Content deleted Content added
Tag: Reverted |
Michaelmalak (talk | contribs) Undid revision 1223249519 by JohnPritchard (talk) cite does not appear to be about map/reduce even though it contains both those words in the title |
||
| (4 intermediate revisions by 4 users not shown) | |||
Line 153:
Each ''Map'' function output is allocated to a particular ''reducer'' by the application's ''partition'' function for [[sharding]] purposes. The ''partition'' function is given the key and the number of reducers and returns the index of the desired ''reducer''.
A typical default is to [[Hash function|hash]] the key and use the hash value [[Modulo operation|modulo]] the number of ''reducers''. It is important to pick a partition function that gives an approximately uniform distribution of data per
(i.e. the reducers assigned the larger shares of the non-uniformly partitioned data).
Between the map and reduce stages, the data are ''shuffled'' (parallel-sorted / exchanged between nodes) in order to move the data from the map node that produced them to the shard in which they will be reduced. The shuffle can sometimes take longer than the computation time depending on network bandwidth, CPU speeds, data produced and time taken by map and reduce computations.
Line 171:
==Theoretical background==
Properties of [[Monoid|monoids]] are the basis for ensuring the validity of
| doi = 10.1017/S0956796817000193
| title = An algebra for distributed Big Data analytics
Line 189:
}}</ref>
In the Algebird package<ref>{{Cite web
|title= Abstract Algebra for Scala
|url=https://twitter.github.io/algebird/}}</ref> a Scala implementation of Map/Reduce explicitly requires
.<ref>{{Cite web
|title= Encoding Map-Reduce As A Monoid With Left Folding
Line 198:
The operations of MapReduce deal with two types: the type ''A'' of input data being mapped, and the type ''B'' of output data being reduced.
The ''Map'' operation takes individual values of type ''A'' and produces, for each ''a:A'' a value ''b:B''; The ''Reduce'' operation requires a binary operation • defined on values of type ''B''; it consists of folding all available ''b:B'' to a single value.
From a basic requirements point of view, any MapReduce operation must involve the ability to arbitrarily regroup data being reduced. Such a requirement amounts to two properties of the operation •:
* associativity: (''x'' • ''y'') • ''z'' = ''x'' • (''y'' • ''z'')
* existence of neutral element ''e'' such that ''e'' • ''x'' = ''x'' • ''e'' = ''x'' for every ''x:B''.
Line 206:
The second property guarantees that, when parallelized over multiple nodes, the nodes that don't have any data to process would have no impact on the result.
These two properties amount to having a [[Monoid|monoid]] (''B'', •, ''e'') on values of type ''B'' with operation • and with neutral element ''e''.
There's no requirements on the values of type ''A''; an arbitrary function ''A'' → ''B'' can be used for the ''Map'' operation. This means that we have a [[Catamorphism|catamorphism]] ''A*'' → (''B'', •, ''e''). Here ''A*'' denotes a [[Kleene star]], also known as the type of lists over ''A''.
The ''Shuffle'' operation per se is not related to the essence of MapReduce; it's needed to distribute calculations over the cloud.
It follows from the above that not every binary ''Reduce'' operation will work in MapReduce. Here are the counter-examples:
Line 236:
==Uses==
MapReduce is useful in a wide range of applications, including distributed pattern-based searching, distributed sorting, web link-graph reversal, Singular Value Decomposition,<ref>{{cite web |last1=Bosagh Zadeh|first1=Reza|last2=Carlsson|first2=Gunnar|title=Dimension Independent Matrix Square Using MapReduce |website=Stanford University |url=https://stanford.edu/~rezab/papers/dimsum.pdf|access-date=12 July 2014|bibcode=2013arXiv1304.1467B|year=2013|arxiv=1304.1467}}</ref> web access log stats, [[inverted index]] construction, [[document clustering]], [[machine learning]],<ref name="mrml">{{cite web| url=http://www.willowgarage.com/map-reduce-machine-learning-multicore| title=Map-Reduce for Machine Learning on Multicore|first1=Andrew Y. |last1=Ng |first2=Gary |last2=Bradski |first3=Cheng-Tao |last3=Chu |first4=Kunle |last4=Olukotun |first5=Sang Kyun |last5=Kim |first6=Yi-An |last6=Lin |first7=YuanYuan |last7=Yu| publisher=NIPS 2006| year=2006}}</ref> and [[statistical machine translation]]. Moreover, the MapReduce model has been adapted to several computing environments like multi-core and many-core systems,<ref name="evalMR">{{Cite book | doi = 10.1109/HPCA.2007.346181| chapter = Evaluating MapReduce for Multi-core and Multiprocessor Systems| title = 2007 IEEE 13th International Symposium on High Performance Computer Architecture| pages = 13| year = 2007| last1 = Ranger | first1 = C. | last2 = Raghuraman | first2 = R. | last3 = Penmetsa | first3 = A. | last4 = Bradski | first4 = G. | last5 = Kozyrakis | first5 = C. | isbn = 978-1-4244-0804-7| citeseerx = 10.1.1.220.8210| s2cid = 12563671}}</ref><ref name="graphicsMR">{{Cite book | doi = 10.1145/1454115.1454152| chapter = Mars: a MapReduce framework on graphics processors| title = Proceedings of the 17th international conference on Parallel architectures and compilation techniques – PACT '08| pages = 260| year = 2008| last1 = He | first1 = B. | last2 = Fang | first2 = W. | last3 = Luo | first3 = Q. | last4 = Govindaraju | first4 = N. K. | chapter-url = http://wenbin.org/doc/papers/Wenbin08PACT.pdf| last5 = Wang | first5 = T. | isbn = 9781605582825| s2cid = 207169888}}</ref><ref name="tiledMR">{{Cite book | doi = 10.1145/1854273.1854337| chapter = Tiled-MapReduce: optimizing resource usages of data-parallel applications on multicore with tiling| title = Proceedings of the 19th international conference on Parallel architectures and compilation techniques – PACT '10| pages = 523| year = 2010| last1 = Chen | first1 = R. | last2 = Chen | first2 = H. | last3 = Zang | first3 = B. | isbn = 9781450301787| s2cid = 2082196}}</ref> desktop grids,<ref name="gridMR">{{Cite book | doi = 10.1109/3PGCIC.2010.33| chapter = Towards MapReduce for Desktop Grid Computing| title = 2010 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing| pages = 193| year = 2010| last1 = Tang | first1 = B. | last2 = Moca | first2 = M. | last3 = Chevalier | first3 = S. | last4 = He | first4 = H. | last5 = Fedak | first5 = G. | chapter-url = http://graal.ens-lyon.fr/~gfedak/papers/xtremmapreduce.3pgcic10.pdf| isbn = 978-1-4244-8538-3| citeseerx = 10.1.1.671.2763| s2cid = 15044391}}</ref>
multi-cluster,<ref name="HMR">{{Cite book | doi = 10.1145/1996023.1996026| chapter = A Hierarchical Framework for Cross-Domain MapReduce Execution|chapter-url = http://yuanluo.net/publications/LUO_ECMLS2011.pdf| title = Proceedings of the second international workshop on Emerging computational methods for the life sciences (ECMLS '11)| year = 2011| last1 = Luo | first1 = Y. | last2 = Guo | first2 = Z. | last3 = Sun | first3 = Y.| last4 = Plale | first4 = B. |author4-link=Beth Plale| last5 = Qiu | first5 = J. | last6=Li|
first6=W. |isbn = 978-1-4503-0702-4| citeseerx = 10.1.1.364.9898| s2cid = 15179363}}</ref> volunteer computing environments,<ref name="volunteerMR">{{Cite book | doi = 10.1145/1851476.1851489| chapter = MOON: MapReduce On Opportunistic eNvironments| title = Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing – HPDC '10| pages = 95| year = 2010| last1 = Lin | first1 = H. | last2 = Ma | first2 = X. | last3 = Archuleta | first3 = J. | last4 = Feng | first4 = W. C. | last5 = Gardner | first5 = M. | last6 = Zhang | first6 = Z. | chapter-url = http://eprints.cs.vt.edu/archive/00001089/01/moon.pdf| isbn = 9781605589428| s2cid = 2351790}}</ref> dynamic cloud environments,<ref name="dynCloudMR">{{Cite journal| doi = 10.1016/j.jcss.2011.12.021| title = P2P-MapReduce: Parallel data processing in dynamic Cloud environments| journal = [[Journal of Computer and System Sciences]]| volume = 78| issue = 5| pages = 1382–1402| year = 2012| last1 = Marozzo| first1 = F.| last2 = Talia| first2 = D.| last3 = Trunfio| first3 = P.| doi-access = free}}</ref> mobile environments,<ref name="mobileMR">{{Cite book | doi = 10.1145/1839294.1839332| chapter = Misco: a MapReduce framework for mobile systems| title = Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments – PETRA '10| pages = 1| year = 2010| last1 = Dou | first1 = A. | last2 = Kalogeraki | first2 = V. | last3 = Gunopulos | first3 = D. | last4 = Mielikainen | first4 = T. | last5 = Tuulos | first5 = V. H. | isbn = 9781450300711| s2cid = 14517696}}</ref> and high-performance computing environments.<ref>{{cite book|chapter=Characterization and Optimization of Memory-Resident MapReduce on HPC Systems|publisher=IEEE|date=May 2014|doi=10.1109/IPDPS.2014.87|isbn=978-1-4799-3800-1|title=2014 IEEE 28th International Parallel and Distributed Processing Symposium|last1=Wang|first1=Yandong|last2=Goldstone|first2=Robin|last3=Yu|first3=Weikuan|last4=Wang|first4=Teng|pages=799–808|s2cid=11157612}}</ref>
| |||