使用spark將內存中的數據寫入到hive表中

hive-site.xml

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>

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<configuration>
    <!--hive 的元數據服務, 供spark SQL 使用-->
    <property>
        　　　　<name>hive.metastore.uris</name>
        　　　　<value>thrift://master:9083</value>
        　　　　<description>Thrift URI for the remote metastore. Used by metastore client to connect to remote metastore.</description>
        　　</property>

    <!--配置mysql數據庫的鏈接URL和數據庫名metastore,?后面的表達式代表如果這個數據庫
    不存在,會自動創建-->
    <property>
        <name>javax.jdo.option.ConnectionURL</name>
        <value>jdbc:mysql://master:3306/metastore?createDatabaseIfNotExist=true</value>
        <description>JDBC connect string for a JDBC metastore</description>
    </property>
    <!--指定mysql的鏈接驅動,配置jdbc的驅動-->
    <property>
        <name>javax.jdo.option.ConnectionDriverName</name>
        <value>com.mysql.jdbc.Driver</value>
        <description>Driver class name for a JDBC metastore</description>
    </property>
    <!--配置mysql的用戶名和密碼-->
    <property>
        <name>javax.jdo.option.ConnectionUserName</name>
        <value>root</value>
        <description>username to use against metastore database</description>
    </property>
    <property>
        <name>javax.jdo.option.ConnectionPassword</name>
        <value>123456</value>
        <description>password to use against metastore database</description>
    </property>

    <property>
        <name>hive.cli.print.header</name>
        <value>true</value>
        <description>Whether to print the names of the columns in query output.</description>
    </property>
    <property>
        <name>hive.cli.print.current.db</name>
        <value>true</value>
        <description>Whether to include the current database in the Hive prompt.</description>
    </property>

</configuration>

下面是示例代碼

package spark_sql

import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types.{StringType, StructField, StructType}
import test.ProductData

/**
  * @Program: spark01
  * @Author: 努力就是魅力
  * @Since: 2018-10-19 08:30
  *         Description:
  *
  *         使用spark將內存中的數據寫入到hive表中，這是一個可以完整運行的例子
  *
  *
  *    下面是hive表查詢的結果
  *         hive (hadoop10)> select * from data_block;
  *         OK
  *         data_block.ip	data_block.time	data_block.phonenum
  *         40.234.66.122	2018-10-12 09:35:21
  *         5.150.203.160	2018-10-03 14:41:09	13389202989
  *
  **/

case class Datablock(ip: String, time:String, phoneNum:String)

object WriteTabletoHive {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession
      .builder()
      .master("local[*]")
      .appName("WriteTableToHive")
      .config("spark.sql.warehouse.dir","D:\\reference-data\\spark01\\spark-warehouse")
      .enableHiveSupport()
      .getOrCreate()

    import spark.implicits._

    val schemaString = "ip time phoneNum"

    val fields = schemaString.split(" ")
      .map(fieldName => StructField(fieldName, StringType,nullable = true))

    val schema = StructType(fields)

   // val datablockDS = Seq(Datablock(ProductData.getRandomIp,ProductData.getRecentAMonthRandomTime("yyyy-MM-dd HH:mm:ss"),ProductData.getRandomPhoneNumber)).toDS()

 // val datablockDS = Seq(Datablock("192.168.40.122","2018-01-01 12:25:25","18866556699")).toDS()

    datablockDS.show()

    datablockDS.toDF().createOrReplaceTempView("dataBlock")


      spark.sql("select * from dataBlock")
        .write.mode("append")
        .saveAsTable("hadoop10.data_block")


  }
}