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实验5 MapReduce初级编程实践

news 2026/5/12 1:44:14

实验步骤

（一）编程实现文件合并和去重操作

对于两个输入文件，即文件A和文件B，请编写MapReduce程序，对两个文件进行合并，并剔除其中重复的内容，得到一个新的输出文件C。下面是输入文件和输出文件的一个样例供参考。

输入文件A的样例如下：

20170101 x

20170102 y

20170103 x

20170104 y

20170105 z

20170106 x

输入文件B的样例如下：

20170101 y

20170102 y

20170103 x

20170104 z

20170105 y

根据输入文件A和B合并得到的输出文件C的样例如下：

20170101 x

20170101 y

20170102 y

20170103 x

20170104 y

20170104 z

20170105 y

20170105 z

20170106 x

（二）编写程序实现对输入文件的排序

现在有多个输入文件，每个文件中的每行内容均为一个整数。要求读取所有文件中的整数，进行升序排序后，输出到一个新的文件中，输出的数据格式为每行两个整数，第一个数字为第二个整数的排序位次，第二个整数为原待排列的整数。下面是输入文件和输出文件的一个样例供参考。

输入文件1的样例如下：

输入文件2的样例如下：

输入文件3的样例如下：

根据输入文件1、2和3得到的输出文件如下：

1 1

2 4

3 5

4 12

5 16

6 25

7 33

8 37

9 39

10 40

11 45

（三）对给定的表格进行信息挖掘

下面给出一个child-parent的表格，要求挖掘其中的父子辈关系，给出祖孙辈关系的表格。

输入文件内容如下：

child parent

Steven Lucy

Steven Jack

Jone Lucy

Jone Jack

Lucy Mary

Lucy Frank

Jack Alice

Jack Jesse

David Alice

David Jesse

Philip David

Philip Alma

Mark David

Mark Alma

输出文件内容如下：

grandchild grandparent

Steven Alice

Steven Jesse

Jone Alice

Jone Jesse

Steven Mary

Steven Frank

Jone Mary

Jone Frank

Philip Alice

Philip Jesse

Mark Alice

Mark Jesse

具体代码

导入依赖

pom.xml

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"xsi:schemaLocation="http://maven.apache.org/POM/4.0.0http://maven.apache.org/xsd/maven-4.0.0.xsd"><modelVersion>4.0.0</modelVersion><groupId>com.example</groupId><artifactId>step1</artifactId><version>1.0-SNAPSHOT</version><properties><maven.compiler.source>1.8</maven.compiler.source><maven.compiler.target>1.8</maven.compiler.target><project.build.sourceEncoding>UTF-8</project.build.sourceEncoding></properties><dependencies><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-common</artifactId><version>3.4.0</version></dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-client</artifactId><version>3.3.4</version></dependency></dependencies><build><plugins><plugin><groupId>org.apache.maven.plugins</groupId><artifactId>maven-compiler-plugin</artifactId><version>3.8.1</version><configuration><source>1.8</source><target>1.8</target></configuration></plugin></plugins></build>
</project>

任务一

MergeDeduplicateMapper

package com.example;import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;public class MergeDeduplicateMapper extends Mapper<LongWritable, Text, Text, Text> {private final Text outputKey = new Text();private final Text outputValue = new Text();@Overrideprotected void map(LongWritable key, Text value, Context context)throws IOException, InterruptedException {String line = value.toString().trim();// 跳过空行if (line.isEmpty()) {return;}// 按制表符或空格分割String[] parts = line.split("\\s+");if (parts.length >= 2) {// 以日期和值的组合作为key，这样可以去除重复的记录String date = parts[0];String val = parts[1];// 创建复合键：日期+值String compositeKey = date + "\t" + val;outputKey.set(compositeKey);outputValue.set("");  // 值为空，因为我们只需要keycontext.write(outputKey, outputValue);}}
}

MergeDeduplicateReducer

package com.example;import java.io.IOException;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;public class MergeDeduplicateReducer extends Reducer<Text, Text, Text, Text> {@Overrideprotected void reduce(Text key, Iterable<Text> values, Context context)throws IOException, InterruptedException {// 由于在mapper阶段已经用复合键去重，这里每个key只会出现一次// 直接输出键（包含日期和值）String compositeKey = key.toString();String[] parts = compositeKey.split("\t");if (parts.length == 2) {Text outputKey = new Text(parts[0]);Text outputValue = new Text(parts[1]);context.write(outputKey, outputValue);}}
}

MergeDeduplicateDriver

package com.example;import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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 MergeDeduplicateDriver {public static void main(String[] args) throws Exception {if (args.length != 3) {System.err.println("Usage: MergeDeduplicate <input path A> <input path B> <output path>");System.exit(-1);}Configuration conf = new Configuration();Job job = Job.getInstance(conf, "Merge and Deduplicate Files");// 设置Jar类job.setJarByClass(MergeDeduplicateDriver.class);// 设置Mapper和Reducer类job.setMapperClass(MergeDeduplicateMapper.class);job.setReducerClass(MergeDeduplicateReducer.class);// 设置输出键值类型job.setOutputKeyClass(Text.class);job.setOutputValueClass(Text.class);// 设置输入输出格式job.setInputFormatClass(TextInputFormat.class);job.setOutputFormatClass(TextOutputFormat.class);// 设置输入和输出路径FileInputFormat.addInputPath(job, new Path(args[0]));  // 文件AFileInputFormat.addInputPath(job, new Path(args[1]));  // 文件BFileOutputFormat.setOutputPath(job, new Path(args[2])); // 输出文件C// 设置Reducer任务数量job.setNumReduceTasks(1);// 等待作业完成System.exit(job.waitForCompletion(true) ? 0 : 1);}
}

任务二

SortMapper

package com.example;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 SortMapper extends Mapper<LongWritable, Text, IntWritable, IntWritable> {private final IntWritable number = new IntWritable();private static final IntWritable one = new IntWritable(1);@Overrideprotected void map(LongWritable key, Text value, Context context)throws IOException, InterruptedException {String line = value.toString().trim();// 跳过空行if (line.isEmpty()) {return;}try {// 将字符串转换为整数int num = Integer.parseInt(line);number.set(num);// 输出 (数字, 1)，其中1是占位符context.write(number, one);} catch (NumberFormatException e) {// 如果遇到非数字内容，跳过System.err.println("跳过非数字内容: " + line);}}
}

SortReducer

package com.example;import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Reducer;public class SortReducer extends Reducer<IntWritable, IntWritable, IntWritable, IntWritable> {private final IntWritable rank = new IntWritable(1);@Overrideprotected void reduce(IntWritable key, Iterable<IntWritable> values, Context context)throws IOException, InterruptedException {// 由于MapReduce已经按key（数字）排序，我们只需要按顺序输出排名for (IntWritable value : values) {context.write(rank, key);// 排名递增rank.set(rank.get() + 1);}}
}

SortDriver

package com.example;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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 SortDriver {public static void main(String[] args) throws Exception {if (args.length != 2) {System.err.println("Usage: SortDriver <input path> <output path>");System.exit(-1);}Configuration conf = new Configuration();Job job = Job.getInstance(conf, "Number Sort with Rank");// 设置Jar类job.setJarByClass(SortDriver.class);// 设置Mapper和Reducer类job.setMapperClass(SortMapper.class);job.setReducerClass(SortReducer.class);// 设置Mapper输出键值类型job.setMapOutputKeyClass(IntWritable.class);job.setMapOutputValueClass(IntWritable.class);// 设置最终输出键值类型job.setOutputKeyClass(IntWritable.class);job.setOutputValueClass(IntWritable.class);// 设置输入输出格式job.setInputFormatClass(TextInputFormat.class);job.setOutputFormatClass(TextOutputFormat.class);// 设置输入和输出路径FileInputFormat.addInputPath(job, new Path(args[0]));  // 输入目录（包含所有输入文件）FileOutputFormat.setOutputPath(job, new Path(args[1])); // 输出目录// 设置Reducer任务数量为1，确保全局排序job.setNumReduceTasks(1);// 等待作业完成System.exit(job.waitForCompletion(true) ? 0 : 1);}
}

任务三

GrandParentMapper

package com.example;import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;public class GrandParentMapper extends Mapper<LongWritable, Text, Text, Text> {private final Text outputKey = new Text();private final Text outputValue = new Text();@Overrideprotected void map(LongWritable key, Text value, Context context)throws IOException, InterruptedException {String line = value.toString().trim();// 跳过空行和标题行if (line.isEmpty() || line.startsWith("child") || line.startsWith("grandchild")) {return;}// 按制表符或空格分割String[] parts = line.split("\\s+");if (parts.length >= 2) {String child = parts[0];String parent = parts[1];// 输出两种关系：// 1. 作为子代关系：key=子, value="1:"+父 (1表示这是子代关系)// 2. 作为父代关系：key=父, value="2:"+子 (2表示这是父代关系)outputKey.set(child);outputValue.set("1:" + parent);context.write(outputKey, outputValue);outputKey.set(parent);outputValue.set("2:" + child);context.write(outputKey, outputValue);}}
}

GrandParentReducer

package com.example;import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;public class GrandParentReducer extends Reducer<Text, Text, Text, Text> {private final Text outputKey = new Text();private final Text outputValue = new Text();@Overrideprotected void reduce(Text key, Iterable<Text> values, Context context)throws IOException, InterruptedException {// 存储该人的子女列表和父母列表List<String> children = new ArrayList<>();List<String> parents = new ArrayList<>();for (Text value : values) {String valStr = value.toString();if (valStr.startsWith("1:")) {// 这是父母关系：1:父母名parents.add(valStr.substring(2));} else if (valStr.startsWith("2:")) {// 这是子女关系：2:子女名children.add(valStr.substring(2));}}// 生成祖孙关系：该人的父母（祖父母） × 该人的子女（孙子）for (String parent : parents) {for (String child : children) {outputKey.set(child);        // 孙子outputValue.set(parent);     // 祖父母context.write(outputKey, outputValue);}}}
}

GrandParentDriver

package com.example;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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 GrandParentDriver {public static void main(String[] args) throws Exception {if (args.length != 2) {System.err.println("Usage: GrandParentDriver <input path> <output path>");System.exit(-1);}Configuration conf = new Configuration();Job job = Job.getInstance(conf, "Find Grandparent Relationships");// 设置Jar类job.setJarByClass(GrandParentDriver.class);// 设置Mapper和Reducer类job.setMapperClass(GrandParentMapper.class);job.setReducerClass(GrandParentReducer.class);// 设置输出键值类型job.setOutputKeyClass(Text.class);job.setOutputValueClass(Text.class);// 设置输入输出格式job.setInputFormatClass(TextInputFormat.class);job.setOutputFormatClass(TextOutputFormat.class);// 设置输入和输出路径FileInputFormat.addInputPath(job, new Path(args[0]));FileOutputFormat.setOutputPath(job, new Path(args[1]));// 设置Reducer任务数量job.setNumReduceTasks(1);// 等待作业完成System.exit(job.waitForCompletion(true) ? 0 : 1);}
}

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