Blog

• PayLoad − Applications implement the Map and the Reduce functions, and form the core of the job.
• Mapper − Mapper maps the input key/value pairs to a set of intermediate key/value pair.
• NamedNode − Node that manages the Hadoop Distributed File System (HDFS).
• DataNode − Node where data is presented in advance before any processing takes place.
• MasterNode − Node where JobTracker runs and which accepts job requests from clients.
• SlaveNode − Node where Map and Reduce program runs.
• JobTracker − Schedules jobs and tracks the assign jobs to Task tracker.
• Task Tracker − Tracks the task and reports status to JobTracker.
• Job − A program is an execution of a Mapper and Reducer across a dataset.
• Task − An execution of a Mapper or a Reducer on a slice of data.
• Task Attempt − A particular instance of an attempt to execute a task on a SlaveNode.

• They will take a lot of time to execute.
• There will be a heavy network traffic when we move data from source to network server and so on.

1979   23   23   2   43   24   25   26   26   26   26   25   26  25 
1980   26   27   28  28   28   30   31   31   31   30   30   30  29 
1981   31   32   32  32   33   34   35   36   36   34   34   34  34 
1984   39   38   39  39   39   41   42   43   40   39   38   38  40 
1985   38   39   39  39   39   41   41   41   00   40   39   39  45 

package hadoop; 

import java.util.*; 

import java.io.IOException; 
import java.io.IOException; 

import org.apache.hadoop.fs.Path; 
import org.apache.hadoop.conf.*; 
import org.apache.hadoop.io.*; 
import org.apache.hadoop.mapred.*; 
import org.apache.hadoop.util.*; 

public class ProcessUnits {
   //Mapper class 
   public static class E_EMapper extends MapReduceBase implements 
   Mapper<LongWritable ,/*Input key Type */ 
   Text,                /*Input value Type*/ 
   Text,                /*Output key Type*/ 
   IntWritable>        /*Output value Type*/ 
   {
      //Map function 
      public void map(LongWritable key, Text value, 
      OutputCollector<Text, IntWritable> output,   
      
      Reporter reporter) throws IOException { 
         String line = value.toString(); 
         String lasttoken = null; 
         StringTokenizer s = new StringTokenizer(line,"\t"); 
         String year = s.nextToken(); 
         
         while(s.hasMoreTokens()) {
            lasttoken = s.nextToken();
         }
         int avgprice = Integer.parseInt(lasttoken); 
         output.collect(new Text(year), new IntWritable(avgprice)); 
      } 
   }
   
   //Reducer class 
   public static class E_EReduce extends MapReduceBase implements Reducer< Text, IntWritable, Text, IntWritable > {
   
      //Reduce function 
      public void reduce( Text key, Iterator <IntWritable> values, 
      OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException { 
         int maxavg = 30; 
         int val = Integer.MIN_VALUE; 
            
         while (values.hasNext()) { 
            if((val = values.next().get())>maxavg) { 
               output.collect(key, new IntWritable(val)); 
            } 
         }
      } 
   }

   //Main function 
   public static void main(String args[])throws Exception { 
      JobConf conf = new JobConf(ProcessUnits.class); 
      
      conf.setJobName("max_eletricityunits"); 
      conf.setOutputKeyClass(Text.class);
      conf.setOutputValueClass(IntWritable.class); 
      conf.setMapperClass(E_EMapper.class); 
      conf.setCombinerClass(E_EReduce.class); 
      conf.setReducerClass(E_EReduce.class); 
      conf.setInputFormat(TextInputFormat.class); 
      conf.setOutputFormat(TextOutputFormat.class); 
      
      FileInputFormat.setInputPaths(conf, new Path(args[0])); 
      FileOutputFormat.setOutputPath(conf, new Path(args[1])); 
      
      JobClient.runJob(conf); 
   } 
} 

$ mkdir units 

$ javac -classpath hadoop-core-1.2.1.jar -d units ProcessUnits.java 
$ jar -cvf units.jar -C units/ . 

$HADOOP_HOME/bin/hadoop fs -mkdir input_dir 

$HADOOP_HOME/bin/hadoop fs -put /home/hadoop/sample.txt input_dir 

$HADOOP_HOME/bin/hadoop fs -ls input_dir/ 

$HADOOP_HOME/bin/hadoop jar units.jar hadoop.ProcessUnits input_dir output_dir 

INFO mapreduce.Job: Job job_1414748220717_0002 
completed successfully 
14/10/31 06:02:52 
INFO mapreduce.Job: Counters: 49 
   File System Counters 
 
FILE: Number of bytes read = 61 
FILE: Number of bytes written = 279400 
FILE: Number of read operations = 0 
FILE: Number of large read operations = 0   
FILE: Number of write operations = 0 
HDFS: Number of bytes read = 546 
HDFS: Number of bytes written = 40 
HDFS: Number of read operations = 9 
HDFS: Number of large read operations = 0 
HDFS: Number of write operations = 2 Job Counters 


   Launched map tasks = 2  
   Launched reduce tasks = 1 
   Data-local map tasks = 2  
   Total time spent by all maps in occupied slots (ms) = 146137 
   Total time spent by all reduces in occupied slots (ms) = 441   
   Total time spent by all map tasks (ms) = 14613 
   Total time spent by all reduce tasks (ms) = 44120 
   Total vcore-seconds taken by all map tasks = 146137 
   Total vcore-seconds taken by all reduce tasks = 44120 
   Total megabyte-seconds taken by all map tasks = 149644288 
   Total megabyte-seconds taken by all reduce tasks = 45178880 
   
Map-Reduce Framework 
 
   Map input records = 5  
   Map output records = 5   
   Map output bytes = 45  
   Map output materialized bytes = 67  
   Input split bytes = 208 
   Combine input records = 5  
   Combine output records = 5 
   Reduce input groups = 5  
   Reduce shuffle bytes = 6  
   Reduce input records = 5  
   Reduce output records = 5  
   Spilled Records = 10  
   Shuffled Maps  = 2  
   Failed Shuffles = 0  
   Merged Map outputs = 2  
   GC time elapsed (ms) = 948  
   CPU time spent (ms) = 5160  
   Physical memory (bytes) snapshot = 47749120  
   Virtual memory (bytes) snapshot = 2899349504  
   Total committed heap usage (bytes) = 277684224
     
File Output Format Counters 
 
   Bytes Written = 40 

$HADOOP_HOME/bin/hadoop fs -ls output_dir/ 

1981    34 
1984    40 
1985    45 

$HADOOP_HOME/bin/hadoop fs -cat output_dir/part-00000/bin/hadoop dfs get output_dir /home/hadoop 

$ $HADOOP_HOME/bin/hadoop job -status <JOB-ID> 
e.g. 
$ $HADOOP_HOME/bin/hadoop job -status job_201310191043_0004 

$ $HADOOP_HOME/bin/hadoop job -history <DIR-NAME> 
e.g. 
$ $HADOOP_HOME/bin/hadoop job -history /user/expert/output 

$ $HADOOP_HOME/bin/hadoop job -kill <JOB-ID> 
e.g. 
$ $HADOOP_HOME/bin/hadoop job -kill job_201310191043_0004