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MapReduce--平均分，最高，低分以及及格率的计算

MapReduce--平均分，最高，低分以及及格率的计算

计算班级的平均分，以及个人的最高最低分，以及每个班级的及格率。

来先看一下我的数据。

时间			班级		姓名		科目			成绩

20180501 		1708a1 		li 		bishi 			80

20180501 		1708a1 		li 		jishi 			55

20180501 		1708a1 		li 		project 		90

20180501 		1708a1 		li2		bishi 			80

20180501 		1708a1 		li2		jishi 			20

20180501 		1708a1 		li2		project 		90

20180501 		1708a1 		li3		bishi 			50

20180501 		1708a1 		li3		jishi 			70

20180501 		1708a1 		li3		project 		60

20180501 		1708a1 		zhangsan 	bishi 			88

20180501 		1708a1 		zhangsan 	jishi 			55

20180501 		1708a1 		zhangsan 	project 		98

20180501 		1708a1 		lishi 		bishi 			18

20180501 		1708a1 		lishi 		jishi 			15

20180501 		1708a1 		lishi 		project 		15

20180501 		1708a1 		wangwu		bishi 			88

20180501 		1708a1 		wangwu		jishi 			76

20180501 		1708a1 		wangwu		project 		70

20180501 		1708a2 		li1 		bishi 			80

20180501 		1708a2 		li1 		jishi 			71

20180501 		1708a2 		li1 		project 		96

20180501 		1708a2 		li2 		bishi 			80

20180501 		1708a2 		li2 		jishi 			26

20180501 		1708a2 		li2 		project 		90

20180501 		1708a2 		li3 		bishi 			80

20180501 		1708a2 		li3 		jishi 			55

20180501 		1708a2 		li3 		project 		90

20180501 		1708a2 		zhangliang 	bishi 			81

20180501 		1708a2 		zhangliang 	jishi 			55

20180501 		1708a2 		zhangliang 	project 		98

20180501 		1708a2 		liuli 		bishi	 		70

20180501 		1708a2 		liuli 		jishi 			95

20180501 		1708a2 		liuli 		project 		75

20180501 		1708a2 		wangwu 		bishi 			80

20180501 		1708a2 		wangwu 		jishi 			76

20180501 		1708a2 		wangwu 		project 		70

20180501 		1708a2 		zhangxi 	bishi 			18

20180501 		1708a2 		zhangxi 	jishi 			16

20180501 		1708a2 		zhangxi 	project 		10

数据之间是空格。。。。

代码来了 -- 平均分，最高分，最低分

package com.huhu.day01;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

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.output.FileOutputFormat;

/**

 * 切割文本: 平均分，最高低分

 *

 * @author huhu_k

 *

 */

public class HomeWork2 {

	// map

	public static class MyMapper extends Mapper<LongWritable, Text, Text, Text> {

		Text keys = new Text();

		Text values = new Text();

		@Override

		protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

			// 数据切割方式(文本中的内容)

			// 按行分

			String[] line = value.toString().split(" ");

			keys.set(line[0] + ":" + line[2]);

			values.set(line[3] + ":" + line[4]);

			context.write(keys, values);

		}

	}

	// reduce

	public static class MyReducer extends Reducer<Text, Text, Text, Text> {

		@Override

		protected void reduce(Text key, Iterable<Text> value, Context context)

				throws IOException, InterruptedException {

			int max = Integer.MIN_VALUE;

			int min = Integer.MAX_VALUE;

			// 和

			int sum = 0;

			// 人数

			int count = 0;

			// 分数

			int score = 0;

			String classs = "";

			for (Text t : value) {

				classs = t.toString().split(":")[0];

				score = Integer.parseInt(t.toString().split(":")[1]);

				if (max < score)

					max = score;

				if (min > score)

					min = score;

				switch (classs) {

				case "bishi":

					score += score * 0.4;

					break;

				case "jishi":

					score += score * 0.3;

					break;

				case "project":

					score += score * 0.3;

					break;

				}

				sum += score;

				count++;

			}

			int avg = (int) sum / count;

			String[] student = key.toString().split(":");

			Text ky = new Text(student[0] + "\t" + student[1]);

			context.write(ky, new Text("平均分   " + avg));

			context.write(ky, new Text("最高值为   " + max));

			context.write(ky, new Text("最低值  " + min));

		}

	}

	public static void main(String[] args) throws Exception {

		// 配置容器

		Configuration conf = new Configuration();

		// 创建一个job

		@SuppressWarnings("deprecation")

		Job job = new Job(conf, "MyMapReduce Two");

		// 配置job

		job.setJarByClass(HomeWork2.class);

		job.setMapperClass(MyMapper.class);

		job.setMapOutputKeyClass(Text.class);

		job.setMapOutputValueClass(Text.class);

		job.setReducerClass(MyReducer.class);

		job.setOutputKeyClass(Text.class);

		job.setOutputValueClass(Text.class);

		// 输入输出

		FileInputFormat.addInputPath(job, new Path(args[0]));

		FileOutputFormat.setOutputPath(job, new Path(args[1]));

		// 执行程序

		boolean waitForCompletion = job.waitForCompletion(true);

		System.exit(waitForCompletion ? 0 : 1);

	}

}

运行结果：

MapReduce--平均分，最高，低分以及及格率的计算-LMLPHP

MapReduce--平均分，最高，低分以及及格率的计算-LMLPHP

MapReduce--平均分，最高，低分以及及格率的计算-LMLPHP

2.及格率

package com.huhu.day01;

import java.io.IOException;

import java.text.DecimalFormat;

import java.util.HashMap;

import java.util.Map;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

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.output.FileOutputFormat;

/**

 * 切割文本:及格率

 *

 * @author huhu_k

 *

 */

public class HomeWork3 {

	// map

	public static class MyMapper extends Mapper<LongWritable, Text, Text, Text> {

		Text keys = new Text();

		Text values = new Text();

		@Override

		protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

			// 数据切割方式(文本中的内容)

			// 按行分

			String[] line = value.toString().split(" ");

			keys.set(line[0] + ":" + line[1]);

			context.write(keys, value);

		}

	}

	// reduce

	public static class MyReducer extends Reducer<Text, Text, Text, Text> {

		Map<String, Double> map = new HashMap<>();

		Map<String, String> maps = new HashMap<>();

		@Override

		protected void reduce(Text key, Iterable<Text> value, Context context)

				throws IOException, InterruptedException {

			for (Text t : value) {

				String[] values = t.toString().split(" ");

				String student = values[2] + ":" + values[0] + ":" + values[1];

				String subject = values[3];

				double score = Integer.valueOf(values[4]);

				if ("bishi".equals(subject)) {

					score *= 0.4;

				} else {

					score *= 0.3;

				}

				// 如果map中有学生，累加学生的没门课程的分数

				if (map.containsKey(student)) {

					double scores = map.get(student);

					scores += score;

					map.put(student, scores);

				} else {

					// 第一次进入时不包含，则直接添加

					map.put(student, score);

				}

			}

			for (Map.Entry<String, Double> m : map.entrySet()) {

				String classname = m.getKey().split(":")[2];

				Double score = m.getValue();

				if (maps.containsKey(classname) && score >= 60) {

					String k = Integer.parseInt(maps.get(classname).split(":")[0]) + 1 + "";

					String v = Integer.parseInt(maps.get(classname).split(":")[1]) + 1 + "";

					maps.put(classname, k + ":" + v);

				} else if (maps.containsKey(classname) && score < 60) {

					String k = Integer.parseInt(maps.get(classname).split(":")[0]) + 1 + "";

					String v = Integer.parseInt(maps.get(classname).split(":")[1]) + "";

					maps.put(classname, k + ":" + v);

				} else if (!maps.containsKey(classname) && score < 60) {

					maps.put(classname, "1:0");

				} else if (!maps.containsKey(classname) && score >= 60) {

					maps.put(classname, "1:1");

				}

			}

		}

		@Override

		protected void cleanup(Reducer<Text, Text, Text, Text>.Context context)

				throws IOException, InterruptedException {

			for (Map.Entry<String, String> m : maps.entrySet()) {

				DecimalFormat d = new DecimalFormat("0.00%");

				double pass = Double.valueOf(m.getValue().split(":")[1]) / Double.valueOf(m.getValue().split(":")[0]);

				context.write(new Text(m.getKey()), new Text("及格率为:" + d.format(pass)));

			}

		}

	}

	public static void main(String[] args) throws Exception {

		// 配置容器

		Configuration conf = new Configuration();

		// 创建一个job

		@SuppressWarnings("deprecation")

		Job job = new Job(conf, "MyMapReduce Count");

		// 配置job

		job.setJarByClass(HomeWork3.class);

		job.setMapperClass(MyMapper.class);

		job.setMapOutputKeyClass(Text.class);

		job.setMapOutputValueClass(Text.class);

		job.setReducerClass(MyReducer.class);

		job.setOutputKeyClass(Text.class);

		job.setOutputValueClass(Text.class);

		// 输入输出

		FileInputFormat.addInputPath(job, new Path(args[0]));

		FileOutputFormat.setOutputPath(job, new Path(args[1]));

		// 执行程序

		boolean waitForCompletion = job.waitForCompletion(true);

		System.exit(waitForCompletion ? 0 : 1);

	}

}

MapReduce--平均分，最高，低分以及及格率的计算-LMLPHP

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MapReduce--平均分，最高，低分以及及格率的计算-LMLPHP

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MapReduce--平均分，最高，低分以及及格率的计算-LMLPHP

05-08 15:20
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