hadoop 裏執行 MapReduce 任務的幾種常見方式

說明：

測試文件：

echo -e "aa\tbb \tcc\nbb\tcc\tdd" > 3.txt

hadoop fs -put 3.txt /tmp/3.txt

全文的例子均以該文件作測試用例，統計單詞出現的次數（WordCount）。

一、原生態的方式：java 源碼編譯打包成jar包後，由 hadoop 腳本調度執行，舉例：

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;

public class WordCount {

	public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> {
		/**
		 * LongWritable, IntWritable, Text 均是 Hadoop 中實現的用於封裝 Java 數據類型的類，
		 * 這些類實現了WritableComparable接口， 都可以被串行化從而便於在分佈式環境中進行數據交換，
		 * 你能夠將它們分別視爲long,int,String 的替代品。
		 */
		// IntWritable one 至關於 java 原生類型 int 1
		private final static IntWritable one = new IntWritable(1);
		private Text word = new Text();

		public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
			// 每行記錄都會調用 map 方法處理，此處是每行都被分詞
			StringTokenizer itr = new StringTokenizer(value.toString());
			while (itr.hasMoreTokens()) {
				word.set(itr.nextToken());
				// 輸出每一個詞及其出現的次數 1，相似 <word1,1><word2,1><word1,1>
				context.write(word, one);
			}
		}
	}

	public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
		private IntWritable result = new IntWritable();

		public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException,
				InterruptedException {
			// key 相同的鍵值對會被分發到同一個 reduce中處理
			// 例如 <word1,<1,1>>在 reduce1 中處理，而<word2,<1>> 會在 reduce2 中處理
			int sum = 0;
			// 相同的key（單詞）的出現次數會被 sum 累加
			for (IntWritable val : values) {
				sum += val.get();
			}
			result.set(sum);
			// 1個 reduce 處理完1 個鍵值對後，會輸出其 key（單詞）對應的結果（出現次數）
			context.write(key, result);
		}
	}

	public static void main(String[] args) throws Exception {
		Configuration conf = new Configuration();
		// 多隊列hadoop集羣中，設置使用的隊列
		conf.set("mapred.job.queue.name", "regular");
		// 之因此此處不直接用 argv[1] 這樣的，是爲了排除掉運行時的集羣屬性參數，例如隊列參數，
		// 獲得用戶輸入的純參數，如路徑信息等
		String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
		for (String argsStr : otherArgs) {
			System.out.println("-->> " + argsStr);
		}
		if (otherArgs.length < 2) {
			System.err.println("Usage: wordcount <in> <out>");
			System.exit(2);
		}
		Job job = new Job(conf, "word count");
		job.setJarByClass(WordCount.class);
		// map、reduce 輸入輸出類
		job.setMapperClass(TokenizerMapper.class);
		job.setCombinerClass(IntSumReducer.class);
		job.setReducerClass(IntSumReducer.class);
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(IntWritable.class);
		// 輸入輸出路徑
		FileInputFormat.addInputPath(job, new Path(otherArgs[1]));
		FileOutputFormat.setOutputPath(job, new Path(otherArgs[2]));
		// 多子job的類中，能夠保證各個子job串行執行
		System.exit(job.waitForCompletion(true) ? 0 : 1);
	}
}

執行：

bin/hadoop jar /tmp/wordcount.jar WordCount /tmp/3.txt /tmp/5

結果：

hadoop fs -cat /tmp/5/*
aa      1
bb      2
cc      2
dd      1

參考資料：

Hadoop - Map/Reduce 經過WordCount例子的變化來了解新版hadoop接口的變化

http://blog.csdn.net/derekjiang/article/details/6836209

Hadoop示例程序WordCount運行及詳解

http://samuschen.iteye.com/blog/763940

官方的 wordcount v1.0 例子

http://hadoop.apache.org/docs/r1.1.1/mapred_tutorial.html#Example%3A+WordCount+v1.0

二、基於 MR 的數據流 Like SQL 腳本開發語言：pig

A1 = load '/data/3.txt';
A = stream A1 through `sed "s/\t/ /g"`;
B = foreach A generate flatten(TOKENIZE((chararray)$0)) as word;
C = filter B by word matches '\\w+';
D = group C by word;
E = foreach D generate COUNT(C), group;
dump E;

注意：不一樣分隔符對load及後面的$0的影響。

詳情請見：

https://gist.github.com/186460

http://www.slideshare.net/erikeldridge/a-brief-handson-introduction-to-hadoop-pig

三、構建數據倉庫的類 SQL 開發語言：hive

create table textlines(text string);
load data inpath '/data/3.txt' overwrite into table textlines;
SELECT wordColumn, count(1) FROM textlines LATERAL VIEW explode(split(text,'\t+')) wordTable AS wordColumn GROUP BY wordColumn;

詳情請見：

http://my.oschina.net/leejun2005/blog/83045

http://blog.csdn.net/techdo/article/details/7433222

四、跨平臺的腳本語言：python

map：

#!/usr/bin/python
import os,re,sys
for line in sys.stdin:
	for i in line.strip().split("\t"):
		print i

reduce:

#!/usr/bin/python
import os,re,sys
arr = {}
for words in sys.stdin:
	word = words.strip()
	if word not in arr:
		arr[word] = 1
	else:
		arr[word] += 1
for k, v in arr.items():
	print str(k) + ": " + str(v)

最後在shell下執行：

hadoop jar $HADOOP_HOME/contrib/streaming/hadoop-streaming-0.20.203.0.jar -file map.py -file reduce.py  -mapper map.py -reducer reduce.py -input /data/3.txt -output /data/py

注意：腳本開頭須要顯示指定何種解釋器以及賦予腳本執行權限

詳情請見：

http://blog.csdn.net/jiedushi/article/details/7390015

五、Linux 下的瑞士軍刀：shell 腳本

map:

#!/bin/bash
tr '\t' '\n'

reduce:

#!/bin/bash
sort|uniq -c

最後在shell下執行：

june@deepin:~/hadoop/hadoop-0.20.203.0/tmp>
hadoop jar $HADOOP_HOME/contrib/streaming/hadoop-streaming-0.20.203.0.jar -file map.py -file reduce.py  -mapper map.py -reducer reduce.py -input /data/3.txt -output /data/py
packageJobJar: [map.py, reduce.py, /home/june/data_hadoop/tmp/hadoop-unjar2676221286002400849/] [] /tmp/streamjob8722854685251202950.jar tmpDir=null
12/10/14 21:57:00 INFO mapred.FileInputFormat: Total input paths to process : 1
12/10/14 21:57:00 INFO streaming.StreamJob: getLocalDirs(): [/home/june/data_hadoop/tmp/mapred/local]
12/10/14 21:57:00 INFO streaming.StreamJob: Running job: job_201210141552_0041
12/10/14 21:57:00 INFO streaming.StreamJob: To kill this job, run:
12/10/14 21:57:00 INFO streaming.StreamJob: /home/june/hadoop/hadoop-0.20.203.0/bin/../bin/hadoop job  -Dmapred.job.tracker=localhost:9001 -kill job_201210141552_0041
12/10/14 21:57:00 INFO streaming.StreamJob: Tracking URL: http://localhost:50030/jobdetails.jsp?jobid=job_201210141552_0041
12/10/14 21:57:01 INFO streaming.StreamJob:  map 0%  reduce 0%
12/10/14 21:57:13 INFO streaming.StreamJob:  map 67%  reduce 0%
12/10/14 21:57:19 INFO streaming.StreamJob:  map 100%  reduce 0%
12/10/14 21:57:22 INFO streaming.StreamJob:  map 100%  reduce 22%
12/10/14 21:57:31 INFO streaming.StreamJob:  map 100%  reduce 100%
12/10/14 21:57:37 INFO streaming.StreamJob: Job complete: job_201210141552_0041
12/10/14 21:57:37 INFO streaming.StreamJob: Output: /data/py
june@deepin:~/hadoop/hadoop-0.20.203.0/tmp>
hadoop fs -cat /data/py/part-00000
      1 aa	
      1 bb 	
      1 bb	
      2 cc	
      1 dd	
june@deepin:~/hadoop/hadoop-0.20.203.0/tmp>

特別提示：上述有些方法對字段後的空格忽略或計算，請注意仔細甄別。

說明：列舉了上述幾種方法主要是給你們一個不一樣的思路，

在解決問題的過程當中，開發效率、執行效率都是咱們須要考慮的，不要太侷限某一種方法了。