Nt1330 Unit 3 Problem Analysis Paper

The cluster software can access data on the disk through two ways, one is asymmetric clustering and the other is parallel clustering.

When a file is written in HDFS, it is divided into fixed size blocks. The client first contacts the NameNode, which get the list of DataNode where actual data can be stored. The data blocks are distributed across the Hadoop cluster. Figure \ref{fig.clusternode} shows the architecture of the Hadoop cluster node used for both computation and storage. The MapReduce engine (running inside a Java virtual machine) executes the user application. When the application reads or writes data, requests are passed through the Hadoop \textit{org.apache.hadoop.fs.FileSystem} class, which provides a standard interface for distributed file systems, including the default HDFS. An HDFS client is then responsible for retrieving data from the distributed file system by contacting a DataNode with the desired block. In the common case, the DataNode is running on the same node, so no external network traffic is necessary. The DataNode, also running inside a Java virtual machine, accesses the data stored on local disk using normal file I/O

This paper proposes backup task mechanism to improve the straggler tasks which are the final set of MapReduce tasks that take unusually longer time to complete. The simplified programming model proposed in this paper opened up the parallel computation field to general purpose programmers. This paper served as the foundation for the open source distributing computing software – Hadoop as well as tackles various common error scenarios that are encountered in a compute cluster and provides fault tolerance solution on a framework

GFS: Google File System is a distributed file system which is developed by Google in order to provide efficient, reliable access to data. . It is designed and implemented inorder to meet the requirements provided by Google’s data processing. The file system consists of hundreds of storage machines to provide inexpensive parts and it is accessed by different client machines. Here the search engine is providing huge amounts data that should be stored. GFS has 1,000 nodes with 300TB disk storage.

0166: However, it is important to state that a MapReduce application can be favored to an MPI implementation only if the data to be processed are enormous.

The Hadoop employs MapReduce paradigm of computing which targets batch-job processing. It does not directly support the real time query execution i.e OLTP. Hadoop can be integrated with Apache Hive that supports HiveQL query language which supports query firing, but still not provide OLTP tasks (such as updates and deletion at row level) and has late response time (in minutes) due to absence of pipeline

Hadoop1 provides a distributed filesystem and a framework for the analysis and transformation of very large data sets using the MapReduce [DG04] paradigm. While the interface to HDFS is patterned after the Unix filesystem, faithfulness to standards was sacrificed in favor of improved performance for the applications at hand.

Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a Parallel and distributed computing environment. It makes Use of the commodity hardware Hadoop is Highly Scalable and Fault Tolerant. Hadoop runs in cluster and eliminates the use of a Super computer. Hadoop is the widely used big data processing engine with a simple master slave setup. Big Data in most companies are processed by Hadoop by submitting the jobs to Master. The Master distributes the job to its cluster and process map and reduce tasks sequencially.But nowdays the growing data need and the and competition between Service Providers leads to the increased submission of jobs to the Master. This Concurrent job submission on Hadoop forces us to do Scheduling on Hadoop Cluster so that the response time will be acceptable for each job.

Listed below are the concepts that should be learned in order to properly understand HADOOP technology.

Faster, better decision making: With the speed of Hadoop and in-memory analytics, combined with the capability to analyze new sources of data, businesses are able to analyze data immediately and make decisions based on what they’ve learned.

The paper “A Comparison to Approaches to Large-Scale Data Analysis” by Pavlo, compares and analyze the MapReduce framework with the parallel DBMSs, for large scale data analysis. It benchmarks the open source Hadoop, build over MapReduce, with two parallel SQL databases, Vertica and a second system form a major relational vendor (DBMS-X), to conclude that parallel databases clearly outperform Hadoop on the same hardware over 100 nodes. Averaged across 5 tasks on 100 nodes, Vertica was 2.3 faster than DBMS-X which in turn was 3.2 times faster than MapReduce. In general, the parallel SQL DBMSs were significantly faster and required less code to implement each task, but took longer to tune and load the data. Finally, the paper talk about

Abstract - Hadoop Distributed File System, a Java based file system provides reliable and scalable storage for data. It is the key component to understand how a Hadoop cluster can be scaled over hundreds or thousands of nodes. The large amounts of data in Hadoop cluster is broken down to smaller blocks and distributed across small inexpensive servers using HDFS. Now, MapReduce functions are executed on these smaller blocks of data thus providing the scalability needed for big data processing. In this paper I will discuss in detail on Hadoop, the architecture of HDFS, how it functions and the advantages.

With the progress of the enterprise big data project, the importance of data analysis speed is increasingly highlighted. To further enhance the speed of data analysis, IBM unveiled a Hadoop data machine, designed to help enterprise users to meet demands of more variety and more large-scale data (lower cost) real-time analysis.

MapReduce is a simple and powerful programming model which enables development of scalable parallel applications to process large amount of data which is scattered on a cluster of machines. The original implementations of Map Reduce framework had some limitations which have been faced by many research follow up work after its introduction. It is gaining a lot of attraction in both research and industrial community as it has the capacity of processing large data. Map reduce framework used in different applications and for different purposes.

The aim of this paper is to explore different aspects of the MapReduce framework. The primary focus will be given on how MapReduce framework follows the principles and techniques of distributed and parallel programming in the context of concurrent, parallel and distributed computing. In the following sections of the report, there will be a brief introduction of the MapReduce platform and how it is related to distributed and parallel computing. Following that, the discussion will be on the phases and job life cycle of MapReduce-based programming, the functionalities of the different components of a MapReduce job, implementation of MapReduce and the challenges in the implementations. Hence, the paper covers different aspects of the methodology, implementations, issues and examples of implementation of the MapReduce framework.