MapReduce

MapReduce作业

Hadoop Streaming

准备

1.您已经创建KMR集群以及相关其他服务，KMR集群默认安装Hadoop，详情参考 集群创建

2.您已经使用SSH连接到集群，详情参考 使用SSH访问集群,或者已经开通云主机作为客户端节点，以下操作均在集群主节点进行。

3.如需使用KS3存储，请开通KS3服务，如何使用KS3，请参考 KS3文档

MapReduce作业

以WordCount为例，介绍如何使用Hadoop实现单词统计功能

程序准备

以下代码来源于Hadoop官网 Wrodcount实例代码

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;

public class WordCount {

  public static class TokenizerMapper
       extends Mapper<Object, Text, Text, IntWritable>{

    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 {
      StringTokenizer itr = new StringTokenizer(value.toString());
      while (itr.hasMoreTokens()) {
        word.set(itr.nextToken());
        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 {
      int sum = 0;
      for (IntWritable val : values) {
        sum += val.get();
      }
      result.set(sum);
      context.write(key, result);
    }
  }

  public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();
    Job job = Job.getInstance(conf, "word count");
    job.setJarByClass(WordCount.class);
    job.setMapperClass(TokenizerMapper.class);
    job.setCombinerClass(IntSumReducer.class);
    job.setReducerClass(IntSumReducer.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class);
    FileInputFormat.addInputPath(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));
    System.exit(job.waitForCompletion(true) ? 0 : 1);
  }
}

设置JDK和Hadoop环境变量，假设java安装路径为/usr/java/jdk1.7.0_51

export JAVA_HOME=/usr/java/jdk1.7.0_51  
export PATH=${JAVA_HOME}/bin:${PATH}  
export HADOOP_CLASSPATH=${JAVA_HOME}/lib/tools.jar 

编译程序

mkdir wordcount_classes 
hadoop com.sun.tools.javac.Main -d wordcount_classes/ WordCount.java 
jar cvf wordcount.jar -C wordcount_classes . 

这样就得到了wordcount.jar

作业输入输出

hadoop作业的输入和输出文件，可以放在HDFS上，也可以选择放在KS3上。

使用HDFS

将输入文件放到HDFS上，假设输入文件为TWILIGHT.txt

hadoop dfs -mkdir -p /user/hadoop/examples/input  
hadoop dfs -put TWILIGHT.txt /user/hadoop/examples/input  

使用KS3

1. 可以在KS3控制台上直接创建目录、上传本地文件

1. 也可以用命令行将jar包和输入文件放到KS3上

 hadoop dfs -mkdir -p ks3://kmrtest9/wordcount/lib  
 hadoop dfs -mkdir -p ks3://kmrtest9/wordcount/input  
 hadoop dfs -put wordcount.jar ks3://kmrtest9/wordcount/lib  
 hadoop dfs -put TWILIGHT.txt ks3://kmrtest9/wordcount/input

作业提交

命令行提交

输入输出在HDFS上：

hadoop jar wordcount.jar WordCount /user/hadoop/examples/input/ /user/hadoop/examples/output

输入输出在KS3上：

hadoop jar wordcount.jar WordCount ks3://kmrtest9/wordcount/input/ ks3://kmrtest9/wordcount/output

KMR控制台提交

对于临时集群，可以在控制台提交作业。

在作业列表标签页中，点击“创建作业”按钮，添加作业。注意：需要将Jar包放到ks3上。

Hadoop Streaming

Hadoop Streaming允许用户使用可执行的命令或者脚本作为mapper和reducer。以下用几个示例说明Hadoop Streaming如何使用。 详细可参考Hadoop官网Hadoop Streaming

使用shell命令作为mapper和reducer

hadoop jar /opt/hadoop/share/hadoop/tools/lib/hadoop-streaming-*.jar -input /user/hadoop/examples/input/ -output /user/hadoop/examples/output1 -mapper /bin/cat -reducer /usr/bin/wc

使用python脚本作为mapper和reducer

mapper.py

#!/usr/bin/env python
import sys
for line in sys.stdin:
    line = line.strip()
    words = line.split()
    for word in words:
        print "%s\t%s" % (word, 1)

reducer.py

#!/usr/bin/env python
from operator import itemgetter
import sys

current_word = None
current_count = 0
word = None

for line in sys.stdin:
    line = line.strip()
    word, count = line.split('\t', 1)
    try:
        count = int(count)
    except ValueError:
        continue
    if current_word == word:
        current_count += count
    else:
        if current_word:
            print "%s\t%s" % (current_word, current_count)
        current_count = count
        current_word = word

if word == current_word:
    print "%s\t%s" % (current_word, current_count)

命令行执行streaming作业

 chmod +x mapper.py  
 chmod +x reducer.py  
 hadoop jar /opt/hadoop/share/hadoop/tools/lib/hadoop-streaming-*.jar -input /user/hadoop/examples/input/ -output /user/hadoop/examples/output2 -mapper mapper.py -reducer reducer.py -file mapper.py  -file reducer.py  

对于临时集群，可以在KMR控制台提交作业