Almost everyone has heard of Google's MapReduce framework, but very few have ever hacked around with the idea of map and reduce. These two idioms are borrowed from functional programming, and form the basis of Google's framework. Although Python is not a functional programming language, it has built-in support for both of these concepts. A…
Disco is an open-source implementation of the Map-Reduce framework for distributed computing. As the original framework, Disco supports parallel computations over large data sets on unreliable cluster of computers.
MRQL (the Map-Reduce Query Language) is an SQL-like query language for map-reduce computations. It is implemented on top of Apache's Hadoop. MRQL is powerful enough to express most common data analysis tasks over many different kinds of raw data, including hierarchical data and nested collections, such as XML data. It is more powerful than other current languages, such as Hive and Pig Latin, since it can operate on more complex data and supports more powerful query constructs, thus eliminating the need for using explicit map-reduce code.
Sqoop is a tool designed to import data from relational databases into Hadoop. Sqoop uses JDBC to connect to a database. It examines each table’s schema and automatically generates the necessary classes to import data into the Hadoop Distributed File System (HDFS). Sqoop then creates and launches a MapReduce job to read tables from the database via DBInputFormat, the JDBC-based InputFormat. Tables are read into a set of files in HDFS. Sqoop supports both SequenceFile and text-based target and includes performance enhancements for loading data from MySQL.
Following up on KMeans Clustering Now Running on Elastic MapReduce, Stephen Green has generously documented the steps that was necessary to get an example of k-Means clustering up and running on Amazon’s Elastic MapReduce (EMR) on the Apache Lucene Mahout wiki.
One night at the pub we discussed whether one could replace Hadoop (a massive and comprehensive implementation of Mapreduce) with a single bash script, an awk command, sort, and a sprinkling of netcat. This turned into a weekend project dubbed bashreduce
Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well.
Introduction This document describes how Map and Reduce operations are carried out in Hadoop. If you are not familiar with the Google [WWW] MapReduce programming model you should get acquainted with it first.
HBase: Bigtable-like structured storage for Hadoop HDFS Just as Google's [WWW] Bigtable leverages the distributed data storage provided by the [WWW] Google File System, HBase provides Bigtable-like capabilities on top of Hadoop Core. Data is organized into tables, rows and columns. An Iterator-like interface is available for scanning through a row range (and of course there is the ability to retrieve a column value for a specific key). Any particular column may have multiple versions for the same row key.
Peregrine is a map reduce framework designed for running iterative jobs across partitions of data. Peregrine is designed to be FAST for executing map reduce jobs by supporting a number of optimizations and features not present in other map reduce frameworks.
Map-Reduce is on its way out. But we shouldn’t measure its importance in the number of bytes it crunches, but the fundamental shift in data processing architectures it helped popularise.
Katta is a scalable, failure tolerant, distributed, data storage for real time access.
Katta serves large, replicated, indices as shards to serve high loads and very large data sets. These indices can be of different type. Currently implementations are available for Lucene and Hadoop mapfiles.
* Makes serving large or high load indices easy
* Serves very large Lucene or Hadoop Mapfile indices as index shards on many servers
* Replicate shards on different servers for performance and fault-tolerance
* Supports pluggable network topologies
* Master fail-over
* Fast, lightweight, easy to integrate
* Plays well with Hadoop clusters
* Apache Version 2 License
Dumbo is a project that allows you to easily write and run Hadoop programs in Python (it’s named after Disney’s flying circus elephant, since the logo of Hadoop is an elephant and Python was named after the BBC series “Monty Python’s Flying Circus”). More generally, Dumbo can be considered to be a convenient Python API for writing MapReduce programs.
Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well.
This course is about scalable approaches to processing large amounts of information (terabytes and even petabytes). We focus mostly on MapReduce, which is presently the most accessible and practical means of computing at this scale, but will discuss other approaches as well.
Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large data sets. At the present time, Pig's infrastructure layer consists of a compiler that produces sequences of Map-Reduce programs, for which large-scale parallel implementations already exist Pig's language layer currently consists of a textual language called Pig Latin, which has the following key properties: * Ease of programming. It is trivial to achieve parallel execution of simple, "embarrassingly parallel" data analysis tasks. * Optimization opportunities. The way in which tasks are encoded permits the system to optimize their execution automatically * Extensibility.
A list of Group papers for MapReduce Applications. Articles include: 'Nephele: Genotyping via Complete Composition Vectors and MapReduce' by Marc E Colosimo, Matthew W Peterson, Scott Mardis et al., 'Clustering Very Large Multi-dimensional Datasets with MapReduce' by Robson L F Cordeiro, Julio López, Christos Faloutsos and 'Yahoo! Research Small World Experiment' by Yahoo!, Facebook
M. Bayir, I. Toroslu, A. Cosar, und G. Fidan. WWW '09: Proceedings of the 18th international conference on World wide web, Seite 161--170. New York, NY, USA, ACM, (2009)
M. Becker, H. Mewes, A. Hotho, D. Dimitrov, F. Lemmerich, und M. Strohmaier. International Conference Companion on World Wide Web, Seite 17--18. Republic and Canton of Geneva, Switzerland, International World Wide Web Conferences Steering Committee, (2016)
C. Bellettini, M. Camilli, L. Capra, und M. Monga. Reachability Problems, Volume 8169 von Lecture Notes in Computer Science, Springer Berlin Heidelberg, (2013)
C. Bellettini, M. Camilli, L. Capra, und M. Monga. Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2012 14th International Symposium on, Seite 295-302. IEEE Computer Society, (сентября 2012)
Q. Chen, A. Therber, M. Hsu, H. Zeller, B. Zhang, und R. Wu. Proceedings of the 2009 International Database Engineering & Applications Symposium, Seite 43--53. New York, NY, USA, ACM, (2009)
F. Chierichetti, R. Kumar, und A. Tomkins. WWW '10: Proceedings of the 19th international conference on World wide web, Seite 231--240. New York, NY, USA, ACM, (2010)