Hadoop中将数据切分成块存在HDFS不同的DataNode中，如果想汇总，按照常规想法就是，移动数据到统计程序：先把数据读取到一个程序中，再进行汇总。
但是HDFS存的数据量非常大时，对汇总程序所在的服务器将产生巨大压力，并且网络IO也十分消耗资源。
为了解决这种问题，MapReduce提出一种想法：将统计程序移动到DataNode，每台DataNode（就近）统计完再汇总，充分利用DataNode的计算资源。YARN的调度决定了MapReduce程序所在的Node。
下面这个图描述了具体的流程
Hadoop中可以通过Java来编写MapReduce，针对不熟悉Java的开发者，Hadoop提供了通过可执行程序或者脚本的方式创建MapReduce的Hadoop Streaming。
hadoop streaming通过用户编写的map函数中标准输入读取数据（一行一行地读取），按照map函数的处理逻辑处理后，将处理后的数据由标准输出进行输出到下一个阶段。
reduce函数也是按行读取数据，按照函数的处理逻辑处理完数据后，将它们通过标准输出写到hdfs的指定目录中。
不管使用的是何种编程语言，在map函数中，原始数据会被处理成<key,value>的形式，但是key与value之间必须通过t分隔符分隔，分隔符左边的是key，分隔符右边的是value,如果没有使用t分隔符，那么整行都会被当作key
首先，新增测试数据
vi mpdata I love Beijing I love China Beijing is the capital of China
然后，将文件上传到hdfs
[root@localhost ~]# hadoop fs -put mrdata /chesterdata
新建dotnet6的console项目mapper，修改Program.cs
using System; using System.Text.RegularExpressions; namespace mapper { class Program { static void Main(string[] args) { string line; //Hadoop passes data to the mapper on STDIN while((line = Console.ReadLine()) != null) { // We only want words, so strip out punctuation, numbers, etc. var onlyText = Regex.Replace(line, @".|;|:|,|[0-9]|'", ""); // Split at whitespace. var words = Regex.Matches(onlyText, @"[w]+"); // Loop over the words foreach(var word in words) { //Emit tab-delimited key/value pairs. //In this case, a word and a count of 1. Console.WriteLine("{0}t1",word); } } } } }
发布mapper
cd /demo/dotnet/mapper/ dotnet publish -c Release -r linux-x64 /p:PublishSingleFile=true
新建dotnet6的console项目reducer，修改Program.cs
using System; using System.Collections.Generic; namespace reducer { class Program { static void Main(string[] args) { //Dictionary for holding a count of words Dictionary<string, int> words = new Dictionary<string, int>(); string line; //Read from STDIN while ((line = Console.ReadLine()) != null) { // Data from Hadoop is tab-delimited key/value pairs var sArr = line.Split('t'); // Get the word string word = sArr[0]; // Get the count int count = Convert.ToInt32(sArr[1]); //Do we already have a count for the word? if(words.ContainsKey(word)) { //If so, increment the count words[word] += count; } else { //Add the key to the collection words.Add(word, count); } } //Finally, emit each word and count foreach (var word in words) { //Emit tab-delimited key/value pairs. //In this case, a word and a count of 1. Console.WriteLine("{0}t{1}", word.Key, word.Value); } } } }
发布reducer
/demo/dotnet/reducer dotnet publish -c Release -r linux-x64 /p:PublishSingleFile=true
执行mapepr reduce
hadoop jar /usr/local/hadoop323/hadoop-3.2.3/share/hadoop/tools/lib/hadoop-streaming-3.2.3.jar -input /chesterdata/mrdata -output /dotnetmroutput -mapper "./mapper" -reducer "./reducer" -file /demo/dotnet/mapper/bin/Release/net6.0/linux-x64/publish/mapper -f /demo/dotnet/reducer/bin/Release/net6.0/linux-x64/publish/reducer
查看mapreduce结果
[root@localhost reducer]# hadoop fs -ls /dotnetmroutput -rw-r--r-- 1 root supergroup 0 2022-05-01 16:40 /dotnetmroutput/_SUCCESS -rw-r--r-- 1 root supergroup 55 2022-05-01 16:40 /dotnetmroutput/part-00000
查看part-00000内容
[root@localhost reducer]# hadoop fs -cat /dotnetmroutput/part-00000 Beijing 2 China 2 I 2 capital 1 is 1 love 2 of 1 the 1
可以看到dotnet模式的Hadoop Streaming已经执行成功。
# mapper.py import sys import re p = re.compile(r'w+') for line in sys.stdin: words = line.strip().split(' ') for word in words: w = p.findall(word) if len(w) < 1: continue s = w[0].strip().lower() if s != "": print("%st%s" % (s, 1))
编写reducer
# reducer.py import sys res = dict() for word_one in sys.stdin: word, one = word_one.strip().split('t') if word in res.keys(): res[word] = res[word] + 1 else: res[word] = 1 print(res)
执行mapreduce
hadoop jar /usr/local/hadoop323/hadoop-3.2.3/share/hadoop/tools/lib/hadoop-streaming-3.2.3.jar -input /chesterdata/mrdata -output /mroutput -mapper "python3 mapper.py" -reducer "python3 reducer.py" -file /root/mapper.py -file /root/reducer.py
查看mapreduce结果
[root@localhost lib]# hadoop fs -ls /mroutput -rw-r--r-- 1 root supergroup 0 2022-05-01 05:00 /mroutput/_SUCCESS -rw-r--r-- 1 root supergroup 89 2022-05-01 05:00 /mroutput/part-00000
查看part-00000内容
[root@localhost lib]# hadoop fs -cat /mroutput/part-00000 {'beijing': 2, 'capital': 1, 'china': 2, 'i': 2, 'is': 1, 'love': 2, 'of': 1, 'the': 1}
可以看到python模式的Hadoop Streaming已经执行成功。
