In today's modern business world, the number of companies turning to Hadoop Big Data solutions is increasing. In order to meet the Big Data problems, they have as well as the client market segments they serve. In addition to the HPCC that is manufactured by LexisNexis Risk Solution, there are also other alternative solutions on the market, such as Qubole, Cassandra, Statwing, Pentaho, Slink, CouchDB, Storm, and so on. These products may be purchased. Then, why is it that Hadoop is so well-liked by all of them? In this section, we will investigate the fundamental industrial-ready properties that give Hadoop its widespread appeal and make it the standard in the industry.

Hadoop is a framework that was designed in Java with some C and Shell Script code. It runs on a group of modest commodity hardware and uses a very basic level of programming to analyze enormous datasets. Doug Cutting and Mike Cafarella were the ones who initially developed it, and the Apache 2.0 License is the one that governs its use at the moment. Now, having experience with Hadoop will be considered a fundamental competency for data scientists and technologies using Big Data. Companies are making significant investments in it, and there is a likelihood that in the future, it will be a sought-after area of expertise. Hadoop 3.x is the most up-to-date version available. Thus, it becomes necessary to understand what is a daemon in Hadoop

The Daemons are the processes that run in the background of the system. The components of Hadoop known as daemons include NameNode, Secondary NameNode, DataNode, JobTracker, and TaskTracker. Each daemon conducts its operations autonomously within its JVM. Hadoop Daemons are a group of Hadoop processes that work together. Hadoop is a platform built on Java; hence, each of these processes is a Java Process.

The components that makeup Apache Hadoop 2 are as follows:

• NameNode

• Resource Manager

• DataNode

• NameNode DataNode

• Node Manager

• Secondary NameNode

Daemons like Namenode, Secondary NameNode, and Resource Manager are operated on the Master System. Daemons like the Node Manager and DataNode are operated on the Slave System.

NameNode

The NameNode Daemon is used over the Master System. The management of all MetaData is the primary responsibility of Namenode. The listing of HDFS (Hadoop Distributed File-System) files is MetaData. It is in fact this very MetaData that is stored in Hadoop clusters as ‘blocks’. Therefore, the DataNode, or more pertinently, the place at which the file block is saved, is indicated in the MetaData. MetaData will record all of the information on the file logs of the transactions which occur in a Hadoop cluster (i.e., the time and date / the entity which read/wrote the Data). Memory is put to use in the process of storing this MetaData.

Features of NameNode

• It does not store any of the included information within a file.

• Because Namenode runs on the Master System, the latter has the greater processing power and memory capacity than the Slave systems.

• It stores information on DataNodes, such as their Block ids and Block Counts, among other things. 

DataNode

The DataNode system is built on Slave-based architecture. As a result of this command, DataNode is always instructed to store the Data. On the slave system, DataNode is a program that reads and writes data in response to a client's request. An extensive memory capacity is necessary for this DataNode since it saves data. The first file contains data, while the second file stores the block's information. HDFS Metadata contains checksums for data. Each Datanode connects to its matching Namenode and performs handshaking during launch. DataNode's namespace ID and software version are validated through handshaking. When a discrepancy is detected, DataNode shuts down automatically.

Secondary NameNode

Backups of the primary NameNode's data are performed every hour on the secondary NameNode. The Hadoop cluster's secondary Namenode will create backups or checkpoints of the Data on an hourly basis and store them in a file called ‘image’. This file will be used if the Hadoop cluster fails or crashes. After this process has taken place, this file is transferred to a separate computer system. A fresh set of MetaData is allotted to the new system. What follows is that with the help of this MetaData, a new Master is logged. Afterward, the cluster is repaired to perform in its desired use case once more.

One of the pros of using a Secondary NameNode is that it offers the aforementioned functionality. It is pertinent to mention that the relevance of this Secondary NameNode has lessened now that Hadoop 2 is available because of its High-Availability and Federation features.

The Primary Features of the Secondary NameNode:

• It compiles the Edit logs and the Fsimage that NameNode generates.

• It repeatedly accesses the Random Access Memory (RAM) of the NameNode and copies the MetaData to the hard disc.

• Because it is responsible for keeping track of checkpoints, the secondary NameNode in an HDFS, is sometimes referred to as the ‘Checkpoint Node’.

Resource Manager

The Resource Manager (Global Master Daemon) is in charge of administering the application resources for the Hadoop Cluster. The functions of the Resource Manager may be broken down primarily into two different parts.

• Application Manager: To host the Application Master, it is the responsibility of an Application Manager to receive requests from clients and then create memory resources on the Slave Systems that make up a Hadoop cluster to fulfill that request.

• Scheduler: The Hadoop cluster apps require the scheduler to get resources, and the scheduler is also used to monitor this application. 

NodeManager

The Node Manager is an application that runs on the Slave System. This system is in charge of managing the memory resource contained within the Node and the Memory Disk. A single instance of the NodeManager Daemon is installed on each Slave node that makes up a Hadoop cluster. The Resource Manager will also get this information when it is sent.

JobTracker: Master Process

A MapReduce Daemon is what JobTracker boils down to being. It is the Ultimate Approach. One JobTracker and an unlimited number of TaskTrackers are allowed for each cluster. JobTracker's principal function is Resource Management, which encompasses tracking TaskTrackers, monitoring their progress, and fault tolerance. This job also involves fault tolerance. The job is generated and carried out on the NameNode by the JobTracker, which then passes it along to the TaskTracker. When a customer sends a job to JobTracker, that job gets broken down into its parts and assigned jobs. After then, the JobTracker decides which jobs should be assigned to each worker node. The process of distributing work to several worker nodes is called "task scheduling." JobTracker is responsible for maintaining a record of the responsibilities that have been assigned to the worker node. Communication between the client and the TaskTracker is handled by the JobTracker, which does so through the utilization of Remote Procedure Calls (RPC). It is possible to think about RPC as a language that processes speak to one another to communicate. In the main memory, JobTracker keeps a record of all jobs and the linked tasks. Memory needs are demanding since they are dependent on the number of jobs and change from one job to the next.

TaskTracker

TaskTracker is a daemon that runs MapReduce. It is a method used by Slaves. Multiple instances of TaskTracker can run simultaneously inside of a cluster. The TaskTracker is accountable for completing every one of the responsibilities delegated to them by the JobTracker. A single TaskTracker can be running beneath each DataNode and SlaveNode. Each TaskTracker has many maps and reduced slots in its database. These vacancies are known as Task slots in the industry. The number of simultaneous Maps and Reduce operations is proportional to the number of available Maps and Reduce slots. The number of tasks that TaskTracker may accept is directly proportional to the number of slots. When a JobTracker needs to schedule a job, it first looks for a free slot in a TaskTracker running on the same server as the DataNode. The DataNode is the location where the data for the task is stored. If the machine is not found, it continues to look in the same rack. 

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Conclusion

In this article, we discussed Daemon in Hadoop. Basically, Daemons in computer terminology is a process that operates in the background. Hadoop contains five such daemons. NameNode, Secondary NameNode, DataNode, JobTracker, and TaskTracker are all components of the NameNode. We discussed the different types of Daemons and their features. Hadoop's File System (HDFS) is its foundational component. HDFS is in charge of storing vast volumes of data on the cluster, while MapReduce is in charge of processing this data. The Master-Slave Model is used extensively in its architecture. A cluster is comprised of thousands of nodes that are all linked to one another in some way. One of the nodes in the cluster is designated as the Master node, and this node is also needed to be referred to as the Head of the cluster. The remaining nodes are referred to as the Slave nodes or the Worker nodes, depending on the context.