[Spark SQL]Spark SQL读取Kudu，写入Hive

SparkUnit

Function：用于获取Spark Session

package com.example.unitl

import org.apache.spark.sql.SparkSession

object SparkUnit {
  def getLocal(appName: String): SparkSession = {
    SparkSession.builder().appName(appName).master("local[*]").getOrCreate()
  }

  def getLocal(appName: String, supportHive: Boolean): SparkSession = {
    if (supportHive) getLocal(appName,"local[*]",true)
    else getLocal(appName)
  }

  def getLocal(appName:String,master:String,supportHive:Boolean): SparkSession = {
    if (supportHive) SparkSession.builder().appName(appName).master(master).enableHiveSupport().getOrCreate()
    else  SparkSession.builder().appName(appName).master(master).getOrCreate()
  }

  def stopSs(ss:SparkSession): Unit ={
    if (ss != null) {
      ss.stop()
    }
  }
}

log4j.properties

Function：设置控制台输出级别

# Set everything to be logged to the console
log4j.rootCategory=ERROR, console
log4j.appender.console=org.apache.log4j.ConsoleAppender
log4j.appender.console.target=System.err
log4j.appender.console.layout=org.apache.log4j.PatternLayout
log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n

# Set the default spark-shell log level to WARN. When running the spark-shell, the
# log level for this class is used to overwrite the root logger's log level, so that
# the user can have different defaults for the shell and regular Spark apps.
log4j.logger.org.apache.spark.repl.Main=WARN

# Settings to quiet third party logs that are too verbose
log4j.logger.org.spark_project.jetty=WARN
log4j.logger.org.spark_project.jetty.util.component.AbstractLifeCycle=ERROR
log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
log4j.logger.org.apache.parquet=ERROR
log4j.logger.parquet=ERROR

# SPARK-9183: Settings to avoid annoying messages when looking up nonexistent UDFs in SparkSQL with Hive support
log4j.logger.org.apache.hadoop.hive.metastore.RetryingHMSHandler=FATAL
log4j.logger.org.apache.hadoop.hive.ql.exec.FunctionRegistry=ERROR

KTV

Function：读取kudu，写入hive。Kudu_To_Hive，简称KTV

package com.example.dao

import com.example.unitl.SparkUnit
import org.apache.spark.sql.SparkSession

object KTV {
  def getKuduTableDataFrame(ss: SparkSession): Unit = {
    // 读取kudu
    // 获取tb对象
    val kuduTb = ss.read.format("org.apache.kudu.spark.kudu")
      .option("kudu.master", "10.168.1.12:7051")
      .option("kudu.table", "impala::realtimedcs.bakup_db") // Tips：注意指定库
      .load()

    // create view
    kuduTb.createTempView("v1")

    val kudu_unit1_df = ss.sql(
      """
        |SELECT * FROM `sources_tb1`
        |WHERE `splittime` = "2021-07-11"
        |""".stripMargin)

    // print
    kudu_unit1_df.printSchema()
    kudu_unit1_df.show()

    // load of memory
    kudu_unit1_df.createOrReplaceTempView("v2")
  }

  def insertHive(ss: SparkSession): Unit = {
    // create table
    ss.sql(
      """
        |USE `bakup_db`
        |""".stripMargin)

    ss.sql(
      """
        |  CREATE TABLE IF NOT EXISTS `bak_tb1`(
        |   `id` int,
        |   `packtimestr` string,
        |   `dcs_name` string,
        |   `dcs_type` string,
        |   `dcs_value` string,
        |   `dcs_as` string,
        |   `dcs_as2` string)
        | PARTITIONED BY (
        |   `splittime` string)
        |""".stripMargin)
    println("创建表成功！")

    // create view
    ss.sql(
      """
        |INSERT INTO `bakup_db`
        |SELECT * FROM bak_tb1
        |""".stripMargin)
    println("保存成功！")
  }

  def main(args: Array[String]): Unit = {
    //get ss
    val ss = SparkUnit.getLocal("KTV", true)
    // 做动态分区, 所以要先设定partition参数
    // default是false, 需要额外下指令打开这个开关
    ss.sqlContext.setConf("hive.exec.dynamic.partition;","true");
    ss.sqlContext.setConf("hive.exec.dynamic.partition.mode","nonstrict");

    // 调用方法
    getKuduTableDataFrame(ss)
    insertHive(ss)

    // 关闭连接
    SparkUnit.stopSs(ss)
  }
}

运行：

运行时请将hive的配置文件 hive-site.xml文件，复制到项目resource下。

hue查看写入的数据：