Hadoop History and General
Apache Hadoop is an open-source software framework for distributed storage and distributed
processing of very large data sets on computer clusters built from commodity hardware.
All the modules in Hadoop are designed with a fundamental assumption that hardware failures
are common and should be automatically handled by the framework.
Hadoop Main Components
Hadoop consists of MapReduce, the Hadoop distributed file system (HDFS) and a number of
related projects such as Apache Hive, HBase and Zookeeper. MapReduce and Hadoop
distributed file system (HDFS) are the main component of Hadoop.
Hadoop framework includes following four modules:
Hadoop Common: These are Java libraries and utilities required by other Hadoop
modules. These libraries provides filesystem and OS level abstractions and contains the
necessary Java files and scripts required to start Hadoop.
Hadoop YARN: This is a framework for job scheduling and cluster resource management.
Hadoop Distributed File System (HDFS™): A distributed file system that provides high-
throughput access to application data.
Hadoop MapReduce: This is YARN-based system for parallel processing of large data
Normally any set of loosely connected or tightly connected computers that work together as a
single system is called Cluster. In simple words, a computer cluster used for Hadoop is called
Hadoop cluster is a special type of computational cluster designed for storing and analyzing
vast amount of unstructured data in a distributed computing environment. These clusters run
on low cost commodity computers.
Hadoop clusters are often referred to as "shared nothing" systems because the only thing
that is shared between nodes is the network that connects them.
Large Hadoop Clusters are arranged in several racks. Network traffic between different
nodes in the same rack is much more desirable than network traffic across the racks.
1. Each Task Tracker is responsible to execute and manage the individual tasks assigned by Job Tracker.
2. Task Tracker also handles the data motion between the map and reduce phases.
3. One Prime responsibility of Task Tracker is to constantly communicate with the Job Tracker the status
of the Task.
4. If the JobTracker fails to receive a heartbeat from a TaskTracker within a specified amount of time, it will
assume the TaskTracker has crashed and will resubmit the corresponding tasks to other nodes in the
Hadoop Heart - MapReduce
MapReduce is a programming model which is used to process large data sets in a batch processing
A MapReduce program is composed of
a Map() procedure that performs filtering and sorting (such as sorting students by first name into
queues, one queue for each name)
and a Reduce() procedure that performs a summary operation (such as counting the number of
students in each queue, yielding name frequencies).
Facts about MapReduce
Apache Hadoop Map-Reduce is an open source implementation of Google's Map Reduce Framework.
Although there are so many map-reduce implementation like Dryad from Microsoft, Dicso from Nokia
which have been developed for distributed systems but Hadoop being the most popular among them
offering open source implementation of Map-reduce framework.
Hadoop Map-Reduce framework works on Master/Slave architecture.
Hadoop MapReduce is composed of two components
Job tracker playing the role of master and runs on MasterNode (Namenode)
Task tracker playing the role of slave per data node and runs o
Job Tracker is the one to which client application submit mapreduce programs(jobs).
Job Tracker schedule clients jobs and allocates task to the slave task trackers that are running on
individual worker machines(date nodes).
Job tracker manage overall execution of Map-Reduce job.
Job tracker manages the resources of the cluster
Manage the data nodes i.e. task tracker.
To keep track of the consumed and available resource.
To keep track of already running task, to provide fault-tolerance for task etc.
Hadoop File System was developed using distributed file system design. It is run on
commodity hardware. Unlike other distributed systems, HDFS is highly faulttolerant and
designed using low-cost hardware.
HDFS holds very large amount of data and provides easier access. To store such huge
data, the files are stored across multiple machines. These files are stored in redundant
fashion to rescue the system from possible data losses in case of failure. HDFS also
makes applications available to parallel processing.
Features of HDFS
It is suitable for the distributed storage and processing.
Hadoop provides a command interface to interact with HDFS.
The built-in servers of namenode and datanode help users to easily check the status of
Streaming access to file system data.
HDFS provides file permissions and authentication.
Hadoop - Big Data Solutions
In this approach, an enterprise will have a computer to store and process big data.
Here data will be stored in an RDBMS like Oracle Database, MS SQL Server or DB2 and
sophisticated softwares can be written to interact with the database, process the
required data and present it to the users for analysis purpose.