<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Literature on Bruno Pedro</title><link>https://brunopedro.com/tags/literature/</link><description>Recent content in Literature on Bruno Pedro</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Tue, 08 May 2018 10:00:15 -0700</lastBuildDate><atom:link href="https://brunopedro.com/tags/literature/index.xml" rel="self" type="application/rss+xml"/><item><title>MapReduce literature</title><link>https://brunopedro.com/2010/12/22/mapreduce-literature/</link><pubDate>Wed, 22 Dec 2010 13:13:45 +0000</pubDate><guid>https://brunopedro.com/2010/12/22/mapreduce-literature/</guid><description>&lt;p>Just a quick note to point to two valuable MapReduce related resources:&lt;/p>
&lt;ul>
&lt;li>&amp;ldquo;&lt;a href="http://labs.google.com/papers/mapreduce.html">MapReduce: Simplified Data Processing on Large Clusters&lt;/a>&amp;rdquo;, by Jeffrey Dean and Sanjay Ghemawat: the original paper describing Google&amp;rsquo;s MapReduce strategy;&lt;/li>
&lt;/ul>
&lt;blockquote>
&lt;p>MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Many real world tasks are expressible in this model, as shown in the paper.&lt;/p></description></item></channel></rss>