MapReduce - Wikipedia, the free encyclopedia - The Diigo Meta page

• 29 Nov 09
  

  yarmiky shilla
  • MapReduce is a framework for processing huge datasets on certain kinds of distributable problems using a large number of computers (nodes), collectively referred to as a cluster. Computational processing can occur on data stored either in a filesystem (unstructured) or within a database (structured)
  • MapReduce
• 10 Nov 09
  

  Eric Wang
  • MapReduce is a software framework introduced by Google to support distributed computing on large data sets on clusters of computers
• 02 Nov 09
  

  rg443322

  MapReduce Wikipedia Google

• 05 May 09
  

  Dustin Burke
• 03 May 09
  

  Pierre Tran

  distributed computing parallel computing mapreduce google

• 30 Apr 09
  

  Kenyth Zeng

  distributed algorithm wikipedia scalability Google

  • The framework is inspired by map and reduce functions commonly used in functional programming,^[2]
  • defined with respect to data structured in (key, value) pairs.
  • 7 more annotations...
  • • ct to data stru

    • Map takes one pair of data with a type on a data domain, and returns a list of pairs in a different domain:

      
      

      Map(k1,v1) -> list(k2,v2)

    • the MapReduce framework collects all pairs with the same key from all lists and groups them together, thus creating one group for each one of the different generated keys.

    • The Reduce function is then applied in parallel to each group, which in turn produces a collection of values in the same domain:

      
      

      Reduce(k2, list (v2)) -> list(v2)

    • This behavior is different from the functional programming map and reduce combination, which accepts a list of arbitrary values and returns one single value that combines all the values returned by map.
    • count the appearances of each different word in a set of documents
    • each document is split in words, and each word is counted initially with a "1" value by the Map function, using the word as the result key. The framework puts together all the pairs with the same key and feeds them to the same call to Reduce, thus this function just needs to sum all of its input values to find the total appearances of that word.
• 26 Apr 09
  

  Shekhar Tiwatne
• 31 Mar 09
  

  xu ling
  • master node
  • master node
  • 2 more annotations...
  • • The master node takes the input, chops it up into smaller sub-problems, and distributes those to worker nodes. (A wor
    • master node
• 01 Mar 09
  

  Ahmed Diaa

  mapreduce google functional

• 17 Feb 09
  

  Simon Reavely

  google mapreduce bigtable largedatasets

• 12 Jan 09
  

  doxyer Liu

  google mapreduce

  • The framework is inspired by map and reduce functions commonly used in functional programming,^[2] although their purpose in the MapReduce framework is not the same as their original forms
  • MapReduce is a framework for computing certain kinds of distributable problems using a large number of computers (nodes), collectively referred to as a cluster.
  • 10 more annotations...
  • • The Map and Reduce functions of MapReduce are both defined with respect to data structured in (key, value) pairs.
    • Map(k1,v1) -> list(k2,v2)
    • Reduce(k2, list (v2)) -> list(v2)
    • Thus the MapReduce framework transforms a list of (key, value) pairs into a list of values. This behavior is different from the functional programming map and reduce combination, which accepts a list of arbitrary values and returns one single value that combines all the values returned by map.
    • The canonical example application of MapReduce is a process to count the appearances of each different word in a set of documents
    • map(String name, String document):
      // key: document name
      // value: document contents
      for each word w in document:
      EmitIntermediate(w, 1);
      
      reduce(String word, Iterator partialCounts):
      // key: a word
      // values: a list of aggregated partial counts
      int result = 0;
      for each v in partialCounts:
      result += ParseInt(v);
      Emit(result);
    • The frozen part of the MapReduce framework is a large distributed sort

    • The hot spots, which the application defines, are:

      
      
      • an input reader
      • a Map function
      • a partition function
      • a compare function
      • a Reduce function
      • an output writer
    • MapReduce is useful in a wide range of applications, including: "distributed grep, distributed sort, web link-graph reversal, term-vector per host, web access log stats, inverted index construction, document clustering, machine learning, statistical machine translation..."
    • The Google MapReduce framework is implemented in C++ with interfaces in Python and Java.
• 30 Dec 08
  

  madzientist

  largedatasets

• 21 Dec 08
  

  Todd Suomela

  includes links to current projects in a variety of languages: java, python, erlang, etc

  programming google mapreduce functional python erlang java

• 24 Sep 08
  

  lucidus
• 04 Jun 08
  

  Joel Liu

  MapReduce

• 24 Apr 08
  

  Gregory Watts

  mapreduce distributed computing framework

• 07 Aug 07
  

  M G
• 03 Aug 07
  

  Mats Svensson

  Google Misc Webservice programming webdev

• 25 Jul 07
  

  Matti Narkia

  MapReduce is a software framework originally developed by Google to support parallel computations over large (greater than 100 terabyte) data sets on unreliable clusters of computers. The name is derived from the map and reduce functions commonly used in

  MapReduce Wikipedia software framework parallel computations computing processing google map reduce functional programming popular

• 30 Jun 07
  

  York Jong

  Google MapReduce

• 25 Feb 07
  

  Deepak

  google highperformancecomputing mapreduce parallelization

• 28 Jan 07
  

  Dan Armano

  programming

• 17 Aug 06
  

  Simon

  algorithms distributed-computing

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