spark操作kudu之DML操作

Kudu支持许多DML类型的操作，其中一些操作包含在Spark on Kudu集成

包括：

  • INSERT - 将DataFrame的行插入Kudu表。请注意，虽然API完全支持INSERT，但不鼓励在Spark中使用它。使用INSERT是有风险的，因为Spark任务可能需要重新执行，这意味着可能要求再次插入已插入的行。这样做会导致失败，因为如果行已经存在，INSERT将不允许插入行（导致失败）。相反，我们鼓励使用下面描述的INSERT_IGNORE。

  • INSERT-IGNORE - 将DataFrame的行插入Kudu表。如果表存在，则忽略插入动作。

  • DELETE - 从Kudu表中删除DataFrame中的行

  • UPSERT - 如果存在，则在Kudu表中更新DataFrame中的行，否则执行插入操作。

  • UPDATE - 更新dataframe中的行

Insert操作

import org.apache.kudu.spark.kudu.KuduContext
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.SparkSession
import org.apache.kudu.spark.kudu._
/**
  * Created by angel；
  */
object Insert {
  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
    //使用spark创建kudu表
    val kuduMasters = "hadoop01:7051,hadoop02:7051,hadoop03:7051"
    val kuduContext = new KuduContext(kuduMasters, sqlContext.sparkContext)
    //TODO 1：定义kudu表
    val kuduTableName = "spark_kudu_tbl"

    //TODO 2：配置kudu参数
    val kuduOptions: Map[String, String] = Map(
      "kudu.table"  -> kuduTableName,
      "kudu.master" -> kuduMasters)
    import sqlContext.implicits._
    //TODO 3：定义数据
    val customers = Array(
      Customer("jane", 30, "new york"),
      Customer("jordan", 18, "toronto"))

    //TODO 4：创建RDD
    val customersRDD = sparkContext.parallelize(customers)
    //TODO 5：将RDD转成dataFrame
    val customersDF = customersRDD.toDF()

    //TODO 6：将数据插入kudu表
    kuduContext.insertRows(customersDF, kuduTableName)

    //TODO 7：将插入的数据读取出来
    sqlContext.read.options(kuduOptions).kudu.show
  }
}

Delete操作

import org.apache.kudu.spark.kudu._
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.SparkSession

/**
  * Created by angel；
  */
object Delete {
  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
    //使用spark创建kudu表
    val kuduMasters = "hadoop01:7051,hadoop02:7051,hadoop03:7051"
    val kuduContext = new KuduContext(kuduMasters, sqlContext.sparkContext)
    //TODO 1：定义kudu表
    val kuduTableName = "spark_kudu_tbl"

    //TODO 2：配置kudu参数
    val kuduOptions: Map[String, String] = Map(
      "kudu.table"  -> kuduTableName,
      "kudu.master" -> kuduMasters)
    import sqlContext.implicits._
    //TODO 3：定义数据
    val customers = Array(
      Customer("jane", 30, "new york"),
      Customer("jordan", 18, "toronto"))

    //TODO 4：创建RDD
    val customersRDD = sparkContext.parallelize(customers)
    //TODO 5：将RDD转成dataFrame
    val customersDF = customersRDD.toDF()
    //TODO 6：注册表
    customersDF.registerTempTable("customers")

    //TODO 7：编写SQL语句，过滤出想要的数据
    val deleteKeysDF = sqlContext.sql("select name from customers where age > 20")

    //TODO 8：使用kuduContext执行删除操作
    kuduContext.deleteRows(deleteKeysDF, kuduTableName)

    //TODO 9：查看kudu表中的数据
    sqlContext.read.options(kuduOptions).kudu.show
  }
}

Upsert操作

如果存在，则在Kudu表中更新DataFrame中的行，否则执行插入操作。

import org.apache.kudu.spark.kudu._
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.SparkSession

/**
  * Created by angel；
  */
object Upsert {
  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
    //使用spark创建kudu表
    val kuduMasters = "hadoop01:7051,hadoop02:7051,hadoop03:7051"
    val kuduContext = new KuduContext(kuduMasters, sqlContext.sparkContext)
    //TODO 1：定义kudu表
    val kuduTableName = "spark_kudu_tbl"

    //TODO 2：配置kudu参数
    val kuduOptions: Map[String, String] = Map(
      "kudu.table"  -> kuduTableName,
      "kudu.master" -> kuduMasters)
    import sqlContext.implicits._

    //TODO 3：定义数据集
    val newAndChangedCustomers = Array(
      Customer("michael", 25, "chicago"),
      Customer("denise" , 43, "winnipeg"),
      Customer("jordan" , 19, "toronto"))

    //TODO 4：将数据集转换成dataframe
    val newAndChangedRDD = sparkContext.parallelize(newAndChangedCustomers)
    val newAndChangedDF  = newAndChangedRDD.toDF()

    //TODO 5：使用upsert来更新数据集
    kuduContext.upsertRows(newAndChangedDF, kuduTableName)

    //TODO 6：读取kudu中的数据
    sqlContext.read.options(kuduOptions).kudu.show
  }
}

Update操作

更新kudu行数据

import org.apache.kudu.spark.kudu._
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.SparkSession

/**
  * Created by angel；
  */
object Update {
  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
    //使用spark创建kudu表
    val kuduMasters = "hadoop01:7051,hadoop02:7051,hadoop03:7051"
    val kuduContext = new KuduContext(kuduMasters, sqlContext.sparkContext)
    //TODO 1：定义kudu表
    val kuduTableName = "spark_kudu_tbl"

    //TODO 2：配置kudu参数
    val kuduOptions: Map[String, String] = Map(
      "kudu.table"  -> kuduTableName,
      "kudu.master" -> kuduMasters)

    //TODO 3：准备数据集
    val modifiedCustomers = Array(Customer("michael", 25, "toronto"))
    val modifiedCustomersRDD = sparkContext.parallelize(modifiedCustomers)
    //TODO 4：将数据集转化成dataframe
    import sqlContext.implicits._
    val modifiedCustomersDF  = modifiedCustomersRDD.toDF()

    //TODO 5：执行更新操作
    kuduContext.updateRows(modifiedCustomersDF, kuduTableName)

    //TODO 6：查看kudu数据
    sqlContext.read.options(kuduOptions).kudu.show
  }
}
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原文地址：https://www.cnblogs.com/niutao/p/10555302.html