MapReduce编程实战

MapReduce是什么 

MapReduce是Hadoop（这种大数据处理生态环境）的编程模型。既然称为模型，则意味着它有固定的形式。

MapReduce编程模型，就是Hadoop生态环境进行数据分析处理的固定的编程形式。

这种固定的编程形式描述如下：

MapReduce任务过程被分为两个阶段：map阶段和reduce阶段。每个阶段都以键/值对作为输入和输出，并由程序员选择他们的类型。

也就是说，程序员只需要定义两个函数：map函数和reduce函数就好了，其他的计算过程交给hadoop就好了。

通过以上描述，我们可以看出：

MapReduce所能处理的场景实际是非常具体的，非常有限的，只是“数据的统计分析”场景。 

输入数据准备 

天气预报官方网址：ftp://ftp.ncdc.noaa.gov/pub/data/gsod/

https://github.com/tomwhite/hadoop-book/tree/master/input/ncdc/all

如果简单测试，也可以把下面这几行粘贴到一个文本文件也行，这就是正确的天气文件：

0035029070999991902010113004+64333+023450FM-12+000599999V0201401N011819999999N0000001N9-01001+99999100311ADDGF104991999999999999999999MW1381
0035029070999991902010120004+64333+023450FM-12+000599999V0201401N013919999999N0000001N9-01171+99999100121ADDGF108991999999999999999999MW1381
0035029070999991902010206004+64333+023450FM-12+000599999V0200901N009819999999N0000001N9-01611+99999100121ADDGF108991999999999999999999MW1381
0029029070999991902010213004+64333+023450FM-12+000599999V0200901N011819999999N0000001N9-01721+99999100121ADDGF108991999999999999999999
0029029070999991902010220004+64333+023450FM-12+000599999V0200901N009819999999N0000001N9-01781+99999100421ADDGF108991999999999999999999

本文中，我们把存储天气格式的文本文件命名为：temperature.txt

MapReduce Java编程

有两套JavaAPI，旧的是org.apache.hadoop.mapred包，MapReduce编程是使用实现接口的方式；新的是org.apache.hadoop.marreduce包，MapReduce编程是使用继承抽象基类的方式；其实都差不多，下面都会有显示。

Maven

 <dependency>
  <groupId>org.apache.hadoop</groupId>
  <artifactId>hadoop-core</artifactId>
  <version>1.0.4</version>
 </dependency>

也可以不用官方的，用别人修改重新编译过的，可以直接在Eclipse里面像运行普通Java程序一样运行MapReduce。

编译过的hadoop-core-1.0.4.jar，可以在本地模拟MapReduce

如果Eclipse workspace在d:，则我们可以把d:的某个目录，比如d:\input作为输入目录；d:\output作为输出目录。

MapReduce编程模型里面这样写就可以了：

FileInputFormat.setInputPaths(job, new Path("/input"));

FileOutputFormat.setOutputPath(job, new Path("/output"));

下载地址：

具体下载目录在 /2014年资料/4月/16日/MapReduce编程实战

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或者：

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下载后，直接覆盖maven资源库位置的文件即可。

接口方式

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

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;

public class MaxTemperature {

 public static void main(String[] args) throws Exception {
  JobConf conf = new JobConf(MaxTemperature.class);
  conf.setJobName("Max Temperature");

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

  FileInputFormat.setInputPaths(conf, new Path("/hadooptemp/input/2"));
  FileOutputFormat.setOutputPath(conf, new Path("/hadooptemp/output"));

  conf.setMapperClass(MaxTemperatureMapper.class);
  conf.setReducerClass(MaxTemperatureReduce.class);

  conf.setOutputKeyClass(Text.class);
  conf.setOutputValueClass(IntWritable.class);

  JobClient.runJob(conf);
 }
}

class MaxTemperatureMapper extends MapReduceBase implements
  Mapper<LongWritable, Text, Text, IntWritable> {
 private static final int MISSING = 9999;

 public void map(LongWritable key, Text value,
   OutputCollector<Text, IntWritable> output, Reporter reporter)
   throws IOException {
  String line = value.toString();
  String year = line.substring(15, 19);
  int airTemperature;
  if (line.charAt(87) == '+') {
   airTemperature = Integer.parseInt(line.substring(88, 92));
  } else {
   airTemperature = Integer.parseInt(line.substring(87, 92));
  }
  String quality = line.substring(92, 93);
  if (airTemperature != MISSING && quality.matches("[01459]")) {
   output.collect(new Text(year), new IntWritable(airTemperature));
  }
 }
}

class MaxTemperatureReduce 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 maxValue = Integer.MIN_VALUE;
  while (values.hasNext()) {
   maxValue = Math.max(maxValue, values.next().get());
  }
  output.collect(key, new IntWritable(maxValue));

 }
}

抽象类方式

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

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.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 NewMaxTemperature {

 public static void main(String[] args) throws Exception {

  Job job = new Job();
  job.setJarByClass(NewMaxTemperature.class);

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

  FileInputFormat.setInputPaths(job, new Path("/hadooptemp/input/2"));
  FileOutputFormat.setOutputPath(job, new Path("/hadooptemp/output"));

  job.setMapperClass(NewMaxTemperatureMapper.class);
  job.setReducerClass(NewMaxTemperatureReduce.class);

  job.setOutputKeyClass(Text.class);
  job.setOutputValueClass(IntWritable.class);

  System.exit(job.waitForCompletion(true) ? 0 : 1);
 }
}

class NewMaxTemperatureMapper extends
  Mapper<LongWritable, Text, Text, IntWritable> {
 private static final int MISSING = 9999;

 public void map(LongWritable key, Text value, Context context)
   throws IOException, InterruptedException {
  String line = value.toString();
  String year = line.substring(15, 19);
  int airTemperature;
  if (line.charAt(87) == '+') {
   airTemperature = Integer.parseInt(line.substring(88, 92));
  } else {
   airTemperature = Integer.parseInt(line.substring(87, 92));
  }
  String quality = line.substring(92, 93);
  if (airTemperature != MISSING && quality.matches("[01459]")) {
   context.write(new Text(year), new IntWritable(airTemperature));
  }
 }
}

class NewMaxTemperatureReduce extends
  Reducer<Text, IntWritable, Text, IntWritable> {
 public void reduce(Text key, Iterator<IntWritable> values, Context context)
   throws IOException, InterruptedException {
  int maxValue = Integer.MIN_VALUE;
  while (values.hasNext()) {
   maxValue = Math.max(maxValue, values.next().get());
  }
  context.write(key, new IntWritable(maxValue));

 }
}