自定义用户函数有两种方式，区别：是否使用强类型，参考demo：https://github.com/asker124143222/spark-demo
1、不使用强类型，继承UserDefinedAggregateFunction
package com.home.spark import org.apache.spark.SparkConf
import org.apache.spark.sql.{DataFrame, Row, SparkSession}
import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
import org.apache.spark.sql.types._ object Ex_sparkUDAF {
def main(args: Array[String]): Unit = {
val conf = new SparkConf(true).setAppName("spark udf").setMaster("local[*]")
val spark = SparkSession.builder().config(conf).getOrCreate() //自定义聚合函数
//创建聚合函数对象
val myUdaf = new MyAgeAvgFunc //注册自定义函数
spark.udf.register("ageAvg",myUdaf) //使用聚合函数
val frame: DataFrame = spark.read.json("input/userinfo.json")
frame.createOrReplaceTempView("userinfo")
spark.sql("select ageAvg(age) from userinfo").show() spark.stop()
}
} //声明自定义函数
//实现对年龄的平均，数据如：{ "name": "tom", "age" : 20}
class MyAgeAvgFunc extends UserDefinedAggregateFunction {
//函数输入的数据结构，本例中只有年龄是输入数据
override def inputSchema: StructType = {
new StructType().add("age", LongType)
} //计算时的数据结构（缓冲区）
// 本例中有要计算年龄平均值，必须有两个计算结构，一个是年龄总计（sum），一个是年龄个数（count）
override def bufferSchema: StructType = {
new StructType().add("sum", LongType).add("count", LongType)
} //函数返回的数据类型
override def dataType: DataType = DoubleType //函数是否稳定
override def deterministic: Boolean = true //计算前缓冲区的初始化,结构类似数组，这里缓冲区与之前定义的bufferSchema顺序一致
override def initialize(buffer: MutableAggregationBuffer): Unit = {
//sum
buffer(0) = 0L
//count
buffer(1) = 0L
} //根据查询结果更新缓冲区数据，input是每次进入的数据,其数据结构与之前定义的inputSchema相同
//本例中每次输入的数据只有一个就是年龄
override def update(buffer: MutableAggregationBuffer, input: Row): Unit = {
if(input.isNullAt(0)) return
//sum
buffer(0) = buffer.getLong(0) + input.getLong(0) //count，每次来一个数据加1
buffer(1) = buffer.getLong(1) + 1
} //将多个节点的缓冲区合并到一起（因为spark是分布式的）
override def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = {
//sum
buffer1(0) = buffer1.getLong(0) + buffer2.getLong(0) //count
buffer1(1) = buffer1.getLong(1) + buffer2.getLong(1)
} //计算最终结果，本例中就是(sum / count)
override def evaluate(buffer: Row): Any = {
buffer.getLong(0).toDouble / buffer.getLong(1)
}
}
2、使用强类型，
package com.home.spark import org.apache.spark.SparkConf
import org.apache.spark.sql._
import org.apache.spark.sql.expressions.Aggregator object Ex_sparkUDAF2 {
def main(args: Array[String]): Unit = {
val conf = new SparkConf(true).setAppName("spark udf class").setMaster("local[*]")
val spark = SparkSession.builder().config(conf).getOrCreate() //rdd转换成df或者ds需要SparkSession实例的隐式转换
//导入隐式转换，注意这里的spark不是包名，而是SparkSession的对象名
import spark.implicits._ //创建聚合函数对象
val myAvgFunc = new MyAgeAvgClassFunc
val avgCol: TypedColumn[UserBean, Double] = myAvgFunc.toColumn.name("avgAge")
val frame = spark.read.json("input/userinfo.json")
val userDS: Dataset[UserBean] = frame.as[UserBean]
//应用函数
userDS.select(avgCol).show() spark.stop()
}
} case class UserBean(name: String, age: BigInt) case class AvgBuffer(var sum: BigInt, var count: Int) //声明用户自定义函数（强类型方式）
//继承Aggregator，设定泛型
//实现方法
class MyAgeAvgClassFunc extends Aggregator[UserBean, AvgBuffer, Double] {
//初始化缓冲区
override def zero: AvgBuffer = {
AvgBuffer(0, 0)
} //聚合数据
override def reduce(b: AvgBuffer, a: UserBean): AvgBuffer = {
if(a.age == null) return b
b.sum = b.sum + a.age
b.count = b.count + 1 b
} //缓冲区合并操作
override def merge(b1: AvgBuffer, b2: AvgBuffer): AvgBuffer = {
b1.sum = b1.sum + b2.sum
b1.count = b1.count + b2.count b1
} //完成计算
override def finish(reduction: AvgBuffer): Double = {
reduction.sum.toDouble / reduction.count
} override def bufferEncoder: Encoder[AvgBuffer] = Encoders.product override def outputEncoder: Encoder[Double] = Encoders.scalaDouble
}
继承Aggregator