MapReduce编程

阳光穿透心脏的1/2处 2022-05-26 09:53 174阅读 0赞

> # 实验目的（[转载地址][Link 1]） #

1.  通过实验掌握基本的MapReduce编程方法。
2.  掌握用MapReduce解决一些常见的数据处理问题，包括数据去重、数据排序和数据挖掘等。
3.  通过操作MapReduce的实验，模仿实验内容，深入理解MapReduce的过程，熟悉MapReduce程序的编程方式。

> # 实验平台 #

*  操作系统:Ubuntu-16.04
 *  Hadoop版本:2.6.0
 *  JDK版本:1.8
 *  IDE:Eclipse

> # 实验内容和要求 #

### 一，编程实现文件合并和去重操作： ###

1.  ##### 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f1.txt的样例如下：

20150101     x
    20150102     y
    20150103     x
    20150104     y
    20150105     z
    20150106     x

*  输入文件f2.txt的样例如下：

20150101     y
    20150102     y
    20150103     x
    20150104     z
    20150105     y

*  根据输入文件f1和f2合并得到的输出文件的样例如下：

20150101      x
    20150101      y
    20150102      y
    20150103      x
    20150104      y
    20150104      z
    20150105      y
    20150105      z
    20150106      x

实验过程:

1.  创建文件f1.txt和f2.txt
    
      
    
    
    ![Image 1][]
    
    将上面样例内容复制进去
2.  在HDFS建立input文件夹（执行这步之前要开启hadoop相关进程）
    
      
    
    
    ![Image 1][]
3.  上传样例到HDFS中的input文件夹
    
      
    
    
    ![Image 1][]
4.  接着打开eclipse  
    Eclipse的使用
    
    1.  点开项目,找到 src 文件夹，右键选择 New -> Class
        
          
        
        
        ![Image 1][]
    2.  输入 Package 和 Name,然后Finish
        
          
        
        
        ![Image 1][]
    3.  写好Java代码（给的代码里要修改HDFS和本地路径),右键选择 Run As -> Run on Hadoop,结果在HDFS系统中查看
        
          
        
        
        ![Image 1][]

实验代码:

package cn.edu.zucc.mapreduce;
    
    import java.io.IOException;
    
    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.Mapper;
    import org.apache.hadoop.mapreduce.Reducer;
    import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
    import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
    
    public class Merge {
    
        public static class Map extends Mapper<Object, Text, Text, Text> {
            private static Text text = new Text();
    
            @Override
            public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
                text = value;
                context.write(text, new Text(""));
            }
        }
    
        public static class Reduce extends Reducer<Text, Text, Text, Text> {
            @Override
            public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
                context.write(key, new Text(""));
            }
        }
    
        public static void main(String[] args) throws Exception {
            Configuration conf = new Configuration();
            conf.set("fs.defaultFS", "hdfs://localhost:9000");
            String[] otherArgs = new String[]{
          "input", "output"};
            if (otherArgs.length != 2) {
                System.err.println("Usage: Merge and duplicate removal <in> <out>");
                System.exit(2);
            }
            Job job = Job.getInstance(conf, "Merge");
            job.setJarByClass(Merge.class);
            job.setMapperClass(Map.class);
            job.setReducerClass(Reduce.class);
            job.setOutputKeyClass(Text.class);
            job.setOutputValueClass(Text.class);
            FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
            FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
            System.exit(job.waitForCompletion(true) ? 0 : 1);
        }
    
    }

##### 模仿上题完成以下内容:对于两个输入文件，即文件A和文件B，请编写MapReduce程序，对两个文件进行统计单词数量，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.txt的样例如下:

hello world 
    wordcount java
    android hbase
    hive pig

*  输入文件b.txt的样例如下:

hello hadoop 
    spring mybatis
    hive hbase
    pig android

*  输出文件的结果为:

android  2
    hadoop    1
    hbase      2
    hello      2
    hive        2
    java        1
    mybatis  1
    pig      2
    spring    1
    wordcount   1
    world      1

实验代码:

package cn.edu.zucc.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.Mapper;
    import org.apache.hadoop.mapreduce.Reducer;
    import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
    import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
    
    public class WordCount {
    
        public static class Map extends Mapper<Object, Text, Text, IntWritable> {
            private static final IntWritable one = new IntWritable(1);
            private Text word = new Text();
    
            @Override
            public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
                String lineValue = value.toString();
                String[] words = lineValue.split(" ");
                for (String singleWord : words) {
                    word.set(singleWord);
                    context.write(word, one);
                }
    
            }
        }
    
        public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> {
            private IntWritable result = new IntWritable();
    
            @Override
            public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
                int sum = 0;
                for (IntWritable value : values) {
                    sum += value.get();
                }
                result.set(sum);
                context.write(key, result);
            }
        }
    
        public static void main(String[] args) throws Exception {
            Configuration conf = new Configuration();
            conf.set("fs.defaultFS", "hdfs://localhost:9000");
            String[] otherArgs = new String[]{
          "input_1", "output_1"};
            if (otherArgs.length != 2) {
                System.err.println("Usage: Wordcount <in> <out>");
                System.exit(2);
            }
            Job job = Job.getInstance(conf, "Wordcount");
            job.setJarByClass(WordCount.class);
            job.setMapperClass(Map.class);
            job.setReducerClass(Reduce.class);
            job.setOutputKeyClass(Text.class);
            job.setOutputValueClass(IntWritable.class);
            FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
            FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
            System.exit(job.waitForCompletion(true) ? 0 : 1);
        }
    }

### 二，编写程序实现对输入文件的排序： ###

1.  ##### 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file1.txt的样例如下：

33
    37
    12
    40

*  输入文件file2.txt的样例如下：

4
    16
    39
    5

*  输入文件file3.txt的样例如下：

1
    45
    25

*  根据输入文件file1.txt、file2.txt和file3.txt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

实验过程:

1.  创建文件file1.txt、file2.txt和file3.txt
    
      
    
    
    ![Image 1][]
    
    将上面样例内容复制进去
2.  在HDFS建立input2文件夹
    
      
    
    
    ![Image 1][]
3.  上传样例到HDFS中的input2文件夹
    
      
    
    
    ![Image 1][]
4.  到eclipse上执行代码

实验代码:

package cn.edu.zucc.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.Mapper;
    import org.apache.hadoop.mapreduce.Reducer;
    import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
    import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
    
    public class ContentSort {
    
        public static class Map extends Mapper<Object, Text, IntWritable, IntWritable> {
            private static IntWritable data = new IntWritable();
    
            @Override
            public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
                String line = value.toString();
                data.set(Integer.parseInt(line));
                context.write(data, new IntWritable(1));
            }
        }
    
        public static class Reduce extends Reducer<IntWritable, IntWritable, IntWritable, IntWritable> {
            private static IntWritable linenum = new IntWritable(1);
    
            @Override
            public void reduce(IntWritable key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
                for (IntWritable val : values) {
                    context.write(linenum, key);
                    linenum = new IntWritable(linenum.get() + 1);
                }
    
            }
    
        }
    
        public static void main(String[] args) throws Exception {
            Configuration conf = new Configuration();
            conf.set("fs.defaultFS", "hdfs://localhost:9000");
            String[] otherArgs = new String[]{
          "input2", "output2"};
            if (otherArgs.length != 2) {
                System.err.println("Usage: ContentSort <in> <out>");
                System.exit(2);
            }
            Job job = Job.getInstance(conf, "ContentSort");
            job.setJarByClass(ContentSort.class);
            job.setMapperClass(Map.class);
            job.setReducerClass(Reduce.class);
            job.setOutputKeyClass(IntWritable.class);
            job.setOutputValueClass(IntWritable.class);
            FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
            FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
            System.exit(job.waitForCompletion(true) ? 0 : 1);
    
        }
    
    }

##### 模仿上题完成以下内容:对于三个输入文件，即文件math、文件china和文件english，请编写MapReduce程序，对三个文件进行统计平均分，得到一个新的输出文件。下面是输入文件和输出文件的一个样例供参考。 #####

*  输入文件math.txt的样例如下:

张三    88
    李四    99
    王五    66
    赵六    77

*  输入文件algs.txt的样例如下:

张三    78
    李四    89
    王五    96
    赵六    67

*  输入文件english.txt的样例如下:

张三    80
    李四    82
    王五    84
    赵六    86

*  输出文件结果为:

张三    82
    李四    90
    王五    82
    赵六    76

实验代码:

package cn.edu.zucc.mapreduce;
    
    import java.io.IOException;
    import java.util.ArrayList;
    import java.util.List;
    
    import org.apache.hadoop.conf.Configuration;
    import org.apache.hadoop.fs.Path;
    import org.apache.hadoop.io.IntWritable;
    import org.apache.hadoop.io.LongWritable;
    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.input.TextInputFormat;
    import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
    import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
    
    public class AvgScore {
    
        public static class Map extends Mapper<LongWritable, Text, Text, IntWritable> {
            @Override
            public void map(LongWritable key, Text value, Context context)
                    throws IOException, InterruptedException {
                String line = value.toString();
                String[] nameAndScore = line.split(" ");
                List<String> list = new ArrayList<>(2);
                for (String nameOrScore : nameAndScore) {
                    if (!"".equals(nameOrScore)) {
                        list.add(nameOrScore);
                    }
                }
                context.write(new Text(list.get(0)), new IntWritable(Integer.parseInt(list.get(1))));
            }
        }
    
        public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> {
            @Override
            public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
                int sum = 0;
                int count = 0;
                for (IntWritable value : values) {
                    sum += Integer.parseInt(value.toString());
                    count++;
                }
                int average = sum / count;
                context.write(key, new IntWritable(average));
            }
        }
    
        public static void main(String[] args) throws Exception {
            Configuration conf = new Configuration();
            conf.set("fs.defaultFS", "hdfs://localhost:9000");
            String[] otherArgs = new String[]{
          "input_2", "output_2"};
            if (otherArgs.length != 2) {
                System.err.println("Usage: AvgScore <in> <out>");
                System.exit(2);
            }
            Job job = Job.getInstance(conf, "AvgScore");
            job.setJarByClass(AvgScore.class);
            job.setMapperClass(Map.class);
            job.setReducerClass(Reduce.class);
            job.setOutputKeyClass(Text.class);
            job.setOutputValueClass(IntWritable.class);
            job.setInputFormatClass(TextInputFormat.class);
            job.setOutputFormatClass(TextOutputFormat.class);
            FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
            FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
            System.exit(job.waitForCompletion(true) ? 0 : 1);
        }
    }

### 三，对给定的表格进行信息挖掘： ###

1.  ##### 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table.txt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
    Mark    Jesse
    Mark    Alice
    Philip  Jesse
    Philip  Alice
    Jone    Jesse
    Jone    Alice
    Steven  Jesse
    Steven  Alice
    Steven  Frank
    Steven  Mary
    Jone    Frank
    Jone    Mary

实验过程:

1.  创建文件table
    
      
    
    
    ![Image 1][]
    
    将上面样例内容复制进去
2.  在HDFS建立input3文件夹
    
      
    
    
    ![Image 1][]
3.  上传样例到HDFS中的input3文件夹
    
      
    
    
    ![Image 1][]
4.  到eclipse上执行代码

实验代码:

package cn.edu.zucc.mapreduce;
    
    import java.io.IOException;
    import java.util.ArrayList;
    import java.util.List;
    
    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.Mapper;
    import org.apache.hadoop.mapreduce.Reducer;
    import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
    import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
    
    public class STJoin {
        public static int time = 0;
    
        public static class Map extends Mapper<Object, Text, Text, Text> {
            @Override
            public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
                String line = value.toString();
                String[] childAndParent = line.split(" ");
                List<String> list = new ArrayList<>(2);
                for (String childOrParent : childAndParent) {
                    if (!"".equals(childOrParent)) {
                        list.add(childOrParent);
                    }
                }
                if (!"child".equals(list.get(0))) {
                    String childName = list.get(0);
                    String parentName = list.get(1);
                    String relationType = "1";
                    context.write(new Text(parentName), new Text(relationType + "+"
                            + childName + "+" + parentName));
                    relationType = "2";
                    context.write(new Text(childName), new Text(relationType + "+"
                            + childName + "+" + parentName));
                }
            }
        }
    
        public static class Reduce extends Reducer<Text, Text, Text, Text> {
            @Override
            public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
                if (time == 0) {
                    context.write(new Text("grand_child"), new Text("grand_parent"));
                    time++;
                }
                List<String> grandChild = new ArrayList<>();
                List<String> grandParent = new ArrayList<>();
                for (Text text : values) {
                    String s = text.toString();
                    String[] relation = s.split("\\+");
                    String relationType = relation[0];
                    String childName = relation[1];
                    String parentName = relation[2];
                    if ("1".equals(relationType)) {
                        grandChild.add(childName);
                    } else {
                        grandParent.add(parentName);
                    }
                }
                int grandParentNum = grandParent.size();
                int grandChildNum = grandChild.size();
                if (grandParentNum != 0 && grandChildNum != 0) {
                    for (int m = 0; m < grandChildNum; m++) {
                        for (int n = 0; n < grandParentNum; n++) {
                            context.write(new Text(grandChild.get(m)), new Text(
                                    grandParent.get(n)));
                        }
                    }
                }
            }
        }
    
        public static void main(String[] args) throws Exception {
            Configuration conf = new Configuration();
            conf.set("fs.defaultFS", "hdfs://localhost:9000");
            String[] otherArgs = new String[]{
          "input3", "output3"};
            if (otherArgs.length != 2) {
                System.err.println("Usage: Single Table Join <in> <out>");
                System.exit(2);
            }
            Job job = Job.getInstance(conf, "Single table Join ");
            job.setJarByClass(STJoin.class);
            job.setMapperClass(Map.class);
            job.setReducerClass(Reduce.class);
            job.setOutputKeyClass(Text.class);
            job.setOutputValueClass(Text.class);
            FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
            FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
            System.exit(job.waitForCompletion(true) ? 0 : 1);
    
        }
    
    }

##### 模仿上题完成以下内容:现有两个输入文件两个文件，一个是工厂名与地址编号的对应关系；另一个是地址编号和地址名的对应关系。要求从输入数据中找出工厂名和地址名的对应关系，输出"工厂名——地址名"表。 #####

*  输入文件factory.txt:

factoryname addressID
    Beijing Red Star   1
    Shenzhen Thunder   3
    Guangzhou Honda   2
    Beijing Rising   1
    Guangzhou Development Bank    2
    Tencent   3
    Bank of Beijing   1

*  输入文件address.txt:

addressID    addressname
    1            Beijing
    2            Guangzhou
    3            Shenzhen
    4            Xian

*  输出文件内容如下:

factoryname addressname
    Back of Beijing 　   　　Beijing 
    Beijing Rising  　　Beijing 
    Beijing Red Star 　  　　Beijing 
    Guangzhou Development Bank  　　Guangzhou 
    Guangzhou Honda 　　　     　　Guangzhou 
    Tencent 　　　　　   　　Shenzhen 
    Shenzhen Thunder 　　　    　　Shenzhen

实验代码:

package cn.edu.zucc.mapreduce;
    
    import java.io.IOException;
    import java.util.ArrayList;
    import java.util.List;
    
    import org.apache.commons.lang.StringUtils;
    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.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;
    
    public class MTJoin {
        public static int time = 0;
    
        public static class Map extends Mapper<Object, Text, Text, Text> {
    
            @Override
            protected void map(Object key, Text value, Context context) throws IOException, InterruptedException {
                String line = value.toString();
                if (line.contains("factoryname") || line.contains("addressID")) {
                    return;
                }
                String[] strings = line.split(" ");
                List<String> list = new ArrayList<>();
                for (String information : strings) {
                    if (!"".equals(information)) {
                        list.add(information);
                    }
                }
                String addressID;
                StringBuilder stringBuilder = new StringBuilder();
                if (StringUtils.isNumeric(list.get(0))) {
                    addressID = list.get(0);
                    for (int i = 1; i < list.size(); i++) {
                        if (i != 1) {
                            stringBuilder.append(" ");
                        }
                        stringBuilder.append(list.get(i));
                    }
                    context.write(new Text(addressID), new Text("1+" + stringBuilder.toString()));
                } else {
                    addressID = list.get(list.size() - 1);
                    for (int i = 0; i < list.size() - 1; i++) {
                        if (i != 0) {
                            stringBuilder.append(" ");
                        }
                        stringBuilder.append(list.get(i));
                    }
                    context.write(new Text(addressID), new Text("2+" + stringBuilder.toString()));
                }
            }
        }
    
        public static class Reduce extends Reducer<Text, Text, Text, Text> {
    
            @Override
            protected void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
                if (time == 0) {
                    context.write(new Text("factoryname"), new Text("addressname"));
                    time++;
                }
                List<String> factory = new ArrayList<>();
                List<String> address = new ArrayList<>();
                for (Text text : values) {
                    String s = text.toString();
                    String[] relation = s.split("\\+");
                    if ("1".equals(relation[0])) {
                        address.add(relation[1]);
                    } else {
                        factory.add(relation[1]);
                    }
                }
                int factoryNum = factory.size();
                int addressNum = address.size();
                if (factoryNum != 0 && addressNum != 0) {
                    for (int m = 0; m < factoryNum; m++) {
                        for (int n = 0; n < addressNum; n++) {
                            context.write(new Text(factory.get(m)),
                                    new Text(address.get(n)));
                        }
                    }
                }
            }
    
        }
    
        public static void main(String[] args) throws Exception {
            Configuration conf = new Configuration();
            conf.set("fs.defaultFS", "hdfs://localhost:9000");
            String[] ioArgs = new String[]{
          "input_3", "output_3"};
            String[] otherArgs = new GenericOptionsParser(conf, ioArgs)
                    .getRemainingArgs();
            if (otherArgs.length != 2) {
                System.err.println("Usage: Multiple Table Join <in> <out>");
                System.exit(2);
            }
            Job job = Job.getInstance(conf, "Mutiple table join ");
            job.setJarByClass(MTJoin.class);
            job.setMapperClass(Map.class);
            job.setReducerClass(Reduce.class);
            job.setOutputKeyClass(Text.class);
            job.setOutputValueClass(Text.class);
            FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
            FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
            System.exit(job.waitForCompletion(true) ? 0 : 1);
    
        }
    
    }

作者：Tiny\_16  
链接：https://www.jianshu.com/p/7328bb45a7cd  
來源：简书  
著作权归作者所有。商业转载请联系作者获得授权，非商业转载请注明出处。

[Link 1]: https://www.jianshu.com/p/7328bb45a7cd
[Image 1]: