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Hadoop日记Day15---MapReduce新旧api的比较

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  我使用hadoop的是hadoop1.1.2,而很多公司也在使用hadoop0.2x版本,因此市面上的hadoop资料版本不一,为了扩充自己的知识面,MapReduce的新旧api进行了比较研究。
  hadoop版本1.x的包一般是mapreduce
  hadoop版本0.x的包一般是mapred
我们还是以单词统计为例进行研究,代码如下,如代码1.1所示:
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package old;

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

import mapreduce.WordCountApp;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
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;
/**
 * hadoop版本1.x的包一般是mapreduce
 * hadoop版本0.x的包一般是mapred
 *
 */
public class OldAPP {
    static final String INPUT_PATH = "hdfs://hadoop:9000/hello";
    static final String OUT_PATH = "hdfs://hadoop:9000/out";
    /**
     * 改动:
     * 1.不再使用Job,而是使用JobConf
     * 2.类的包名不再使用mapreduce,而是使用mapred
     * 3.不再使用job.waitForCompletion(true)提交作业,而是使用JobClient.runJob(job);
     * 
     */
    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        final FileSystem fileSystem = FileSystem.get(new URI(INPUT_PATH), conf);
        final Path outPath = new Path(OUT_PATH);
        if(fileSystem.exists(outPath)){
            fileSystem.delete(outPath, true);
        }
        
        final JobConf job = new JobConf(conf , WordCountApp.class);
        //1.1指定读取的文件位于哪里
        FileInputFormat.setInputPaths(job, INPUT_PATH);
        //指定如何对输入文件进行格式化,把输入文件每一行解析成键值对
        //job.setInputFormatClass(TextInputFormat.class);
        
        //1.2 指定自定义的map类
        job.setMapperClass(MyMapper.class);
        //map输出的<k,v>类型。如果<k3,v3>的类型与<k2,v2>类型一致,则可以省略
        //job.setMapOutputKeyClass(Text.class);
        //job.setMapOutputValueClass(LongWritable.class);
        
        //1.3 分区
        //job.setPartitionerClass(HashPartitioner.class);
        //有一个reduce任务运行
        //job.setNumReduceTasks(1);
        
        //1.4 TODO 排序、分组
        
        //1.5 TODO 规约
        
        //2.2 指定自定义reduce类
        job.setReducerClass(MyReducer.class);
        //指定reduce的输出类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);
        
        //2.3 指定写出到哪里
        FileOutputFormat.setOutputPath(job, outPath);
        //指定输出文件的格式化类
        //job.setOutputFormatClass(TextOutputFormat.class);
        
        //把job提交给JobTracker运行
        JobClient.runJob(job);
    }

    
    
    /**
     * 新api:extends Mapper
     * 老api:extends MapRedcueBase implements Mapper
     */
    static class MyMapper extends MapReduceBase implements Mapper<LongWritable, Text, Text, LongWritable>{
        @Override
        public void map(LongWritable k1, Text v1,
                OutputCollector<Text, LongWritable> collector, Reporter reporter)
                throws IOException {
            final String[] splited = v1.toString().split("\t");
            for (String word : splited) {
                collector.collect(new Text(word), new LongWritable(1));
            }
        }
    }
    
    static class MyReducer extends MapReduceBase implements Reducer<Text, LongWritable, Text, LongWritable>{
        @Override
        public void reduce(Text k2, Iterator<LongWritable> v2s,
                OutputCollector<Text, LongWritable> collector, Reporter reporter)
                throws IOException {
            long times = 0L;
            while (v2s.hasNext()) {
                final long temp = v2s.next().get();
                times += temp;
            }
            collector.collect(k2, new LongWritable(times));
        }
    }
}
View Code

代码 1.1

一、自定义Mapper类的不同

  在新api中,是继承类org.apache.hadoop.mapreduce.Mapper<KEYIN, VALUEIN, KEYOUT, VALUEOUT>。在旧api中,是继承类org.apache.hadoop.mapred.MapReduceBase,然后实现接口 org.apache.hadoop.mapred.Mapper<K1, V1, K2, V2>。在新api中,覆盖的map方法的第三个参数是Context类;在旧api中,覆盖的map方法的第三、四个形参分别是OutputCollectorReporter类。在新api的Context中已经把两个类的功能合并到一起了,用户操作更简单。使用旧api的自定义Mapper类,如代码1.2所示所示。key、value对。每一个键值对调用一次map函数。

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 1 /**
 2      * 新api:extends Mapper
 3      * 老api:extends MapRedcueBase implements Mapper
 4      */
 5     static class MyMapper extends MapReduceBase implements Mapper<LongWritable, Text, Text, LongWritable>{
 6         @Override
 7         public void map(LongWritable k1, Text v1,
 8                 OutputCollector<Text, LongWritable> collector, Reporter reporter)
 9                 throws IOException {
10             final String[] splited = v1.toString().split("\t");
11             for (String word : splited) {
12                 collector.collect(new Text(word), new LongWritable(1));
13             }
14         }
15     }
View Code

代码 1.2

二、自定义Reducer类的不同

  在新api中,是继承类org.apache.hadoop.mapreduce.Reducer<KEYIN, VALUEIN, KEYOUT, VALUEOUT>。在旧api中,是继承类org.apache.hadoop.mapred.MapReduceBase,然后实现接口 org.apache.hadoop.mapred. Reducer<K1, V1, K2, V2>。在新api中覆盖的reduce方法的第二个参数是java.lang.Iterable<VALUEIN>。在旧api中,覆盖的 reduce方法的第二个参数是java.util.Iterator<V 2>。前者可以使用增强for循环进行处理,后者只能使用 while循环处理了。在新api中,覆盖的reduce方法的第三个参数是Context类;在旧api中,覆盖的reduce方法的第三、四个形参分别是OutputCollectorReporter类。在新api的Context中已经把两个类的功能合并到一起了,用户操作更简单。使用旧api的自定义Reducer类,代码如2.1所示。

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 1 static class MyReducer extends MapReduceBase implements Reducer<Text, LongWritable, Text, LongWritable>{
 2         @Override
 3         public void reduce(Text k2, Iterator<LongWritable> v2s,
 4                 OutputCollector<Text, LongWritable> collector, Reporter reporter)
 5                 throws IOException {
 6             long times = 0L;
 7             while (v2s.hasNext()) {
 8                 final long temp = v2s.next().get();
 9                 times += temp;
10             }
11             collector.collect(k2, new LongWritable(times));
12         }
13     }
View Code

代码 2.1

三、 驱动代码main方法的不同

  在新api中驱动代码主要是通过org.apache.hadoop.mapreduce.Job类实现的,通过该类管理各种配置,然后调用waitForCompleti on(boolean)方法把代码提交给JobTracker执行。在旧api中驱动代码主要是通过 org.apache.hadoop.mapred.JobConf.JobConf(Con figuration, Class)类实现的,通过该类管理各种配置。对于job的提交,是通过org.apache.hadoop.mapred.JobClient类的 runJob(JobC onf)方法实现的。可见,新api中把JobConfJobClient的功能进行了合并,用户调用更方便。

  其中,JobConf类与Job类的方法名称几乎一致,只是传递的形参类型大不相同了。在新api中的Job类,要求setXXX(…)的形参必须是org .apache.hadoop.mapreduce及其子包下面的类;而旧api中的JobConf类,要求setXXX(…)的形参必须是 org.apache.hadoop.mapred及其子包下面的类。使用旧api的驱动代码main方法,如代码3.1所示。

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 1 package old;
 2 
 3 import java.io.IOException;
 4 import java.net.URI;
 5 import java.util.Iterator;
 6 
 7 import mapreduce.WordCountApp;
 8 
 9 import org.apache.hadoop.conf.Configuration;
10 import org.apache.hadoop.fs.FileSystem;
11 import org.apache.hadoop.fs.Path;
12 import org.apache.hadoop.io.LongWritable;
13 import org.apache.hadoop.io.Text;
14 import org.apache.hadoop.mapred.FileInputFormat;
15 import org.apache.hadoop.mapred.FileOutputFormat;
16 import org.apache.hadoop.mapred.JobClient;
17 import org.apache.hadoop.mapred.JobConf;
18 import org.apache.hadoop.mapred.MapReduceBase;
19 import org.apache.hadoop.mapred.Mapper;
20 import org.apache.hadoop.mapred.OutputCollector;
21 import org.apache.hadoop.mapred.Reducer;
22 import org.apache.hadoop.mapred.Reporter;
23 import org.apache.hadoop.mapred.TextInputFormat;
24 import org.apache.hadoop.mapred.TextOutputFormat;
25 import org.apache.hadoop.mapred.lib.HashPartitioner;
26 /**
27  * hadoop版本1.x的包一般是mapreduce
28  * hadoop版本0.x的包一般是mapred
29  *
30  */
31 public class OldAPP {
32     static final String INPUT_PATH = "hdfs://hadoop:9000/hello";
33     static final String OUT_PATH = "hdfs://hadoop:9000/out";
34     /**
35      * 改动:
36      * 1.不再使用Job,而是使用JobConf
37      * 2.类的包名不再使用mapreduce,而是使用mapred
38      * 3.不再使用job.waitForCompletion(true)提交作业,而是使用JobClient.runJob(job);
39      * 
40      */
41     public static void main(String[] args) throws Exception {
42         
43         Configuration conf = new Configuration();
44         final FileSystem fileSystem = FileSystem.get(new URI(INPUT_PATH), conf);
45         final Path outPath = new Path(OUT_PATH);
46         if(fileSystem.exists(outPath)){
47             fileSystem.delete(outPath, true);
48         }
49         
50         final JobConf job = new JobConf(conf , WordCountApp.class);
51         
52         FileInputFormat.setInputPaths(job, INPUT_PATH);//1.1指定读取的文件位于哪里
53         job.setMapperClass(MyMapper.class);//1.2 指定自定义的map类
54         job.setMapOutputKeyClass(Text.class);//map输出的<k,v>类型。如果<k3,v3>的类型与<k2,v2>类型一致,则可以省略
55         job.setMapOutputValueClass(LongWritable.class);
56         job.setPartitionerClass(HashPartitioner.class);//1.3 分区
57         job.setNumReduceTasks(1);//有一个reduce任务运行
58         job.setReducerClass(MyReducer.class);//2.2 指定自定义reduce类
59         job.setOutputKeyClass(Text.class);//指定reduce的输出类型
60         job.setOutputValueClass(LongWritable.class);
61         FileOutputFormat.setOutputPath(job, outPath);//2.3 指定写出到哪里
62         JobClient.runJob(job);//把job提交给JobTracker运行
63     }
64 
65     
66     
67     /**
68      * 新api:extends Mapper
69      * 老api:extends MapRedcueBase implements Mapper
70      */
71     static class MyMapper extends MapReduceBase implements Mapper<LongWritable, Text, Text, LongWritable>{
72         @Override
73         public void map(LongWritable k1, Text v1,
74                 OutputCollector<Text, LongWritable> collector, Reporter reporter)
75                 throws IOException {
76             final String[] splited = v1.toString().split("\t");
77             for (String word : splited) {
78                 collector.collect(new Text(word), new LongWritable(1));
79             }
80         }
81     }
82     
83     static class MyReducer extends MapReduceBase implements Reducer<Text, LongWritable, Text, LongWritable>{
84         @Override
85         public void reduce(Text k2, Iterator<LongWritable> v2s,
86                 OutputCollector<Text, LongWritable> collector, Reporter reporter)
87                 throws IOException {
88             long times = 0L;
89             while (v2s.hasNext()) {
90                 final long temp = v2s.next().get();
91                 times += temp;
92             }
93             collector.collect(k2, new LongWritable(times));
94         }
95     }
96 }
View Code

代码 3.1

Hadoop日记Day15---MapReduce新旧api的比较

标签:style   blog   http   color   io   os   使用   ar   java   

原文地址:http://www.cnblogs.com/sunddenly/p/3997836.html

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