spark操作Kudu之读 - 使用DataFrame API

虽然我们可以通过上面显示的KuduContext执行大量操作，但我们还可以直接从默认数据源本身调用读/写API。

要设置读取，我们需要为Kudu表指定选项，命名我们要读取的表以及为表提供服务的Kudu集群的Kudu主服务器列表。

import org.apache.kudu.spark.kudu._

import org.apache.spark.{SparkConf, SparkContext}

import org.apache.spark.sql.SparkSession

/**

  * Created by angel；

  */

object DataFrame_read {

  def main(args: Array[String]): Unit = {

    val sparkConf = new SparkConf().setAppName("AcctfileProcess")

      //设置Master_IP并设置spark参数

      .setMaster("local")

      .set("spark.worker.timeout", "500")

      .set("spark.cores.max", "10")

      .set("spark.rpc.askTimeout", "600s")

      .set("spark.network.timeout", "600s")

      .set("spark.task.maxFailures", "1")

      .set("spark.speculationfalse", "false")

      .set("spark.driver.allowMultipleContexts", "true")

      .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")

    val sparkContext = SparkContext.getOrCreate(sparkConf)

    val sqlContext = SparkSession.builder().config(sparkConf).getOrCreate().sqlContext

    //TODO 1:定义表名

    val kuduTableName = "spark_kudu_tbl"

    val kuduMasters = "hadoop01:7051,hadoop02:7051,hadoop03:7051"

    //使用spark创建kudu表

    val kuduContext = new KuduContext(kuduTableName, sqlContext.sparkContext)

    //TODO 2：配置kudu参数

    val kuduOptions: Map[String, String] = Map(

      "kudu.table"  -> kuduTableName,

      "kudu.master" -> kuduMasters)

    //TODO 3：执行读取操作

    val customerReadDF = sqlContext.read.options(kuduOptions).kudu

    val filterData = customerReadDF.select("name" ,"age", "city").filter("age<30")

    //TODO 4：打印

    filterData.show()

  }

}