Spark-SQL核心编程-EW帮帮网

Spark-SQL核心编程（四）

实验内容：

利用IDEA开发Spark-SQL。

实验步骤：

利用IDEA开发Spark-SQL

创建子模块Spark-SQL，并添加依赖

<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-sql_2.12</artifactId>
    <version>3.0.0</version>
</dependency>

创建Spark-SQL的测试代码：

case class User(id:Int,name:String,age:Int)

object SparkSQLDemo {
  def main(args: Array[String]): Unit = {
    //创建上下文环境配置对象
    val sparkConf = new SparkConf().setMaster("local[*]").setAppName("SQLDemo")
    //创建SparkSession对象
    val spark :SparkSession = SparkSession.builder().config(sparkConf).getOrCreate()

    import spark.implicits._
    //读取json文件
    val df : DataFrame = spark.read.json("Spark-SQL/input/user.json")
    df.show()
    //SQL风格语法
    df.createOrReplaceTempView("user")
    spark.sql("select * from user").show
    spark.sql("select avg(age) from user").show

    //DSL风格语法
    df.select("username","age").show()

    //RDD=>DataFrame=>DataSet
    //RDD
    val rdd1 :RDD[(Int,String,Int)] = spark.sparkContext.makeRDD(
      List((1,"zhangsan",30),(2,"lisi",40))
    )
    //DataFrame
    val df1 :DataFrame = rdd1.toDF("id","name","age")
    df1.show()
    //DataSet
    val ds1 :Dataset[User] = df1.as[User]
    ds1.show()

    //DataSet=>DataFrame=>RDD
    val df2 =ds1.toDF()
    df2.show()

    val rdd2 :RDD[Row] = df2.rdd
    rdd2.foreach(a=>println(a.getString(1)))

    rdd1.map{
      case (id,name,age)=>User(id,name,age)
    }.toDS().show()

    val rdd3 = ds1.rdd
    rdd3.foreach(a=>println(a.age))
    rdd3.foreach(a=>println(a.id))
    rdd3.foreach(a=>println(a.name))

    spark.stop()
  }

}

Spark-SQL核心编程（五）

自定义函数：

UDF：

val sparkConf = new SparkConf().setMaster("local[*]").setAppName("SQLDemo")
//创建SparkSession对象
val spark :SparkSession = SparkSession.builder().config(sparkConf).getOrCreate()

import spark.implicits._
//读取json文件
val df : DataFrame = spark.read.json("Spark-SQL/input/user.json")

spark.udf.register("addName",(x:String)=>"Name:"+x)

df.createOrReplaceTempView("people")
spark.sql("select addName(username),age from people").show()

spark.stop()

UDAF（自定义聚合函数）

强类型的 Dataset 和弱类型的 DataFrame 都提供了相关的聚合函数，如 count()，

countDistinct()，avg()，max()，min()。除此之外，用户可以设定自己的自定义聚合函数。Spark3.0之前我们使用的是UserDefinedAggregateFunction作为自定义聚合函数，从 Spark3.0 版本后可以统一采用强类型聚合函数 Aggregator

实验需求：计算平均工资

实现方式一：RDD

val sparkconf: SparkConf = new SparkConf().setAppName("app").setMaster("local[*]")
val sc: SparkContext = new SparkContext(conf)
val resRDD: (Int, Int) = sc.makeRDD(List(("zhangsan", 20), ("lisi", 30), ("wangwu",40))).map {
  case (name, salary) => {
    (salary, 1)
  }
}.reduce {
  (t1, t2) => {
    (t1._1 + t2._1, t1._2 + t2._2)
  }
}
println(resRDD._1/resRDD._2)
// 关闭连接
sc.stop()

实现方式三：强类型UDAF

case class Buff(var sum:Long,var cnt:Long)
class MyAverageUDAF extends Aggregator[Long,Buff,Double]{
  override def zero: Buff = Buff(0,0)
  override def reduce(b: Buff, a: Long): Buff = {
    b.sum += a
    b.cnt += 1
    b
  }
  override def merge(b1: Buff, b2: Buff): Buff = {
    b1.sum += b2.sum
    b1.cnt += b2.cnt
    b1
  }
  override def finish(reduction: Buff): Double = {
    reduction.sum.toDouble/reduction.cnt
  }
  override def bufferEncoder: Encoder[Buff] = Encoders.product
  override def outputEncoder: Encoder[Double] = Encoders.scalaDouble

}

val sparkconf: SparkConf = new SparkConf().setAppName("app").setMaster("local[*]")
val spark:SparkSession = SparkSession.builder().config(conf).getOrCreate()

import spark.implicits._
val res :RDD[(String,Int)]= spark.sparkContext.makeRDD(List(("zhangsan", 20), ("lisi", 30), ("wangwu",40)))

val df :DataFrame = res.toDF("name","salary")
df.createOrReplaceTempView("user")
var myAverage = new MyAverageUDAF
//在 spark 中注册聚合函数
spark.udf.register("avgSalary",functions.udaf(myAverage))
spark.sql("select avgSalary(salary) from user").show()

// 关闭连接
spark.stop()

Spark-SQL核心编程