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。
天气预报官方网址：ftp://ftp.ncdc.noaa.gov/pub/data/gsod/
但是，发现这个官方网址的文件格式和《Hadoop权威指南》所用的格式不一致，不知道是时间久了，官网的格式变了，还是作者对原始格式进行过处理，亦或这个网址根本不对，所以继而又到《Hadoop权威指南》指定的地址下载了一个，地址如下：
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：
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
本文中，我们把存储天气格式的文本文件命名为：temperature.txt
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。
<dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-core</artifactId> <version>1.0.4</version> </dependency>
下载地址：http://download.csdn.net/detail/puma_dong/7199329
下载后，直接覆盖maven资源库位置的文件即可。
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));
}
}
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));
}
}
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://blog.csdn.net/puma_dong/article/details/17244089
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#!/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_keycat 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。
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。
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
原文：http://blog.csdn.net/puma_dong/article/details/23711103