THE SOLUTION FOR BIG DATA

NAME:SIVAKOTI TARAKA SATYA PHANINDRAROLL NO:15K81D5824COURSE: CSE M.TECH/SEM-1

HADOOP

CONTENT:

Data – Trends in storing data. BigData – Problems in IT industry Why BigData ? Introduction to HADOOP HDFS (Hadoop Distributed File System)  MapReduce  Prominent users of Hadoop. Conclusion 

Data – Trends in storing data What is data--- Any real world symbol (character, numeric, special character) or a of group of them is said to be data it may be of the visual or audio or scriptural , images, etc .,

 File system

Databases 

Cloud (internet) 

BIG DATA:

What is big data—In IT, it is a collection of data sets so large and complex data that it becomes difficult to process using on-hand database management tools or traditional data processing applications.

As of 2016, limits on the size of data sets that are feasible to process in reasonable time were on the order of Exabyte of data. (KBs MBs GBs TBs PB ZB )

 BIGDATA and problems with it.  Daily about 0.8 Petabytes of updates are being made

into FACEBOOK including 50 millions photos.

Daily, YOUTUBE is loaded with videos that can be watched for one year

continuously

Limitations are encountered due to large data sets in many areas, including

meteorology, genomics, complex physics simulations, and biological and

environmental research. 

Also affect Internet search, finance and business informatics. 

The challenges include in capture, retrieval, storage, search, sharing, analysis,

and visualization.

 

Why BIG DATA ?

Unstructured DATA growth !

THEN WHAT COULD BE THE SOLUTION FOR BIGDATA  ?

Hadoop’s Developers: 2005: Doug Cutting and  Michael J. Cafarella developed Hadoop to

support distribution for the Nutch search engine project.

The project was funded by Yahoo.

2006: Yahoo gave the project to Apache Software Foundation.

Doug Cutting

What is Hadoop? It is a open source software written in java Hadoop software library is a framework that allows for

the distributed processing of large data sets across clusters of computers using simple programming models.

It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

• Apache top level project, open-source implementation of frameworks for reliable, scalable, distributed computing and data storage.

• It is a flexible and highly-available architecture for large scale computation and data processing on a network of commodity hardware.

The project includes these modules:

Hadoop Common

Hadoop Distributed File System(HDFS)

Hadoop MapReduce

1.Hadoop Commons 

It provides access to the filesystems supported by Hadoop. 

The Hadoop Common package contains the necessary JAR files and scripts needed to start Hadoop.

 The package also provides source code, documentation, and a contribution section which includes projects from the Hadoop Community (Avro, Cassandra, Chukwa, Hbase, Hive, Mahout, Pig, ZooKeeper) 

2. Hadoop Distributed File System (HDFS):

Hadoop uses HDFS, a distributed file system based on GFS

(Google File System), as its shared filesystem.

HDFS architecture divides files into large chunks (~64MB)

distributed across data servers (this is configurable).

It has a namenode and datanodes

What does a HDFS contain

HDFS consists of a global namenodes or namespaces and they

are federated.

The datanodes are used as common storage for blocks by all the

Namenodes. 

Each datanode registers with all the Namenodes in the cluster.

Datanodes send periodic heartbeats and block reports and

handles commands from the Namenodes

Structure of Hadoop system: Master Node :

Name NodeSecondary Name Node

Job Tracker

Slaves :

Data

Node

Task

Tracker

MASTER NODE:

Master node Keeps track of namespace and metadata about items Keeps track of MapReduce jobs in the system Hadoop currently configured with centurion064 as the

master node Hadoop is locally installed in each system. Installed location is in /localtmp/hadoop/hadoop-

0.15.3

SLAVE NODES :

Slave nodes Manage blocks of data sent from master node

In common, these are the chunkservers

Currently centurion060, centurion064 are the two slave nodes being used.

Slave nodes store their data in /localtmp/hadoop/hadoop-dfs (this is automatically created by the DFS)

Once you use the DFS, relative paths are from /usr/{your usr id}

Advantages and Limitations of HDFS : Reduce traffic on job scheduling. File access can be achieved through the native Java or

language of the users' choice (C++, Java, Python, PHP, Ruby, Erlang, Perl, Haskell, C#, Cocoa, Smalltalk, and OCaml),

It cannot be directly mounted by an existing operating system.

It should be provided with UNIX or LUNIX system.

3.Hadoop MAPREDUCE SYSTEM:

The Hadoop MapReduce framework harnesses a cluster of machines and executes user defined MapReduce jobs across the nodes in the cluster.

A MapReduce computation has two phases a map phase and a reduce phase.

MAP AND REDUCE METHODS USAGE…

Map function

Reduce function

Run this program as aMapReduce job

WORD COUNT OVER A GIVEN SET OF STRINGS

We love India

We Play Tennis

We1

love1

India 1

We1

Play1

Tennis 1

Love1

India1

We

2Tennis

1Play

1

Map Reduce

MAPREDUCE IN WITH NO REDUCE TASKS

MAPREDUCE WITH TWO REDUCE TASKS - AUTOMATIC PARALLEL EXECUTION IN MAPREDUCE

Shuffle and sort in MapReduce with multiple reduce tasks

Prominent users of HADOOP

Amazon – 100 nodes Facebook – two clusters of 8000 and 3000 nodes Adobe – 80 node system EBay – 532 node cluster yahoo – cluster of about 4500 nodes IIIT Hyderabad – 30 node cluster

Trending :Hadoop Job’s

Salaries Tend in Hadoop:

Achievements : 2008 - Hadoop Wins Terabyte Sort Benchmark (sorted 1 terabyte

of data in 209 seconds, compared to previous record of 297 seconds)

2009 - Avro and Chukwa became new members of Hadoop Framework family

2010 - Hadoop's Hbase, Hive and Pig subprojects completed, adding more computational power to Hadoop framework

2011 - ZooKeeper Completed March 2011 - Apache Hadoop takes top prize at Media Guardian

Innovation Award

2013 - Hadoop 1.1.2 and Hadoop 2.0.3 alpha. - Ambari, Cassandra, Mahout have been added

Conclusion:It reduce traffic on capture, storage, search, sharing, analysis,

and visualization.

A huge amount of data could be stored and large computations could be done in a single compound with full safety and security at cheap cost.

BIGDATA and BIGDATA-SOLUTIONS is one of the burning issues in the present IT industry so, work on those will surely make you more useful to that.