReducer(ReduceTask.java:468)
{
Configuration conf = getConf();
JobConf job = new JobConf(conf, DataJoin.class);
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if (otherArgs.length != 3)
{
System.err.println("Usage: wordcount <in> <in1> <out>");
System.exit(2);
}
Path in = new Path(args[0]);
Path in1 = new Path(args[1]);
Path out = new Path(args[2]);
FileInputFormat.setInputPaths(job,in,in1);
FileOutputFormat.setOutputPath(job, out);
job.setJobName("DataJoin");
job.setMapperClass(MapClass.class);
job.setReducerClass(Reduce.class);
job.setInputFormat(TextInputFormat.class);
job.setOutputFormat(TextOutputFormat.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(TaggedWritable.class);
job.set("mapred.textoutputformat.separator", ",");
JobClient.runJob(job);
return 0;
}
答案 0 :(得分：6)
您需要TaggedWritable的默认构造函数（Hadoop使用反射来创建此对象，并且需要默认构造函数（无args）。
你的readFields方法也有问题，你在可写接口上调用data.readFields(in) - 但不知道data的实际运行时类。
我建议您在输出数据对象本身之前写出数据类名，或者查看GenericWritable类（您需要扩展它以定义可以使用的可允许可写类的集合）。
所以你可以修改如下：
public static class TaggedWritable extends TaggedMapOutput {
private Writable data;
public TaggedWritable() {
this.tag = new Text();
}
public TaggedWritable(Writable data) {
this.tag = new Text("");
this.data = data;
}
public Writable getData() {
return data;
}
public void setData(Writable data) {
this.data = data;
}
public void write(DataOutput out) throws IOException {
this.tag.write(out);
out.writeUTF(this.data.getClass().getName());
this.data.write(out);
}
public void readFields(DataInput in) throws IOException {
this.tag.readFields(in);
String dataClz = in.readUTF();
if (this.data == null
|| !this.data.getClass().getName().equals(dataClz)) {
this.data = (Writable) ReflectionUtils.newInstance(
Class.forName(dataClz), null);
}
this.data.readFields(in);
}
}
答案 1 :(得分：0)
由于您的代码仅使用Text，因此在TaggedWritable中链接默认构造函数应该：
public TaggedWritable() {
this(new Text(""));
}