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Hello; I'm a new contributor; sorry for this comment's simplicity... This article on (what I would have thought) an important topic is pretty bad; the C grade it's received reflects this. It's also considered of "low-importance"; I have not yet located the project's importance scale, so I don't understand this. MY QUESTION: why is there no "needs work" indicator at the top of the article itself, like I see in so many others? Readers should be warned. DrTLesterThomas ( talk) 15:01, 26 January 2014 (UTC)
Hi there,
I think a comparison with fork-join would be helpful. These two concepts seem similar, and pointing out both the similarities and differences would be helpful for the reader. There's an academic paper on the subject actually. Thank you. 205.175.116.125 ( talk) 21:03, 18 March 2014 (UTC)
I first heard about map-reduce frameworks in a lecture at Imperial College in 1994 given by Qian Wu. [ This paper] covers some of that work. I wish I could find the lecture notes because they actually contained a diagram which was map-reduce. I'm just putting this here for people looking for prior art against Google's patent. Richard W.M. Jones ( talk) 12:14, 3 May 2014 (UTC)
I started fresh on this topic on Wikipedia (I am bit new to this page of Wikipedia), though i had some good understanding of the concept (MapReduce). As suggested on the top of the article, I wanted to improve the article to make it easy to understand. For this, i tried to look at the previous discussions and feedback, which seems pretty old (more than 3-4 years old now in 2015). Also it is difficult to understand about which comments are already addressed, and which ones need attention. For instance talks regarding the examples (K1, K2) and citation cleanup seems like already addressed. If some existing followers of this topic may throw some light of what is done and what is pending for action, that could be helpful. — Preceding unsigned comment added by Vishal0soni ( talk • contribs) 13:30, 2 January 2015 (UTC)
Regarding Yarn, can you please provide references for your explainations. For the changes i made, i had already mentioned appropriate source. As far my understanding, with MRv2, the entire architecture and functioning of MapReduce has changed. Now Job Tracker and Task Tracker does not exists, these are replaced by resource manager,Application manager and few other additional components. So the entire processing workflow has been redefined. Vishal0soni ( talk) 02:15, 3 January 2015 (UTC)
Statement by 138.246.2.241 (talk) As for your YARN change, I reverted it, sorry. YARN is not a "programming model"; and MapReducev2 is not a new model either. Yarn is an Hadoop API change, but I don't think it is notable on its own for Wikipedia. It coincides with the Hadoop 2 milestone, and it cannot be used without Hadoop. It is a refactoring of the Hadoop codebase to allow sharing certain code between MapReduce and other jobs. MapReducev2 isn't fundamentally different - it's simply Hadoops MR, now using YARN for resource management instead of having an own resource management. I'm not aware of any major breakthrough on MR enabled by YARN; but from a MR point of view this is only maintainance. The appropriate article for YARN is Apache Hadoop, and it is already covered there. —138.246.2.241 (talk) 18:00, 2 January 2015 (UTC)
References
[1] Does this reference support "MapReduce as a big data processing model is considered dead by many domain experts, as development has moved on to more capable and less disk-oriented mechanisms that incorporate full map and reduce capabilities."? 199.64.7.56 ( talk) 06:11, 24 September 2015 (UTC)
References
This section was unsourced and does not reflect how MapReduce works in the real world. The statement that a Reducer must be a monoid in particular is not correct. The only reason for a Reducer to be a monoid would be if the process of reducing a set of pairs with the same key was split across processes, however neither Google's MapReduce nor Hadoop do this, and neither require that a Reducer be monoidal. When key-value pairs (output by a Mapper) are shuffled, all the pairs with the same key are sent to a single process, which then processes these pairs in a single call to the Reducer. There are therefore no requirements for the Reducer to be associative, commutative, or to have an identity element. The example of taking an average, mentioned as inappropriate for MapReduce, is in reality a perfectly ordinary operation for a MapReduce. — Preceding unsigned comment added by 98.239.129.142 ( talk) 07:51, 25 May 2017 (UTC)
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FYI a new user has submitted an AfC for Draft:Reduce (algorithmics), since there's some overlap I thought I would mention it here. Rolf H Nelson ( talk) 05:18, 4 May 2018 (UTC)
I expect it to be "map reduce", "map-reduce", "map/reduce". Because without spaces, it is difficult to distinguish the programming model from the Hadoop MapReduce program. — Preceding unsigned comment added by Sunapi386 ( talk • contribs) 21:59, 8 January 2019 (UTC)
I was trying to understand the examples on this page, in light of the type signatures given for Map and Reduce. I was confused by the second example of averaging social network contacts, which said "The count info in the record is important if the processing is reduced more than one time". Is this supposed to refer to calling Reduce more than once for the same key, and that it's essential to have Cnew in the output (Y,(A,Cnew)) as opposed to just outputting (Y,A) ? If Reduce is called more than one time for the same key, then either Reduce needs to be able to fold its output with previous calls (which puts more rigid type constraints and invariants than the ones given in this article for Reduce), or MapReduce doesn't result in a finalized average. Stackoverflow summary of the confusion here. Metaeducation ( talk) 07:00, 9 October 2023 (UTC)