Hadoop Cluster

A Hadoop cluster is a networked collection of computers known as nodes that perform these types of linear computations on huge amounts of data. A Hadoop cluster is intended for the distributed storage and analysis of large amounts of various types of data. In this article, we will talk more about Hadoop clusters, their properties, and various types of Hadoop clusters. We will also talk about various components of it along with the best practices associated with Hadoop.

What is the Hadoop Cluster?

Apache Hadoop is an open source, Java-based, software framework with a data parallel processing engine. It is possible to split down huge analytics handling activities into small activities that may be required to carry out concurrently by utilizing an algorithm like MapReduce and then distributing the tasks easily across a Hadoop cluster. A Hadoop cluster is a collection of linked computers. They are used to perform parallel computation on huge amounts of data.
Unlike traditional computers, clusters in Hadoop are designed to store and process massive amounts of organized as well as unstructured data in a distributed computing environment. Hadoop ecosystems differ from various other clusters in terms of their own architecture and structure. Networks of worker and master nodes make up Hadoop clusters, which coordinate and carry out the numerous tasks across the distributed system of Hadoop. The name node, secondary name node, and job tracker are three components of the master nodes that often run on distinct pieces of higher-end hardware.

Additionally, a Hadoop cluster is made up of a variety of commodity hardware. Together, these hardware parts function as a single system. The Name node, Resource Manager, and Node Manager all function as Masters and Slaves in the cluster of Hadoop, which consists of numerous nodes (including computers and servers). A single Hadoop cluster's master nodes serve as a guide for the slave nodes. We create Hadoop clusters to store, analyze, comprehend, and discover the details buried in data or datasets that hold important information. The Hadoop cluster processes and stores a variety of data kinds.

  • Structured Data: Data that is properly organized, such as MySQL.
  • Semi-Structured Data: Data that is structured but does not have a specific data type, such as XML or JSON
  • Unstructured data: Data without any organizational structure, such as audio

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Hadoop cluster properties

Let us see various properties of Hadoop clusters:


These are particularly capable of increasing and decreasing the node numbers, like generic hardware or servers.


This is one of the most key traits of a Hadoop cluster. According to this feature, the Hadoop cluster I know manages any type of data, regardless of the type or structure. This feature enables Hadoop to handle any type of data via other online browser platforms.


Because the data is dispersed across the cluster, as well as due to its data mapping abilities (i.e. the MapReduce design, which uses the Master-Slave phenomenon), Hadoop clusters are particularly effective at working at a very quick speed.

No Loss of Data:

Because a Hadoop cluster can imitate data on some other node, there is zero chance that a node can encounter data loss. As a result, even if a node fails, no data is lost since a backup of that data is managed to keep on file.


The distributed storage strategy used by Hadoop clusters, where the data is dispersed among all of the cluster's nodes, makes them extremely cost-effective. In order to expand storage, we would only have to install one more piece of inexpensive hardware storage.

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Types of Hadoop clusters

There are two types of Hadoop clusters which are Single Node Hadoop Cluster and Multiple Node Hadoop Cluster. Let us talk about them in detail:

  1. Single Node Cluster: All of our Hadoop Daemons, including Name Node, Secondary Name Node, Data Node, Node Manager, and Resource Manager runs within the same platform or machine in a Single Node Hadoop Cluster, as the name suggests, as there is only one node in the cluster. Additionally, it means that a single JVM Process Instance manages all of our processes.
  2. Multiple Node Cluster: Hadoop clusters with various nodes, as the name implies, have multiple nodes. All Hadoop Daemons will be stored on different nodes all through the cluster in a specific type of cluster configuration. In a multi-node Hadoop cluster, we frequently aim to use our quicker response nodes for the Master, like name node, Resource Manager, and the less expensive platform.

Components of a Hadoop Cluster

Master Node:

In a Hadoop cluster, the master node is in charge of keeping data into HDFS and then doing parallel MapReduce computations on the data. NameNode, Secondary NameNode, and JobTracker are the 3 nodes that makeup Master Node. MapReduce-based parallel data processing is tracked by JobTracker, while HDFS-based data storage is handled by NameNode. The metadata on files, like the bandwidth of a file, the user who is now accessing it, and which files are stored in which Hadoop cluster are all kept care of by NameNode. The duplicate of the NameNode data is kept on the secondary NameNode.

Worker Node:

This part of the Hadoop cluster is in charge of both data storage and computation. To connect with the Master node in the cluster, each slave/worker node in the cluster runs both DataNode and TaskTrackerservice. The service of TaskTracker is subordinate to the JobTracker, while the DataNode service is subordinate to the NameNode.

Client Node:

The client node is in charge of uploading all data into the Hadoop cluster because it has installed Hadoop and has all the necessary cluster configuration settings. Client nodes submit MapReduce tasks that specify how data should be processed, and when the task processing is complete, the client nodes obtain the output.

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Best Practices for Building a Hadoop Cluster

Choosing the Right Hardware for a Hadoop Cluster:

Many businesses face difficulties while building up their Hadoop infrastructure because they are unsure of the best configuration to utilize and the kind of computers they should buy in order to set up an optimal Hadoop environment. Choosing the right hardware for the Hadoop cluster is the main issue that users face. Hadoop operates on hardware that is industry standard, however, there isn't a perfect cluster setup like giving a list of system requirements to build up a cluster of Hadoop. For a given workload, the hardware selected for the Hadoop cluster configuration should offer the ideal compromise between performance and economy.
In order to completely optimize it after careful testing and validation, choosing the best power for a Hadoop cluster needs a full understanding of both the I/O bound as well as the CPU bound workloads). The number of machines or their hardware requirements relies on things like -

  • Quantity of the Data
  • The kind of workload that must be handled (CPU-driven or Use-Case/IO-bound)
  • Data storage techniques (Data compression, data container)
  • policy on data retention
Sizing a Hadoop Cluster

In sizing the Hadoop cluster, the amount of data that the Hadoop users would process should be taken into account. Knowing the amount of data to be processed can assist determine the number of nodes or machines that will be needed to handle the data effectively and how much RAM each machine would need. The ideal method for sizing a Hadoop cluster is to do it according to the required amount of storage. More CPU resources are added to the additional storage capacity each when a new node is introduced to the Hadoop cluster.

Configuring a Hadoop Cluster

The proper configuration of a Hadoop cluster is necessary to get the best performance out of it. Finding the best setup for a Hadoop cluster is challenging, though. The framework of Hadoop should be customized for both the task and the cluster on which it is being used. Running the Hadoop jobs using the configuration is the best method to establish a baseline before deciding on the cluster's optimum setup. The job record data files can then be examined to determine whether there are any resource weaknesses or whether the time required to run the jobs is longer than anticipated. Repeating the same procedure can assist optimize the setup of the Hadoop cluster so that it best meets the needs of the business.

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Cluster size in Hadoop

A set of parameters known as the size of a Hadoop cluster describes the storage and computing power needed to operate Hadoop workloads, specifically:
The number of nodes, including the number of worker nodes, edge nodes, and master nodes.
Configuration of every type of node, including RAM, disc volume, and the number of CPUs per node.

Hadoop Cluster Architecture

Hadoop clusters are worker networks and a few master nodes that coordinate as well as carry out a lot of tasks from across the Hadoop distributed system. The node name, 2nd node name, as well as the job tracker are 3 master node components that frequently run on separate pieces of relatively high hardware. The real work to store and process the jobs is instructed by master nodes is performed by the workers, which are virtual machines running both the Services like TaskTracker and DataNode on common hardware. The Client Nodes, the last component of the system, are in charge of loading the information and obtaining the results.

Hadoop Cluster Architecture

  1. Master nodes are in charge of managing crucial processes like using MapReduce to do parallel processing on the information and storing data in HDFS.
  2. A Hadoop cluster's worker nodes, which make up the majority of virtual machines, are responsible for storing data and conducting calculations. The services of DataNode and TaskTracker, being used to get the commands from the master nodes, are operated by each worker node.
  3. Data loading into the cluster is the responsibility of the client nodes. Client nodes initially submit MapReduce tasks outlining the data processing to be done, then once that processing is complete, get the results.


"Yet Another Resource Negotiator" is abbreviated as YARN. It was added in Hadoop 2.0 to alleviate the gap on Job Tracker that existed in Hadoop 1.0. YARN was initially marketed as a "Redesigned Resource Manager," but it has since evolved into a large-scale distributed system that is used for the processing of Big Data.


Let us see various advantages of YARN:


The scheduling algorithm in the YARN architecture's Resource manager enables Hadoop to broaden as well as enhance the management of thousands of clusters and nodes.


Because YARN supports existing map-reduce application forms without interruption, it is also compatible with Hadoop 1.0.

Cluster Utilization:

Because YARN supports dynamic cluster utilization in Hadoop, optimized Cluster Utilization is possible.


It provides organizations with the benefit of multi-tenancy by allowing multiple engine access.

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Setting up a Hadoop Cluster:

We must ensure that we have all the necessary components before we can begin building up our Hadoop Cluster.

These are the prerequisites:

  • CentOS
  • Java
  • Hadoop

You can consult the blog on Hadoop installation if you haven't yet installed Hadoop. For setting up the Hadoop Cluster using 1 Master and 2 Slaves, please refer to the procedures below.

  • Step 1: Download and install VM Workstation 15 on your host computer.
  • Step 2: Look through your system files and choose the CentOS virtual machine that already exists on your host machine.
  • Step 3: Accept the conditions and launch your virtual Linux OS after doing so.
  • Step 4: The Slave Machines should be set up using the same procedure. Your Workstation interface will appear once the Virtual OS has loaded.
  • Step 5: Open a fresh terminal on each machine, start the Master and then all of the Slaves, and then look up each machine's IP address. For checking the IP address, enter the following code into your browser.
  • Step 6: The very next step would be to configure your machines as Master as well as Slaves once you have determined their IP addresses.
  • Step 7: Let's start the local host, all the daemons, and the master and slaves when they have been configured for checking the HDFS user interface.

You must launch the terminal from the sbin folder in order to start all of the daemons.

Use the command: start-all.sh for launching each daemon after the terminal has been opened in the sbin folder.

You can now simply ping the master and slave to view the communication.

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Let us have a look at the advantages of the Hadoop cluster:

  1. Given its capacity to divide huge computing jobs into smaller jobs that can be performed in a parallel, distributed fashion, Hadoop clusters can increase the processing capability of several big data activities.
  2. When faced with growing data blocks, Hadoop clusters are easy to scale and may quickly add nodes to boost throughput and maintain processing capability.
  3. Hadoop clusters are very simple and affordable to set up and operate because of the usage of low costing, and high-availability commodity technology.
  4. A data set is replicated throughout the distributed system by a Hadoop cluster, making it resistant to loss of data and even cluster failure.
  5. Using data from numerous diverse source systems and file formats is made possible by Hadoop clusters.
  6. For testing reasons, Hadoop can be set up as a single-node installation.


Let us have a look at the disadvantages of the Hadoop cluster:

  1. Hadoop has trouble handling high numbers of little files that are smaller than the usual Hadoop block of either size 128MB or 256MB. Big data was not intended to be supported by it in a scalable manner. Hadoop performs well if there are few but many huge files. The Namenode, storing the namespace of the system, becomes overloaded when the number of smaller files increases.
  2. High processing costs - Hadoop reading and writing operations, particularly when processing massive amounts of data, can become highly expensive very quickly. It all boils down to Hadoop's incapability to process data in-memory; as a result, data is read from and written to the disc.
  3. Hadoop is designed for tiny volumes of huge files being processed in batches; mainly batch processing can be supported. This is related to the manner in which data must be gathered and stored prior to processing. In the end, this means that real-time, low-latency processing for data streams is not enabled.
  4. Hadoop's data flow topology is built up in sequential stages, making it unable to perform iterative analysis or use for machine learning.


This article is about Hadoop clusters. A Hadoop cluster is a group of connected computers, or "nodes," that are used to carry out these sorts of parallel operations on massive amounts of data. We have discussed various components, features, and properties of Hadoop clusters along with their advantages and disadvantages. We have also talked about the architecture of the Hadoop cluster and the various steps required to install it.

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One system serves as the master and runs the NameNode daemon in a multi-node cluster arrangement, while the other machines serve as slave or worker nodes and run other Hadoop daemons.

The 3 main components of the Hadoop cluster are HDFS, MapReduce, and YARN.

It is used to carry out these sorts of parallel operations on massive amounts of data

Facebook is the world's largest Hadoop cluster

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