[[PageOutline]]
 = Hadoop 程式 Debug =


 == 原始程式 ==

{{{
#!java
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.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.FileSplit;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;

public class WordIndex {
	 public static class wordindexM extends
			Mapper<LongWritable, Text, Text, Text> {
		public void map(LongWritable key, Text value,
				OutputCollector<Text, Text> output, Reporter reporter)
				throws IOException {

			FileSplit fileSplit = (FileSplit) reporter.getInputSplit();
			Text map_key = new Text();
			Text map_value = new Text();
			String line = value.toString();
			StringTokenizer st = new StringTokenizer(line.toLowerCase());
			while (st.hasMoreTokens()) {
				String word = st.nextToken();
				map_key.set(word);
				map_value.set(fileSplit.getPath().getName() + ":" + line);
				output.collect(map_key, map_value);
			}
		}
	}


	static public class wordindexR extends Reducer<Text, Text, Text, Text> {

		public void reduce(Text key, Iterable<Text> values,
				OutputCollector<Text, Text> output, Reporter reporter)
				throws IOException {
			String v = "";
			StringBuilder ret = new StringBuilder("\n");
			for (Text val : values) {
				v += val.toString().trim();
				if (v.length() > 0)
					ret.append(v + "\n");
			}

			output.collect((Text) key, new Text(ret.toString()));
		}
	}

	public static void main(String[] args) throws IOException,
			InterruptedException, ClassNotFoundException {
		// debug using
		String[] argv = { "/user/waue/input", "/user/waue/output-wi" };
		args = argv;

		Configuration conf = new Configuration();
		String[] otherArgs = new GenericOptionsParser(conf, args)
				.getRemainingArgs();
		if (otherArgs.length < 2) {
			System.out.println("hadoop jar WordIndex.jar <inDir> <outDir>");
			return;
		}
		Job job = new Job(conf, "word index");
		job.setJobName("word inverted index");
		job.setJarByClass(WordIndex.class);

		job.setMapOutputKeyClass(Text.class);
		job.setMapOutputValueClass(Text.class);
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(Text.class);

		job.setMapperClass(wordindexM.class);
		job.setReducerClass(wordindexR.class);
		job.setCombinerClass(wordindexR.class);

		FileInputFormat.setInputPaths(job, args[0]);
		FileOutputFormat.setOutputPath(job, new Path(args[1]));

		long start = System.nanoTime();

		job.waitForCompletion(true);

		long time = System.nanoTime() - start;
		System.err.println(time * (1E-9) + " secs.");
	}
}

}}}

 == 遇到問題 ==

{{{
#!text
10/01/18 20:52:39 INFO input.FileInputFormat: Total input paths to process : 2
10/01/18 20:52:39 INFO mapred.JobClient: Running job: job_201001181452_0038
10/01/18 20:52:40 INFO mapred.JobClient:  map 0% reduce 0%
10/01/18 20:52:50 INFO mapred.JobClient: Task Id : attempt_201001181452_0038_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

}}}

 == 研判 ==

{{{
#!java
	 public static class wordindexM extends
			Mapper<LongWritable, Text, Text, Text> {
		public void map(LongWritable key, Text value,
				OutputCollector<Text, Text> output, Reporter reporter)
				throws IOException {
}}}

用了新的 org.apache.hadoop.mapreduce.Mapper  可以省略掉不用 extends ..  implements ... 的宣告，

不過它預設搭配的map 實做方法是 ''' "public void map(LongWritable key, Text value, Context context) " ''' 而非 

'''  "public void map(LongWritable key, Text value, OutputCollector<Text, Text> output, Reporter reporter) " '''

原因是因為 使用 org.apache.hadoop.mapreduce.Mapper 卻使用  OutputCollector<Text, Text> output, Reporter reporter 

會造成''' OutputCollector<Text, Text>''' 的<Text, Text>無法作用，因此   output type 會以為是要跟 input type 一致

所以程式認定 output.collect( longWritable, Text );  但我們覺得輸出應該是  output.collect( Text , Text ) 因此不斷告訴我們

" Type mismatch in key from map: expected org.apache.hadoop.io.Text, recieved org.apache.hadoop.io.LongWritable "

 == 解決辦法 ==

Mapper.Context 就有宣告了getInputSplit();方法，因此使用 context 幾乎可以滿足之前的 ''' OutputCollector 與 Reporter '''

=== 完整可以work 的程式碼 ===

{{{
#!java
public class WordIndex {
	 public static class wordindexM extends
			Mapper<LongWritable, Text, Text, Text> {
		public void map(LongWritable key, Text value, Context context)
				throws IOException, InterruptedException {

			FileSplit fileSplit = (FileSplit) context.getInputSplit();
			
			Text map_key = new Text();
			Text map_value = new Text();
			String line = value.toString();
			StringTokenizer st = new StringTokenizer(line.toLowerCase());
			while (st.hasMoreTokens()) {
				String word = st.nextToken();
				map_key.set(word);
				map_value.set(fileSplit.getPath().getName() + ":" + line);
				context.write(map_key, map_value);
			}
		}
	}


	static public class wordindexR extends Reducer<Text, Text, Text, Text> {

		public void reduce(Text key, Iterable<Text> values,
				OutputCollector<Text, Text> output, Reporter reporter)
				throws IOException {
			String v = "";
			StringBuilder ret = new StringBuilder("\n");
			for (Text val : values) {
				v += val.toString().trim();
				if (v.length() > 0)
					ret.append(v + "\n");
			}

			output.collect((Text) key, new Text(ret.toString()));
		}
	}

	public static void main(String[] args) throws IOException,
			InterruptedException, ClassNotFoundException {
		// debug using
		String[] argv = { "/user/waue/input", "/user/waue/output-wi" };
		args = argv;

		Configuration conf = new Configuration();
		String[] otherArgs = new GenericOptionsParser(conf, args)
				.getRemainingArgs();
		if (otherArgs.length < 2) {
			System.out.println("hadoop jar WordIndex.jar <inDir> <outDir>");
			return;
		}
		Job job = new Job(conf, "word index");
		job.setJobName("word inverted index");
		job.setJarByClass(WordIndex.class);

		job.setMapOutputKeyClass(Text.class);
		job.setMapOutputValueClass(Text.class);
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(Text.class);

		job.setMapperClass(wordindexM.class);
		job.setReducerClass(wordindexR.class);
		job.setCombinerClass(wordindexR.class);

		FileInputFormat.setInputPaths(job, args[0]);
		HelloHadoopV2.checkAndDelete(args[1], conf);
		FileOutputFormat.setOutputPath(job, new Path(args[1]));

		long start = System.nanoTime();

		job.waitForCompletion(true);

		long time = System.nanoTime() - start;
		System.err.println(time * (1E-9) + " secs.");
	}
}

}}}