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前言

本文隶属于专栏《1000个问题搞定大数据技术体系》，该专栏为笔者原创，引用请注明来源，不足和错误之处请在评论区帮忙指出，谢谢！

本专栏目录结构和参考文献请见1000个问题搞定大数据技术体系

正文

需求： Word Count，大数据领域的 Hello World。

Mapper

package com.shockang.study.bigdata.mapreduce;

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> { 
   
    protected void map(LongWritable key, Text value, Context context)
            throws IOException, InterruptedException { 
   
        String[] words = value.toString().split(" ");
        for (String word : words) { 
   
            // 每个单词出现１次，作为中间结果输出
            context.write(new Text(word), new IntWritable(1));
        }
    }
}

Reducer

package com.shockang.study.bigdata.mapreduce;

import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> { 
   
    /* key: hello value: List(1, 1, ...) */
    protected void reduce(Text key, Iterable<IntWritable> values,
                          Context context) throws IOException, InterruptedException { 
   
        int sum = 0;

        for (IntWritable count : values) { 
   
            sum = sum + count.get();
        }
        context.write(key, new IntWritable(sum));
    };
}

Main

package com.shockang.study.bigdata.mapreduce;
import java.io.IOException;
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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
public class WordCountMain { 

public static void main(String[] args) throws IOException,
ClassNotFoundException, InterruptedException { 

if (args.length != 2) { 

System.out.println("please input Path!");
System.exit(0);
}
Configuration configuration = new Configuration();
Job job = Job.getInstance(configuration, WordCountMain.class.getSimpleName());
// 打jar包
job.setJarByClass(WordCountMain.class);
// 通过job设置输入/输出格式，默认的就是 TextInputFormat/TextOutputFormat
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
// 设置输入/输出路径
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
// 设置处理Map/Reduce阶段的类
job.setMapperClass(WordCountMapper.class);
job.setReducerClass(WordCountReducer.class);
//如果map、reduce的输出的kv对类型一致，直接设置reduce的输出的kv对就行
//如果不一样，需要分别设置map, reduce的输出的kv类型
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);
// 设置最终输出key/value的类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
// 提交作业
job.waitForCompletion(true);
}
}

Combiner

package com.shockang.study.bigdata.mapreduce;
import java.io.IOException;
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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
public class WordCountMainWithCombiner { 

public static void main(String[] args) throws IOException,
ClassNotFoundException, InterruptedException { 

if (args.length != 2) { 

System.out.println("please input Path!");
System.exit(0);
}
Configuration configuration = new Configuration();
Job job = Job.getInstance(configuration, WordCountMainWithCombiner.class.getSimpleName());
// 打jar包
job.setJarByClass(WordCountMainWithCombiner.class);
// 通过job设置输入/输出格式
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
// 设置输入/输出路径
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
// 设置处理Map/Reduce阶段的类
job.setMapperClass(WordCountMapper.class);
job.setCombinerClass(WordCountReducer.class);
job.setReducerClass(WordCountReducer.class);
//如果map、reduce的输出的kv对类型一致，直接设置reduce的输出的kv对就行
// 如果不一样，需要分别设置map, reduce的输出的kv类型
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);
// 设置最终输出key/value的类型m
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
// 提交作业
job.waitForCompletion(true);
}
}

二次排序

package com.shockang.study.bigdata.mapreduce;
import org.apache.hadoop.io.WritableComparable;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
/** * 现有需求，需要自定义key类型，并自定义key的排序规则，如按照人的salary降序排序，若相同，则再按age升序排序 */
public class Person implements WritableComparable<Person> { 

private String name;
private int age;
private int salary;
public Person() { 

}
public Person(String name, int age, int salary) { 

this.name = name;
this.age = age;
this.salary = salary;
}
public String getName() { 

return name;
}
public void setName(String name) { 

this.name = name;
}
public int getAge() { 

return age;
}
public void setAge(int age) { 

this.age = age;
}
public int getSalary() { 

return salary;
}
public void setSalary(int salary) { 

this.salary = salary;
}
@Override
public String toString() { 

return this.salary + " " + this.age + " " + this.name;
}
public int compareTo(Person o) { 

//先比较salary，高的排序在前；若相同，age小的在前
int compareResult1 = this.salary - o.salary;
if (compareResult1 != 0) { 

return -compareResult1;
} else { 

return this.age - o.age;
}
}
public void write(DataOutput dataOutput) throws IOException { 

//序列化，将NewKey转化成使用流传输的二进制
dataOutput.writeUTF(name);
dataOutput.writeInt(age);
dataOutput.writeInt(salary);
}
public void readFields(DataInput dataInput) throws IOException { 

//使用in读字段的顺序，要与write方法中写的顺序保持一致
this.name = dataInput.readUTF();
this.age = dataInput.readInt();
this.salary = dataInput.readInt();
}
}

自定义分区

package com.shockang.study.bigdata.mapreduce;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Partitioner;
import java.util.HashMap;
public class CustomPartitioner extends Partitioner<Text, IntWritable> { 

public static HashMap<String, Integer> dict = new HashMap<>();
static { 

dict.put("Dear", 0);
dict.put("Bear", 1);
dict.put("River", 2);
dict.put("Car", 3);
}
public int getPartition(Text text, IntWritable intWritable, int i) { 

return dict.get(text.toString());
}
}

数据倾斜处理

当遇到数据倾斜的时候，我们可以在 Reducer 中日志记录哪些超过阈值的 key

package com.shockang.study.bigdata.mapreduce;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;
import java.util.Iterator;
public class WordCountReducerWithDataSkew extends Reducer<Text, IntWritable, Text, IntWritable> { 

public static final String MAX_VALUES = "skew.maxvalues";
private int maxValueThreshold;
@Override
protected void setup(Context context) throws IOException, InterruptedException { 

Configuration conf = context.getConfiguration();
maxValueThreshold = Integer.parseInt(conf.get(MAX_VALUES));
}
/* key: hello value: List(1, 1, ...) */
protected void reduce(Text key, Iterable<IntWritable> values,
Context context) throws IOException, InterruptedException { 

int i = 0;
for (IntWritable value : values) { 

System.out.println(value);
i++;
}
if (++i > maxValueThreshold) { 

System.out.println("Received " + i + " values for key " + key);
}
}
}