In this blog, we will take a close look at the classes and their methods that are involved in the operations of MapReduce programming. We will primarily keep our focus on the following −
• JobContext Interface
• Job Class
• Mapper Class
• Reducer Class

JobContext Interface

The JobContext interface is the super interface for all the classes, which defines different jobs in MapReduce. It gives you a read-only view of the job that is provided to the tasks while they are running.
The following are the sub-interfaces of JobContext interface.

S.No.

Subinterface Description

MapContext<KEYIN, VALUEIN, KEYOUT, VALUEOUT>

Defines the context that is given to the Mapper.

ReduceContext<KEYIN, VALUEIN, KEYOUT, VALUEOUT>

Defines the context that is passed to the Reducer.

Job class is the main class that implements the JobContext interface.

Job Class

The Job class is the most important class in the MapReduce API. It allows the user to configure the job, submit it, control its execution, and query the state. The set methods only work until the job is submitted, afterwards they will throw an IllegalStateException.
Normally, the user creates the application, describes the various facets of the job, and then submits the job and monitors its progress.
Here is an example of how to submit a job −
// Create a new Job
Job job = new Job(new Configuration());
job.setJarByClass(MyJob.class);

// Specify various job-specific parameters
job.setJobName(“myjob”);
job.setInputPath(new Path(“in”));
job.setOutputPath(new Path(“out”));

job.setMapperClass(MyJob.MyMapper.class);
job.setReducerClass(MyJob.MyReducer.class);

// Submit the job, then poll for progress until the job is complete
job.waitForCompletion(true);
Constructors
Following are the constructor summary of Job class.

S.No	Constructor Summary
1	Job()
2	Job(Configuration conf)
3	Job(Configuration conf, String jobName)

Methods

S.No	Method Description
1	getJobName() User-specified job name.
2	getJobState() Returns the current state of the Job.
3	isComplete() Checks if the job is finished or not.
4	setInputFormatClass() Sets the InputFormat for the job.
5	setJobName(String name) Sets the user-specified job name.
6	setOutputFormatClass() Sets the Output Format for the job.
7	setMapperClass(Class) Sets the Mapper for the job.
8	setReducerClass(Class) Sets the Reducer for the job.
9	setPartitionerClass(Class) Sets the Partitioner for the job.
10	setCombinerClass(Class) Sets the Combiner for the job.

Mapper Class

The Mapper class defines the Map job. Maps input key-value pairs to a set of intermediate key-value pairs. Maps are the individual tasks that transform the input records into intermediate records. The transformed intermediate records need not be of the same type as the input records. A given input pair may map to zero or many output pairs.
Method
map is the most prominent method of the Mapper class. The syntax is defined below −
map(KEYIN key, VALUEIN value, org.apache.hadoop.mapreduce.Mapper.Context context)
This method is called once for each key-value pair in the input split.

Reducer Class

The Reducer class defines the Reduce job in MapReduce. It reduces a set of intermediate values that share a key to a smaller set of values. Reducer implementations can access the Configuration for a job via the JobContext.getConfiguration() method. A Reducer has three primary phases − Shuffle, Sort, and Reduce.
• Shuffle − The Reducer copies the sorted output from each Mapper using HTTP across the network.
• Sort − The framework merge-sorts the Reducer inputs by keys (since different Mappers may have output the same key). The shuffle and sort phases occur simultaneously, i.e., while outputs are being fetched, they are merged.
• Reduce − In this phase the reduce (Object, Iterable, Context) method is called for each <key, (collection of values)> in the sorted inputs.
Method
reduce is the most prominent method of the Reducer class. The syntax is defined below −
reduce(KEYIN key, Iterable<VALUEIN> values, org.apache.hadoop.mapreduce.Reducer.Context context)
This method is called once for each key on the collection of key-value pairs.

So, this brings us to the end of blog. This Tecklearn ‘Deep dive into the MapReduce API’ helps you with commonly asked questions if you are looking out for a job in Big Data and Hadoop Domain.
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YARN and MapReduce
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Hadoop Security
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• What Kerberos Is and How it Works
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Managing and Scheduling Jobs
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Cluster Maintenance
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Data Management
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Data Storage and Performance
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Relational Data Analysis with Hive and Impala
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Working with Impala
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Analyzing Text and Complex Data with Hive
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Extending Hive
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Importing Relational Data with Apache Sqoop
• Sqoop Overview
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• Limiting Results
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Introduction to Impala and Hive
• Introduction to Impala and Hive
• Why Use Impala and Hive?
• Comparing Hive to Traditional Databases
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Modelling and Managing Data with Impala and Hive
• Data Storage Overview
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Data Formats
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Data Partitioning
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Capturing Data with Apache Flume
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Spark Basics
• What is Apache Spark?
• Using the Spark Shell
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• Functional Programming in Spark
Working with RDDs in Spark
• A Closer Look at RDDs
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• MapReduce
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Writing and Deploying Spark Applications
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Parallel Programming with Spark
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Spark Caching and Persistence
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• Spark SQL and the SQL Context
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Hadoop Testing
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Big Data Testing
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