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Mapreduce | 案例

news 2025/8/18 16:10:14

根据提供的数据文件【test.log】

数据文件格式：姓名,语文成绩,数学成绩,英语成绩

完成如下2个案例：

（1）求每个学科的平均成绩

（2）将三门课程中任意一门不及格的学生过滤出来

（1）求每个学科的平均成绩

上传到hdfs

Idea代码：

package zz;import demo5.Sort1Job;
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.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;import java.io.IOException;public class ScoreAverageDriver {public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException {Configuration conf = new Configuration();conf.set("fs.defaultFS","hdfs://hadoop10:8020");Job job = Job.getInstance(conf);job.setJarByClass(ScoreAverageDriver.class);job.setInputFormatClass(TextInputFormat.class);job.setOutputFormatClass(TextOutputFormat.class);TextInputFormat.addInputPath(job,new Path("/test.log"));TextOutputFormat.setOutputPath(job,new Path("/test1"));job.setMapperClass(ScoreAverageMapper.class);job.setReducerClass(ScoreAverageReducer.class);//map输出的键与值类型job.setMapOutputKeyClass(Text.class);job.setMapOutputValueClass(IntWritable.class);//reducer输出的键与值类型job.setOutputKeyClass(Text.class);job.setOutputValueClass(IntWritable.class);boolean b = job.waitForCompletion(true);System.out.println(b);}static class ScoreAverageMapper extends Mapper<LongWritable, Text, Text, IntWritable> {// 定义一个Text类型的变量subject，用于存储科目名称private Text subject = new Text();// 定义一个IntWritable类型的变量score，用于存储分数private IntWritable score = new IntWritable();// 重写Mapper类的map方法@Overrideprotected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, IntWritable>.Context context) throws IOException, InterruptedException {// 将输入的Text值转换为字符串，并按逗号分割成数组String[] fields = value.toString().split(",");// 假设字段的顺序是：姓名,语文成绩,数学成绩,英语成绩String name = fields[0]; // 提取姓名int chinese = Integer.parseInt(fields[1]); // 提取语文成绩int math = Integer.parseInt(fields[2]); // 提取数学成绩int english = Integer.parseInt(fields[3]); // 提取英语成绩// 为Chinese科目输出成绩subject.set("Chinese"); // 设置科目为Chinesescore.set(chinese); // 设置分数为语文成绩context.write(subject, score); // 写入输出// 为Math科目输出成绩subject.set("Math"); // 设置科目为Mathscore.set(math); // 设置分数为数学成绩context.write(subject, score); // 写入输出// 为English科目输出成绩subject.set("English"); // 设置科目为Englishscore.set(english); // 设置分数为英语成绩context.write(subject, score); // 写入输出}}static class ScoreAverageReducer extends Reducer<Text, IntWritable, Text, IntWritable> {// 定义一个IntWritable类型的变量average，用于存储平均分数private IntWritable average = new IntWritable();// 重写Reducer类的reduce方法@Overrideprotected void reduce(Text key, Iterable<IntWritable> values, Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException {int sum = 0; // 初始化分数总和为0int count = 0; // 初始化科目成绩的个数为0// 遍历该科目下的所有分数for (IntWritable val : values) {sum += val.get(); // 累加分数count++; // 计数加一}// 如果存在分数（即count大于0）if (count > 0) {// 计算平均分并设置到average变量中average.set(sum / count);// 写入输出，键为科目名称，值为平均分数context.write(key, average);}}}}

结果：

（2）将三门课程中任意一门不及格的学生过滤出来

Idea代码

package zz;import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;import java.io.IOException;public class FailingStudentDriver {public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException {Configuration conf = new Configuration();conf.set("fs.defaultFS","hdfs://hadoop10:8020");Job job = Job.getInstance(conf);job.setJarByClass(FailingStudentDriver .class);job.setInputFormatClass(TextInputFormat.class);job.setOutputFormatClass(TextOutputFormat.class);TextInputFormat.addInputPath(job,new Path("/test.log"));TextOutputFormat.setOutputPath(job,new Path("/test2"));job.setMapperClass(FailingStudentMapper.class);//map输出的键与值类型job.setMapOutputKeyClass(Text.class);job.setMapOutputValueClass(IntWritable.class);job.setNumReduceTasks(0);boolean b = job.waitForCompletion(true);System.out.println(b);}// 定义一个静态类FailingStudentMapper，它继承了Hadoop的Mapper类
// 该Mapper类处理的是Object类型的键和Text类型的值，并输出Text类型的键和NullWritable类型的值static class FailingStudentMapper extends Mapper<Object, Text, Text, NullWritable> {// 定义一个Text类型的变量studentName，用于存储不及格的学生姓名private Text studentName = new Text();// 定义一个NullWritable类型的变量nullWritable，由于输出值不需要具体的数据，所以使用NullWritableprivate NullWritable nullWritable = NullWritable.get();// 重写Mapper类的map方法，这是处理输入数据的主要方法@Overrideprotected void map(Object key, Text value, Mapper<Object, Text, Text, NullWritable>.Context context) throws IOException, InterruptedException {// 将输入的Text值转换为字符串，并按逗号分割成数组// 假设输入的Text值是"姓名,语文成绩,数学成绩,英语成绩"这样的格式String[] fields = value.toString().split(",");// 从数组中取出学生的姓名String name = fields[0];// 从数组中取出语文成绩，并转换为整数int chineseScore = Integer.parseInt(fields[1]);// 从数组中取出数学成绩，并转换为整数int mathScore = Integer.parseInt(fields[2]);// 从数组中取出英语成绩，并转换为整数int englishScore = Integer.parseInt(fields[3]);// 检查学生的三门成绩中是否有任意一门不及格（即小于60分）// 如果有，则将该学生的姓名写入输出if (chineseScore < 60 || mathScore < 60 || englishScore < 60) {studentName.set(name); // 设置studentName变量的值为学生的姓名context.write(studentName, nullWritable); // 使用Mapper的Context对象将学生的姓名写入输出}}}}