Working with MapReduce

- The MapReduce framework is used while creating the Hadoop program which is typically a java file. It is saved with the .java extension and it can be compiled and executed to get the desired output. Let us consider that the current location is the home directory of a Hadoop user (e.g. /home/hadoop). Below steps would help the user to compile the program
  Follow the steps given below to compile and execute the above program.
  - Enter the below command is to create a directory to store the compiled java classes.
  $ mkdir units
  Step 2
  Download Hadoop-core-1.2.1.jar, which is used to compile and execute the MapReduce program. Visit the following link http://mvnrepository.com/artifact/org.apache.hadoop/hadoop-core/1.2.1 to download the jar. Let us assume the downloaded folder is /home/hadoop/.
  - The following commands are used for compiling the ProcessUnits.java program and creating a jar for the program.
  $ javac -classpath hadoop-core-1.2.1.jar -d units ProcessUnits.java
  $ jar -cvf units.jar -C units/ .
  - The following command is used to create an input directory in HDFS.
  $HADOOP_HOME/bin/hadoop fs -mkdir input_dir
  - The following command is used to copy the input file named sample.txtin the input directory of HDFS.
  $HADOOP_HOME/bin/hadoop fs -put /home/hadoop/sample.txt input_dir
  - The following command is used to verify the files in the input directory.
  $HADOOP_HOME/bin/hadoop fs -ls input_dir/
  - The following command is used to run the Eleunit_max application by taking the input files from the input directory.
  $HADOOP_HOME/bin/hadoop jar units.jar hadoop.ProcessUnits input_dir output_dir
  Wait for a while until the file is executed. After execution, as shown below, the output will contain the number of input splits, the number of Map tasks, the number of reducer tasks, etc.
  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
  - The following command is used to verify the resultant files in the output folder.
  $HADOOP_HOME/bin/hadoop fs -ls output_dir/
  - The following command is used to see the output in Part-00000 file. This file is generated by HDFS.
  $HADOOP_HOME/bin/hadoop fs -cat output_dir/part-00000
  Below is the output generated by the MapReduce program.
  1981    34
  1984    40
  1985    45
  Step 10
  The following command is used to copy the output folder from HDFS to the local file system for analyzing.
  $HADOOP_HOME/bin/hadoop fs -cat output_dir/part-00000/bin/hadoop dfs get output_dir /home/hadoop