Detailed explanation of Hive integration between SparkSQL and IDEA

Introduction: Everyone can type the code, the key is to have clear logic

• 1- Why does SparkOnHive ( SparkSQL ) appear ?
  • 1- In essence, SparkOnHive is designed to solve the problem of slow Hive calculations
  • 2- Now more offline analysis in industrial scenarios is based on the integration of Hive+Spark
• 2- Note:
• SparkOnHive only uses Hive metadata information, the others are all spark technology
• You need to copy hive-site.xml of hive to spark/conf to realize that spark knows the metadata information of hive,
• 3-step:
• 1-Principle: Need to integrate Hive metadata information to start metator service
  

1. SparkSQL integrates Hive

Step 1: Copy hive-site.xml to the conf directory of the spark installation path

因为Hive仅仅只是一个操作MapReduce的一个客户端工具,所以我就在我的第三台主机上安装了Hive

在node3执行以下命令来拷贝hive-site.xml到所有的spark安装服务器上面去

cd /export/server/hive/conf
cp hive-site.xml /export/server/spark/conf/
scp hive-site.xml root@node2:/export/server/spark/conf/
scp hive-site.xml root@node1:/export/server/spark/conf/

Step 2: Copy the mysql connection driver package to the spark jars directory

node3执行以下命令将连接驱动包拷贝到spark的jars目录下，三台机器都要进行拷贝
cd /export/server/hive/lib
cp mysql-connector-java-5.1.38.jar  /export/server/spark/jars/
scp mysql-connector-java-5.1.38.jar  root@node2:/export/server/spark/jars/
scp mysql-connector-java-5.1.38.jar  root@node1:/export/server/spark/jars/

Step 3: Hive opens MetaStore service

1: 修改 hive/conf/hive-site.xml 新增如下配置
<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
    <property>
      <name>hive.metastore.warehouse.dir</name>
      <value>/user/hive/warehouse</value>
    </property>
    <property>
      <name>hive.metastore.local</name>
      <value>false</value>
    </property>
    <property>
      <name>hive.metastore.uris</name>
      <value>thrift://node3:9083</value>
    </property>
 </configuration>

2: 后台启动 Hive MetaStore服务
nohup /export/server/hive/bin/hive --service metastore &
或者
nohup /export/server/hive/bin/hive --service metastore 2>&1 >> /var/log.log &

Step 4: Test whether Sparksql integrates Hive successfully

先启动hadoop集群，在启动spark集群，确保启动成功之后node01执行命令：

Spark-Shell方式启动：
bin/spark-shell --master local[3]
spark.sql("show databases").show

看到数据的结果，说明sparksql整合hive成功！
日志太多，我们可以修改spark的日志输出级别(conf/log4j.properties)

The tables created in Hive can be seen in the Spark shell

2. IDEA integrates Hive

1. Preparation:

2. Profile explained

3- Note that setting the parameters of the shuffle sql
spark.default.parallieize rdd only be used in the shuffle sparkcore the
official website screenshot

4-code

/**
 * @author liu a fu
 * @date 2021/1/18 0018
 * @version 1.0
 * @DESC: 整合Hive和SparkSQL
 *      1-准备SparkSession的环境
 *      2-使用spark.sql(hive的创建数据库的语句)
 *      3-使用spark.sql(hive的创建表的语句)
 *      4-使用spark.sql(hive的展示数据库表的语句)
 *      5-使用spark.sql(hive的加载本地文件的数据)
 *      6-使用spark.sql查询
 *      7-停止sparksession
 */
object SparkToHive {
    
    
  def main(args: Array[String]): Unit = {
    
    
    //1-准备SparkSession的环境
    val spark: SparkSession = SparkSession.builder()
      .appName(this.getClass.getSimpleName.stripSuffix("$"))
      .master("local[4]")
      .enableHiveSupport()
      .config("spark.sql.shuffle.partitions", "4")//默认200
      //元数据的信息放在那里
      //thrift的地址是哪个
      //指定hive元数据在hdfs上的位置
      .config("spark.sql.warehouse.dir", "hdfs://node1:8020/user/hive/warehouse")
      // 指定Hive MetaStore服务地址，客户端连接metastore服务，metastore再去连接MySQL数据库来存取元数据有了metastore服务，就可以有多个客户端同时连接，而且这些客户端不需要知道MySQL数据库的用户名和密码，只需要连接metastore 服务即可。 
      .config("hive.metastore.uris", "thrift://node3:9083")   //Hive安装的位置
      // TODO: 告诉Spark要集成Hive，读取Hive表的数据
      .getOrCreate()
    spark.sparkContext.setLogLevel("WARN")


    //2-使用spark.sql(hive的创建数据库的语句)
    spark.sql("show databases")
    spark.sql("use sparkhive")
    //3-使用spark.sql(hive的创建表的语句)
    spark.sql("create table student(id  int,name String,age int) row format delimited fields terminated by \",\"")
    //4-使用spark.sql(hive的展示数据库表的语句)
    spark.sql("load data local inpath 'data/input/sql/hive/student.csv' overwrite into table student")

    //5-使用spark.sql(hive的加载本地文件的数据)
    //6-使用spark.sql查询
    spark.sql("desc student").show()
    spark.sql("select * from student").show()

    //7-停止sparksession
    spark.stop()
  }

}