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Big data Analysis in R using Hadoop

HAMS Technologieswww.hams.co.indirector@hams.co.inpriyank@hams.co.invivek@hams.co.in

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» Data size is continuously increasing due to ˃ Multiple source of data˃ Easy and fast availability of data sources

» R is widely known and accepted language in field of statistical analysis and prediction techniques. But performing an analysis over Big data is always a challenging task.

» MapReduce is a mechanism to handle large volume of data. Hadoop is one of open source MapReduce implementation provided by Apache.

» R and Hadoop can be used together with the help of a package called RHadoop.

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Before moving further, We can take a look of MapReduce architecture and Hadoop implementation..

General flow in MapReduce architecture

1. Create a clustered network2. Load the data into cluster using Map (mapper task)3. Fetch the processing data with help of Map (mapper task)4. Aggregate the result with Reducer ( Reducer task)

Local Data Local Data Local Data

Partial Result-1

Partial Result-2

Partial Result-3

Map Map Map

Reduce Aggregated Result

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General attributes of in MapReduce architecture

1. Distributed file system (DFS)2. Data locality3. Data redundancy for fault tolerance 4. Map tasks applied to partitioned data it scheduled so that input blocks are

on same machine5. Reducer tasks applied to process data partitioned by MAP task

Local Data Local Data Local Data

Partial Result-1

Partial Result-2

Partial Result-3

Map Map Map

Reduce Aggregated Result

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Hadoop is an open source implementation of MapReduced architecture maintained by Apache

Hadoop

HDFSHadoop Distributed file system

MapReduceJob trackers

name node/s

Data node/s Job tracker node/s

Data NodeData node/s

Tracker node/s

Data NodeData node/s

Tracker node/s

Data NodeData node/s

Tracker node/s

Master nodes

Slavenodes

Hive(HadoopinteractIVE)

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» Hadoop-streaming allow to create and run MapReducde job as Mapper and/or as Reducer.

» HDFS (Hadoop Distributed File System) is a clustered network used to store data. HDFS contain the script to replicate and track the different data blocks. HDFS write is show below. In same reverse manner we retrieve data from HDFS.

hams.txtBlock-1

Block-2

Block-3 Name Node

Data Node-1

Data node/s

Tracker node/s

Data Node-2

Data node/s

Tracker node/s

Data Node-3

Data node/s

Tracker node/s

Data Node-n

Data node/s

Tracker node/s

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33 3

I am having a file contains 3 blocks.. Where should I write

these? Okey, Write these on data-node 1 ,2

and 3

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» HIVE (Hadoop InteractIVE) support high level function for handling hadoop framework like hive.start(), hive.create().. .etc.

» It provided functions like hive.stream() to support Hadoop-streaming

» DFS functions in R like DFS.put(), DFS.list()…» To start working with HIVE

˃ Download it from https://hadoop/apache.org/core˃ Configure files

> mapred-site.xml – to configure mapReduce> Core-site.xml – to configure basic hadoop> Hdfs-site.xml – for configuration related to HDFS

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» R and Hadoop : a package Rhadoop is available in R-Forge

» In R prompt, ˃ Hadoop package can be loaded as

» Library(‘hive’);˃ To start hadoop

» hive_start()˃ Put the data, list the data

» DFS.put(‘source_data’, ‘/router_list’)» DFS.list(‘/router_list’);

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» Hive stream can be initialized as ˃ hive_stream(mapper = <mapper_function_name>

reducer= <reducer_function_name> input = <routers> output = <routers_out> )

» Other important functions˃ DFS_put_object()˃ DFS_cat()˃ Hive_create()˃ Hive_get_parameter()

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Thank you

Kindly drop us a mail at below mention address for any suggestion and clarification. We like to hear from you

HAMS Technologieswww.hams.co.indirector@hams.co.inpriyank@hams.co.invivek@hams.co.in