MapReduce编写的正确姿势

• 先看一下目录结构

这里是job接口，负责参数的传递和定时的调用

下面的图是MR 程序相关的目录图片，其中MR的入口程序负责读取数据，并指定对应的Map、Reduce程序。

• 程序的流程

首先简单的说一下，整体的流程：

1. 首先是一个Job（定时任务），定时调用 入口程序，拼装参数。
2. job调用 MR的入口程序，入口程序获得job传入的参数，根据参数获得所需的数据；可以去Hbase、mysql、HDFS中获取数据；这个文件会配置job名、要调用的Mapper、Reduce，添加需要的jar包
3. 数据传入Mapper程序，Mapper程序进行数据的整合，整合完成的数据会变成：key:{value1,value2,value3,...}这个样子，所以传入Reduce的value应该是一个可迭代的参数（在这里坑了一会）。Mapper将所有的数据整合完成后，会进入Reduce程序
4. Reduce程序，接受参数，参数类型要和Mapper的返回类型是一样的；values 的入参是 一个可迭代的类型，泛型必须与Mapper的value返回类型一致。然后根据需求进行处理。

• 上代码

Job程序：

package com.sky.cy.mapreduce.dispatch;
 
import com.sky.cy.mapreduce.util.BasicMapreduceJob;
import com.ssports.util.LogFormat;
import com.ssports.util.ToolUtil;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.springframework.stereotype.Component;
 
import java.util.Date;
 
/**
 * @描述:
 * @文件名: UserActionRecordStatistJob
 * @创建人: YangLianjun
 * @创建时间: 2019/4/2 9:58
 * @修改人:
 * @修改备注: Copyright 北京和信金谷科技有限公司 2019/4/2
 */
@Component
public class UserActionRecordStatistJob extends BasicMapreduceJob {
    public static Log log = LogFactory.getLog(UserActionRecordStatistJob.class);
    @Override
    public String[] preProcessJob(String... args) {
        log.info(LogFormat.formatMsg("UserActionRecordStatistJob.preProcessJob","","UserActionRecordStatistJob is start"));
        String statTimeStr = "";
        if (args != null && args.length == 1) {
            statTimeStr = args[0];
        } else {
            Date date = ToolUtil.addDay(new Date(), -1);
            String statTime = ToolUtil.getDateStr(date, "yyyyMMdd");
//            String statTime = "20190401";
            log.info(LogFormat.formatMsg("UserActionRecordStatistJob.preProcessJob","statTime:"+statTime,""));
            statTimeStr = statTime;
        }
 
        String[] retArgs = new String[2];
        // 这个参数是指定哪个 MR入口程序
        retArgs[0] = "com.sky.cy.mapreduce.job.UserActionRecordStatistMR" ;
        retArgs[1] = statTimeStr;
        return retArgs;
    }
}

MR入口程序，负责数据的读取，指定对应的Map、Reduce程序：

package com.sky.cy.mapreduce.job;
 
import com.sky.cy.mapreduce.job.mapper.UserActionRecordMapper;
import com.sky.cy.mapreduce.job.mapper.UserActionRecordStatistMapper;
import com.sky.cy.mapreduce.job.reducer.UserActionRecordReducer;
import com.sky.cy.mapreduce.job.reducer.UserActionRecordStatistReducer;
import com.sky.cy.mapreduce.util.BasicMapreduce;
import com.ssports.util.LogFormat;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.client.Scan;
import org.apache.hadoop.hbase.filter.*;
import org.apache.hadoop.hbase.mapreduce.MultiTableOutputFormat;
import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
 
import java.io.IOException;
 
/**
 * @描述:
 * @文件名: UserActionRecordStatistMR
 * @创建人: YangLianjun
 * @创建时间: 2019/4/2 14:47
 * @修改人:
 * @修改备注: Copyright 北京和信金谷科技有限公司 2019/4/2
 */
public class UserActionRecordStatistMR extends BasicMapreduce {
    public static final Log LOG = LogFactory.getLog(UserActionRecordStatistMR.class);
 
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        LOG.info(LogFormat.formatMsg("UserActionRecordStatistMR.main","","start ..."));
        init();
        String statTime = args[0];
        // 将日期作为查询条件
        Filter filter = new SingleColumnValueFilter(Bytes.toBytes("f"),Bytes.toBytes("stat_time"), CompareFilter.CompareOp.EQUAL,Bytes.toBytes(statTime)) ;
        Scan scan = new Scan();
        scan.setFilter(filter);
 
        Configuration configuration = defaultHbaseConfiguration();
        configuration.set("statTime",statTime);
 
        Job job = Job.getInstance(configuration, "UserActionRecordStatistMR");
 
        TableMapReduceUtil.initTableMapperJob(UserActionRecordStatistMR.Constants.HBASE_FILTER_TABLE, scan, UserActionRecordStatistMapper.class, Text.class, Text.class, job);
        job.setReducerClass(UserActionRecordStatistReducer.class);
        job.setNumReduceTasks(1);
        job.setOutputFormatClass(MultiTableOutputFormat.class);
        //添加mysql驱动包
        job.addFileToClassPath(new Path(UserActionMR.Constants.MYSQL_JAR_PATH));
        //添加spring的jar
        job.addFileToClassPath(new Path(ActionFilterStatistMR.Constants.SPRING_CONTEXT_JAR_PATH));
        job.addFileToClassPath(new Path(ActionFilterStatistMR.Constants.SPRING_TX_JAR_PATH));
        job.addFileToClassPath(new Path(ActionFilterStatistMR.Constants.SPRING_AOP_JAR_PATH));
        job.addFileToClassPath(new Path(ActionFilterStatistMR.Constants.SPRING_BEAN_HADOOP_PATH));
        job.addFileToClassPath(new Path(ActionFilterStatistMR.Constants.SPRING_DATA_HADOOP_PATH));
 
        job.waitForCompletion(true);
 
    }
 
    public static class Constants {
        //需要获取的hbase数据的表名
        public static final String HBASE_FILTER_TABLE = "sky_user_action_record";
    }
 
}

Map程序：

 package com.sky.cy.mapreduce.job.mapper;
 
 import com.sky.cy.mapreduce.util.HbaseUtil;
 import com.sky.cy.mapreduce.util.RegexUtil;
 import com.ssports.util.LogFormat;
 import com.ssports.util.SpringHelper;
 import com.ssports.util.ToolUtil;
 import org.apache.commons.logging.Log;
 import org.apache.commons.logging.LogFactory;
 import org.apache.hadoop.hbase.client.Result;
 import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
 import org.apache.hadoop.hbase.mapreduce.TableMapper;
 import org.apache.hadoop.io.Text;
 
 import java.io.IOException;
 
 /**
  * @描述:
  * @文件名: UserActionRecordStatistMapper
  * @创建人: YangLianjun
  * @创建时间: 2019/4/2 14:46
  * @修改人:
  * @修改备注: Copyright 北京和信金谷科技有限公司 2019/4/2
  */
 public class UserActionRecordStatistMapper extends TableMapper<Text, Text> {
     private static final Log log = LogFactory.getLog(UserActionRecordStatistMapper.class);
     public static final String FAMILY_F = "f";
 
     protected void map(ImmutableBytesWritable key, Result result, Context context) throws IOException, InterruptedException {
         log.info(LogFormat.formatMsg("UserActionRecordStatistMapper.map", "", "mapper start ..."));
         String userId = HbaseUtil.getValue(FAMILY_F, "user_id", result);
         String actionName = HbaseUtil.getValue(FAMILY_F,"action_name",result) ;
         String actionId = HbaseUtil.getValue(FAMILY_F,"action_id",result) ;
         String statTime = context.getConfiguration().get("statTime");
         String keyOut = userId + ":" + statTime;
         log.info(LogFormat.formatMsg("UserActionRecordStatistMapper.map", "keyOut:"+keyOut, ""));
         Text text = new Text(keyOut);
         String valueOut = actionId + ":"+actionName;
         log.info(LogFormat.formatMsg("UserActionRecordStatistMapper.map", "valueOut:"+valueOut, ""));
         Text value = new Text(valueOut);
         context.write(text, value);
         log.info(LogFormat.formatMsg("UserActionRecordStatistMapper.map", "", "mapper end ..."));
     }
 }

Reduce程序：

package com.sky.cy.mapreduce.job.reducer;
 
import com.sky.cy.log.base.dao.SkyUserActionRecordStatistMapper;
import com.ssports.util.LogFormat;
import com.ssports.util.SpringHelper;
import com.ssports.util.ToolUtil;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.apache.hadoop.hbase.mapreduce.TableReducer;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
 
import java.io.IOException;
import java.util.*;
 
/**
 * @描述:
 * @文件名: UserActionRecordStatistReducer
 * @创建人: YangLianjun
 * @创建时间: 2019/4/2 14:47
 * @修改人:
 * @修改备注: Copyright 北京和信金谷科技有限公司 2019/4/2
 */
public class UserActionRecordStatistReducer extends TableReducer<Text, Text, NullWritable> {
    private static final Log log = LogFactory.getLog(UserActionRecordStatistReducer.class);
    private static final Integer ALL = 1 ;  // 统计状态，全部统计
 
    protected void setup(Context context) throws IOException, InterruptedException {
        SpringHelper.init("classpath*:spring/*.xml");
    }
 
    protected void reduce(Text key, Iterable<Text> values, Context context){
        log.info(LogFormat.formatMsg("UserActionRecordStatistReducer.reduce","","start ..."));
        String keyRow = new String(key.getBytes());
        String[] keys = keyRow.split(":");
        String userId = keys[0] ;
        log.info(LogFormat.formatMsg("UserActionRecordStatistReducer.reduce","userId:"+userId,""));
        String statTime = keys[1] ;
        log.info(LogFormat.formatMsg("UserActionRecordStatistReducer.reduce", "key:"+keyRow, ""));
        List<String> actionList = new ArrayList<>();
        for (Text value : values) {
            log.info(LogFormat.formatMsg("UserActionRecordStatistReducer.reduce", "value:"+value.toString(), ""));
            actionList.add(value.toString()) ;
        }
        SkyUserActionRecordStatistMapper mapper = SpringHelper.getBean("skyUserActionRecordStatistMapper") ;
        com.sky.cy.log.base.bean.SkyUserActionRecordStatistEntity entity = new com.sky.cy.log.base.bean.SkyUserActionRecordStatistEntity() ;
 
        Set<String> uniqueSet = new HashSet<>(actionList) ;
        for (String s : uniqueSet) {
            String actionId = s.split(":")[0] ;
            log.info(LogFormat.formatMsg("UserActionRecordStatistReducer.reduce","actionId:"+actionId,""));
            String actionName = s.split(":")[1] ;
            int actionNumber = Collections.frequency(actionList, s) ;  //统计出来数量
            entity = mapper.selectTotalByIdAndType(userId,actionId,ALL) ;  //查询全部统计 的信息
            if (null == entity){ //不存在 这个信息，插入
                log.info(LogFormat.formatMsg("UserActionRecordStatistReducer.reduce","","insert start ..."));
                com.sky.cy.log.base.bean.SkyUserActionRecordStatistEntity entity1 = new com.sky.cy.log.base.bean.SkyUserActionRecordStatistEntity() ;
                entity1.setUserId(userId);
                entity1.setActionId(actionId);
                entity1.setActionName(actionName);
                entity1.setStatistTotal(actionNumber);
                entity1.setStatistTime(statTime);
                entity1.setStatistType(ALL);
                int insert = mapper.insertSelective(entity1) ;
                log.info(LogFormat.formatMsg("UserActionRecordStatistReducer.reduce","insert number:"+insert,""));
            }else { //存在，进行更新
                log.info(LogFormat.formatMsg("UserActionRecordStatistReducer.reduce","","update start..."));
                int update = mapper.updateTotalAndDays(userId,actionId,actionNumber,ALL) ;  //更新
                log.info(LogFormat.formatMsg("UserActionRecordStatistReducer.reduce","update number:"+update,"update end..."));
            }
        }
 
    }
}

说明：

利用集群进行整合、计算、归纳，本身是一个特别复杂的事情，Hadoop中的MR框架可以让我们从复杂的操作中解脱出来，只关注于逻辑本身，无疑是程序员的福音。只要理解了MR的流程和基本的运作原理，就可以像写java程序那样简单的对数据进行处理，但是却比单机的java程序效率高得多。当然既然使用到了MR，数据量应该是巨大的，如果只是对单机mysql中的数据进行统计与计算，建议还是使用普通的方式，毕竟最适合自己才是最好的！！！