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MapReduce/Hadoop

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Data-Intensive Text Processing with MapReduce. BigData and MapReduce with Hadoop. Large-scale Data Mining: MapReduce and Beyond. Data are becoming available in unprecedented volumes. This difference in scale is difference in kind, presenting new opportunities. Map-reduce has drawn a lot of attention recent years for large-scale data processing and mining. In this tutorial, we introduce Map-reduce and its application and research in data mining. In particular, we want to answer the following questions: •What is Map-reduce and why do we need it for data mining? •What mining applications need Map-reduce? •What are the advantages and limitations using Map-Reduce? 1.MapReduce basic includes MapReduce programming model, system architecture, its OpenSource implementation Hadoop and its extensions such as HBase, Pig, Cascading, Hive. 2.MapReduce algorithms cover MapReduce implementation of standard data mining algorithms such as clustering (K-means), classification (k-NN, naive Bayes), graph mining (page rank).

Would you like to put a link to this lecture on your homepage? MapReduce Introduction. MapReduce Introduction - Tutorial Copyright © 2010 Lars Vogel MapReduce Tutorial This article gives an overview of MapReduce and lists several resources which describes MapReduce. 1. 1.1. Parallel programming describes a means to divide a problem into several smaller subproblems and solve these in parallel. The requirement for a problem to be solved by parallel programming is that a part of the program can be identified which can be processed concurrently.

MapReduce is a parallel and distributed solution approach developed by Google for processing large datasets. MapReduce has two key components. The approach assumes that there are no dependencies between the input data. MapReduce incorporates usually also a framework which supports MapReduce operations. The classical example for using MapReduce is logfile analysis. Another example if full text indexing. Other applications are: MapReduce can also be applied to lots of other problems. 2. 4. 4.2. Your first Hadoop Map-Reduce Job « Recipes for Geeks. Writing An Hadoop MapReduce Program In Python - Michael G. In this tutorial I will describe how to write a simple MapReduce program for Hadoop in the Python programming language. Even though the Hadoop framework is written in Java, programs for Hadoop need not to be coded in Java but can also be developed in other languages like Python or C++ (the latter since version 0.14.1).

However, Hadoop’s documentation and the most prominent Python example on the Hadoop website could make you think that you must translate your Python code using Jython into a Java jar file. Obviously, this is not very convenient and can even be problematic if you depend on Python features not provided by Jython. Another issue of the Jython approach is the overhead of writing your Python program in such a way that it can interact with Hadoop – just have a look at the example in $HADOOP_HOME/src/examples/python/WordCount.py and you see what I mean.

Our program will mimick the WordCount, i.e. it reads text files and counts how often words occur. Map step: mapper.py #! #! #! Lintool.github.com/MapReduceAlgorithms/MapReduce-book-final.pdf. Db.cs.berkeley.edu/papers/nsdi10-hop.pdf. Www.cs.arizona.edu/~bkmoon/papers/sigmodrec11.pdf. MapReduce for the Masses: Zero to Hadoop in Five Minutes with Common Crawl | CommonCrawl. Common Crawl aims to change the big data game with our repository of over 40 terabytes of high-quality web crawl information into the Amazon cloud, the net total of 5 billion crawled pages. In this blog post, we’ll show you how you can harness the power of MapReduce data analysis against the Common Crawl dataset with nothing more than five minutes of your time, a bit of local configuration, and 25 cents. When Google unveiled its MapReduce algorithm to the world in an academic paper in 2004, it shook the very foundations of data analysis.

By establishing a basic pattern for writing data analysis code that can run in parallel against huge datasets, speedy analysis of data at massive scale finally became a reality, turning many orthodox notions of data analysis on their head. When you’ve got a taste of what’s possible when open source meets open data, we’d like to whet your appetite by asking you to remix this code. Ready to get started?

Watch our screencast and follow along below: 1. 2. 3. Gridka-school.scc.kit.edu/2011/downloads/Hadoop_tutorial-2_4-MapReduce.pdf. Developer.yahoo.com/hadoop/tutorial/module4.html. Introduction MapReduce is a programming model designed for processing large volumes of data in parallel by dividing the work into a set of independent tasks. MapReduce programs are written in a particular style influenced by functional programming constructs, specifically idioms for processing lists of data. This module explains the nature of this programming model and how it can be used to write programs which run in the Hadoop environment.

Goals for this Module: Understand functional programming as it applies to MapReduce Understand the MapReduce program flow Understand how to write programs for Hadoop MapReduce Learn about additional features of Hadoop designed to aid software development. Outline Prerequisites This module requires that you have set up a build environment as described in Module 3. MapReduce Basics Functional Programming Concepts MapReduce programs are designed to compute large volumes of data in a parallel fashion. List Processing Mapping Lists Reducing Lists The Driver Method. MapReduce Tutorial. You can view this page in Belorussian here! This document gives a quick example of how to use the MapReduce implementation described in [1], by means of a simple example.

The example is available as a tarball here, Updated 31 July 2010. Please note that the MapReduceScheduler.c file differs slightly from the version released by the original authors. The version in the tarball has been modified to compile cleanly with GCC 4.0.2. The files are also available as syntax-highlighted HTML here (the MapReduce implementation is not shown, and fatals.* are elided). This document assumes the reader is following along using the syntax-highlighted Makefile and main.C. Notice: Updated 31 July 2010: Since this tutorial has gained some popularity for non-Wisconsin users, I've modified the files in the tarball to build by default on x86/Linux instead of SPARC/Solaris.

Caveat: The MapReduce implementation seems to break if when a very large number of keys are emitted (eg. 2 billion+). The Map Function. MapReduce, SQL-MapReduce Resources and Hadoop Integration – Teradata Aster. What is MapReduce? What is SQL-MapReduce? Applications Hadoop Integration What is MapReduce? MapReduce, or map reduce, is a programming framework developed by Google to simplify data processing across massive data sets. As people rapidly increase their online activity and digital footprint, organizations are finding it vital to quickly analyze the huge amounts of data their customers and audiences generate to better understand and serve them.

What is SQL-MapReduce? SQL-MapReduce is a framework created by Teradata Aster to allow developers to write powerful and highly expressive SQL-MapReduce functions in languages such as Java, C#, Python, C++, and R and push them into the discovery platform for advanced in-database analytics. SQL-MapReduce functions are simple to write and are seamlessly integrated within SQL statements.

MapReduce functions seamlessly integrate into SQL queries Applications Hadoop Integration Learn More. Appengine-mapreduce - Google App Engine API for running MapReduce jobs. Map / Reduce – A visual explanation. Map/Reduce is a term commonly thrown about these days, in essence, it is just a way to take a big task and divide it into discrete tasks that can be done in parallel.

A common use case for Map/Reduce is in document database, which is why I found myself thinking deeply about this. Let us say that we have a set of documents with the following form: And we want to answer a question over more than a single document. That sort of operation requires us to use aggregation, and over large amount of data, that is best done using Map/Reduce, to split the work. Map / Reduce is just a pair of functions, operating over a list of data. In C#, LInq actually gives us a great chance to do things in a way that make it very easy to understand and work with. Let us say that we want to be about to get a count of comments per blog. There are a couple of things to note here: The first query is the map query, it maps the input document into the final format. And the final step is: Well, yes, but not all of them. Map Reduce. Introduction MapReduce (M/R) is a technique for dividing work across a distributed system.

This takes advantage of the parallel processing power of distributed systems, and also reduces network bandwidth as the algorithm is passed around to where the data lives, rather than a potentially huge dataset transferred to a client algorithm. Developers can use MapReduce for things like filtering documents by tags, counting words in documents, and extracting links to related data. In Riak, MapReduce is one method for non-key-based querying. Features Map phases execute in parallel with data localityReduce phases execute in parallel on the node where the job was submitted Javascript MapReduce support Erlang MapReduce support When to Use MapReduce When you know the set of objects you want to MapReduce over (the bucket-key pairs) When you want to return actual objects or pieces of the object – not just the keys, as do Search & Secondary Indexes When you need utmost flexibility in querying your data.

MapReduce-MPI Library. Learn Hadoop & Big Data with Free Courses Online | Big Data University. CS6240: Parallel Data Processing in MapReduce. This course covers techniques for analyzing very large data sets. We introduce the MapReduce programming model and the core technologies it relies on in practice, such as a distributed file system. Related approaches and technologies from distributed databases and Cloud Computing will also be introduced. Particular emphasis is placed on practical examples and hands-on programming experience. Both plain MapReduce and database-inspired advanced programming models running on top of a MapReduce infrastructure will be used. News Link to Piazza discussion forum: Acknowledgment: This course was kindly supported by an AWS in Education Coursework Grant award from Amazon.com, Inc. [12/11/2012] Lecture audio updated Lectures (Future lectures and events are tentative.)

Course Information Instructor: Mirek Riedewald Office hours: Tuesday 4-5:30pm in 332 WVH Send email (including Alper) to set up an appointment if you cannot make it during these times.