学习笔记Hadoop—— MapReduce开发入门

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一、MapReduce


MapReduce是Google提出的一个软件架构,用于大规模数据集(大于1TB)的并行运算。概念“Map(映射)”和“Reduce(归纳)”,及他们的主要思想,都是从函数式编程语言借来的,还有从矢量编程语言借来的特性。

当前的软件实现是指定一个Map(映射)函数,用来把一组键值对映射成一组新的键值对,指定并发的Reduce(归纳)函数,用来保证所有映射的键值对中的每一个共享相同的键组。


二、MapReduce开发环境搭建

环境准备: Java, Intellij IDEA, Maven
开发环境搭建方式

java安装链接及步骤:https://www.cnblogs.com/de-ming/p/13909440.html

2.1、Maven环境


添加依赖

https://search.maven.org/artifact/org.apache.hadoop/hadoop-client/3.1.4/jar


添加源码

2.2、手动导入Jar包

Hadoop安装包链接:https://pan.baidu.com/s/1teHwnBH2Qm6F7iWZ3q-hSQ
提取码:cgnb

新建一个java工程


然后,搜JobClient.class,点击’Choose Sources’

这样就OK了,可以看到JobClient.java

三、MapReduce单词计数源码分析

3.1、打开WordCount.java

打开:https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-mapreduce-examples/3.1.4,复制Maven里面的内容

粘贴到源码

搜索WordCount

3.2、源码分析

3.2.1、MapReduce单词计数源码 : Map任务

3.2.2、MapReduce单词计数源码 : Reduce任务

3.2.3、MapReduce单词计数源码 : main 函数

设置必要参数及组装MapReduce程序


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)

  1. 参考WordCount程序,修改Mapper;
  2. 直接复制 Reducer程序;
  3. 直接复制Main函数,并做相应修改;
  4. 编译打包 ;
  5. 上传Jar包;
  6. 上传数据;
  7. 运行程序;
  8. 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);
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