Hadoop - MapReduce

MapReduce is a framework using which we can write applications to process huge amounts of data, in parallel, on large clusters of commodity hardware in a reliable manner.
MapReduce is a processing technique and a program model for distributed computing based on java. The MapReduce algorithm contains two important tasks, namely Map and Reduce. Map takes a set of data and converts it into another set of data, where individual elements are broken down into tuples (key/value pairs). Secondly, reduce task, which takes the output from a map as an input and combines those data tuples into a smaller set of tuples. As the sequence of the name MapReduce implies, the reduce task is always performed after the map job.
The major advantage of MapReduce is that it is easy to scale data processing over multiple computing nodes. Under the MapReduce model, the data processing primitives are called mappers and reducers. Decomposing a data processing application into mappers and reducers is sometimes nontrivial. But, once we write an application in the MapReduce form, scaling the application to run over hundreds, thousands, or even tens of thousands of machines in a cluster is merely a configuration change. This simple scalability is what has attracted many programmers to use the MapReduce model.
Generally MapReduce paradigm is based on sending the computer to where the data resides!
Generally MapReduce paradigm is based on sending the computer to where the data resides!
MapReduce program executes in three stages, namely map stage, shuffle stage, and reduce stage.

Map stage − The map or mapper’s job is to process the input data. Generally the input data is in the form of file or directory and is stored in the Hadoop file system (HDFS). The input file is passed to the mapper function line by line. The mapper processes the data and creates several small chunks of data. 
Reduce stage − This stage is the combination of the  Shuffle  stage and the Reduce stage. The Reducer’s job is to process the data that comes from the mapper. After processing, it produces a new set of output, which will be stored in the HDFS.


MapReduce program executes in three stages, namely map stage, shuffle stage, and reduce stage.
Map stage − The map or mapper’s job is to process the input data. Generally the input data is in the form of file or directory and is stored in the Hadoop file system (HDFS). The input file is passed to the mapper function line by line. The mapper processes the data and creates several small chunks of data. 
Map stage − The map or mapper’s job is to process the input data. Generally the input data is in the form of file or directory and is stored in the Hadoop file system (HDFS). The input file is passed to the mapper function line by line. The mapper processes the data and creates several small chunks of data. 
Reduce stage − This stage is the combination of the  Shuffle  stage and the Reduce stage. The Reducer’s job is to process the data that comes from the mapper. After processing, it produces a new set of output, which will be stored in the HDFS.
Reduce stage − This stage is the combination of the  Shuffle  stage and the Reduce stage. The Reducer’s job is to process the data that comes from the mapper. After processing, it produces a new set of output, which will be stored in the HDFS.
During a MapReduce job, Hadoop sends the Map and Reduce tasks to the appropriate servers in the cluster.
During a MapReduce job, Hadoop sends the Map and Reduce tasks to the appropriate servers in the cluster.
The framework manages all the details of data-passing such as issuing tasks, verifying task completion, and copying data around the cluster between the nodes.
The framework manages all the details of data-passing such as issuing tasks, verifying task completion, and copying data around the cluster between the nodes.
Most of the computing takes place on nodes with data on local disks that reduces the network traffic.
Most of the computing takes place on nodes with data on local disks that reduces the network traffic.
After completion of the given tasks, the cluster collects and reduces the data to form an appropriate result, and sends it back to the Hadoop server.
After completion of the given tasks, the cluster collects and reduces the data to form an appropriate result, and sends it back to the Hadoop server.
The MapReduce framework operates on <key, value> pairs, that is, the framework views the input to the job as a set of <key, value> pairs and produces a set of <key, value> pairs as the output of the job, conceivably of different types.
The key and the value classes should be in serialized manner by the framework and hence, need to implement the Writable interface. Additionally, the key classes have to implement the Writable-Comparable interface to facilitate sorting by the framework. Input and Output types of a MapReduce job − (Input) <k1, v1> → map → <k2, v2> → reduce → <k3, v3>(Output).
PayLoad − Applications implement the Map and the Reduce functions, and form the core of the job.
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.
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).
NamedNode − Node that manages the Hadoop Distributed File System (HDFS).
DataNode − Node where data is presented in advance before any processing takes place.
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.
MasterNode − Node where JobTracker runs and which accepts job requests from clients.
SlaveNode − Node where Map and Reduce program runs.
SlaveNode − Node where Map and Reduce program runs.
JobTracker − Schedules jobs and tracks the assign jobs to Task tracker.
JobTracker − Schedules jobs and tracks the assign jobs to Task tracker.
Task Tracker − Tracks the task and reports status to JobTracker. 
Task Tracker − Tracks the task and reports status to JobTracker. 
Job − A program is an execution of a Mapper and Reducer across a dataset.
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 − 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.
Task Attempt − A particular instance of an attempt to execute a task on a SlaveNode.
Given below is the data regarding the electrical consumption of an organization. It contains the monthly electrical consumption and the annual average for various years.
If the above data is given as input, we have to write applications to process it and produce results such as finding the year of maximum usage, year of minimum usage, and so on. This is a walkover for the programmers with finite number of records. They will simply write the logic to produce the required output, and pass the data to the application written.
But, think of the data representing the electrical consumption of all the largescale industries of a particular state, since its formation.
When we write applications to process such bulk data,
They will take a lot of time to execute.
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.
There will be a heavy network traffic when we move data from source to network server and so on.
To solve these problems, we have the MapReduce framework.
The above data is saved as sample.txtand given as input. The input file looks as shown below.
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 

Given below is the program to the sample data using MapReduce framework.
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); 
   } 
} 
Save the above program as ProcessUnits.java. The compilation and execution of the program is explained below.
Let us assume we are in the home directory of a Hadoop user (e.g. /home/hadoop).
Follow the steps given below to compile and execute the above program.
The following command is to create a directory to store the compiled java classes.
$ mkdir units 

Download Hadoop-core-1.2.1.jar, which is used to compile and execute the MapReduce program. Visit the following link  mvnrepository.com 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 

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 

All Hadoop commands are invoked by the $HADOOP_HOME/bin/hadoop command. Running the Hadoop script without any arguments prints the description for all commands.
Usage − hadoop [--config confdir] COMMAND
The following table lists the options available and their description.
namenode -format
Formats the DFS filesystem.
secondarynamenode
Runs the DFS secondary namenode.
namenode
Runs the DFS namenode.
datanode
Runs a DFS datanode.
dfsadmin
Runs a DFS admin client.
mradmin
Runs a Map-Reduce admin client.
fsck
Runs a DFS filesystem checking utility.
fs
Runs a generic filesystem user client.
balancer
Runs a cluster balancing utility.
oiv
Applies the offline fsimage viewer to an fsimage.
fetchdt
Fetches a delegation token from the NameNode.
jobtracker
Runs the MapReduce job Tracker node.
pipes
Runs a Pipes job.
tasktracker
Runs a MapReduce task Tracker node.
historyserver
Runs job history servers as a standalone daemon.
job
Manipulates the MapReduce jobs.
queue
Gets information regarding JobQueues.
version
Prints the version.
jar <jar>
Runs a jar file.
distcp <srcurl> <desturl>
Copies file or directories recursively.
distcp2 <srcurl> <desturl>
DistCp version 2.
archive -archiveName NAME -p <parent path> <src>* <dest>
Creates a hadoop archive.
classpath
Prints the class path needed to get the Hadoop jar and the required libraries.
daemonlog
Get/Set the log level for each daemon
Usage − hadoop job [GENERIC_OPTIONS]
The following are the Generic Options available in a Hadoop job.
-submit <job-file>
Submits the job.
-status <job-id>
Prints the map and reduce completion percentage and all job counters.
-counter <job-id> <group-name> <countername>
Prints the counter value.
-kill <job-id>
Kills the job.
-events <job-id> <fromevent-#> <#-of-events>
Prints the events' details received by jobtracker for the given range.
-history [all] <jobOutputDir> - history < jobOutputDir>
Prints job details, failed and killed tip details. More details about the job such as successful tasks and task attempts made for each task can be viewed by specifying the [all] option.
-list[all]
Displays all jobs. -list displays only jobs which are yet to complete.
-kill-task <task-id>
Kills the task. Killed tasks are NOT counted against failed attempts.
-fail-task <task-id>
Fails the task. Failed tasks are counted against failed attempts.
-set-priority <job-id> <priority>
Changes the priority of the job. Allowed priority values are VERY_HIGH, HIGH, NORMAL, LOW, VERY_LOW
$ $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