HADOOP MapReduce 处理 Spark 抽取的 Hive 数据【解决方案一】

开端：

今天咱先说问题，经过几天测试题的练习，我们有从某题库中找到了新题型，并且成功把我们干趴下，昨天今天就干了一件事，站起来。
沙问题？
java mapeduce 清洗 hive 中的数据 ，清晰之后将driver代码 进行截图提交。

坑号1: spark之前抽取的数据是.parquet格式的， 对 mapreduce 不太友好，我决定从新抽取， 还是用spark技术，换一种文件格式
坑号2: 使用新方法进行sink的时候我是直接like别的现成表结构折磨干的，后来hive分割字段都TM乱套啦，赞看看！

需求：

1.使用scala+spark技术实现抽取mysql到Hive中
2.使用java+ Mapeduce 技术实现清洗Hive数据

问题产生：

• Mapeduce 无法 正常读取Hive数据
• Mapeduce 无法 正常将结果sink到Hive中

解决路线：

首先从spark入手
为了解决spark写入hive后文件格式为 .parquet 问题

首先我们需要创建一个表，至于为什么不用自动建表,是因为自动建表 spark使用的是.parquet文件格式存储的

hive>  CREATE TABLE `ods.region2`(
       >   `regionkey` string,
       >    `name` string,
       >    `comment` string)
       >    PARTITIONED BY (
       >   `etldate` string
       >    )
       >   row format delimited
       >   fields terminated by '|' ;
    OK
Time taken: 0.055 seconds

spark sink hive 部分代码

    spark.sql("select *,'20220616' as etldate from data ")
      .write
      .partitionBy("etldate")
      .mode(saveMode = SaveMode.Overwrite)
      .format("hive")
      .option("delimiter","|")
      .insertInto("ods.region2")

重点是这两条
.format("hive")
.insertInto("ods.region2")

我们看一下写好的数据
hdfs dfs -cat /user/hive/warehouse/ods.db/region2/etldate=20220616/*

3|EUROPE|ly final courts cajole furiously final excuse
4|MIDDLE EAST|uickly special accounts cajole carefully blithely close requests. carefully final asymptotes haggle furiousl
0|AFRICA|lar deposits. blithely final packages cajole. regular waters are final requests. regular accounts are according to 
1|AMERICA|hs use ironic, even requests. s
2|ASIA|ges. thinly even pinto beans ca

可以正常编写和运行java mapReduce 代码啦
代码不再一一贴出，放一个driver把

	
	<groupId>org.li</groupId>
    <artifactId>mapreduce_06-21</artifactId>
    <version>1.0</version>
 
   <properties>
        <maven.compiler.source>8</maven.compiler.source>
        <maven.compiler.target>8</maven.compiler.target>
        <parquet.version>1.8.1</parquet.version>
        <!-- JDateTime 依赖 -->
        <jodd.version>3.3.8</jodd.version>
    </properties>    


<dependencies>

        <!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-common -->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-common</artifactId>
            <version>2.7.6</version>
        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-client -->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>2.7.6</version>
        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-hdfs -->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-hdfs</artifactId>
            <version>2.7.6</version>
        </dependency>

        <!-- parquet-hadoop -->
        <dependency>
            <groupId>org.apache.parquet</groupId>
            <artifactId>parquet-hadoop</artifactId>
            <version>${parquet.version}</version>
        </dependency>

        <!-- jodd -->
        <dependency>
            <groupId>org.jodd</groupId>
            <artifactId>jodd</artifactId>
            <version>${jodd.version}</version>
        </dependency>
    </dependencies>

package com.li.mapreduce;


import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
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.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.db.DBInputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.parquet.hadoop.ParquetInputFormat;
//import org.apache.parquet.hadoop.ParquetInputFormat;

import java.io.File;
import java.io.IOException;
import java.net.URI;
import java.net.URISyntaxException;

public class HiveDriver{
    
    
    public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException, URISyntaxException {
    
    

        System.setProperty("HADOOP_USER_NAME","root");
        System.out.println("删除本地目录" + new File("/home/rjxy/output").delete());
        Configuration configuration = new Configuration();
        configuration.set("dfs.client.use.datanode.hostname","true");

        //hadoop配值文件

        //获取i工作势力
        Job instance = Job.getInstance(configuration);

        //关联driver
        instance.setJarByClass(HiveDriver.class);

        //关联mapper reduce
        instance.setMapperClass(HiveMapper.class);
        instance.setReducerClass(HiveReduce.class);

        //设置map输出的kv类型
        instance.setMapOutputKeyClass(LongWritable.class);
        instance.setMapOutputValueClass(Text.class);

        //设置最终的输入输出类型
        instance.setOutputKeyClass(NullWritable.class);
        instance.setOutputValueClass(Text.class);

        //Parquet
//        instance.setInputFormatClass();
//        instance.setInputFormatClass(ParquetInputFormat.class);

        //设置输入输出路径
        FileInputFormat.setInputPaths(instance,new Path("hdfs://master:9000/user/hive/warehouse/ods.db/" + "region2" + "/*/*"));

        Path outputDir = new Path("hdfs://master:9000/test4");
//        Path outputDir = new Path("/home/rjxy/output");
        FileOutputFormat.setOutputPath(instance, outputDir);

        //7 提交job
        boolean result = instance.waitForCompletion(true);
        System.exit(result?0:1);
    }
}

这些代码就包含啦我的resource 数据信息 sink 位置
现在看一下怎么将hdfs数据进行load进hive表中
先建好表

hive>  CREATE TABLE `ods.region2`(
       >   `regionkey` string,
       >    `name` string,
       >    `comment` string)
       >    PARTITIONED BY (
       >   `etldate` string
       >    )
       >   row format delimited
       >   fields terminated by '|' ;
    OK
Time taken: 0.055 seconds

LOAD DATA INPATH '/test2' INTO TABLE ods.region2 partition(etldate="20220622");

查看hive清洗后的数据

hive (default)> select * from ods.region2 where etldate="20220622";
OK
region2.regionkey	region2.name	region2.comment	region2.etldate
3	EUROPE	ly final courts cajole furiously final excuse	20220622
3	EUROPE	ly final courts cajole furiously final excuse	20220622
4	MIDDLE EAST	uickly special accounts cajole carefully blithely close requests. carefully final asymptotes haggle furiousl	20220622
4	MIDDLE EAST	uickly special accounts cajole carefully blithely close requests. carefully final asymptotes haggle furiousl	20220622
0	AFRICA	lar deposits. blithely final packages cajole. regular waters are final requests. regular accounts are according to 	20220622
0	AFRICA	lar deposits. blithely final packages cajole. regular waters are final requests. regular accounts are according to 	20220622
1	AMERICA	hs use ironic, even requests. s	20220622
1	AMERICA	hs use ironic, even requests. s	20220622
2	ASIA	ges. thinly even pinto beans ca	20220622
2	ASIA	ges. thinly even pinto beans ca	20220622

Time taken: 0.194 seconds, Fetched: 10 row(s)