继上篇SpringBoot集成Hadoop系列一 ---- 对HDFS的文件操作建的工程,接下来使用MapReduce进行一些数据文件的统计开发.这里做一个很经典的统计功能,词频统计.
代码:
package com.hadoop.reduce.mapper;
import java.io.ByteArrayInputStream;
import java.io.IOException;
import java.io.InputStream;
import java.io.InputStreamReader;
import java.io.Reader;
import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import org.wltea.analyzer.core.IKSegmenter;
import org.wltea.analyzer.core.Lexeme;
/**
* 统计单个字符出现的次数
* @author linhaiy
* @date 2019.05.18
*/
public class WordCountMap extends Mapper<Object, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
/**
* 读取 sgyy.txt或者dpcq.txt 内容格式为小说内容
* @param key
* 默认情况下，是mapreduce所读取到的一行文本的起始偏移量
* @param value
* 默认情况下，是mapreduce所读取到的一行文本的内容，hadoop中的序列化类型为Text
* @param context
* 是用户自定义逻辑处理完成后输出的KEY，在此处是单词，String
* @throws IOException
* @throws InterruptedException
*/
public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
// 防止中文乱码
String line = new String(value.getBytes(), 0, value.getLength(), "UTF-8").trim();
if (StringUtils.isNotEmpty(line)) {
// 使用分词器，分隔文件行内容根据常用的短语分隔，比如我们，被分隔成 <我,1>,<们,1><我们,1>
byte[] btValue = line.getBytes();
InputStream inputStream = new ByteArrayInputStream(btValue);
Reader reader = new InputStreamReader(inputStream);
IKSegmenter ikSegmenter = new IKSegmenter(reader, true);
Lexeme lexeme;
while ((lexeme = ikSegmenter.next()) != null) {
word.set(lexeme.getLexemeText());
context.write(word, one);
}
}
}
}
package com.hadoop.reduce.reducer;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
/**
* 统计单个字符出现的次数
* @author linhaiy
* @date 2019.05.18
*/
public class WordCountReduce extends Reducer<Text, IntWritable, Text, IntWritable> {
private IntWritable result = new IntWritable();
private String text = "孙权";
private int textSum = 0;
private List<String> textList = null;
public WordCountReduce() {
textList = new ArrayList<>();
textList.add("曹操");
textList.add("孙权");
}
/**
* @param key
* 第一个Text: 是传入的单词名称，是Mapper中传入的
* @param values
* 第二个：LongWritable 是该单词出现了多少次，这个是mapreduce计算出来的，比如 hello出现了11次
* @param context
* 第三个Text: 是输出单词的名称 ，这里是要输出到文本中的内容
* @throws IOException
* @throws InterruptedException
*/
@Override
public void reduce(Text key, Iterable<IntWritable> values, Context context)
throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
String keyStr = key.toString();
// 未使用分词器，需要根据map传过来的行内容检索并累加
// boolean isHas = keyStr.contains(text);
// if (isHas) {
// textSum++;
// System.out.println("============ " + text + " 统计分词为: " + textSum + "
// ============");
// }
// 使用分词器，内容已经被统计好了，直接输出即可
if (textList.contains(keyStr)) {
System.out.println("============ " + keyStr + " 统计分词为: " + sum + " ============");
}
}
}
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;
}
/**
* 获取单词统计的配置信息
*
* @param jobName
* @param inputPath
* @param outputPath
* @throws IOException
* @throws ClassNotFoundException
* @throws InterruptedException
*/
public static void getWordCountJobsConf(String jobName, String inputPath, String outputPath)
throws IOException, ClassNotFoundException, InterruptedException {
Configuration conf = getConfiguration();
Job job = Job.getInstance(conf, jobName);
job.setMapperClass(WordCountMap.class);
job.setCombinerClass(WordCountReduce.class);
job.setReducerClass(WordCountReduce.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
// 小文件合并设置
job.setInputFormatClass(CombineTextInputFormat.class);
// 最大分片
CombineTextInputFormat.setMaxInputSplitSize(job, 4 * 1024 * 1024);
// 最小分片
CombineTextInputFormat.setMinInputSplitSize(job, 2 * 1024 * 1024);
FileInputFormat.addInputPath(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";
/**
* 单词统计，统计某个单词出现的次数
* @param jobName
* @param inputPath
* @throws Exception
*/
public void wordCount(String jobName, String inputPath) throws Exception {
if (StringUtils.isEmpty(jobName) || StringUtils.isEmpty(inputPath)) {
return;
}
// 输出目录 = output/当前Job,如果输出路径存在则删除，保证每次都是最新的
String outputPath = OUTPUT_PATH + "/" + jobName;
if (HdfsService.existFile(outputPath)) {
HdfsService.deleteFile(outputPath);
}
ReduceJobsUtils.getWordCountJobsConf(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;
/**
* 单词统计(统计指定key单词的出现次数)
* @param jobName
* @param inputPath
* @return
* @throws Exception
*/
@RequestMapping(value = "wordCount", method = RequestMethod.POST)
@ResponseBody
public Result wordCount(@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.wordCount(jobName, inputPath);
return new Result(Result.SUCCESS, "单词统计成功");
}
}
