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HADOOP - INTRODUCTION

Hadoop is an Apache open source framework written in java that allows distributed processing of

large datasets across clusters of computers using simple programming models. A Hadoop frame-

worked application works in an environment that provides distributed storage and computation

across clusters of computers. Hadoop is designed to scale up from single server to thousands of

machines, each offering local computation and storage.

Hadoop Architecture

Hadoop framework includes following four modules:

Hadoop Common: These are Java libraries and utilities required by other Hadoop modules.

These libraries provides filesystem and OS level abstractions and contains the necessary

Java files and scripts required to start Hadoop.

Hadoop YARN: This is a framework for job scheduling and cluster resource management.

Hadoop Distributed File System HDFS™: A distributed file system that provides high-

throughput access to application data.

Hadoop MapReduce: This is YARN-based system for parallel processing of large data sets.

We can use following diagram to depict these four components available in Hadoop framework.

Since 2012, the term "Hadoop" often refers not just to the base modules mentioned above but also

to the collection of additional software packages that can be installed on top of or alongside

Hadoop, such as Apache Pig, Apache Hive, Apache HBase, Apache Spark etc.

MapReduce

Hadoop MapReduce is a software framework for easily writing applications which process big

amounts of data in-parallel on large clusters thousands of nodes of commodity hardware in a

reliable, fault-tolerant manner.

The term MapReduce actually refers to the following two different tasks that Hadoop programs

perform:

The Map Task: This is the first task, which takes input data and converts it into a set of data,

where individual elements are broken down into tuples key/value pairs.

The Reduce Task: This task takes the output from a map task as input and combines those

data tuples into a smaller set of tuples. The reduce task is always performed after the map

task.

Typically both the input and the output are stored in a file-system. The framework takes care of

scheduling tasks, monitoring them and re-executes the failed tasks.

The MapReduce framework consists of a single master JobTracker and one slave TaskTracker

per cluster-node. The master is responsible for resource management, tracking resource

consumption/availability and scheduling the jobs component tasks on the slaves, monitoring them

and re-executing the failed tasks. The slaves TaskTracker execute the tasks as directed by the

master and provide task-status information to the master periodically.

The JobTracker is a single point of failure for the Hadoop MapReduce service which means if

JobTracker goes down, all running jobs are halted.

Hadoop Distributed File System

Hadoop can work directly with any mountable distributed file system such as Local FS, HFTP FS, S3

FS, and others, but the most common file system used by Hadoop is the Hadoop Distributed File

System HDFS.

The Hadoop Distributed File System HDFS is based on the Google File System GFS and provides a

distributed file system that is designed to run on large clusters thousands of computers of small

computer machines in a reliable, fault-tolerant manner.

HDFS uses a master/slave architecture where master consists of a single NameNode that

manages the file system metadata and one or more slave DataNodes that store the actual data.

A file in an HDFS namespace is split into several blocks and those blocks are stored in a set of

DataNodes. The NameNode determines the mapping of blocks to the DataNodes. The DataNodes

takes care of read and write operation with the file system. They also take care of block creation,

deletion and replication based on instruction given by NameNode.

HDFS provides a shell like any other file system and a list of commands are available to interact

with the file system. These shell commands will be covered in a separate chapter along with

appropriate examples.

How Does Hadoop Work?

Stage 1

A user/application can submit a job to the Hadoop a hadoop job client for required process by

specifying the following items:

1. The location of the input and output files in the distributed file system.

2. The java classes in the form of jar file containing the implementation of map and reduce

functions.

3. The job configuration by setting different parameters specific to the job.

Stage 2

The Hadoop job client then submits the job jar/executable etc and configuration to the JobTracker

which then assumes the responsibility of distributing the software/configuration to the slaves,

scheduling tasks and monitoring them, providing status and diagnostic information to the job-

client.

Stage 3

The TaskTrackers on different nodes execute the task as per MapReduce implementation and

output of the reduce function is stored into the output files on the file system.

Advantages of Hadoop

Hadoop framework allows the user to quickly write and test distributed systems. It is efficient,

and it automatic distributes the data and work across the machines and in turn, utilizes the

underlying parallelism of the CPU cores.

Hadoop does not rely on hardware to provide fault-tolerance and high availability FTHA,

rather Hadoop library itself has been designed to detect and handle failures at the

application layer.

Servers can be added or removed from the cluster dynamically and Hadoop continues to

operate without interruption.

Another big advantage of Hadoop is that apart from being open source, it is compatible on

all the platforms since it is Java based.

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Hadoop - Introduction

Hadoop - Introduction