Hadoop 2

This document describes how to set up a single-node Hadoop installation to perform MapReduce operations. It discusses supported platforms, required software including Java and SSH, and preparing the Hadoop cluster in either local, pseudo-distributed, or fully-distributed mode. The main components of the MapReduce execution pipeline are explained, including the driver, mapper, reducer, and input/output formats. Finally, a simple word count example MapReduce job is described to demonstrate how it works.

Optimal Execution Of MapReduce Jobs In Cloud Anshul Aggarwal, Software Engineer, Cisco Systems Session Length: 1 Hour Tue March 10 21:30 PST Wed March 11 0:30 EST Wed March 11 4:30:00 UTC Wed March 11 10:00 IST Wed March 11 15:30 Sydney Voices 2015 www.globaltechwomen.com We use MapReduce programming paradigm because it lends itself well to most data-intensive analytics jobs run on cloud these days, given its ability to scale-out and leverage several machines to parallel process data. Research has demonstrates that existing approaches to provisioning other applications in the cloud are not immediately relevant to MapReduce -based applications. Provisioning a MapReduce job entails requesting optimum number of resource sets (RS) and configuring MapReduce parameters such that each resource set is maximally utilized. Each application has a different bottleneck resource (CPU :Disk :Network), and different bottleneck resource utilization, and thus needs to pick a different combination of these parameters based on the job profile such that the bottleneck resource is maximally utilized. The problem at hand is thus defining a resource provisioning framework for MapReduce jobs running in a cloud keeping in mind performance goals such as Optimal resource utilization with Minimum incurred cost, Lower execution time, Energy Awareness, Automatic handling of node failure and Highly scalable solution.

MapReduce is a programming model for processing large datasets in parallel. It works by breaking the dataset into independent chunks which are processed by the map function, and then grouping the output of the maps into partitions to be processed by the reduce function. Hadoop uses MapReduce to provide fault tolerance by restarting failed tasks and monitoring the JobTracker and TaskTrackers. MapReduce programs can be written in languages other than Java using Hadoop Streaming.

1. The document discusses installing Hadoop in single node cluster mode on Ubuntu, including installing Java, configuring SSH, extracting and configuring Hadoop files. Key configuration files like core-site.xml and hdfs-site.xml are edited. 2. Formatting the HDFS namenode clears all data. Hadoop is started using start-all.sh and the jps command checks if daemons are running. 3. The document then moves to discussing running a KMeans clustering MapReduce program on the installed Hadoop framework.

Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. It consists of HDFS for distributed storage and MapReduce for distributed processing. HDFS stores large files across multiple machines and provides high throughput access to application data. MapReduce allows processing of large datasets in parallel by splitting the work into independent tasks called maps and reduces. Companies use Hadoop for applications like log analysis, data warehousing, machine learning, and scientific computing on large datasets.

The document provides an overview of MapReduce and how it addresses the problem of processing large datasets in a distributed computing environment. It explains how MapReduce inspired by functional programming works by splitting data, mapping functions to pieces in parallel, and then reducing the results. Examples are given of word count and sorting word counts to find the most frequent word. Finally, it discusses how Hadoop popularized MapReduce by providing an open-source implementation and ecosystem.

This document provides performance optimization tips for Hadoop jobs, including recommendations around compression, speculative execution, number of maps/reducers, block size, sort size, JVM tuning, and more. It suggests how to configure properties like mapred.compress.map.output, mapred.map/reduce.tasks.speculative.execution, and dfs.block.size based on factors like cluster size, job characteristics, and data size. It also identifies antipatterns to avoid like processing thousands of small files or using many maps with very short runtimes.

Slides of the workshop conducted in Model Engineering College, Ernakulam, and Sree Narayana Gurukulam College, Kadayiruppu Kerala, India in December 2010

The document provides an introduction to Hadoop, including an overview of its core components HDFS and MapReduce, and motivates their use by explaining the need to process large amounts of data in parallel across clusters of computers in a fault-tolerant and scalable manner. It also presents sample code walkthroughs and discusses the Hadoop ecosystem of related projects like Pig, HBase, Hive and Zookeeper.

This document provides a high-level overview of MapReduce and Hadoop. It begins with an introduction to MapReduce, describing it as a distributed computing framework that decomposes work into parallelized map and reduce tasks. Key concepts like mappers, reducers, and job tracking are defined. The structure of a MapReduce job is then outlined, showing how input is divided and processed by mappers, then shuffled and sorted before being combined by reducers. Example map and reduce functions for a word counting problem are presented to demonstrate how a full MapReduce job works.

This document provides an overview of Hadoop and MapReduce. It discusses how Hadoop uses HDFS for distributed storage and replication of data blocks across commodity servers. It also explains how MapReduce allows for massively parallel processing of large datasets by splitting jobs into mappers and reducers. Mappers process data blocks in parallel and generate intermediate key-value pairs, which are then sorted and grouped by the reducers to produce the final results.

Big Data with Hadoop & Spark Training: http://bit.ly/2skCodH This CloudxLab Understanding MapReduce tutorial helps you to understand MapReduce in detail. Below are the topics covered in this tutorial: 1) Thinking in Map / Reduce 2) Understanding Unix Pipeline 3) Examples to understand MapReduce 4) Merging 5) Mappers & Reducers 6) Mapper Example 7) Input Split 8) mapper() & reducer() Code 9) Example - Count number of words in a file using MapReduce 10) Example - Compute Max Temperature using MapReduce 11) Hands-on - Count number of words in a file using MapReduce on CloudxLab

This document provides an overview of Apache Hadoop, HDFS, and MapReduce. It describes how Hadoop uses a distributed file system (HDFS) to store large amounts of data across commodity hardware. It also explains how MapReduce allows distributed processing of that data by allocating map and reduce tasks across nodes. Key components discussed include the HDFS architecture with NameNodes and DataNodes, data replication for fault tolerance, and how the MapReduce engine works with a JobTracker and TaskTrackers to parallelize jobs.

This slide deck is used as an introduction to the internals of Hadoop MapReduce, as part of the Distributed Systems and Cloud Computing course I hold at Eurecom. Course website: http://michiard.github.io/DISC-CLOUD-COURSE/ Sources available here: https://github.com/michiard/DISC-CLOUD-COURSE

The document discusses fault tolerance in Apache Hadoop. It describes how Hadoop handles failures at different layers through replication and rapid recovery mechanisms. In HDFS, data nodes regularly heartbeat to the name node, and blocks are replicated across racks. The name node tracks block locations and initiates replication if a data node fails. HDFS also supports name node high availability. In MapReduce v1, task and task tracker failures cause re-execution of tasks. YARN improved fault tolerance by removing the job tracker single point of failure.

This presentation will give you Information about : 1.Configuring HDFS 2.Interacting With HDFS 3.HDFS Permissions and Security 4.Additional HDFS Tasks HDFS Overview and Architecture 5.HDFS Installation 6.Hadoop File System Shell 7.File System Java API

Hadoop is an open-source framework that allows for the distributed processing of large data sets across clusters of computers. It addresses problems like massive data storage needs and scalable processing of large datasets. Hadoop uses the Hadoop Distributed File System (HDFS) for storage and MapReduce as its processing engine. HDFS stores data reliably across commodity hardware and MapReduce provides a programming model for distributed computing of large datasets.

This document summarizes a pen pc technology called P-ISM. P-ISM includes 5 functions: a CPU pen, camera, virtual keyboard, visual output via an LED projector, and cellular calling capability. It uses Bluetooth and WiFi for wireless connectivity and allows the user to access computing functions by writing on any flat surface and using a projected virtual keyboard. While portable and convenient, challenges include cost, battery life, and precise keyboard positioning.

Laser communication uses lasers to transmit information through free space instead of fiber optic cables. It works similarly to fiber optics but transmits the beam through the atmosphere instead of cables. The transmitter converts signals into laser light and the receiver includes a telescope to capture the beam and detectors to convert it back into signals. Laser communication has advantages over radio frequency and fiber optics for applications where laying cable is not possible or practical such as for satellites, remote areas, and emergencies due to its high bandwidth, directivity, security, and smaller antenna size.

This document summarizes a seminar presentation on brain fingerprinting technology. Brain fingerprinting uses EEG to measure electrical brain wave responses, specifically the P300 wave, to stimuli presented on a computer in order to determine if individuals have hidden information stored in their brains. It works by presenting probes, targets, and irrelevant stimuli and analyzing the brain's differential response. There are four phases: evidence collection, brain evidence collection, computer analysis, and determining guilt or innocence. Unlike polygraph tests, it does not rely on physiological responses but on cognitive brain responses. Case studies showed it correctly identified information stored in a murder suspect's brain and its potential use in identifying trained terrorists.

Laser communications offer a viable alternative to RF communications for inter satellite links and other applications where high-performance links are a necessity.

This document provides an overview of 3D on the web (3D internet). It discusses what 3D internet is, the applications and importance of 3D content on the web, the history and current status. Key enablers for 3D on the web have been increased bandwidth and computer processing power. 3D can be used for e-commerce, training, games, entertainment, social interaction, and education. The document also discusses technologies, design, animation, interactivity, and content creation for 3D on the web. A simple example of a 3D forest walk site is provided to illustrate how easy it can be to create basic 3D web content.

Brain fingerprinting is a technique developed by Lawrence Farwell that uses electroencephalography (EEG) to detect electrical brainwave responses called MERMERs that are elicited when a person recognizes familiar stimuli. It works by measuring the brain's response when a subject is exposed to words or images related to a crime. If the brainwave patterns match those that would be expected from someone familiar with the crime details, it suggests the person has knowledge of the crime. Brain fingerprinting has been used to help solve criminal cases and evaluate brain functioning, though further research with larger samples is still needed to fully validate its accuracy and capabilities.

The document discusses the concept of 3D Internet, which combines the power of the Internet with 3D graphics to provide interactive, real-time 3D content over the web. It outlines how improvements in bandwidth, processor speeds, and graphics accelerators have now made 3D Internet possible. Examples are given of potential applications in e-commerce, education, entertainment, and more. Challenges that must still be overcome include complexity, slow adoption rates, and underutilization by advertisers. The future of 3D Internet is predicted to include highly immersive experiences that integrate the virtual and real world.

The document discusses big data and distributed computing. It provides examples of the large amounts of data generated daily by organizations like the New York Stock Exchange and Facebook. It explains how distributed computing frameworks like Hadoop use multiple computers connected via a network to process large datasets in parallel. Hadoop's MapReduce programming model and HDFS distributed file system allow users to write distributed applications that process petabytes of data across commodity hardware clusters.

This document provides an overview of key concepts in Hadoop including: - Hadoop was tested on a 4000 node cluster with 32,000 cores and 16 petabytes of storage. - MapReduce is Hadoop's programming model and consists of mappers that process input splits in parallel, and reducers that combine the outputs of the mappers. - The JobTracker manages jobs, TaskTrackers run tasks on slave nodes, and Tasks are individual mappers or reducers. Data is distributed to nodes implicitly based on the HDFS file distribution. Configurations are set using a JobConf object.

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