1.BigData-Hadoop an Introduction

8/11/2019 1.BigData-Hadoop an Introduction

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HADOOP-

Nagarjuna K



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Why and What Hadoop ?

!A tool to process big data



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What is BIG Data ?

!Facebook, Google+ etc.,

!

Machines too generate lots of data

!We are having a online discussion now ,

certainly how many of us are in this conference

will also be recorded as data.



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What is BIG Data ? ..continued

!Exponential growth of data "challenges to Google,

Yahoo, Microsoft, Amazon

!Need to go through TBs and PBs of data ?

! Which websites and books were popular ?

! What kind of Ads appeal to them ?

!

Existing tools became inadequate to process such largedata sets.



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Why is the data so BIG ?

!Till Couple of decade back #Floppy disks

!

From then on #CD/DVD Drives

!Half a decade back #Hard drives (500 GB)

!

Now#Hard Drives(I TB) are available in abundance



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!

So WHAT ?

!

Even the technology to read has taken a leap.



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Year Device Volume

DataTransfer

speed

Time toprocess

1990Optical Drive

1370 MB 4.4 MB/s 5 minutes

2012

1 TB SATA

Drives 1 TB 100 MB/s 2.5 Hrs



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How to handle such BIG ?

! BIG elephant

!

Numerous small chicken ?



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How to handle such BIG ?

!Concept of Torrents

!Reduce time to read by reading it from multiple

sources simultaneously.

!!"#$%&' %) *' +#, -.. ,/%0'12 '#3+ +45,%&$ 4&' +6&,/',7+

4) 7+' ,#7#8 94/:%&$ %& ;#/#55'52 *' 3465, /'#, 7+' ,#7# %&

5'11 7+#& 7*4 "%&67'18



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How to handle such BIG ? --

Issues

!

How to handle a system up and downs ?

!

How to combine the data from all the

systems ?



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Problem1 : Systems Ups and

Downs!Commodity hard ware for data storage and analysis

!Chances of failure are very high

!

So, have a redundant copy of the same data across somemachines

! In case of eventuality of one machine, you have the other

!Google came up with a file system # GFS (Google File System)

which implemented all these details.



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GFS! Divides data into chunks and stores in the file System

! Can store data in ranges of PBs also



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Problem 2 : How to combine the

data ?

!Analyze data across different machines , But how do we mergethem to get a meaningful outcome ?

!Yes, all (some) of the data has to travel across network. Then only

merging of the data can occur.

!Doing this is notoriously challenging

!

Again Google"MapReduce



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Map Reduce!Provides a programming model "abstracts the

problem of disk reads and writes transforming in to a

computation of keys and values.

!Two phases

!Map

!

Reduce



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So what is Hadoop ?

!

An operating system ?

!Provides

1. A reliable shared storage system

2.

Analysis system



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History of Hadoop

!Google was the first to launch GFS and

MapReduce

!

They published a paper in 2004 announcing the

world a brand new technology

!This technology was well proven in Google by 2004

itself

MapReduce paper by Googlehttp://www.excelonlineclasses.co.nr/


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History of Hadoop

!Doug Cutting saw an opportunity and led the

charge to develop an open sourceversion of this

MapReduce system called Hadoop .

! Soon after, Yahoo and othersrallied around tosupport this effort.

!

Now Hadoop is core part in :

!Facebook, Yahoo, LinkedIn, Twitter!



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History of Hadoop

!GFS # HDFS

!MapReduce #MapReduce



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HDFS -- A Brief

Design # Streaming very large files on commodity cluster.

1. Very Large Files$MBs to PBs

2. Streaming$Write once read many approach

$After huge data being placed #We tend to use the data not modify it

$Time to read the whole data is more important

3.

Commodity Cluster$No High end Servers

$

Yes, high chance of failure (But HDFS is tolerant enoguh)$Replication is done



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MapReduce -- A Brief

!

Large scale data processing in parallel.

!

MapReduce provides:

$Automatic parallelization and distribution

$

Fault-tolerance

$I/O scheduling

$Status and monitoring

!

Two phases in MapReduce!Map

!Reducehttp://www.excelonlineclasses.co.nr/


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!Map phase

! map (in_key, in_value) -> list(out_key, intermediate_value)

! Processes input key/value pair

!

Produces set of intermediate pairs

!Reduce Phase

! reduce (out_key, list(intermediate_value)) -> list(out_value)

! Combines all intermediate values for a particular key

!

Produces a set of merged output values (usually just one)



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Hadoop Cluster



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Hadoop Ecosystems


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Version of Hadoop

!

We will deal with either of

!Apache hadoop-0.20

!Cloudera hadoop - cdh3



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Pre-Requisites

!

Core-Java

!Acquaintance with LINUX will help.

! For better experience :- Linux installation on your

machines.



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Thank you %!Your feedback is highly important to improve our

course material and teaching methodologies.

! Please email your suggestions to

[email protected]@outlook.com



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