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figure 7-4. Configuring the command-line arguments for debugging. Note the need to make the working directory. Once the directory /tmp/pidebug has been made, the Run button in the bottom-right corner of the Run Configurations window will be active. Click this button just to verify that the job will work in this setting. The output should be as follows:

@DataLen = 4000 - DATALENGTH(@DataRemain) / 2 @Temp = @DataRemain + SUBSTRING(@Items, @DataPos, @DataLen) @DataPos = @DataPos + @DataLen @Pos = CHARINDEX(@Delimiter, @Temp)



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if (e instanceof RuntimeException) { throw (RuntimeException) e; } throw new IOException(e); } } } It begins by establishing several member variables that will be used in the reduce() method to save object generation: /** Save object churn. */ Text outputKey = new Text(); Text outputValue = new Text(); /** Used in building the textual representation of the output key and values. */ StringBuilder sb = new StringBuilder(); Formatter fmt = new Formatter(sb); The working body of the reduce() method is within a try block that catches Throwables, and the input count, output count, and failure count are reported to the framework: reporter.incrCounter("Reduce Input Keys", "Total", 1); try { .... reporter.incrCounter("Reduce Output Keys", "Total", 1); output.collect(outputKey, outputValue); } catch( Throwable e) { reporter.incrCounter("Reduce Input Keys", "Exception", 1); In the body of the example in Listing 5-10, each value that is passed in is examined and aggregated: /** Examine each of the values that grouped with this key. */ while (values.hasNext()) { final long value = values.next().get(); if (value>max) { max = value; } if (value<min) { min = value; } total += value; count++; }





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You cannot expect to get a useful answer if you are swearing or aggressively demanding in any way Last but not least, you should be careful with advertising your products in forums Some forums provide specific boards for such items In general, you should post news about your products only when they are of real interest to J2ME developers..

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Finally, the output key and value are constructed with the aggregated data: sb.setLength(0); fmt.format("%12d %3d %12d %12d %12d", total/count, count, min, max, max-min); fmt.flush(); outputValue.set(sb.toString()); sb.setLength(0); fmt.format("%4d", key.get()); outputKey.set(sb.toString()); The example that runs this reducer also uses an output grouping comparator that groups the records in sets of ten. The comparator Utils.GroupByLongGroupingComparator.java (supplied with the downloadable code for this chapter) handles grouping LongWritable values in sets of 10, 0 9, 10 19, and so on. The following is the core code in SimpleReduce that sets up the job that runs SimpleReduceTransformingReducer: job.setInputFormat(KeyValueTextInputFormat.class); FileInputFormat.setInputPaths(job, inputDir); job.setMapperClass(SimpleReduceTransformingMapper.class); job.setMapOutputValueClass(LongWritable.class); job.setMapOutputKeyClass(LongWritable.class); /** Force the reduce to take text as the output value class, * instead of the default. */ job.setOutputValueClass(Text.class); job.setOutputKeyClass(Text.class); job.setReducerClass(SimpleReduceTransformingReducer.class); /** Cause the keys to be grouped by 10s. */ job.setOutputValueGroupingComparator(GroupByLongGroupingComparator.class); job.setNumReduceTasks(1); /** Ensure that all keys go to 1 reduce so * the group by is stable. */ The following command will run the SimpleReduce job (your output will vary slightly): % HADOOP_CLASSPATH=/misc/HadoopSource/commons-lang-2.4.jar bin/hadoop jar /misc/HadoopSource/hadoop-0.19.0/hadoopprobook.jar com.apress.hadoopbook.examples.ch5.SimpleReduce -libjars /misc/HadoopSource/commons-lang-2.4.jar

English and German forums exist for discussing J2ME Polish related problems. You can also join the J2ME Polish user mailing list. On http://www.j2mepolish.org, select Discussion for accessing the forums. Or get directly in touch with other J2ME Polish users by joining the irc://irc.synyx.de#j2mepolish Internet Relay Chat.

Total input paths to process : 5 Running job: job_200902221346_0079 map 0% reduce 0% map 20% reduce 0% map 60% reduce 0% map 80% reduce 0% map 100% reduce 0% map 100% reduce 100% Job complete: job_200902221346_0079 Counters: 20 File Systems HDFS bytes read=7103 HDFS bytes written=2135 Local bytes read=9006 Local bytes written=18176 Job Counters Launched reduce tasks=1 Launched map tasks=5 Data-local map tasks=5 Map Input Keys Total=500 Reduce Output Keys Total=35 Map Output Keys Total=500 Reduce Input Keys Total=35 Map-Reduce Framework Reduce input groups=35 Combine output records=0 Map input records=500 Reduce output records=35 Map output bytes=8000 Map input bytes=7103 Combine input records=0 Map output records=500 Reduce input records=500 The Job is complete and successfull Note how the output keys are multiples of tens. This is the result of the output value grouping. The actual output is the key, the average value, the number of values averaged, the minimum value, the maximum value, and the difference between the minimum and the maximum.

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