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Michaelmalak (talk | contribs) →Lack of novelty: Non-parallel map/reduce in Common Lisp in 1984 |
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 |
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{{Short description|Parallel programming model}}
'''MapReduce''' is a [[programming model]] and an associated implementation for processing and generating [[big data]] sets with a [[Parallel computing|parallel]], [[distributed computing|distributed]] algorithm on a [[Cluster (computing)|cluster]].<ref>{{cite web|url=https://hadoop.apache.org/docs/r1.2.1/mapred_tutorial.html|title=MapReduce Tutorial|access-date=3 July 2019|website=Apache Hadoop}}</ref><ref>{{cite web|url=http://news.cnet.com/8301-10784_3-9955184-7.html|title=Google spotlights data center inner workings|date=30 May 2008|website=cnet.com|access-date=31 May 2008|archive-date=19 October 2013|archive-url=https://web.archive.org/web/20131019063218/http://news.cnet.com/8301-10784_3-9955184-7.html|url-status=dead}}</ref><ref name="GoogleMapReduce">{{cite web|url=http://static.googleusercontent.com/media/research.google.com/es/us/archive/mapreduce-osdi04.pdf|title=MapReduce: Simplified Data Processing on Large Clusters|website=googleusercontent.com}}</ref>
A MapReduce program is composed of a [[map (parallel pattern)|''map'']] [[procedure (computing)|procedure]], which performs filtering and sorting (such as sorting students by first name into queues, one queue for each name), and a ''[[Reduce (parallel pattern)|reduce]]'' method, which performs a summary operation (such as counting the number of students in each queue, yielding name frequencies). The "MapReduce System" (also called "infrastructure" or "framework") orchestrates the processing by [[Marshalling (computer science)|marshalling]] the distributed servers, running the various tasks in parallel, managing all communications and data transfers between the various parts of the system, and providing for [[Redundancy (engineering)|redundancy]] and [[Fault-tolerant computer system|fault tolerance]].
The model is a specialization of the ''split-apply-combine'' strategy for data analysis.<ref>{{Cite journal | doi = 10.18637/jss.v040.i01| title = The split-apply-combine strategy for data analysis| journal = Journal of Statistical Software| volume = 40| pages = 1–29| year = 2011| last1 = Wickham| first1 = Hadley | doi-access = free}}</ref>
It is inspired by the [[map (higher-order function)|map]] and [[reduce (higher-order function)|reduce]] functions commonly used in [[functional programming]],<ref name="map">"Our abstraction is inspired by the map and reduce primitives present in Lisp and many other functional languages." -[http://research.google.com/archive/mapreduce.html "MapReduce: Simplified Data Processing on Large Clusters"], by Jeffrey Dean and Sanjay Ghemawat; from Google Research</ref> although their purpose in the MapReduce framework is not the same as in their original forms.<ref>{{Cite journal | doi = 10.1016/j.scico.2007.07.001| title = Google's Map ''Reduce'' programming model — Revisited| journal = Science of Computer Programming| volume = 70| pages = 1–30| year = 2008| last1 = Lämmel | first1 = R. | doi-access =
| url = https://stackoverflow.com/questions/3947889/mongodb-terrible-mapreduce-performance
| title = MongoDB: Terrible MapReduce Performance
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# '''Reduce:''' worker nodes now process each group of output data, per key, in parallel.
MapReduce allows for the distributed processing of the map and reduction operations. Maps can be performed in parallel, provided that each mapping operation is independent of the others; in practice, this is limited by the number of independent data sources and/or the number of CPUs near each source. Similarly, a set of 'reducers' can perform the reduction phase, provided that all outputs of the map operation that share the same key are presented to the same reducer at the same time, or that the reduction function is [[Associative property|associative]]. While this process often appears inefficient compared to algorithms that are more sequential (because multiple instances of the reduction process must be run), MapReduce can be applied to significantly larger datasets than a single [[Commodity computing|"commodity" server]] can handle – a large [[server farm]] can use MapReduce to sort a [[petabyte]] of data in only a few hours.<ref>{{cite web|last=Czajkowski|first=Grzegorz|title=Sorting Petabytes with MapReduce – The Next Episode|url=http://googleresearch.blogspot.com/2011/09/sorting-petabytes-with-mapreduce-next.html|access-date=7 April 2014|author2=Marián Dvorský |author3=Jerry Zhao |author4=Michael Conley |date=7 September 2011 }}</ref> The parallelism also offers some possibility of recovering from partial failure of servers or storage during the operation: if one mapper or reducer fails, the work can be rescheduled – assuming the input data are still available.
Another way to look at MapReduce is as a 5-step parallel and distributed computation:
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===Examples===
The canonical MapReduce example counts the appearance of each word in a set of documents:<ref>{{cite web|url=http://research.google.com/archive/mapreduce-osdi04-slides/index-auto-0004.html|title=Example: Count word occurrences|publisher=Google Research|access-date=September 18, 2013}}</ref>
'''function''' <u>map</u>(String name, String document):
''// name: document name''
''// document: document contents''
'''for each''' word w '''in''' document:
emit (w, 1)
'''function''' <u>reduce</u>(String word, Iterator partialCounts):
''// word: a word''
''// partialCounts: a list of aggregated partial counts''
sum = 0
'''for each''' pc '''in''' partialCounts:
sum += pc
emit (word, sum)
Here, each document is split into words, and each word is counted 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.
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'''end function'''
Note that in the {{mono|Reduce}} function, {{mono|C}} is the count of people having in total N contacts, so in the {{mono|Map}} function it is natural to write {{mono|1=C=1}}, since every output pair is referring to the contacts of one single person.
The MapReduce system would line up the 1100 Map processors, and would provide each with its corresponding 1 million input records. The Map step would produce 1.1 billion {{mono|(Y,(N,1))}} records, with {{mono|Y}} values ranging between, say, 8 and 103. The MapReduce System would then line up the 96 Reduce processors by performing shuffling operation of the key/value pairs due to the fact that we need average per age, and provide each with its millions of corresponding input records. The Reduce step would result in the much reduced set of only 96 output records {{mono|(Y,A)}}, which would be put in the final result file, sorted by {{mono|Y}}.
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10, 9
If we reduce it with file {{mono|#3}}, we lose the count of how many records we've already seen, so we end up with an average of 9.5 contacts for a 10-year-old person ((9+10)/2), which is wrong. The correct answer is 9.1<span style="text-decoration: overline;">66</span> = 55 / 6 = (
==Dataflow==
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===Output writer===
The ''Output Writer'' writes the output of the ''Reduce'' to the stable storage.
==Theoretical background==
Properties of [[Monoid|monoids]] are the basis for ensuring the validity of MapReduce operations.<ref>{{Cite journal
| doi = 10.1017/S0956796817000193
| title = An algebra for distributed Big Data analytics
| journal = Journal of Functional Programming
| volume = 28
| year = 2017
| last = Fegaras
| first = Leonidas
| s2cid = 44629767
| doi-access =
}}</ref><ref>{{cite arXiv
|last=Lin
|first=Jimmy
|title=Monoidify! Monoids as a Design Principle for Efficient MapReduce Algorithms
|eprint=1304.7544
|date=29 Apr 2013|class=cs.DC
}}</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 a monoid class type
.<ref>{{Cite web
|title= Encoding Map-Reduce As A Monoid With Left Folding
|date= 5 September 2016|url= http://erikerlandson.github.io/blog/2016/09/05/expressing-map-reduce-as-a-left-folding-monoid/}}</ref>
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''.
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:
* building a tree from subtrees: this operation is not associative, and the result will depend on grouping;
* direct calculation of averages: ''avg'' is also not associative (and it has no neutral element); to calculate an average, one needs to calculate [[Moment (mathematics)|moments]].
==Performance considerations==
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==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
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>
At Google, MapReduce was used to completely regenerate Google's index of the [[World Wide Web]]. It replaced the old ''ad hoc'' programs that updated the index and ran the various analyses.<ref name="usage">{{cite web| quote=As of October, Google was running about 3,000 computing jobs per day through MapReduce, representing thousands of machine-days, according to a presentation by Dean. Among other things, these batch routines analyze the latest Web pages and update Google's indexes.| url=http://www.baselinemag.com/c/a/Infrastructure/How-Google-Works-1/5| title=How Google Works| date=7 July 2006| publisher=baselinemag.com}}</ref> Development at Google has since moved on to technologies such as Percolator, FlumeJava<ref name="Chambers2010">{{cite book |last1=Chambers |first1=Craig |last2=Raniwala |first2=Ashish |last3=Perry |first3=Frances |last4=Adams |first4=Stephen |last5=Henry |first5=Robert R. |last6=Bradshaw |first6=Robert |last7=Weizenbaum |first7=Nathan |title
MapReduce's stable inputs and outputs are usually stored in a [[distributed file system]]. The transient data are usually stored on local disk and fetched remotely by the reducers.
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DeWitt and Stonebraker have subsequently published a detailed benchmark study in 2009 comparing performance of [[Hadoop|Hadoop's]] MapReduce and [[RDBMS]] approaches on several specific problems.<ref name="sigmod">{{cite web| url=http://database.cs.brown.edu/projects/mapreduce-vs-dbms| title=A Comparison of Approaches to Large-Scale Data Analysis|first1=Andrew |last1=Pavlo |first2=Erik |last2=Paulson |first3=Alexander |last3=Rasin |first4=Daniel J. |last4=Abadi |first5=Deavid J. |last5=DeWitt |first6=Samuel |last6=Madden |first7=Michael |last7=Stonebraker| publisher=Brown University| access-date=2010-01-11}}</ref> They concluded that relational databases offer real advantages for many kinds of data use, especially on complex processing or where the data is used across an enterprise, but that MapReduce may be easier for users to adopt for simple or one-time processing tasks.
The MapReduce programming paradigm was also described in [[Danny Hillis]]'s 1985 thesis <ref name="WDHmit86">{{cite book |author-first=W. Danny |author-last=Hillis |date=1986 |title=The Connection Machine |publisher=[[MIT Press]] |isbn=0262081571 |url-access=registration |url=https://archive.org/details/connectionmachin00hill }}</ref> intended for use on the [[Connection Machine]], where it was called "xapping/reduction"
In 2010 Google was granted what is described as a patent on MapReduce. The patent, filed in 2004, may cover use of MapReduce by open source software such as [[Hadoop]], [[CouchDB]], and others. In ''[[Ars Technica]]'', an editor acknowledged Google's role in popularizing the MapReduce concept, but questioned whether the patent was valid or novel.<ref>{{cite news |last1=Paul |first1=Ryan |title=Google's MapReduce patent: what does it mean for Hadoop? |url=https://arstechnica.com/information-technology/2010/01/googles-mapreduce-patent-what-does-it-mean-for-hadoop/ |access-date=21 March 2021 |work=Ars Technica |date=20 January 2010 |language=en-us}}</ref><ref name="patent">{{cite web|url=http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=/netahtml/PTO/srchnum.htm&r=1&f=G&l=50&s1=7,650,331.PN.&OS=PN/7,650,331&RS=PN/7,650,331|title=United States Patent: 7650331 - System and method for efficient large-scale data processing|website=uspto.gov}}</ref> In 2013, as part of its "Open Patent Non-Assertion (OPN) Pledge", Google pledged to only use the patent defensively.<ref>{{cite news |last1=Nazer |first1=Daniel |title=Google Makes Open Patent Non-assertion Pledge and Proposes New Licensing Models |url=https://www.eff.org/deeplinks/2013/03/google-makes-open-patent-non-assertion-pledge |access-date=21 March 2021 |work=Electronic Frontier Foundation |date=28 March 2013 |language=en}}</ref><ref>{{cite news |last1=King |first1=Rachel |title=Google expands open patent pledge to 79 more about data center management |url=https://www.zdnet.com/article/google-expands-open-patent-pledge-to-79-more-about-data-center-management/ |access-date=21 March 2021 |work=ZDNet |date=2013 |language=en}}</ref> The patent is expected to expire on 23 December 2026.<ref>{{cite web |title=System and method for efficient large-scale data processing |url=https://patents.google.com/patent/US7650331B1/en |publisher=Google Patents Search |access-date=21 March 2021 |language=en |date=18 June 2004}}</ref>
===Restricted programming framework===
MapReduce tasks must be written as acyclic dataflow programs, i.e. a stateless mapper followed by a stateless reducer, that are executed by a batch job scheduler. This paradigm makes repeated querying of datasets difficult and imposes limitations that are felt in fields such as [[
==See also==
* [[Bird–Meertens formalism]]
* [[Parallelization contract]]
===Implementations of MapReduce===
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{{Commons category|MapReduce}}
{{Google
{{Authority control}}
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