這裡示範如何使用 R 與 Hadoop MapReduce 分析 Stack Exchange 網站的傾印資料。
Stack Exchange 是一個程式設計領域非常知名的問答網站，上面有非常多具有參考價值的問題解答，Stack Exchange 也將其整個網站的內容傾印成 XML 檔，以創用 CC 授權的方式開放出來，放在 archive.org 提供大家免費下載使用。
Stack Exchange 所提供的資料有好多種，我們這裡僅選擇主要的文章資料來進行分析。參考 readme.txt 的說明後，我們可以知道文章的資料儲存於 **posts**.xml 中，而其檔案格式如下：
- Id - PostTypeId - 1: Question - 2: Answer - ParentID (only present if PostTypeId is 2) - AcceptedAnswerId (only present if PostTypeId is 1) - CreationDate - Score - ViewCount - Body - OwnerUserId - LastEditorUserId - LastEditorDisplayName="Jeff Atwood" - LastEditDate="2009-03-05T22:28:34.823" - LastActivityDate="2009-03-11T12:51:01.480" - CommunityOwnedDate="2009-03-11T12:51:01.480" - ClosedDate="2009-03-11T12:51:01.480" - Title= - Tags= - AnswerCount - CommentCount - FavoriteCount
由於 Stack Exchange 旗下有非常多的網站，我們選擇 raspberrypi.stackexchange.com 這個專門討論樹莓派（Raspberry Pi）的網站作為示範。
下載的 raspberrypi.stackexchange.com 傾印資料檔：
wget https://archive.org/download/stackexchange/raspberrypi.stackexchange.com.7z
使用 7zip 解壓縮：
7za x raspberrypi.stackexchange.com.7z
檢查 Posts.xml 的檔案格式：
head Posts.xml
<?xml version="1.0" encoding="utf-8"?> <posts> <row Id="1" PostTypeId="1" AcceptedAnswerId="225" CreationDate="2012-06-12T19:45:29.157" Score="71" ViewCount="46943" Body="<p>I already asked this <a href="http://stackoverflow.com/questions/10973020/cross-compilation-for-raspberry-pi-in-gcc-where-to-start">question</a> on Stack Overflow, but I would like to know if anyone managed to build a GCC 4.7 toolchain for ARM cross-compilation (for a x86/x86-64 Linux host). There are many instructins for building GCC from source and many available cross-compilers for pre-4.7 GCC versions, just not the latest one.</p>

<p>Compiling on Rasp Pi itself works fine but is just a bit too slow for practical purposes.</p>

<p>I am eager to get compiling and I would like to use the latest and the best tools.</p>
" OwnerUserId="13" LastEditorUserId="86" LastEditDate="2012-07-18T21:17:22.913" LastActivityDate="2016-02-20T19:57:27.583" Title="How do I build a GCC 4.7 toolchain for cross-compiling?" Tags="<software-development><cross-compilation><gcc>" AnswerCount="8" CommentCount="3" FavoriteCount="35" /> <row Id="2" PostTypeId="1" CreationDate="2012-06-12T19:52:39.397" Score="14" ViewCount="935" Body="<p>I have come across the <a href="http://elinux.org/RPi_Advanced_Setup" rel="nofollow noreferrer">RPi Advanced Setup</a> but there are incomplete steps. For example, where can I find <code>mkcard.txt</code> from the <strong>Advanced SD card setup</strong> section? Do I just reuse the one from the <a href="http://downloads.angstrom-distribution.org/demo/beaglebone/mkcard.txt" rel="nofollow noreferrer">BeagleBone demo site</a>? Then there is an important steps section under <strong>Finally booting GNU/Linux</strong> that only contains the text "to be completed". </p>

<p>Does anybody know what additional things need to be done to boot into a Debian release for example?</p>
" OwnerUserId="22" LastEditorUserId="46969" LastEditDate="2017-02-17T16:52:52.020" LastActivityDate="2017-02-17T16:52:52.020" Title="On the RPi Advanced setup page, what does mkcard.txt do?" Tags="<setup>" AnswerCount="2" CommentCount="0" /> <row Id="3" PostTypeId="1" AcceptedAnswerId="4" CreationDate="2012-06-12T19:53:29.027" Score="6" ViewCount="1261" Body="<p>Which distributors and partners are officially authorized to sell Raspberry Pi's?</p>
" OwnerUserId="8" LastEditorUserId="8" LastEditDate="2012-06-14T17:35:31.210" LastActivityDate="2012-06-14T17:35:31.210" Title="Which distributors are authorized to sell device units?" Tags="<purchasing>" AnswerCount="6" CommentCount="0" ClosedDate="2012-06-14T23:19:43.213" /> <row Id="4" PostTypeId="2" ParentId="3" CreationDate="2012-06-12T19:55:24.513" Score="16" Body="<p><a href="http://www.farnell.com/" rel="nofollow">Farnell</a> and <a href="http://uk.rs-online.com/web/" rel="nofollow">RS Components</a> are the only companies listed on <a href="http://www.raspberrypi.org/faqs" rel="nofollow">RaspberryPi.org</a> authorized to sell the official devices so far.</p>

<p>Additionally, their is an official <a href="http://elinux.org/RPi_Buying_Links_By_Country" rel="nofollow">Wiki page</a> of additional sources that the RPi can be purchased from, outside the UK. (Thanks <a href="http://raspberrypi.stackexchange.com/a/9/8">@Shane Hudson</a> for this information!)</p>
" OwnerUserId="8" LastEditorUserId="8" LastEditDate="2012-06-13T13:38:42.810" LastActivityDate="2012-06-13T13:38:42.810" CommentCount="1" /> <row Id="5" PostTypeId="1" AcceptedAnswerId="11" CreationDate="2012-06-12T19:55:35.857" Score="58" ViewCount="110720" Body="<p>The site <a href="http://www.raspberrypi.org/quick-start-guide">http://www.raspberrypi.org/quick-start-guide</a> says:</p>

<blockquote>
 <p>You will need an SD card with an operating system preloaded before you
 can boot the Raspberry Pi. A brand-name (not generic) Class 4 card of
 4GB or more is recommended. To obtain an SD card image, and for
 instructions on how to flash an SD card from a Linux or Windows PC,
 please refer to <a href="http://www.raspberrypi.org/downloads">http://www.raspberrypi.org/downloads</a>.</p>
</blockquote>

<p>Which SD cards are compatible with the Raspberry Pi?</p>
" OwnerUserId="37" LastEditorUserId="90" LastEditDate="2012-06-13T14:53:21.153" LastActivityDate="2015-10-01T08:26:45.943" Title="Which SD cards are compatible?" Tags="<sd-card><compatibility>" AnswerCount="1" CommentCount="3" FavoriteCount="11" /> <row Id="6" PostTypeId="2" ParentId="3" CreationDate="2012-06-12T19:56:55.850" Score="5" Body="<p>You need to register your interest before you can purchase:</p>

<p><a href="http://uk.rs-online.com/web/generalDisplay.html?id=raspberrypi">http://uk.rs-online.com/web/generalDisplay.html?id=raspberrypi</a></p>

<p><a href="http://uk.farnell.com/jsp/bespoke/bespoke7.jsp?ICID=I-RASP-HPBLOF-0015&amp;bespokepage=farnell/en_UK/promotions/raspberryPi.jsp">http://uk.farnell.com/jsp/bespoke/bespoke7.jsp?ICID=I-RASP-HPBLOF-0015&amp;bespokepage=farnell/en_UK/promotions/raspberryPi.jsp</a></p>
" OwnerUserId="30" LastActivityDate="2012-06-12T19:56:55.850" CommentCount="0" /> [略]
這個 xml 檔的結構非常簡單，每一筆資料都是用一行 row 表示，所以我們可以直接使用 MapReduce 逐行解析，不需要經過特別的處理。
將資料以 bzip2 壓縮，節省磁碟空間：
bzip2 -z Posts.xml
將資料放進 HDFS 檔案系統：
hadoop fs -mkdir raspberrypi hadoop fs -put Posts.xml.bz2 raspberrypi hadoop fs -ls raspberrypi
Found 1 items -rw-r--r-- 3 s00ser01 s00ser00 11444598 2017-05-04 11:00 raspberrypi/Posts.xml.bz2
R 與 Hadoop 配合使用的方式主要有兩種，一種是使用 RHadoop 的 rmr2 套件，完全在 R 中執行 MapReduce；另外一種是使用 Hadoop Streaming 的方式將資料傳給 R 來處理。
rmr2）這裡我們將分析每篇文章，計算每個作者所獲的 score 總值。
這是使用 rmr2 實作 MapReduce 的 R 程式碼：
library(rmr2) # 資料來源 my.input <- "raspberrypi/Posts.xml.bz2" # 定義 Map 函數 se.map <- function(., lines) { score <- c() owner.uid <- c() for (text in lines) { # 取出 Score 值 pattern.s <- '(?<=Score=")[[:digit:]]+(?=")' m.s <- regexpr(pattern.s, text, perl = TRUE) if ( m.s == -1 ) next # 取出 OwnerUserId 值 pattern.o <- '(?<=OwnerUserId=")[[:digit:]]+(?=")' m.o <- regexpr(pattern.o, text, perl = TRUE) if ( m.o == -1 ) next score <- c(score, regmatches(text, m.s)) owner.uid <- c(owner.uid, regmatches(text, m.o)) } keyval(as.numeric(owner.uid), as.numeric(score)) } # 定義 Reduce 函數 se.reduce <- function(owner.uid, score) { keyval(owner.uid, sum(score)) } # 以 MapReduce 計算使用者的 Score 總值 output <- mapreduce( input = my.input, input.format = "text", map = se.map, reduce = se.reduce, combine = TRUE ) # 從 HDFS 檔案系統取回計算結果 result.list <- from.dfs(output) result.df <- data.frame(owner.uid = result.list$key, score = result.list$val) result.df <- result.df[order(result.df$score, decreasing = TRUE),] # 取出 Score 最高的前 20 名使用者 result.df.top20 <- result.df[1:20,] result.df.top20
owner.uid score 74 5538 2741 13393 13650 2410 8439 56 2164 947 40 1916 1070 8697 1307 1858 86 1157 7498 894 1157 14154 590 938 3705 93 918 6530 13 752 4673 1070 729 14253 7274 726 4775 8496 719 4932 19949 678 13156 35 639 14569 32756 601 1986 8631 533 7479 54 495 2796 181 458 13157 68 440
使用 R 與 Hadoop Streaming API 來實作的版本，概念上大同小異，這是 mapper.R：
#! /usr/bin/env Rscript con <- file("stdin", open = "r") while (length(text <- readLines(con, n = 1, warn = FALSE)) > 0) { # 取出 Score 值 pattern.s <- '(?<=Score=")[[:digit:]]+(?=")' m.s <- regexpr(pattern.s, text, perl = TRUE) if ( m.s == -1 ) next # 取出 OwnerUserId 值 pattern.o <- '(?<=OwnerUserId=")[[:digit:]]+(?=")' m.o <- regexpr(pattern.o, text, perl = TRUE) if ( m.o == -1 ) next score <- regmatches(text, m.s) owner.uid <- regmatches(text, m.o) cat(owner.uid, "\t", score, "\n", sep="") } close(con)
這是 reducer.R：
#! /usr/bin/env Rscript # 移除空白函數 trimWhiteSpace <- function(line) gsub("(^ +)|( +$)", "", line) # 分割欄位函數 splitLine <- function(line) { val <- unlist(strsplit(line, "\t")) list(owner.uid = as.character(val[1]), score = as.integer(val[2])) } # 使用環境空間作為雜湊（hash） env <- new.env(hash = TRUE) con <- file("stdin", open = "r") while (length(line <- readLines(con, n = 1, warn = FALSE)) > 0) { line <- trimWhiteSpace(line) split <- splitLine(line) owner.uid <- split$owner.uid score <- split$score # 檢查 owner.uid 是否已經存在 if (exists(owner.uid, envir = env, inherits = FALSE)) { # 更新既有的 owner.uid 變數 old.score <- get(owner.uid, envir = env) assign(owner.uid, old.score + score, envir = env) } else { # 建立名稱為 owner.uid 的變數 assign(owner.uid, score, envir = env) } } close(con) # 輸出結果 for (o in ls(env, all = TRUE)) cat(o, "\t", get(o, envir = env), "\n", sep = "")
執行：
hadoop jar hadoop-streaming-2.6.0-cdh5.5.1.jar \ -file ./mapper.R \ -file ./reducer.R \ -mapper ./mapper.R \ -reducer ./reducer.R \ -numReduceTasks 1 \ -input raspberrypi/Posts.xml.bz2 \ -output raspberrypi_output
將資料從 HDFS 取出，並整理出前 20 名使用者：
hadoop fs -cat raspberrypi_output/* | sort -r -n -k 2 | head -n 20
輸出結果為：
5538 2741 13650 2410 56 2164 40 1916 8697 1307 894 1157 86 1157 590 938 93 918 13 752 1070 729 7274 726 8496 719 19949 678 35 639 32756 601 8631 533 54 495 181 458 68 440