SpringBoot集成Hadoop系列二 ---- MapReduce统计数据文件的共同好友

╰半橙微兮° 2022-01-28 09:25 506阅读 0赞

### 代码: ###

package com.hadoop.reduce.mapper;
    
    import java.io.IOException;
    
    import org.apache.hadoop.io.LongWritable;
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.mapreduce.Mapper;
    
    /**
     * 查找共同的好友
     * @author linhaiy
     * @date 2019.05.18
     */
    public class FriendsMapper extends Mapper<LongWritable, Text, Text, Text> {
    	private Text k = new Text();
    	private Text v = new Text();
    
    	/**
    	 * 读取 friends.txt 内容格式 A:B,C,D,F,E,O
    	 * @param key
    	 * @param value
    	 * @param context
    	 * @throws IOException
    	 * @throws InterruptedException
    	 */
    	@Override
    	protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
    		String line = value.toString().trim();
    		// 根据冒号拆分
    		String[] personFriends = line.split(":");
    		// 第一个为用户
    		String person = personFriends[0];
    		// 第二个为好友
    		String friends = personFriends[1];
    		// 好友根据逗号拆分
    		String[] friendsList = friends.split(",");
    		for (String friend : friendsList) {
    			k.set(friend);
    			v.set(person);
    			context.write(k, v);
    		}
    	}
    }

package com.hadoop.reduce.reducer;
    
    import java.io.IOException;
    
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.mapreduce.Reducer;
    
    /**
     * 获取共同好友
     * @author linhaiy
     * @date 2019.05.18
     */
    public class FriendsReduce extends Reducer<Text, Text, Text, Text> {
    	private Text k = new Text();
    	private Text v = new Text();
    
    	/**
    	 * 读取 FriendsMapper1 输出，内容格式 B A
    	 * @param key
    	 * @param values
    	 * @param context
    	 * @throws IOException
    	 * @throws InterruptedException
    	 */
    	@Override
    	protected void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
    		StringBuffer sb = new StringBuffer();
    		// 循环好友
    		for (Text person : values) {
    			sb.append(person).append(",");
    		}
    		k.set(key);
    		v.set(sb.toString());
    		context.write(k, v);
    	}
    }

package com.hadoop.reduce.service;
    
    import java.io.IOException;
    
    import javax.annotation.PostConstruct;
    
    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.NullWritable;
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.mapred.JobConf;
    import org.apache.hadoop.mapred.TextInputFormat;
    import org.apache.hadoop.mapred.TextOutputFormat;
    import org.apache.hadoop.mapreduce.Job;
    import org.apache.hadoop.mapreduce.lib.input.CombineTextInputFormat;
    import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
    import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
    import org.apache.hadoop.mapreduce.lib.output.MultipleOutputs;
    import org.springframework.beans.factory.annotation.Value;
    import org.springframework.stereotype.Component;
    
    import com.hadoop.reduce.bean.StaffProvincePartitioner;
    import com.hadoop.reduce.bean.WeiboInputFormat;
    import com.hadoop.reduce.mapper.CounterMapper;
    import com.hadoop.reduce.mapper.FriendsMapper;
    import com.hadoop.reduce.mapper.JoinMapper;
    import com.hadoop.reduce.mapper.StaffMap;
    import com.hadoop.reduce.mapper.WeatherMap;
    import com.hadoop.reduce.mapper.WeiboMapper;
    import com.hadoop.reduce.mapper.WordCount;
    import com.hadoop.reduce.mapper.WordCountMap;
    import com.hadoop.reduce.model.GroupSortModel;
    import com.hadoop.reduce.model.OrderInfo;
    import com.hadoop.reduce.model.StaffModel;
    import com.hadoop.reduce.model.Weibo;
    import com.hadoop.reduce.reducer.FriendsReduce;
    import com.hadoop.reduce.reducer.JoinReduce;
    import com.hadoop.reduce.reducer.StaffReduce;
    import com.hadoop.reduce.reducer.WeatherReduce;
    import com.hadoop.reduce.reducer.WeiboReduce;
    import com.hadoop.reduce.reducer.WordCountReduce;
    import com.hadoop.util.GroupSort;
    
    /**
     * Map/Reduce工具类
     * @author linhaiy
     * @date 2019.05.18
     */
    @Component
    public class ReduceJobsUtils {
    
    	@Value("${hdfs.path}")
    	private String path;
    
    	private static String hdfsPath;
    
    	/**
    	 * 获取HDFS配置信息
    	 * @return
    	 */
    	public static Configuration getConfiguration() {
    		Configuration configuration = new Configuration();
    		configuration.set("fs.defaultFS", hdfsPath);
    		configuration.set("mapred.job.tracker", hdfsPath);
    		// 运行在yarn的集群模式
    		// configuration.set("mapreduce.framework.name", "yarn");
    		// 这个配置是让main方法寻找该机器的mr环境
    		// configuration.set("yarn.resourcemanmager.hostname", "node1");
    		return configuration;
    	}
    
    	/**
    	 * 获取共同好友
    	 * @param jobName
    	 * @param inputPath
    	 * @param outputPath
    	 * @throws IOException
    	 * @throws ClassNotFoundException
    	 * @throws InterruptedException
    	 */
    	public static void friends(String jobName, String inputPath, String outputPath)
    			throws IOException, ClassNotFoundException, InterruptedException {
    		Configuration config = getConfiguration();
    		Job job = Job.getInstance(config, jobName);
    		// 设置jar中的启动类，可以根据这个类找到相应的jar包
    		job.setJarByClass(FriendsMapper.class);
    
    		job.setMapperClass(FriendsMapper.class);
    		job.setReducerClass(FriendsReduce.class);
    
    		// 一般情况下mapper和reducer的输出的数据类型是一样的，所以我们用上面两条命令就行，如果不一样，我们就可以用下面两条命令单独指定mapper的输出key、value的数据类型
    		// 设置Mapper的输出
    		// job.setMapOutputKeyClass(Text.class);
    		// job.setMapOutputValueClass(Text.class);
    
    		// 设置reduce的输出
    		job.setOutputKeyClass(Text.class);
    		job.setOutputValueClass(Text.class);
    
    		// 指定输入输出文件的位置
    		FileInputFormat.setInputPaths(job, new Path(inputPath));
    		FileOutputFormat.setOutputPath(job, new Path(outputPath));
    
    		job.waitForCompletion(true);
    	}
    
    	@PostConstruct
    	public void getPath() {
    		hdfsPath = this.path;
    	}
    
    	public static String getHdfsPath() {
    		return hdfsPath;
    	}
    }

package com.hadoop.reduce.service;
    
    import org.apache.commons.lang.StringUtils;
    import org.apache.hadoop.fs.Path;
    import org.apache.hadoop.mapred.FileInputFormat;
    import org.apache.hadoop.mapred.FileOutputFormat;
    import org.apache.hadoop.mapred.JobClient;
    import org.apache.hadoop.mapred.JobConf;
    import org.springframework.stereotype.Service;
    import com.hadoop.hdfs.service.HdfsService;
    
    /**
     * 单词统计
     * @author linhaiy
     * @date 2019.05.18
     */
    @Service
    public class MapReduceService {
    
    	// 默认reduce输出目录
    	private static final String OUTPUT_PATH = "/output";
    
    	/**
    	 * 获取共同好友
    	 * @param jobName
    	 * @param inputPath
    	 * @throws Exception
    	 */
    	public void friends(String jobName, String inputPath) throws Exception {
    		if (StringUtils.isEmpty(jobName) || StringUtils.isEmpty(inputPath)) {
    			return;
    		}
    		// 输出目录 = output/当前Job
    		String outputPath = OUTPUT_PATH + "/" + jobName;
    		if (HdfsService.existFile(outputPath)) {
    			HdfsService.deleteFile(outputPath);
    		}
    		ReduceJobsUtils.friends(jobName, inputPath, outputPath);
    	}
    }

package com.hadoop.reduce.controller;
    
    import org.apache.commons.lang.StringUtils;
    import org.springframework.beans.factory.annotation.Autowired;
    import org.springframework.web.bind.annotation.RequestMapping;
    import org.springframework.web.bind.annotation.RequestMethod;
    import org.springframework.web.bind.annotation.RequestParam;
    import org.springframework.web.bind.annotation.ResponseBody;
    import org.springframework.web.bind.annotation.RestController;
    import com.hadoop.reduce.service.MapReduceService;
    import com.hadoop.util.Result;
    
    /**
     * MapReduce处理控制层
     * @author linhaiy
     * @date 2019.05.18
     */
    @RestController
    @RequestMapping("/hadoop/reduce")
    public class MapReduceAction {
    
    	@Autowired
    	MapReduceService mapReduceService;
    
    	/**
    	 * 获取共同好友
    	 * @param jobName
    	 * @param inputPath
    	 * @return
    	 * @throws Exception
    	 */
    	@RequestMapping(value = "friends", method = RequestMethod.POST)
    	@ResponseBody
    	public Result friends(@RequestParam("jobName") String jobName, @RequestParam("inputPath") String inputPath)
    			throws Exception {
    		if (StringUtils.isEmpty(jobName) || StringUtils.isEmpty(inputPath)) {
    			return new Result(Result.FAILURE, "请求参数为空");
    		}
    		mapReduceService.friends(jobName, inputPath);
    		return new Result(Result.SUCCESS, "获取共同好友成功");
    	}
    }