代码:
package com.hadoop.reduce.mapper;
import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reporter;
/**
* 读取一年中某天的最高气温
* @author linhaiy
* @date 2019.05.18
*/
public class WeatherMap extends MapReduceBase implements Mapper<LongWritable, Text, Text, LongWritable> {
private Text word = new Text();
public void map(LongWritable key, Text value, OutputCollector<Text, LongWritable> output, Reporter reporter)
throws IOException {
// 打印输入样本 如 2018120715
System.out.println("==== Before Mapper: ====" + key + "," + value);
String line = value.toString();
StringTokenizer tokenizer = new StringTokenizer(line);
// 截取年份
String year = line.substring(0, 4);
// 截取温度
int temperature = Integer.parseInt(line.substring(8));
word.set(year);
output.collect(word, new LongWritable(temperature));
// 打印输出样本
System.out.println("==== After Mapper: ==== " + new Text(year) + "," + new LongWritable(temperature));
}
}
package com.hadoop.reduce.reducer;
import java.io.IOException;
import java.util.Iterator;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
/**
* 统计一年天气最高温
* @author linhaiy
* @date 2019.05.18
*/
public class WeatherReduce extends MapReduceBase implements Reducer<Text, LongWritable, Text, LongWritable> {
@Override
public void reduce(Text key, Iterator<LongWritable> values, OutputCollector<Text, LongWritable> output,
Reporter reporter) throws IOException {
long maxValue = Integer.MIN_VALUE;
StringBuffer sb = new StringBuffer();
// 取values温度的最大值
while (values.hasNext()) {
long tmp = values.next().get();
maxValue = Math.max(maxValue, tmp);
sb.append(tmp).append(", ");
output.collect(key, new LongWritable(maxValue));
}
// 打印输入样本，如 2000， 15 ，99， 12
System.out.println("==== Before Reduce ==== " + key + ", " + sb.toString());
// 打印输出样本
System.out.println("==== After Reduce ==== " + key + ", " + sb.toString());
}
}
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;
}
/**
* 获取单词一年最高气温计算配置
* @param jobName
* @return
*/
public static JobConf getWeatherJobsConf(String jobName) {
JobConf jobConf = new JobConf(getConfiguration());
jobConf.setJobName(jobName);
jobConf.setOutputKeyClass(Text.class);
jobConf.setOutputValueClass(LongWritable.class);
jobConf.setMapperClass(WeatherMap.class);
jobConf.setReducerClass(WeatherReduce.class);
jobConf.setInputFormat(TextInputFormat.class);
jobConf.setOutputFormat(TextOutputFormat.class);
return jobConf;
}
@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";
/**
* 一年最高气温统计
* @param jobName
* @param inputPath
* @throws Exception
*/
public void weather(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);
}
JobConf jobConf = ReduceJobsUtils.getWeatherJobsConf(jobName);
FileInputFormat.setInputPaths(jobConf, new Path(inputPath));
FileOutputFormat.setOutputPath(jobConf, new Path(outputPath));
JobClient.runJob(jobConf);
}
}
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;
/**
* 一年最高气温统计
* @param jobName
* @param inputPath
* @return
* @throws Exception
*/
@RequestMapping(value = "MaxWeather", method = RequestMethod.POST)
@ResponseBody
public Result weather(@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.weather(jobName, inputPath);
return new Result(Result.SUCCESS, "温度统计成功");
}
}
部分截图:
