This extends the Part 1 tutorial 1. Hadoop MapReduce Basic Tutorial. The key difference of this tutorial is using a “TextInputFormat” instead of “KeyValueTextInputFormat“.
TextInputFormat reads
The key as line offset number starting from 0 and the values as “Science, 80, 75, 89, 90” from the Scores.data file.
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Science, 80, 75, 89, 90 Maths, 90, 87, 78, 92 English, 78, 88, 65, 99 |
Mapper input with TextInputFormat.
Hadoop MapReduce Steps
Step 1: The Hadoop based mapper class “ScoreMapper” that can be executed in parallel by multiple nodes. It processe each input line as key/value pairs. E.g 0/Science, 80, 75, 89, 90. It is imperative to note that the key of type “LongWritable” instead of type “Text”.
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package com.mytutorial; import java.io.IOException; import java.util.StringTokenizer; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Mapper; public class ScoreMapper extends Mapper<LongWritable, Text, Text, Text> { public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { StringTokenizer st = new StringTokenizer(value.toString(), ","); String mappedString = ""; //for the scores String subject = (String) st.nextElement(); while (st.hasMoreElements()) { String score = (String) st.nextElement(); mappedString += score.trim() + "_"; } //subject is the new key context.write(new Text(subject), new Text(mappedString)); } } |
Step 2: The Hadoop based reducer class “ScoreReducer” that can be executed in parallel by multiple nodes. It processe each input line as key/value pairs. E.g Science/80_75_89_90_. The output key/value pairs will be E.g Science/max score is: 90. This is same as the Part 1 example.
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package com.mytutorial; import java.io.IOException; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Reducer; public class ScoreReducer extends Reducer<Text, Text, Text, Text> { @Override protected void reduce(Text key, Iterable<Text> values, Reducer<Text, Text, Text, Text>.Context context) throws IOException, InterruptedException { String mapString = null; String[] split = null; long tempValue = 0; long maxScore = 0; for (Text val : values) { mapString = val.toString(); split = mapString.split("_"); for (int i = 0; i < split.length; i++) { tempValue = new Long(split[i].trim()).longValue(); if(tempValue > maxScore){ maxScore = tempValue; } } } context.write(key, new Text("max score is: " + maxScore)); } } |
Step 3: Finally the executable main Java class “MaxScoreMain” that ties everything together.
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package com.mytutorial; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.input.TextInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat; public class MaxScoreMain { public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); Job job = new Job(conf, "maxscoremain"); job.setJarByClass(MaxScoreMain.class); job.setMapperClass(ScoreMapper.class); job.setReducerClass(ScoreReducer.class); job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(Text.class); job.setInputFormatClass(TextInputFormat.class); job.setOutputFormatClass(TextOutputFormat.class); FileInputFormat.addInputPath(job, new Path( "/Users/arulk/projects/scores.data")); FileOutputFormat.setOutputPath(job, new Path("/Users/arulk/tempMapreduce")); boolean result = job.waitForCompletion(true); System.exit(result ? 0 : 1); } } |
As you can see the “job.setInputFormatClass” uses “TextInputFormat“. The output results will be exactly same as the part 1.
