Critical Study of Hadoop Implementation and Performance Issues

2014 IEEE International Advance Computing Conference (IACC), 2014

Hadoop is an open source cloud computing platform of the Apache Foundation that provides a software programming framework called MapReduce and distributed file system, HDFS. It is a Linux based set of tools that uses commodity hardware, which are relatively inexpensive, to handle, analyze and transform large quantity of data. Hadoop Distributed File System, HDFS, stores huge data set reliably and streams it to user application at high bandwidth and MapReduce is a framework that is used for processing massive data sets in a distributed fashion over a several machines. This paper gives a brief overview of Big Data, Hadoop MapReduce and Hadoop Distributed File System along with its architecture.

Cornell University - arXiv, 2022

In this paper, a technology for massive data storage and computing named Hadoop is surveyed. Hadoop consists of heterogeneous computing devices like regular PCs abstracting away the details of parallel processing and developers can just concentrate on their computational problem. A Hadoop cluster is made of two parts: HDFS and MapReduce. Hadoop cluster uses HDFS for data management. HDFS provides storage for input and output data in MapReduce jobs and is designed with abilities like highfault tolerance, highdistribution capacity and highthroughput. It is also suitable for storing Terabyte or Petabyte data on cluster and it runs on flexible hardware like commodity devices.

Technology Reports of Kansai University, 2020

The last days, the data and internet are become increasingly growing which occurring the problems in big-data. For these problems, there are many software frameworks used to increase the performance of the distributed system. This software is used for available of large data storage. One of the most beneficial software frameworks used to utilize data in distributed systems is Hadoop. This software creates machine clustering and formatting the work between them. The Hadoop consists of two major components which are Hadoop Distributed File System (HDFS) and Map Reduce (MR). By Hadoop, we can process, count and distribute of each word in a large file and know the number of affecting for each of them. In this paper, we will explain what is Hadoop and its architectures, how it works and its performance analysis in a distributed system according to many authors. In addition, assessing each paper and compare with each other.

Hadoop is a software framework that supports data intensive distributed application. Hadoop creates clusters of machine and coordinates the work among them. It include two major component, HDFS (Hadoop Distributed File System) and Map Reduce. HDFS is designed to store large amount of data reliably and provide high availability of data to user application running at client. It creates multiple data blocks and store each of the block redundantly across the pool of servers to enable reliable, extreme rapid computation. Map Reduce is software framework for the analyzing and transforming a very large data set in to desired output. This paper describe introduction of hadoop, types of hadoop, architecture of HDFS and Map Reduce, benefit of HDFS and Map Reduce.

2016

Cloud computing is joined with a new model for supplying of computing infrastructure. Big Data management has been specified as one of the momentous technologies for the next years. This paper shows a comprehensive survey of different approaches of data management applications using MapReduce. The open source framework implementing the MapReduce algorithm is Hadoop. We simulate the different design examples of the MapReduce which stored on the cloud. This paper proposes the application of MapReduce which runs on a huge cluster of machines, in Hadoop framework. The proposed implantation methodology is highly scalable and easy to use for non professional users. The main objective is to improve the performance of the MapReduce data management system in the basis of the Hadoop framework. Simulation result shows the effectiveness of the proposed implementation methodology for the MapReduce.

2021

Apache Hadoop is a collection of open-source software utilities that facilitates using a network of many computers to solve problems involving massive amounts of data and computation. It provides a software framework for distributed storage and processing of big data using the MapReduce programming model. Hadoop was originally designed for computer clusters built from commodity hardware, which is still the common use. It has since also found use on clusters of higher-end hardware. All the modules in Hadoop are designed with a fundamental assumption that hardware failures are common occurrences and should be automatically handled by the framework.

2020

Big Data make conversant with novel technology, skills and processes to your information architecture and the people that operate, design, and utilization them. The big data delineate a holistic information management contrivance that comprise and integrates numerous new types of data and data management together conventional data. The Hadoop is an unlocked source software framework licensed under the Apache Software Foundation, render for supporting data profound applications running on huge grids and clusters, to proffer scalable, credible, and distributed computing. This is invented to scale up from single servers to thousands of machines, every proposition local computation and storage. In this paper, we have endeavored to converse about on the taxonomy for big data and Hadoop technology. Eventually, the big data technologies are necessary in providing more actual analysis, which may leadership to more concrete decision-making consequence in greater operational capacity, cost de...

Semiconductor science and information devices, 2022

The data and internet are highly growing which causes problems in management of the big-data. For these kinds of problems, there are many software frameworks used to increase the performance of the distributed system. This software is used for the availability of large data storage. One of the most beneficial software frameworks used to utilize data in distributed systems is Hadoop. This paper introduces Apache Hadoop architecture, components of Hadoop, their significance in managing vast volumes of data in a distributed system. Hadoop Distributed File System enables the storage of enormous chunks of data over a distributed network. Hadoop Framework maintains fsImage and edits files, which supports the availability and integrity of data. This paper includes cases of Hadoop implementation, such as monitoring weather, processing bioinformatics.

Data is getting bigger and bigger in size that is called as Big Data. Big Data may be structured, unstructured and semi structured. Traditional systems are not good to manage this huge amount of data. So, it is required to use best sources to manage this Big Data. Hadoop is Highly Archived Distributed Object Oriented Programming tool which is an open source software platform. Hadoop is written Java. It is used to store and manage large amount of data. In this paper configuration of Hadoop single node cluster is explained. Hardware and software requirements are also described. Some running commands are also explained for Hadoop. Map Reduce job of Hadoop also presented.

International Journal of Advanced Computer Science and Applications, 2021

Data analysis has become a challenge in recent years as the volume of data generated has become difficult to manage, therefore more hardware and software resources are needed to store and process this huge amount of data. Apache Hadoop is a free framework, widely used thanks to the Hadoop Distributed Files System (HDFS) and its ability to relate to other data processing and analysis components such as MapReduce for processing data, Spark - in-memory Data Processing, Apache Drill - SQL on Hadoop, and many other. In this paper, we analyze the Hadoop framework implementation making a comparative study between Single-node and Multi-node cluster on Hadoop. We will explain in detail the two layers at the base of the Hadoop architecture: HDFS Layer with its deamons NameNode, Secondary NameNode, DataNodes and MapReuce Layer with JobTrackers, TaskTrackers daemons. This work is part of a complex one aiming to perform data processing in Data Lake structures.