MapReduce编程实战(二)——20151112

来源:转载


MapReduce Ruby编程


Hadoop的Streaming使用UNIX标准流作为Hadoop和应用程序之间的接口,所以我们可以使用任何编程语言通过标准输入、输出来写MapReduce程序。


关于Ruby的环境安装,可以参照这篇文章:
http://www.linuxidc.com/Linux/2014-04/100242.htm


Ruby改写的查找最高气温的程序如下。


map.rb


#!/usr/local/rvm/bin/ruby


STDIN.each_line do |line|
  val = line
  year,temp,q = val[15,4],val[87,5],val[92,1]
  puts "#{year}/t#{temp}" if (temp != "+9999" && q =~ /[01459]/)
end


reduce.rb


#!/usr/local/rvm/bin/ruby
last_key,max_val = nil,0
STDIN.each_line do |line|
  key,val = line.split("/t")
  if last_key && last_key != key
    puts "#{last_key}/t#{max_val}"
    last_key,max_val = key,val.to_i
  else
    last_key,max_val = key,[max_val,val.to_i].max
  end
end
puts "#{last_key}/t#{max_val}" if last_key


使用Unix管道来模拟整个MapReduce过程,如下:


 


cat temperature.txt | ./map.rb | sort | ./reduce.rb


或者:cat temperature.txt | ruby map.rb | sort | ruby reduce.rb


可以看到,输出结果和Java是一样的。


 


在集群中运行


 


运行Java的MapReduce:


我在程序中写死了HDFS的输入路径为/hadooptemp/input/2,输出路径为/hadooptemp/output,运行Java的MapReduce的大致步骤如下:


(1)上传jar包到服务器:test.jar


(2)hadoop fs -mkdir -p /hadooptemp/input/2


(3)hadoop fs -put /home/hadoop/temperature.txt /hadooptemp/input/2


(4)运行:hadoop jar test.jar MaxTemperature


(5)查看输出结果:hadoop fs -ls /hadooptemp/output hadoop fs -cat /hadooptemp/output/part-00000


 


运行Ruby的MapReduce:


hadoop jar /home/hadoop/hadoop-2.2.0/share/hadoop/tools/lib/hadoop-streaming-2.2.0.jar /


-input /hadooptemp/input/2


-output /hadooptemp/output


-mapper "map.rb | sort | reduce.rb"


-reducer reduce.rb


这里的mapper部分中的reduce.rb,实际起到了combiner的作用。


 


WordCount MapReduce程序演示


 


代码如下:


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


import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.TextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;


public class WordCount {
 public static void main(String[] args) throws Exception {
  JobConf conf = new JobConf(WordCount.class);
  conf.setJobName("World Count");


  // FileInputFormat.setInputPaths(conf, new Path(args[0]));
  // FileOutputFormat.setOutputPath(conf, new Path(args[1]));


  FileInputFormat.setInputPaths(conf, new Path("/hadooptemp/input/1"));
  FileOutputFormat.setOutputPath(conf, new Path("/hadooptemp/output"));


  conf.setMapperClass(Map.class);
  conf.setCombinerClass(Reduce.class);
  conf.setReducerClass(Reduce.class);
  
  conf.setOutputKeyClass(Text.class);
  conf.setOutputValueClass(IntWritable.class);


  conf.setInputFormat(TextInputFormat.class);
  conf.setOutputFormat(TextOutputFormat.class);


  JobClient.runJob(conf);
 }
}


class Map extends MapReduceBase implements
  Mapper<LongWritable, Text, Text, IntWritable> {
 private final static IntWritable one = new IntWritable(1);
 private Text word = new Text();


 public void map(LongWritable key, Text value,
   OutputCollector<Text, IntWritable> output, Reporter reporter)
   throws IOException {
  String line = value.toString();
  StringTokenizer tokenizer = new StringTokenizer(line);
  while (tokenizer.hasMoreTokens()) {
   word.set(tokenizer.nextToken());
   output.collect(word, one);
  }
 }
}


class Reduce extends MapReduceBase implements
  Reducer<Text, IntWritable, Text, IntWritable> {
 public void reduce(Text key, Iterator<IntWritable> values,
   OutputCollector<Text, IntWritable> output, Reporter reporter)
   throws IOException {
  int sum = 0;
  while (values.hasNext()) {
   sum += values.next().get();
  }
  output.collect(key, new IntWritable(sum));
 }
}


示例数据:


hello world
nihao
hello beijing



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