Architecture and components of Hadoop: 2017

Hadoop includes three main Components

HDFS:

HDFS means Hadoop Distributed File System and it manages big data sets with high volume.
HDFS stores the data at distributed manner and it is the primary storage system
HDFS allows read and write the files but cannot updated the files in HDFS.
When we move file in HDFS that file are automatically split into small files and that small files are replicated of three different servers.
HDFS are implemented by Master Slave architecture.Master means namenode and Slave means datande.

NameNode:

NameNode is the heart and master oh Hadoop
It maintains the namespace system of hadoop
NameNode stores the metadata of data blocks that data are permanently stored on Local disk
It reduced disk space also.
There are two types of NameNode(active and standby)

Secondary Node:

Main role of secondary namenode is copy and merge the namespace.
Secondary namenode requires huge amount of memory to merge the files.
If namenode failure namespace images are stored in secondary namenode and it can be restart the namenode.

DataNode:

MapReduce:

MapReduce is the software framework and it is used to process large amount of data in Parallel.
There are two tasks in MapReduce.(Click Here to know more details about Map Reduce)

1.Mapper Task:

It takes the one input and divide into small parts and distribute to another node.
To solve all small programs and send the results to master node.

2.Reducer Task:

It combines the all master node results and arranging the results at some formats.

YARN:

YARN means Yet Another Resource Negotiator.
YARN is the resource management responsible for managing resources in cluster and scheduling applications.
It also Known as MapReduce2
Responsible of YARN is managing and monitoring the work loads.
Yarn Having following two components

1.Resource Manager(NameNode)

2.Node Manager(DataNode)

Sunday, 5 March 2017