学习笔记Hadoop（十四）—— MapReduce开发入门—— MapReduce API介绍MapReduce实例

Posted 2021-10-22 别呀

tags:

篇首语：本文由小常识网(cha138.com)小编为大家整理，主要介绍了学习笔记Hadoop（十四）—— MapReduce开发入门—— MapReduce API介绍MapReduce实例相关的知识，希望对你有一定的参考价值。

四、MapReduce API介绍

一般MapReduce都是由Mapper， Reducer 及main 函数组成。
Mapper程序一般完成键值对映射操作;
Reducer 程序一般完成键值对聚合操作;
Main函数则负责组装Mapper，Reducer及必要的配置;
高阶编程还涉及到设置输入输出文件格式、设置Combiner、Partitioner优化程序等;

4.1、MapReduce程序模块 : Main 函数

4.2、MapReduce程序模块： Mapper

org.apache.hadoop.mapreduce.Mapper

4.3、MapReduce程序模块： Reducer

org.apache.hadoop.mapreduce.Reducer

五、MapReduce实例

5.1、流程（Mapper、Reducer、Main、打包运行）

参考WordCount程序，修改Mapper;
直接复制 Reducer程序；
直接复制Main函数，并做相应修改;
编译打包 ;
上传Jar包;
上传数据;
运行程序;
查看运行结果;

5.2、实例1：按日期访问统计次数:

1、参考WordCount程序，修改Mapper;
（这里新建一个java程序，然后把下面(1、2、3步代码)复制到类里）

    public static class SpiltMapper
            extends Mapper<Object, Text, Text, IntWritable> {

        private final static IntWritable one = new IntWritable(1);
        private Text word = new Text();
        //value: email_address | date
        public void map(Object key, Text value, Context context
        ) throws IOException, InterruptedException {
            String[] data = value.toString().split("\\\\|",-1);  //
            word.set(data[1]);   //
            context.write(word, one);
        }
    }

2、直接复制 Reducer程序；

    public static class IntSumReducer
            extends Reducer<Text,IntWritable,Text,IntWritable> {
        private IntWritable result = new IntWritable();

        public void reduce(Text key, Iterable<IntWritable> values,
                           Context context
        ) throws IOException, InterruptedException {
            int sum = 0;
            for (IntWritable val : values) {
                sum += val.get();
            }
            result.set(sum);
            context.write(key, result);
        }
    }

3、直接复制Main函数，并做相应修改;

public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
        if (otherArgs.length < 2) {
            System.err.println("Usage: wordcount <in> [<in>...] <out>");
            System.exit(2);
        }
        Job job = Job.getInstance(conf, "word count");
        job.setJarByClass(CountByDate.class);   //我们的主类是CountByDate
        job.setMapperClass(SpiltMapper.class);  //mapper：我们修改为SpiltMapper
        job.setCombinerClass(IntSumReducer.class);
        job.setReducerClass(IntSumReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        for (int i = 0; i < otherArgs.length - 1; ++i) {
            FileInputFormat.addInputPath(job, new Path(otherArgs[i]));
        }
        FileOutputFormat.setOutputPath(job,
                new Path(otherArgs[otherArgs.length - 1]));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }

4、编译打包 (jar打包)

build出现错误及解决办法：

完成

5/6、上传jar包&数据
email_log_with_date.txt数据包链接：https://pan.baidu.com/s/1HfwHCfmvVdQpuL-MPtpAng
提取码：cgnb

上传数据包(注意开启hdfs)：

上传OK（浏览器：master:50070查看）

7、运行程序
(注意开启yarn)

上传完成后：

(master:8088)

8、查看结果
(master:50070)

5.3、实例2：按用户访问次数排序

Mapper、Reducer、Main程序
SortByCountFirst.Mapper

package demo;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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;
import org.apache.hadoop.util.GenericOptionsParser;

import java.io.IOException;

public class SortByCountFirst {
    //1、修改Mapper
    public static class SpiltMapper
            extends Mapper<Object, Text, Text, IntWritable> {

        private final static IntWritable one = new IntWritable(1);
        private Text word = new Text();
        //value: email_address | date
        public void map(Object key, Text value, Context context
        ) throws IOException, InterruptedException {
            String[] data = value.toString().split("\\\\|",-1);
            word.set(data[0]);
            context.write(word, one);
        }
    }

    //2、直接复制 Reducer程序，不用修改
    public static class IntSumReducer
            extends Reducer<Text,IntWritable,Text,IntWritable> {
        private IntWritable result = new IntWritable();

        public void reduce(Text key, Iterable<IntWritable> values,
                           Context context
        ) throws IOException, InterruptedException {
            int sum = 0;
            for (IntWritable val : values) {
                sum += val.get();
            }
            result.set(sum);
            context.write(key, result);
        }
    }

    //3、直接复制Main函数，并做相应修改;
    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
        if (otherArgs.length < 2) {
            System.err.println("Usage: demo.SortByCountFirst <in> [<in>...] <out>");
            System.exit(2);
        }
        Job job = Job.getInstance(conf, "sort by count first ");
        job.setJarByClass(SortByCountFirst.class);   //我们的主类是CountByDate
        job.setMapperClass(SpiltMapper.class);  //mapper：我们修改为SpiltMapper
        job.setCombinerClass(IntSumReducer.class);
        job.setReducerClass(IntSumReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        for (int i = 0; i < otherArgs.length - 1; ++i) {
            FileInputFormat.addInputPath(job, new Path(otherArgs[i]));
        }
        FileOutputFormat.setOutputPath(job,
                new Path(otherArgs[otherArgs.length - 1]));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

SortByCountSecond.Mapper

package demo;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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;
import org.apache.hadoop.util.GenericOptionsParser;

import java.io.IOException;

public class SortByCountSecond {
    //1、修改Mapper
    public static class SpiltMapper
            extends Mapper<Object, Text, IntWritable, Text> {

        private IntWritable count = new IntWritable(1);
        private Text word = new Text();
        //value: email_address \\t count
        public void map(Object key, Text value, Context context
        ) throws IOException, InterruptedException {
            String[] data = value.toString().split("\\t",-1);
            word.set(data[0]);
            count.set(Integer.parseInt(data[1]));
            context.write(count,word);
        }
    }

    //2、直接复制 Reducer程序，不用修改
    public static class ReverseReducer
            extends Reducer<IntWritable,Text,Text,IntWritable> {

        public void reduce(IntWritable key, Iterable<Text> values,
                           Context context
        ) throws IOException, InterruptedException {
            for (Text val : values) {
                context.write(val,key);
            }
        }
    }

    //3、直接复制Main函数，并做相应修改;
    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
        if (otherArgs.length < 2) {
            System.err.println("Usage: demo.SortByCountFirst <in> [<in>...] <out>");
            System.exit(2);
        }
        Job job = Job.getInstance(conf, "sort by count first ");
        job.setJarByClass(SortByCountSecond.class);   //我们的主类是CountByDate
        job.setMapperClass(SpiltMapper.class);  //mapper：我们修改为SpiltMapper
//        job.setCombinerClass(IntSumReducer.class);
        job.setReducerClass(ReverseReducer.class);
        job.setMapOutputKeyClass(IntWritable.class);
        job.setMapOutputValueClass(Text.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        for (int i = 0; i < otherArgs.length - 1; ++i) {
            FileInputFormat.addInputPath(job, new Path(otherArgs[i]));
        }
        FileOutputFormat.setOutputPath(job,
                new Path(otherArgs[otherArgs.length - 1]));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

然后打包上传

yarn jar sortbycount.jar demo.SortByCountSecond -Dmapreduce.job.queuename=prod email_log_with_date.txt sortbycountfirst_output00

yarn jar sortbycount.jar demo.SortByCountSecond -Dmapreduce.job.queuename=prod email_log_with_date.txt sortbycountfirst_output00 sortbycountsecond_output00

以上是关于学习笔记Hadoop（十四）—— MapReduce开发入门—— MapReduce API介绍MapReduce实例的主要内容，如果未能解决你的问题，请参考以下文章

大数据笔记（十四）——HBase的过滤器与Mapreduce

Hadoop学习之路（十四）MapReduce的核心运行机制