spark UDAF根据某列去重求合 distinct sum

spark UDAF根据某列去重求合 distinct sum

例子:

package spark.groupzb

import org.apache.log4j.{Level, Logger}
import org.apache.spark.sql.types._
import org.apache.spark.sql.{Row, SparkSession}

/**
  * create by roy on 2020-03020
  */
object OrderDistinctSumTest {
  Logger.getRootLogger.setLevel(Level.WARN)

  def main(args: Array[String]): Unit = {
    val data = Seq(
      Row("barnd1", "a", 300.0),
      Row("barnd1", "a", 300.0),
      Row("barnd1", "b", 200.0),
      Row("barnd2", "c", 200.0),
      Row("barnd2", "c", 200.0),
      Row("barnd3", "c", 200.0),
      Row("barnd3", "c", 200.0)
      //      Row("a", "400", Array(Row("a", "name_a")))
    )
    val schme = new StructType()
      .add("barnd", StringType)
      .add("orderid", StringType)
      .add("price", DoubleType)
    schme.printTreeString()
    val spark = SparkSession.builder().master("local[*]").getOrCreate()
    val df = spark.createDataFrame(spark.sparkContext.parallelize(data), schme)
    df.show()
    import spark.implicits._
    df.createOrReplaceTempView("tmp_tab")
    //    spark.sql(
    //      """
    //        |select barnd,sum(price) sum_price
    //        |from tmp_tab group by barnd
    //      """.stripMargin).show()
    //    println("orderDistinctSumUDAF  ")
    val orderDistinctSumUDAF = new OrderDistinctSumUDAF
    spark.sqlContext.udf.register("orderDistinctSumUDAF", orderDistinctSumUDAF)
    //订单去重，求次数，订单量，金额
    spark.sql(
      """
        |select barnd,count(1) ct,count(distinct orderid) order_num,orderDistinctSumUDAF(orderid,price) sum_price
        |from tmp_tab  group by barnd
      """.stripMargin).show()
    //+------+---+---------+---------+
    //| barnd| ct|order_num|sum_price|
    //+------+---+---------+---------+
    //|barnd3|  2|        1|    200.0|
    //|barnd2|  2|        1|    200.0|
    //|barnd1|  3|        2|    500.0|
    //+------+---+---------+---------+
    //这样需再次连接求出
    spark.sql(
      """
        |select barnd,sum(price) prices  from (
        |select barnd,orderid,price,count(1)
        |from tmp_tab group by barnd,orderid,price
        |) t1 group by barnd
      """.stripMargin).show()
    //+------+------+
    //| barnd|prices|
    //+------+------+
    //|barnd3| 200.0|
    //|barnd2| 200.0|
    //|barnd1| 500.0|
    //+------+------+

    //    println("partition by barnd,orderid ")
    //    spark.sql(
    //      """
    //        |select barnd,first_value(orderid) over(PARTITION BY orderid) lowid
    //        |from tmp_tab
    //      """.stripMargin).show()
  }

}

UDAF:

package spark.groupzb

import org.apache.spark.sql.Row
import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
import org.apache.spark.sql.types._

/**
  * create by roy 2020-03-18
  * 订单id去重，并求合
  *
  */
class OrderDistinctSumUDAF extends UserDefinedAggregateFunction {

  override def inputSchema: StructType = {
    new StructType()
      //      .add("barnd", StringType)
      .add("orderid", StringType)
      .add("sum_v", DoubleType)

  }

  override def update(buffer: MutableAggregationBuffer, input: Row): Unit = {
//    println("update==>>>", buffer, input) //餐品 有多少个订单
    val orderid = input.getAs[String](0)
    val sum_v = input.getAs[Double](1)
    //取出新加入的行，并加入缓存区
//    buffer(0) = buffer.getSeq[String](0) ++ orderid
    buffer(0) =  Seq[String](orderid)
    buffer(1) = buffer.getAs[Double](1) + sum_v

  }

  override def bufferSchema: StructType = {
    //    new StructType().add("items", ArrayType(new StructType().add("orderid", StringType).add("sumv", DoubleType), true), nullable = true)
    new StructType().add("orderid", ArrayType(StringType)).add("sumv", DoubleType)
  }

  //合并数据
  override def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = {
//    println("merge==>", buffer1, "|", buffer2)
    val b1Seq = buffer1.getSeq[String](0)
    val orderid=buffer2.getSeq[String](0)(0)//第1列，第1个元素
    if (!b1Seq.contains(orderid)) {
      buffer1(0) = b1Seq ++ orderid
      buffer1(1) = buffer1.getAs[Double](1) + buffer2.getAs[Double](1)
    }
  }

  override def initialize(buffer: MutableAggregationBuffer): Unit = {
    buffer(0) = Seq[String]()
    buffer(1) = 0.0

  }

  override def deterministic: Boolean = true

  override def evaluate(buffer: Row): Any = {
    //    buffer.getSeq[String](0).length
    buffer.getAs[Double](1)
  }

  override def dataType: DataType = DoubleType

  case class ObjetValus(orderId: String, sumV: Double)

}

输出:

"C:\Program Files\Java\jdk1.8.0_181\bin\java.exe" "-javaagent:D:\Program Files\JetBrains\IntelliJ IDEA Community Edition 2018.2\lib\idea_rt.jar=64860:D:\Program Files\JetBrains\IntelliJ IDEA Community Edition 2018.2\bin" -Dfile.encoding=UTF-8 -classpath "C:\Program Files\Java\jdk1.8.0_181\jre\lib\charsets.jar;C:\Program Files\Java\jdk1.8.0_181\jre\lib\deploy.jar;C:\Program Files\Java\jdk1.8.0_181\jre\lib\ext\access-bridge-64.jar;C:\Program Files\Java\jdk1.8.0_181\jre\lib\ext\cldrdata.jar;C:\Program Files\Java\jdk1.8.0_181\jre\lib\ext\dnsns.jar;C:\Program Files\Java\jdk1.8.0_181\jre\lib\ext\jaccess.jar;C:\Program Files\Java\jdk1.8.0_181\jre\lib\ext\jfxrt.jar;C:\Program Files\Java\jdk1.8.0_181\jre\lib\ext\localedata.jar;C:\Program Files\Java\jdk1.8.0_181\jre\lib\ext\nashorn.jar;C:\Program Files\Java\jdk1.8.0_181\jre\lib\ext\sunec.jar;C:\Program Files\Java\jdk1.8.0_181\jre\lib\ext\sunjce_provider.jar;C:\Program Files\Java\jdk1.8.0_181\jre\lib\ext\sunmscapi.jar;C:\Program 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spark.groupzb.OrderDistinctSumTest
root
 |-- barnd: string (nullable = true)
 |-- orderid: string (nullable = true)
 |-- price: double (nullable = true)

20/03/20 00:07:03 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
+------+-------+-----+
| barnd|orderid|price|
+------+-------+-----+
|barnd1|      a|300.0|
|barnd1|      a|300.0|
|barnd1|      b|200.0|
|barnd2|      c|200.0|
|barnd2|      c|200.0|
|barnd3|      c|200.0|
|barnd3|      c|200.0|
+------+-------+-----+

+------+---+---------+---------+
| barnd| ct|order_num|sum_price|
+------+---+---------+---------+
|barnd3|  2|        1|    200.0|
|barnd2|  2|        1|    200.0|
|barnd1|  3|        2|    500.0|
+------+---+---------+---------+