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What is Hadoop and How does Hadoop work?

Hadoop is an Apache open source framework written in Java that allows for the distributed processing of large data sets across clusters of computers using simple programming models.

In this article, we will look at Hadoop in detail and will try to answer all your questions with regards to this most popular framework.

Let’s start.


What is Hadoop?

Hadoop is a framework that lets you distribute work across a large cluster of machines.

Hadoop tasks such as the indexing and searching of data can be partitioned and run in parallel on many networked computers, which brings great scalability enabled by the use of clusters. And if one node fails, it does not bring down your entire system.

In the event of a failure, Hadoop can continue operating while some nodes recover from the failure.

Hadoop was developed by Doug Cutting and Mike Cafarella in 2005 while they were both working at Yahoo!

The name “Hadoop” is based on a toy elephant belonging to Cutting’s son, who named it after the child’s toy elephant.

Hadoop implements the MapReduce paradigm, decomposing a problem into many small pieces that can be solved by massively parallel processing.

This approach allows for very rapid development of distributed datacenters, because tasks are easily divided up between machines belonging to different departments or which otherwise have not been carefully coordinated. Hadoop can work with many different input/output formats such as JSON, Avro and XLS.

What is Hadoop used for?

Hadoop is open-source software that runs on Linux, UNIX, Windows, and Mac OS.

Hadoop 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.

Rather than relying on hardware to deliver high aggregate throughput, Hadoop delivers high aggregate throughput by spreading work across more nodes.

How does Hadoop work?

Hadoop is a library that provides common functions for running applications on clusters of nodes, where computation takes place in both the map and reduce stages.

Hadoop manages input splits by distributing them across nodes in the cluster according to an input format’s schema so each node becomes responsible for only one file at a time.

Splits are divided into blocks which are then sorted by key before being sent to multiple reducers where they are scanned for their primary sort key before being subdivided again into new blocks to produce output records having the same order as their inputs.

When there are more reducers than splits, additional maps will be started.

Hadoop framework can be divided into two main components, which are:

1). Hadoop Distributed File System (HDFS)

It is designed to run on commodity hardware. HDFS provides very high aggregate bandwidth across the cluster and uses very efficient data transfer protocols like the local discovery protocol which enables discovery at the file level, and the remote read/write protocol which enables reading and writing data blocks to arbitrary machines.

It is horizontally scalable as adding new machines can increase the total available storage capacity and computation capacity.

HDFS handles reliability by detecting failures and creating replicas of blocks such that no more than one replica exist if there is a failure. This ensures that the failure of any given machine will not compromise data access.

2). Hadoop MapReduce

It is a programming model that provides an implementation of the map and reduce functions.

The input to MapReduce is typically larger than memory, so this format enables distributed processing across many nodes in computers by dividing tasks into independent chunks, which are executed in parallel by worker nodes, and then sorting the output to get a complete picture.

It is designed to distribute the processing for massive datasets across many machines.

Developers around the world have been deploying both HDFS and MapReduce frameworks in production on thousands of nodes over clusters of tens to hundreds of thousands of cores.

What is cluster in Hadoop?

A Hadoop cluster is a group of computers that work together to analyze and process data.

Hadoop compute nodes – the machines that process data in a Hadoop cluster are often dedicated servers, but they don’t have to be.

In fact, you can get more performance out of your Hadoop cluster if its computing nodes also perform other functions.

Every Hadoop installation is unique and takes into account many factors when creating a Hadoop compute cluster.

However, in most cases the data is in HDFS storage and must be moved to local disk before it can be processed by MapReduce.

What is HDFS in Hadoop?

HDFS is the acronym for the Hadoop Distributed File System. A Hadoop cluster is composed of datanodes and one or more namenodes. The datanodes store data across multiple servers. The name node is responsible for managing the file system namespace, replication of files, and serving file systems metadata to clients.

What is Hadoop and Spark?

Hadoop is a framework that supports the distributed processing of large datasets.

Spark is a light-weight cluster computing system that was originally developed at UC Berkeley’s AMPLab to provide high-level abstraction for data analysis and transformation in MapReduce and later grew into a project with its own dataset APIs, higher-level tools and larger ecosystem.

What is YARN Hadoop?

YARN is a scheduler for large compute clusters that can work on both map-reduce and data-intensive computations.

YARN is an important computing framework used in the Hadoop ecosystem.
YARN is a part of the MapReduce family.

In 2012, Hadoop added a new component called YARN (Yet Another Resource Negotiator), which enables Hadoop to manage multiple disparate workloads in the same cluster simultaneously.

This gives users much more flexibility in working with various data types and formats without having to set up multiple clusters.

What is Hadoop Hue?

Hadoop Hue is a visualization and monitoring tool for Hadoop.

It has been designed to provide the real-time visualization of clusters and allows for the monitoring of cluster health, describing metrics and performance counters, and managing alerts for clusters.

Hue includes a set of tasks that can be used to monitor MapReduce, Hadoop and HDFS activities. For example, a user can visualize data-flows between two nodes using the “traceroute” command over a cluster.

It also provides visualization diagrams for various components such as job history, storage subsystems or YARN.

This software is intended to be used by system administrators and users who want to monitor the Hadoop cluster performance.

Hue may be used as a monitoring console, but its goal is not to replace existing enterprise-level tools (such as Nagios).

Hue has three main components: Search, Clients and UI.

The Search component is responsible for crawling the cluster and indexing all information. The Clients are in charge of the interfaces that provide the user access to Hue functionality. Finally, the UI provides a way to visualize information in diagrams.

What is Apache Flume?

Apache Flume provides the ability to reliably move all your log data in real time into HDFS or Hadoop Distributed File System (HDFS) without having to write custom code.

It can also be used with other systems like Kafka and Spark Streaming for advanced analytics.

What is Big Data Hadoop?

Big data is a term used to describe the large amounts of data generated from social media, internet searches, and sensors.

The Hadoop framework lets you work with big data by taking a distributed computing approach that breaks up data into smaller pieces and processes it in parallel or serially to increase efficiency.

When big data first emerged, the only way to manage it was by using expensive supercomputers with high-speed interconnectivity.

Today, this is no longer necessary. Using Hadoop, you can use thousands of cheap commodity computers working together on a large scale to process big data.

What is Hive Hadoop?

Hive Hadoop is a data warehouse and analytics platform that is used in the processing and analyzing of large, unstructured datasets.

The platform provides an easier way for organizations to collect, transform and analyze datasets using SQL-like queries.

Hive Hadoop can be deployed on premise or in the cloud which provides greater flexibility and scalability. Additionally, HDP offers a native MapReduce interface that makes it easy to move data between disparate storage systems.

Hive Hadoop provides ad-hoc querying, real-time query processing and flexible data analysis. The platform also takes advantage of the existing investment in today’s state-of-the-art hardware; whether commodity servers or high performance computing systems.

It is designed to allow users to write queries in the language of their choice, without having to be concerned about how data is stored or managed under the hood.

Hive Hadoop is recommended for users who need to share resources with other teams, collaborate on workflows and provide access to user’s data across a broad spectrum of applications.

Hive is also known for its ability to integrate with existing enterprise data warehouses.

Who uses Hadoop?

Some of the top companies that use Hadoop are: Uber, Airbnb, Netflix, Pinterest, Shopify, Spotify, Twitter, and Slack etc.

What is a slave node in Hadoop?

The slave node in Hadoop is an important part of the distributed file system.

This node is not the same as the master node in Hadoop. The slave nodes in Hadoop are just like any other node in a computer system.

It does not execute any special operations and cannot provide input or output. The slave nodes in Hadoop do, however, provide computational power to the system and help perform tasks such as retrieving data from disks and aggregating them, and transferring data between nodes in the cluster.

How to learn Hadoop?

You can read tutorial on Hadoop online. You can take an online training from Coursera, edX, Udacity, or Udemy. If you have access to a local university with a course that covers Hadoop, that might be the best way to go.

How data is stored in Hadoop?

Data is stored within Hadoop by subjecting it to the MapReduce algorithm.

The MapReduce algorithm breaks up data into smaller tasks and distributes them across various parts of the system.

This will allow for multiple jobs to be processed at once, leading to an improved performance rate. Hadoop uses its own programming language for this process, which is called Pig.

Pig adds a layer on top of MapReduce that allows for easier data manipulation and organization.

What is Hadoop Ecosystem?

The Hadoop ecosystem is a vast and complex network of software and systems that all work together to provide a novel and powerful environment for data storage and analysis.

What is Sqoop Hadoop?

The Sqoop Hadoop is the program that processes data.

It transfers data to or from HDFS. It can also transfer data to the external RDBMS system. It automatically determines which map parameters to use when transferring data.


So this blog post covered lots of aspects about Hadoop.

We will keep adding more useful points in this article in future.

See you next time!


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