|NO.Z.00048|——————————|BigDataEnd|——|Hadoop&Spark.V09|——|Spark.v09|spark sql|UDAF|

一、UDAF

### --- UDAF

~~~     # 数据如下：
id, name, sales, discount, state, saleDate
1, "Widget Co", 1000.00, 0.00, "AZ", "2019-01-01"
2, "Acme Widgets", 2000.00, 500.00, "CA", "2019-02-01"
3, "Widgetry", 1000.00, 200.00, "CA", "2020-01-11"
4, "Widgets R Us", 2000.00, 0.0, "CA", "2020-02-19"
5, "Ye Olde Widgete", 3000.00, 0.0, "MA", "2020-02-28"
~~~     # 最后要得到的结果为：
(2020年的合计值 – 2019年的合计值) / 2019年的合计值 (6000 - 3000) / 3000 = 1

~~~     # 执行以下SQL得到最终的结果：
select userFunc(sales, saleDate) from table1;
~~~     # 即计算逻辑在userFunc中实现

~~~     # 普通的UDF不支持数据的聚合运算。

~~~     如当要对销售数据执行年度同比计算，就需要对当年和上一年的销量分别求和，
~~~     然后再利用公式进行计算。
~~~     此时需要使用UDAF，Spark为所有的UDAF定义了一个父类UserDefinedAggregateFunction 。

### --- 要继承这个类，需要实现父类的几个抽象方法：

~~~     inputSchema用于定义与DataFrame列有关的输入样式
~~~     bufferSchema用于定义存储聚合运算时产生的中间数据结果的Schema
~~~     dataType标明了UDAF函数的返回值类型
~~~     deterministic是一个布尔值，用以标记针对给定的一组输入，UDAF是否总是生成相同的结果
~~~     initialize对聚合运算中间结果的初始化

abstract class UserDefinedAggregateFunction extends Serializble {
    /**...*/
    def inputSchema: StructType
    
    /**...*/
    def bufferSchema: StructType
    
    /**...*/
    def dataType: DataType
    
    /**...*/
    def deterministic: Boolean
    
    /**...*/
    def initialize(buffer: MutableAggregationBuffer): Unit
    
    /**...*/
    def update(buffer: MutableAggregationBuffer, input: Row): Unit
    
    /**...*/
    def merge(buffer1: MutableAggregationBuffer, bufer2: Row): Unit
    
    /**...*/
    def evaluate(buffer： Row)： Any

### --- update函数的第一个参数为bufferSchema中两个Field的索引，默认以0开始；

~~~     UDAF的核心计算都发生在update函数中；update函数的第二个参数input:
~~~     Row对应的并非DataFrame的行，而是被inputSchema投影了的行
~~~     merge函数负责合并两个聚合运算的buffer，再将其存储到MutableAggregationBuffer中
~~~     evaluate函数完成对聚合Buffer值的运算，得到最终的结果

二、编程代码实现：类型不安全

### --- UDAF--类型不安全

package cn.yanqi.sparksql

import org.apache.log4j.{Level, Logger}
import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
import org.apache.spark.sql.types.{DataType, DoubleType, StringType, StructType}
import org.apache.spark.sql.{Row, SparkSession}

class TypeUnsafeUDAF extends UserDefinedAggregateFunction {
  // 定义输入数据的类型
  override def inputSchema: StructType = new StructType().add("sales", DoubleType).add("saleDate", StringType)

  // 定义数据缓存的类型
  override def bufferSchema: StructType = new StructType().add("year2019", DoubleType).add("year2020", DoubleType)

  // 定义最终返回结果的类型
  override def dataType: DataType = DoubleType

  // 对于相同的结果是否有相同的输出
  override def deterministic: Boolean = true

  // 数据缓存的初始化
  override def initialize(buffer: MutableAggregationBuffer): Unit = {
    buffer.update(0, 0.0)
    buffer.update(1, 0.0)
  }

  // 分区内数据合并
  override def update(buffer: MutableAggregationBuffer, input: Row): Unit = {
    // 销售量、销售日期(year)
    val sales = input.getAs[Double](0)
    val saleYear = input.getAs[String](1).take(4)

    saleYear match{
      case "2019" => buffer(0) = buffer.getAs[Double](0) + sales
      case "2020" => buffer(1) = buffer.getAs[Double](1) + sales
      case _ => println("Error!")
    }
  }

  // 分区间数据合并
  override def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = {
    buffer1(0) = buffer1.getAs[Double](0) + buffer2.getAs[Double](0)
    buffer1(1) = buffer1.getAs[Double](1) + buffer2.getAs[Double](1)
  }

  // 计算最终的结果
  override def evaluate(buffer: Row): Double = {
    // (2020年的合计值 – 2019年的合计值) / 2019年的合计值
    if (math.abs(buffer.getAs[Double](0)) < 0.000000001) 0.0
    else (buffer.getAs[Double](1) - buffer.getAs[Double](0)) / buffer.getAs[Double](0)
  }
}

object TypeUnsafeUDAFTest{
  def main(args: Array[String]): Unit = {
    Logger.getLogger("org").setLevel(Level.WARN)
    val spark = SparkSession.builder()
      .appName(s"${this.getClass.getCanonicalName}")
      .master("local[*]")
      .getOrCreate()

    val sales = Seq(
      (1, "Widget Co",        1000.00, 0.00,    "AZ", "2019-01-02"),
      (2, "Acme Widgets",     1000.00, 500.00,  "CA", "2019-02-01"),
      (3, "Widgetry",         1000.00, 200.00,  "CA", "2020-01-11"),
      (4, "Widgets R Us",     2000.00, 0.0,     "CA", "2020-02-19"),
      (5, "Ye Olde Widgete",  3000.00, 0.0,     "MA", "2020-02-28"))
    val salesDF = spark.createDataFrame(sales).toDF("id", "name", "sales", "discount", "state", "saleDate")
    salesDF.createTempView("sales")
    val userFunc = new TypeUnsafeUDAF
    spark.udf.register("userFunc", userFunc)
    spark.sql("select userFunc(sales, saleDate) as rate from sales").show()

    spark.stop()
  }
}

### --- 编译打印

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cn.yanqi.sparksql.TypeUnsafeUDAFTest
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
+----+
|rate|
+----+
| 2.0|
+----+

Process finished with exit code 0

三、编程实现：UDAF--类型安全

### --- 实现流程

abstract class Aggregator [-IN, BUF, OUT] extends Seruakuzable {
    // 定义初值
    def zero: BUF
    
    // 分区内数据合并
    def reduce(b: BUF, a: IN): BUF
    
    // 分区间数据合并
    def merge(b1: BUF , b2: BUF): BUF
    
    // 最终结果的计算
    def finish(reduction: BUF): OUT
    
    // 定义buffer 和输出结果的编译器
    def bufferEncoder: Encoder[BUF]
    def outputEncoder: Encoder[OUT]
    
    def toColumn: TypedColumn[IN, OUT] = { ... ... }
}

### --- 编程实现：UDAF--类型安全

package cn.yanqi.sparksql

import org.apache.log4j.{Level, Logger}
import org.apache.spark.sql.expressions.Aggregator
import org.apache.spark.sql.{Encoder, Encoders, SparkSession, TypedColumn}

case class Sales(id: Int, name1: String, sales: Double, discount: Double, name2: String, stime: String)
case class SalesBuffer(var sales2019: Double, var sales2020: Double)

class TypeSafeUDAF extends Aggregator[Sales, SalesBuffer, Double]{
  // 定义初值
  override def zero: SalesBuffer = SalesBuffer(0.0, 0.0)

  // 分区内的数据合并
  override def reduce(buffer: SalesBuffer, input: Sales): SalesBuffer = {
    val sales: Double = input.sales
    val year = input.stime.take(4)
    year match {
      case "2019" => buffer.sales2019 += sales
      case "2020" => buffer.sales2020 += sales
      case _ => println("ERROR")
    }
    buffer
  }

  // 分区间的数据合并
  override def merge(b1: SalesBuffer, b2: SalesBuffer): SalesBuffer = {
    SalesBuffer(b1.sales2019 + b2.sales2019, b1.sales2020 + b2.sales2020)
  }

  // 计算最终结果
  override def finish(reduction: SalesBuffer): Double = {
    if (math.abs(reduction.sales2019) < 0.0000000001) 0.0
    else (reduction.sales2020 - reduction.sales2019) / reduction.sales2019
  }

  // 定义编码器
  override def bufferEncoder: Encoder[SalesBuffer] = Encoders.product
  override def outputEncoder: Encoder[Double] = Encoders.scalaDouble
}

object TypeSafeUDAFTest{
  def main(args: Array[String]): Unit = {
    Logger.getLogger("org").setLevel(Level.WARN)
    val spark = SparkSession.builder()
      .appName(s"${this.getClass.getCanonicalName}")
      .master("local[*]")
      .getOrCreate()

    val sales = Seq(
      Sales(1, "Widget Co",        1000.00, 0.00,    "AZ", "2019-01-02"),
      Sales(2, "Acme Widgets",     2000.00, 500.00,  "CA", "2019-02-01"),
      Sales(3, "Widgetry",         1000.00, 200.00,  "CA", "2020-01-11"),
      Sales(4, "Widgets R Us",     2000.00, 0.0,     "CA", "2020-02-19"),
      Sales(5, "Ye Olde Widgete",  3000.00, 0.0,     "MA", "2020-02-28"))

    import spark.implicits._
    val ds = spark.createDataset(sales)
    ds.show

    val rate: TypedColumn[Sales, Double] = new TypeSafeUDAF().toColumn.name("rate")
    ds.select(rate).show

    spark.stop()
  }
}

### --- 编译打印

D:\JAVA\jdk1.8.0_231\bin\java.exe "-javaagent:D:\IntelliJIDEA\IntelliJ IDEA 2019.3.3\lib\idea_rt.jar=56919:D:\IntelliJIDEA\IntelliJ IDEA 2019.3.3\bin" -Dfile.encoding=UTF-8 -classpath 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cn.yanqi.sparksql.TypeSafeUDAFTest
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
+---+---------------+------+--------+-----+----------+
| id|          name1| sales|discount|name2|     stime|
+---+---------------+------+--------+-----+----------+
|  1|      Widget Co|1000.0|     0.0|   AZ|2019-01-02|
|  2|   Acme Widgets|2000.0|   500.0|   CA|2019-02-01|
|  3|       Widgetry|1000.0|   200.0|   CA|2020-01-11|
|  4|   Widgets R Us|2000.0|     0.0|   CA|2020-02-19|
|  5|Ye Olde Widgete|3000.0|     0.0|   MA|2020-02-28|
+---+---------------+------+--------+-----+----------+

+----+
|rate|
+----+
| 1.0|
+----+


Process finished with exit code 0

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Walter Savage Landor:strove with none,for none was worth my strife.Nature I loved and, next to Nature, Art:I warm'd both hands before the fire of life.It sinks, and I am ready to depart

                                                                                                                                                   ——W.S.Landor

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