MapReduce is a framework that is used for writing applications to process huge volumes of data on large clusters of commodity hardware in a reliable manner. This blog takes you through the operation of MapReduce in Hadoop framework using Java.

MapReduce Algorithm

Generally MapReduce paradigm is based on sending map-reduce programs to computers where the actual data resides.
• During a MapReduce job, Hadoop sends Map and Reduce tasks to appropriate servers in the cluster.
• The framework manages all the details of data-passing like issuing tasks, verifying task completion, and copying data around the cluster between the nodes.
• Most of the computing takes place on the nodes with data on local disks that reduces the network traffic.
• After completing a given task, the cluster collects and reduces the data to form an appropriate result, and sends it back to the Hadoop server.

Inputs and Outputs (Java Perspective)

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 pair as the output of the job, conceivably of different types.
The key and value classes have to be serializable by the framework and hence, it is required to implement the Writable interface. Additionally, the key classes have to implement the WritableComparable interface to facilitate sorting by the framework.
Both the input and output format of a MapReduce job are in the form of key-value pairs −
(Input) <k1, v1> -> map -> <k2, v2>-> reduce -> <k3, v3> (Output).

	Input	Output
Map	<k1, v1>	list (<k2, v2>)
Reduce	<k2, list(v2)>	list (<k3, v3>)

MapReduce Implementation

The following table shows the data regarding the electrical consumption of an organization. The table includes the monthly electrical consumption and the annual average for five consecutive years.

	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec	Avg
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

We need to write applications to process the input data in the given table to find the year of maximum usage, the year of minimum usage, and so on. This task is easy for programmers with finite amount of records, as they will simply write the logic to produce the required output, and pass the data to the written application.
Let us now raise the scale of the input data. Assume we have to analyze the electrical consumption of all the large-scale industries of a particular state. When we write applications to process such bulk data,
• They will take a lot of time to execute.
• There will be heavy network traffic when we move data from the source to the network server.
To solve these problems, we have the MapReduce framework.
Input Data
The above data is saved as sample.txt and 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

Example Program
The following program for the sample data uses 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(Eleunits.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 into ProcessUnits.java. The compilation and execution of the program is given below.
Compilation and Execution of ProcessUnits Program
Let us assume we are in the home directory of Hadoop user (e.g. /home/hadoop).
Follow the steps given below to compile and execute the above program.
Step 1 − Use the following command 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. Download the jar from mvnrepository.com. Let us assume the download folder is /home/hadoop/.
Step 3 − The following commands are used to compile the ProcessUnits.java program and to create a jar for the program.
$ javac -classpath hadoop-core-1.2.1.jar -d units ProcessUnits.java
$ jar -cvf units.jar -C units/ .
Step 4 − The following command is used to create an input directory in HDFS.
$HADOOP_HOME/bin/hadoop fs -mkdir input_dir
Step 5 − The following command is used to copy the input file named sample.txt in the input directory of HDFS.
$HADOOP_HOME/bin/hadoop fs -put /home/hadoop/sample.txt input_dir
Step 6 − The following command is used to verify the files in the input directory
$HADOOP_HOME/bin/hadoop fs -ls input_dir/
Step 7 − The following command is used to run the Eleunit_max application by taking input files from the input directory.
$HADOOP_HOME/bin/hadoop jar units.jar hadoop.ProcessUnits input_dir output_dir
Wait for a while till the file gets executed. After execution, the output contains a number of input splits, Map tasks, 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
Step 8 − The following command is used to verify the resultant files in the output folder.
$HADOOP_HOME/bin/hadoop fs -ls output_dir/
Step 9 − 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
Following 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.
$HADOOP_HOME/bin/hadoop fs -cat output_dir/part-00000/bin/hadoop dfs -get output_dir /home/Hadoop

So, this brings us to the end of blog. This Tecklearn ‘Operation of MapReduce in Hadoop framework using Java’ helps you with commonly asked questions if you are looking out for a job in Big Data and Hadoop Domain.
If you wish to learn Hive and build a career in Big Data or Hadoop domain, then check out our interactive, Big Data Hadoop-Architect (All in 1) Combo Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

BigData Hadoop-Architect (All in 1) | Combo Course

Big Data Hadoop-Architect (All in 1) Combo Training

About the Course

Tecklearn’s Big Data Hadoop-Architect (All in 1) combo includes the following Courses:
• BigData Hadoop Analyst
• BigData Hadoop Developer
• BigData Hadoop Administrator
• BigData Hadoop Tester
• Big Data Security with Kerberos

Why Should you take Big Data Hadoop Combo Training?

• Average salary for a Hadoop Administrator ranges from approximately $104,528 to $141,391 per annum – Indeed.com
• Average salary for a Spark and Hadoop Developer ranges from approximately $106,366 to $127,619 per annum – Indeed.com
• Average salary for a Big Data Hadoop Analyst is $115,819– ZipRecruiter.com

What you will Learn in this Course?

Introduction
• The Case for Apache Hadoop
• Why Hadoop?
• Core Hadoop Components
• Fundamental Concepts
HDFS
• HDFS Features
• Writing and Reading Files
• NameNode Memory Considerations
• Overview of HDFS Security
• Using the Namenode Web UI
• Using the Hadoop File Shell
Getting Data into HDFS
• Ingesting Data from External Sources with Flume
• Ingesting Data from Relational Databases with Sqoop
• REST Interfaces
• Best Practices for Importing Data
YARN and MapReduce
• What Is MapReduce?
• Basic MapReduce Concepts
• YARN Cluster Architecture
• Resource Allocation
• Failure Recovery
• Using the YARN Web UI
• MapReduce Version 1
Planning Your Hadoop Cluster
• General Planning Considerations
• Choosing the Right Hardware
• Network Considerations
• Configuring Nodes
• Planning for Cluster Management
Hadoop Installation and Initial Configuration
• Deployment Types
• Installing Hadoop
• Specifying the Hadoop Configuration
• Performing Initial HDFS Configuration
• Performing Initial YARN and MapReduce Configuration
• Hadoop Logging
Installing and Configuring Hive, Impala, and Pig
• Hive
• Impala
• Pig
Hadoop Clients
• What is a Hadoop Client?
• Installing and Configuring Hadoop Clients
• Installing and Configuring Hue
• Hue Authentication and Authorization
Cloudera Manager
• The Motivation for Cloudera Manager
• Cloudera Manager Features
• Express and Enterprise Versions
• Cloudera Manager Topology
• Installing Cloudera Manager
• Installing Hadoop Using Cloudera Manager
• Performing Basic Administration Tasks Using Cloudera Manager
Advanced Cluster Configuration
• Advanced Configuration Parameters
• Configuring Hadoop Ports
• Explicitly Including and Excluding Hosts
• Configuring HDFS for Rack Awareness
• Configuring HDFS High Availability
Hadoop Security
• Why Hadoop Security Is Important
• Hadoop’s Security System Concepts
• What Kerberos Is and How it Works
• Securing a Hadoop Cluster with Kerberos
Managing and Scheduling Jobs
• Managing Running Jobs
• Scheduling Hadoop Jobs
• Configuring the Fair Scheduler
• Impala Query Scheduling
Cluster Maintenance
• Checking HDFS Status
• Copying Data Between Clusters
• Adding and Removing Cluster Nodes
• Rebalancing the Cluster
• Cluster Upgrading
Cluster Monitoring and Troubleshooting
• General System Monitoring
• Monitoring Hadoop Clusters
• Common Troubleshooting Hadoop Clusters
• Common Misconfigurations
Introduction to Pig
• What Is Pig?
• Pig’s Features
• Pig Use Cases
• Interacting with Pig
Basic Data Analysis with Pig
• Pig Latin Syntax
• Loading Data
• Simple Data Types
• Field Definitions
• Data Output
• Viewing the Schema
• Filtering and Sorting Data
• Commonly-Used Functions
Processing Complex Data with Pig
• Storage Formats
• Complex/Nested Data Types
• Grouping
• Built-In Functions for Complex Data
• Iterating Grouped Data
Multi-Dataset Operations with Pig
• Techniques for Combining Data Sets
• Joining Data Sets in Pig
• Set Operations
• Splitting Data Sets
Pig Troubleshooting and Optimization
• Troubleshooting Pig
• Logging
• Using Hadoop’s Web UI
• Data Sampling and Debugging
• Performance Overview
• Understanding the Execution Plan
• Tips for Improving the Performance of Your Pig Jobs
Introduction to Hive and Impala
• What Is Hive?
• What Is Impala?
• Schema and Data Storage
• Comparing Hive to Traditional Databases
• Hive Use Cases
Querying with Hive and Impala
• Databases and Tables
• Basic Hive and Impala Query Language Syntax
• Data Types
• Differences Between Hive and Impala Query Syntax
• Using Hue to Execute Queries
• Using the Impala Shell
Data Management
• Data Storage
• Creating Databases and Tables
• Loading Data
• Altering Databases and Tables
• Simplifying Queries with Views
• Storing Query Results
Data Storage and Performance
• Partitioning Tables
• Choosing a File Format
• Managing Metadata
• Controlling Access to Data
Relational Data Analysis with Hive and Impala
• Joining Datasets
• Common Built-In Functions
• Aggregation and Windowing
Working with Impala
• How Impala Executes Queries
• Extending Impala with User-Defined Functions
• Improving Impala Performance
Analyzing Text and Complex Data with Hive
• Complex Values in Hive
• Using Regular Expressions in Hive
• Sentiment Analysis and N-Grams
• Conclusion
Hive Optimization
• Understanding Query Performance
• Controlling Job Execution Plan
• Bucketing
• Indexing Data
Extending Hive
• SerDes
• Data Transformation with Custom Scripts
• User-Defined Functions
• Parameterized Queries
Importing Relational Data with Apache Sqoop
• Sqoop Overview
• Basic Imports and Exports
• Limiting Results
• Improving Sqoop’s Performance
• Sqoop 2
Introduction to Impala and Hive
• Introduction to Impala and Hive
• Why Use Impala and Hive?
• Comparing Hive to Traditional Databases
• Hive Use Cases
Modelling and Managing Data with Impala and Hive
• Data Storage Overview
• Creating Databases and Tables
• Loading Data into Tables
• HCatalog
• Impala Metadata Caching
Data Formats
• Selecting a File Format
• Hadoop Tool Support for File Formats
• Avro Schemas
• Using Avro with Hive and Sqoop
• Avro Schema Evolution
• Compression
Data Partitioning
• Partitioning Overview
• Partitioning in Impala and Hive
Capturing Data with Apache Flume
• What is Apache Flume?
• Basic Flume Architecture
• Flume Sources
• Flume Sinks
• Flume Channels
• Flume Configuration
Spark Basics
• What is Apache Spark?
• Using the Spark Shell
• RDDs (Resilient Distributed Datasets)
• Functional Programming in Spark
Working with RDDs in Spark
• A Closer Look at RDDs
• Key-Value Pair RDDs
• MapReduce
• Other Pair RDD Operations
Writing and Deploying Spark Applications
• Spark Applications vs. Spark Shell
• Creating the SparkContext
• Building a Spark Application (Scala and Java)
• Running a Spark Application
• The Spark Application Web UI
• Configuring Spark Properties
• Logging
Parallel Programming with Spark
• Review: Spark on a Cluster
• RDD Partitions
• Partitioning of File-based RDDs
• HDFS and Data Locality
• Executing Parallel Operations
• Stages and Tasks
Spark Caching and Persistence
• RDD Lineage
• Caching Overview
• Distributed Persistence
Common Patterns in Spark Data Processing
• Common Spark Use Cases
• Iterative Algorithms in Spark
• Graph Processing and Analysis
• Machine Learning
• Example: k-means
Preview: Spark SQL
• Spark SQL and the SQL Context
• Creating DataFrames
• Transforming and Querying DataFrames
• Saving DataFrames
• Comparing Spark SQL with Impala
Hadoop Testing
• Hadoop Application Testing
• Roles and Responsibilities of Hadoop Testing Professional
• Framework MRUnit for Testing of MapReduce Programs
• Unit Testing
• Test Execution
• Test Plan Strategy and Writing Test Cases for Testing Hadoop Application
Big Data Testing
• BigData Testing
• Unit Testing
• Integration Testing
• Functional Testing
• Non-Functional Testing
• Golden Data Set
System Testing
• Building and Set up
• Testing SetUp
• Solary Server
• Non-Functional Testing
• Longevity Testing
• Volumetric Testing
Security Testing
• Security Testing
• Non-Functional Testing
• Hadoop Cluster
• Security-Authorization RBA
• IBM Project
Automation Testing
• Query Surge Tool
Oozie
• Why Oozie
• Installation Engine
• Oozie Workflow Engine
• Oozie security
• Oozie Job Process
• Oozie terminology
• Oozie bundle
Got a question for us? Please mention it in the comments section and we will get back to you.

545