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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

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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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Frank says: Applications that process cross-origin messages should always verify the source origin of every message. Furthermore, message data should be treated with caution. Even if a message comes from a trusted source, it should be treated with the same caution as any other external input. The following two examples show a method of injecting content that can lead to trouble, as well as a safer alternative.

Now you can print the job output (key, average, count, min, max, difference), as follows: % hadoop dfs -cat SampleReduce.ouput/part-00000

<one-to-one name="campaign" class="com.hibernatebook.chapter06.Campaign" property-ref="product"/> The format used in this example is the most common. The body of the element consists of an infrequently used optional element: (meta* | formula*) We discuss the many-to-many element and the alternative approach of composition in some detail in the Mapping Collections section later in this chapter.

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300 310 320 330 340

// Dangerous: e.data is evaluated as markup! element.innerHTML = e.data; // Better element.textContent = e.data;

rdlc pdf 417

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The many-to-many association describes the relationship where multiple instances of this one class can reference a single instance of another class. This enforces a relational rule where the many class has a foreign key into the (usually primary) unique key of the one class. Table 6-9 shows the attributes permissible for the many-to-one element. Table 6-9. The <many-to-one> Attributes

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As a best practice, never evaluate strings received from third parties. Furthermore, avoid using eval with strings originating from your own application. Instead, you can use JSON with window.JSON or the json.org parser. JSON is a data language that is meant to be safely consumed by JavaScript, and the json.org parser is designed to be paranoid.

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