Hadoop Architecture – Meaning, Components, and More

Hadoop, a powerful framework for big data processing, was developed as an open-source project by Doug Cutting and Mike Cafarella in 2004. It was modeled after Google’s MapReduce and Google File System (GFS). The objective was to create a distributed system to process and store enormous amounts of data effectively.

Hadoop architecture in big data has evolved from its initial origins to become a robust and flexible framework for processing and storing large datasets. With the introduction of components like HDFS, MapReduce, YARN, and an expanding ecosystem, Hadoop continues to empower organizations in harnessing the potential of big data. In this blog, we will explore the meaning of Hadoop architecture, its components, and much more.

What is Hadoop Architecture?

Hadoop is an Apache open-source platform written in Java. It extraordinarily stores, processes, and analyzes large volumes of data. By providing a scalable and distributed architecture for storing and analyzing large datasets, Hadoop transformed the way big data processing is done. 

The core of Hadoop comprises HDFS, YARN, and MapReduce, which allow for dependable storage, effective resource management, and simultaneous processing of large amounts of data. You can learn more about managing and modifying large datasets through this comprehensive SQL course.

As a versatile and scalable framework for big data processing, Hadoop finds applications in various industries, such as Media, E-Commerce, Financial Services, Healthcare, etc. All of this makes it an amazing framework to learn and grow for students or employees in their respective fields.

Components of Hadoop

There are four components in Hadoop that work together to enable the storage, processing, and analysis of large datasets. Let’s explore these components in a simplified manner to understand the architecture of Hadoop better.

1. Hadoop Distributed File System (HDFS)

Hadoop Distributed File System (HDFS) is a core component of the Hadoop ecosystem. It is designed to store and manage large volumes of data across a cluster of computers. 

In order to provide fault tolerance, scalability, and high data access, HDFS divides big files into smaller blocks and distributes them over numerous machines. Let’s explore the main features of HDFS in more detail. 

A master/slave architecture is present in HDFS. In this architecture, a single NameNode serves as the master, and numerous DataNodes serve as the slaves.

i. NameNode

The NameNode is a critical component of HDFS. It acts as the file system’s central coordinator and contains metadata about files and directories. It oversees the replication of the data blocks, maintains track of their locations, and responds to client requests for data access.

ii. DataNode

The actual data within HDFS is stored on and served by DataNodes. When performing read and write operations on data blocks, they communicate with the NameNode to report on their status. To ensure data stability and fault tolerance, DataNodes also manages data replication.

iii. Data Classification

Blocks, which are typically 128 megabytes in size by default (but adjustable), are how HDFS organizes data. The Hadoop cluster’s large files are broken up into smaller chunks and distributed among several servers. To maintain data locality, the blocks are stored on the local file systems of the DataNodes (cluster machines).

2. Yet Another Resource Negotiator (YARN)

YARN, which stands for Yet Another Resource Negotiator, is a crucial component of the Apache Hadoop architecture. For Hadoop clusters, it serves as the basis for resource management and job scheduling. 

On the same Hadoop cluster, YARN enables different data processing engines, including MapReduce, Apache Spark, and Apache Flink, to operate concurrently. 

Different frameworks can coexist and effectively share cluster resources thanks to YARN’s decoupling of resource management from the processing frameworks (such as MapReduce, Spark, etc.). It ensures that each program or job operating on the cluster has access to the resources needed to finish its work. 

YARN comprises the following key components:

i. Resource Manager

The Resource Manager is the heart of YARN. It controls how cluster resources are distributed across various applications. Applications submit requests for resources, which it then negotiates, allots, and monitors resource utilization. According to the needs of the application, the resource manager makes sure that the resources are used as efficiently as possible.

ii. Node Manager

Node Managers are in charge of controlling resources on specific cluster nodes by running on those nodes individually. They follow the Resource Manager’s orders and control how tasks are carried out on their particular nodes. Node managers launch and control containers to carry out application task execution, manage resources, and keep an eye on the node’s overall health.

How Does the YARN Application Work?

The YARN application works in the following steps:

• Application Submission: Users submit their applications using the yarn command. 
• Resource Negotiation: YARN’s ResourceManager receives the application submission and negotiates resources with the NodeManagers.
• Application Execution: Once the ResourceManager and NodeManagers agree on the resource allocation, the application is launched on the allocated containers.
• Application Monitoring and Management: It collects information about running applications and provides a web-based ApplicationMaster interface for tracking their status and logs.
• Resource Deallocation: When an application completes or is terminated, YARN releases the allocated resources, making them available for other applications.

3. MapReduce

Hadoop’s MapReduce processing engine and programming model are utilized for the parallel processing and analysis of huge datasets. The map phase and the reduction phase are the two main components. 

Data is broken into smaller pieces, analyzed separately, and then turned into key-value pairs during the map phase. The processed data is combined and condensed to create the final output during the reduction phase. 

It consists of the following crucial steps:

i. Map

The “map” phase is the very first step. The input data is broken up into smaller pieces and processed individually by a cluster of computers. To the allotted data chunk, each computer uses a “map function” to produce intermediate key-value pairs. For instance, the map function would take a document, separate it into words, and give each word a value of 1 if we counted the appearance of words in a collection of documents.

ii. Shuffle and Sort

The framework automatically sorts and shuffles the intermediate key-value pairs based on their keys after the map phase. This is done to assemble all the values linked to the same key. This process ensures that all the data with the same key is collected and prepared for the following stage.

iii. Reduce

The “reduce” phase is the last stage. In this step, the framework uses the sorted key-value pairs to apply a “reduce function” to every set of related values that share a key. The values are processed by the reduction function, generating the result. The reduction function would take the list of counts for each word in the word count example and add them together to get the total count for each word.

4. Hadoop Common

The Hadoop ecosystem can work properly because of Hadoop Common’s provision of the basic infrastructure and elements. It comprises YARN for resource management, utilities and libraries for management and development chores, common interoperability interfaces, and data protection security features. 

It also includes HDFS for distributed storage. Together, these parts make it possible to handle and analyze enormous amounts of data in a distributed computing setting.

Conclusion

Understanding the Hadoop architecture and its components is crucial for harnessing the power of this distributed data processing framework. The core of Hadoop comprises HDFS, YARN, and MapReduce, which allow for reliable storage, effective resource management, and simultaneous processing of large amounts of data. Organizations may harness the potential of their data and acquire priceless insights that drive corporate success by utilizing these components correctly.