Hadoop实战第一篇

前言：
 　　都说现在是草根为尊的时代，近年来hadoop及spark技术在国内越来越流行。而且渐渐现成为企业的新宠。在DT时代全面来临之前，能提早接触大数据的技术必然能先人一步。本文作为Hadoop系列的第一篇，将HDFS和MapRed两个技术核心用2个实例简单实现一些，希望能供hadoop入门的朋友些许参考。

--HDFS

 1 import java.io.IOException;
 2
 3 import org.apache.hadoop.conf.Configuration;
 4 import org.apache.hadoop.fs.FileSystem;
 5 import org.apache.hadoop.fs.Path;
 6
 7 public class  HDFStest {
 8     final static String P_IN="hdfs://hadoop0:9000/data";
 9     final static String P_F1="hdfs://hadoop0:9000/a.txt";
10
11
12     public static void main(String[] args) throws IOException {
13
14         FileSystem fileSystem = FileSystem.get(new Configuration());
15         System.out.println("make diretory:");
16         fileSystem.mkdirs(new Path(P_IN));
17         System.out.println("judgy if exist ‘File‘:");
18         System.out.println(fileSystem.exists(new Path(P_F1)));
19
20     }
21
22 }

--MapReduce

实现文本单词出现次数的统计：

 1 import org.apache.hadoop.conf.Configuration;
 2 import org.apache.hadoop.fs.Path;
 3 import org.apache.hadoop.io.LongWritable;
 4 import org.apache.hadoop.io.Text;
 5 import org.apache.hadoop.mapreduce.Job;
 6 import org.apache.hadoop.mapreduce.Mapper;
 7 import org.apache.hadoop.mapreduce.Reducer;
 8 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
 9 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
10
11
12
13 public class WC {
14
15     static String INPUT="hdfs://hadoop0:9000/hello";
16     static String OUTPUT="hdfs://hadoop0:9000/output";
17
18     public static void main(String[] args) throws Exception{
19
20
21         Job job = new Job(new Configuration(),WC.class.getSimpleName());
22         job.setMapperClass(MyMapper.class);
23         job.setReducerClass(MyReducer.class);
24         job.setJarByClass(WC.class);
25         //输出结果格式
26         job.setMapOutputKeyClass(Text.class);;
27         job.setMapOutputValueClass(LongWritable.class);
28         job.setOutputKeyClass(Text.class);
29         job.setOutputValueClass(LongWritable.class);
30         //路径设置
31         FileInputFormat.setInputPaths(job, INPUT);
32         FileOutputFormat.setOutputPath(job, new Path(OUTPUT));
33         //waitfor
34         job.waitForCompletion(true);
35
36     }
37
38     static class MyMapper extends Mapper<LongWritable, Text,Text,LongWritable >{
39
40         @Override
41         protected void map(LongWritable key, Text value,
42                 Mapper<LongWritable, Text, Text, LongWritable>.Context context)
43                 throws IOException, InterruptedException {
44
45             String[] words = value.toString().split(" ");
46             for(String word:words){
47                 context.write(new Text(word), new LongWritable(1));
48             }
49         }
50     }
51     static class MyReducer extends Reducer<Text, LongWritable, Text, LongWritable>{
52
53         @Override
54         protected void reduce(Text arg0, Iterable<LongWritable> arg1,Context context)
55                 throws IOException, InterruptedException {
56
57             Long sum=0L;
58             for(LongWritable c:arg1){
59                 sum += c.get();
60             }
61             context.write(arg0,new LongWritable(sum));
62         }
63     }
64 }

以上代码相对简单，map读取到一行“Text”之后通过字符串切分函数split()得到各个单词，每个单词出现一次计数为1：

Reduce操作，实际就是一个集合元素累计的操作：