MapReduce is used to compute the huge amount of data. To handle the upcoming data in a parallel and distributed form, the data has to flow from various phases.

Phases of MapReduce data flow

Input reader
The input reader reads the upcoming data and splits it into the data blocks of the appropriate size (64 MB to 128 MB). Each data block is associated with a Map function.
Once input reads the data, it generates the corresponding key-value pairs. The input files reside in HDFS.
Note – The input data can be in any form.
Map function
The map function process the upcoming key-value pairs and generated the corresponding output key-value pairs. The map input and output type may be different from each other.
Partition function
The partition function assigns the output of each Map function to the appropriate reducer. The available key and value provide this function. It returns the index of reducers.
Shuffling and Sorting
The data are shuffled between/within nodes so that it moves out from the map and get ready to process for reduce function. Sometimes, the shuffling of data can take much computation time.
The sorting operation is performed on input data for Reduce function. Here, the data is compared using comparison function and arranged in a sorted form.
Reduce function
The Reduce function is assigned to each unique key. These keys are already arranged in sorted order. The values associated with the keys can iterate the Reduce and generates the corresponding output.
Output writer
Once the data flow from all the above phases, Output writer executes. The role of Output writer is to write the Reduce output to the stable storage.

MapReduce API

In this section of blog , we focus on MapReduce APIs. Here, we learn about the classes and methods used in MapReduce programming.

MapReduce Mapper Class

void cleanup(Context context)	This method called only once at the end of the task.
void map(KEYIN key, VALUEIN value, Context context)	This method can be called only once for each key-value in the input split.
void run(Context context)	This method can be override to control the execution of the Mapper.
void setup(Context context)	This method called only once at the beginning of the task.

MapReduce Reducer Class

void cleanup(Context context)	This method called only once at the end of the task.
void map(KEYIN key, Iterable<VALUEIN> values, Context context)	This method called only once for each key.
void run(Context context)	This method can be used to control the tasks of the Reducer.
void setup(Context context)	This method called only once at the beginning of the task.

MapReduce Job Class

Methods	Description
Counters getCounters()	This method is used to get the counters for the job.
long getFinishTime()	This method is used to get the finish time for the job.
Job getInstance()	This method is used to generate a new Job without any cluster.
Job getInstance(Configuration conf)	This method is used to generate a new Job without any cluster and provided configuration.
Job getInstance(Configuration conf, String jobName)	This method is used to generate a new Job without any cluster and provided configuration and job name.
String getJobFile()	This method is used to get the path of the submitted job configuration.
String getJobName()	This method is used to get the user-specified job name.
JobPriority getPriority()	This method is used to get the scheduling function of the job.
void setJarByClass(Class<?> c)	This method is used to set the jar by providing the class name with .class extension.
void setJobName(String name)	This method is used to set the user-specified job name.
void setMapOutputKeyClass(Class<?> class)	This method is used to set the key class for the map output data.
void setMapOutputValueClass(Class<?> class)	This method is used to set the value class for the map output data.
void setMapperClass(Class<? extends Mapper> class)	This method is used to set the Mapper for the job.
void setNumReduceTasks(int tasks)	This method is used to set the number of reduce tasks for the job
void setReducerClass(Class<? extends Reducer> class)	This method is used to set the Reducer for the job.

So, this brings us to the end of blog. This Tecklearn ‘Phases of MapReduce Dataflow and detailed understanding of MapReduce API’ 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:

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Introduction
• The Case for Apache Hadoop
• Why Hadoop?
• Core Hadoop Components
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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
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• 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
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• Performing Initial HDFS Configuration
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Installing and Configuring Hive, Impala, and Pig
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Hadoop Clients
• What is a Hadoop Client?
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Cloudera Manager
• The Motivation for Cloudera Manager
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Advanced Cluster Configuration
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• 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
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• Non-Functional Testing
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System Testing
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• Solary Server
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Security Testing
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• Hadoop Cluster
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• IBM Project
Automation Testing
• Query Surge Tool
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