का समाधान करना चाहिए के बाद "कुंग फू" का एक बहुत, मैं ChainMapper/ChainReducer उपयोग करने में सक्षम था। अंतिम टिप्पणी उपयोगकर्ता 864846 के लिए धन्यवाद।
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* to you under the Apache License, Version 2.0 (the
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* http://www.apache.org/licenses/LICENSE-2.0
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package myPKG;
/*
* Ajitsen: Sample program for ChainMapper/ChainReducer. This program is modified version of WordCount example available in Hadoop-0.18.0. Added ChainMapper/ChainReducer and made to works in Hadoop 1.0.2.
*/
import java.io.IOException;
import java.util.Iterator;
import java.util.StringTokenizer;
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.*;
import org.apache.hadoop.mapred.lib.ChainMapper;
import org.apache.hadoop.mapred.lib.ChainReducer;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
public class ChainWordCount extends Configured implements Tool {
public static class Tokenizer extends MapReduceBase
implements Mapper<LongWritable, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(LongWritable key, Text value,
OutputCollector<Text, IntWritable> output,
Reporter reporter) throws IOException {
String line = value.toString();
System.out.println("Line:"+line);
StringTokenizer itr = new StringTokenizer(line);
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
output.collect(word, one);
}
}
}
public static class UpperCaser extends MapReduceBase
implements Mapper<Text, IntWritable, Text, IntWritable> {
public void map(Text key, IntWritable value,
OutputCollector<Text, IntWritable> output,
Reporter reporter) throws IOException {
String word = key.toString().toUpperCase();
System.out.println("Upper Case:"+word);
output.collect(new Text(word), value);
}
}
public static class Reduce extends MapReduceBase
implements Reducer<Text, IntWritable, Text, IntWritable> {
public void reduce(Text key, Iterator<IntWritable> values,
OutputCollector<Text, IntWritable> output,
Reporter reporter) throws IOException {
int sum = 0;
while (values.hasNext()) {
sum += values.next().get();
}
System.out.println("Word:"+key.toString()+"\tCount:"+sum);
output.collect(key, new IntWritable(sum));
}
}
static int printUsage() {
System.out.println("wordcount <input> <output>");
ToolRunner.printGenericCommandUsage(System.out);
return -1;
}
public int run(String[] args) throws Exception {
JobConf conf = new JobConf(getConf(), ChainWordCount.class);
conf.setJobName("wordcount");
if (args.length != 2) {
System.out.println("ERROR: Wrong number of parameters: " +
args.length + " instead of 2.");
return printUsage();
}
FileInputFormat.setInputPaths(conf, args[0]);
FileOutputFormat.setOutputPath(conf, new Path(args[1]));
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
JobConf mapAConf = new JobConf(false);
ChainMapper.addMapper(conf, Tokenizer.class, LongWritable.class, Text.class, Text.class, IntWritable.class, true, mapAConf);
JobConf mapBConf = new JobConf(false);
ChainMapper.addMapper(conf, UpperCaser.class, Text.class, IntWritable.class, Text.class, IntWritable.class, true, mapBConf);
JobConf reduceConf = new JobConf(false);
ChainReducer.setReducer(conf, Reduce.class, Text.class, IntWritable.class, Text.class, IntWritable.class, true, reduceConf);
JobClient.runJob(conf);
return 0;
}
public static void main(String[] args) throws Exception {
int res = ToolRunner.run(new Configuration(), new ChainWordCount(), args);
System.exit(res);
}
}
नवीनतम संस्करण में संपादित (कम से कम Hadoop 2.6 से), addMapper में true झंडा जरूरत नहीं है। (वास्तव में हस्ताक्षर ने दमन को बदल दिया है ')।
तो यह सिर्फ
JobConf mapAConf = new JobConf(false);
ChainMapper.addMapper(conf, Tokenizer.class, LongWritable.class, Text.class,
Text.class, IntWritable.class, mapAConf);
वह पहले से ही एक jobconf है, उसे एक विन्यास की जरूरत है। कास्टिंग यहां सही विकल्प नहीं होगा। यह map1 के बारे में है और conf के बारे में नहीं है। –
आपकी नक्शा कक्षा को विस्तारित करना है: org.apache.hadoop.mapred.Mapper org.apache.hadoop.mapreduce.Mapper नहीं – user864846