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Why and What Hadoop ?

A tool to process big data

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.

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 large data sets.

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

Why is the data so BIG ?

So WHAT ?

Even the technology to read has taken a leap.

Why is the data so BIG ?

Year Device VolumeData

Transfer speed

Time to process

1990 Optical Drive 1370 MB 4.4 MB/s 5 minutes

2012 1 TB SATA Drives 1 TB 100 MB/s 2.5 Hrs

How to handle such BIG ?

BIG elephant Numerous small chicken ?

How to handle such BIG ?Concept of Torrents

Reduce time to read by reading it from multiple sources simultaneously.

Imagine if we had 100 drives, each holding one hundredth of the data. Working in parallel, we could read the data in less than two minutes.

How to handle such BIG ? -- Issues

How to handle a system up and downs ?

How to combine the data from all the systems ?

Problem1 : System’s 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 some machines

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.

Problem 2 : How to combine the data ?

Analyze data across different machines , But how do we merge them 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 Map—Reduce

Map ReduceProvides a programming model abstracts the problem of

disk reads and writes transforming in to a computation of keys and values.

Two phases

Map

Reduce

So what is Hadoop ? An operating system ?

Provides

1. A reliable shared storage system

2. Analysis system

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 Google

History of Hadoop

Doug Cutting saw an opportunity and led the charge to develop an open source version of this MapReduce system called Hadoop .

Soon after, Yahoo and others rallied around to support this effort.

Now Hadoop is core part in : Facebook, Yahoo, LinkedIn, Twitter …

History of Hadoop

GFS HDFS

MapReduce MapReduce

HDFS -- A BriefDesign Streaming very large files on commodity cluster.

1. Very Large FilesMBs to PBs

2. Streaming Write once read many approachAfter huge data being placed We tend to use the data not modify itTime to read the whole data is more important

3. Commodity ClusterNo High end ServersYes, high chance of failure (But HDFS is tolerant enoguh)Replication is done

MapReduce -- A BriefLarge scale data processing in parallel.

MapReduce provides:Automatic parallelization and distributionFault-toleranceI/O schedulingStatus and monitoring

Two phases in MapReduceMapReduce

MapReduce -- A Brief

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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MapReduce -- A Brief

Hadoop Cluster

Hadoop Ecosystems

Pre-Requisites

Core-Java

Acquaintance with LINUX will help.

For better experience :- Linux installation on your machines.

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