scala - 如何在 Spark SQL 中定义和使用用户定义的聚合函数？

Question

我知道如何在 Spark SQL 中编写 UDF:

def belowThreshold(power: Int): Boolean = {
        return power < -40
      }

sqlContext.udf.register("belowThreshold", belowThreshold _)

我可以做类似的事情来定义聚合函数吗？这是如何完成的？

对于上下文，我想运行以下 SQL 查询:

val aggDF = sqlContext.sql("""SELECT span, belowThreshold(opticalReceivePower), timestamp
                                    FROM ifDF
                                    WHERE opticalReceivePower IS NOT null
                                    GROUP BY span, timestamp
                                    ORDER BY span""")

它应该返回类似的内容

行(span1, false, T0)

我希望聚合函数告诉我在 span 和 timestamp 定义的组中是否有 opticalReceivePower 的值，这些值低于临界点。我是否需要以不同于上面粘贴的 UDF 的方式编写 UDAF？

Answer 1

支持的方法

Spark >= 3.0

Scala UserDefinedAggregateFunction 已被弃用 (SPARK-30423 弃用 UserDefinedAggregateFunction)，转而使用注册的 Aggregator。

Spark >= 2.3

矢量化 udf(仅限 Python):

from pyspark.sql.functions import pandas_udf
from pyspark.sql.functions import PandasUDFType

from pyspark.sql.types import *
import pandas as pd

df = sc.parallelize([
    ("a", 0), ("a", 1), ("b", 30), ("b", -50)
]).toDF(["group", "power"])

def below_threshold(threshold, group="group", power="power"):
    @pandas_udf("struct<group: string, below_threshold: boolean>", PandasUDFType.GROUPED_MAP)
    def below_threshold_(df):
        df = pd.DataFrame(
           df.groupby(group).apply(lambda x: (x[power] < threshold).any()))
        df.reset_index(inplace=True, drop=False)
        return df

    return below_threshold_

使用示例:

df.groupBy("group").apply(below_threshold(-40)).show()

## +-----+---------------+
## |group|below_threshold|
## +-----+---------------+
## |    b|           true|
## |    a|          false|
## +-----+---------------+

另请参阅Applying UDFs on GroupedData in PySpark (with functioning python example)

Spark >= 2.0(可选 1.6，但 API 略有不同):

可以在类型化的数据集上使用聚合器:

import org.apache.spark.sql.expressions.Aggregator
import org.apache.spark.sql.{Encoder, Encoders}

class BelowThreshold[I](f: I => Boolean)  extends Aggregator[I, Boolean, Boolean]
    with Serializable {
  def zero = false
  def reduce(acc: Boolean, x: I) = acc | f(x)
  def merge(acc1: Boolean, acc2: Boolean) = acc1 | acc2
  def finish(acc: Boolean) = acc

  def bufferEncoder: Encoder[Boolean] = Encoders.scalaBoolean
  def outputEncoder: Encoder[Boolean] = Encoders.scalaBoolean
}

val belowThreshold = new BelowThreshold[(String, Int)](_._2 < - 40).toColumn
df.as[(String, Int)].groupByKey(_._1).agg(belowThreshold)

Spark >= 1.5:

在 Spark 1.5 中，您可以像这样创建 UDAF，尽管这很可能是一种矫枉过正:

import org.apache.spark.sql.expressions._
import org.apache.spark.sql.types._
import org.apache.spark.sql.Row

object belowThreshold extends UserDefinedAggregateFunction {
    // Schema you get as an input
    def inputSchema = new StructType().add("power", IntegerType)
    // Schema of the row which is used for aggregation
    def bufferSchema = new StructType().add("ind", BooleanType)
    // Returned type
    def dataType = BooleanType
    // Self-explaining 
    def deterministic = true
    // zero value
    def initialize(buffer: MutableAggregationBuffer) = buffer.update(0, false)
    // Similar to seqOp in aggregate
    def update(buffer: MutableAggregationBuffer, input: Row) = {
        if (!input.isNullAt(0))
          buffer.update(0, buffer.getBoolean(0) | input.getInt(0) < -40)
    }
    // Similar to combOp in aggregate
    def merge(buffer1: MutableAggregationBuffer, buffer2: Row) = {
      buffer1.update(0, buffer1.getBoolean(0) | buffer2.getBoolean(0))    
    }
    // Called on exit to get return value
    def evaluate(buffer: Row) = buffer.getBoolean(0)
}

使用示例:

df
  .groupBy($"group")
  .agg(belowThreshold($"power").alias("belowThreshold"))
  .show

// +-----+--------------+
// |group|belowThreshold|
// +-----+--------------+
// |    a|         false|
// |    b|          true|
// +-----+--------------+

Spark 1.4 解决方法:

我不确定我是否正确理解您的要求，但据我所知，普通的旧聚合在这里应该足够了:

val df = sc.parallelize(Seq(
    ("a", 0), ("a", 1), ("b", 30), ("b", -50))).toDF("group", "power")

df
  .withColumn("belowThreshold", ($"power".lt(-40)).cast(IntegerType))
  .groupBy($"group")
  .agg(sum($"belowThreshold").notEqual(0).alias("belowThreshold"))
  .show

// +-----+--------------+
// |group|belowThreshold|
// +-----+--------------+
// |    a|         false|
// |    b|          true|
// +-----+--------------+

Spark <= 1.4:

据我所知，目前(Spark 1.4.1)，除了 Hive 之外，不支持 UDAF。 Spark 1.5 应该可以实现(请参阅 SPARK-3947)。

不支持/内部方法

Spark 在内部使用了许多类，包括 ImperativeAggregates和 DeclarativeAggregates 。

它们仅供内部使用，可能会发生变化，恕不另行通知，因此您可能不想在生产代码中使用它，而只是为了完整性 BelowThreshold 和 DeclarativeAggregate 可以这样实现(使用 Spark 2.2-SNAPSHOT 进行测试):

import org.apache.spark.sql.catalyst.expressions.aggregate.DeclarativeAggregate
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.types._

case class BelowThreshold(child: Expression, threshold: Expression) 
    extends  DeclarativeAggregate  {
  override def children: Seq[Expression] = Seq(child, threshold)

  override def nullable: Boolean = false
  override def dataType: DataType = BooleanType

  private lazy val belowThreshold = AttributeReference(
    "belowThreshold", BooleanType, nullable = false
  )()

  // Used to derive schema
  override lazy val aggBufferAttributes = belowThreshold :: Nil

  override lazy val initialValues = Seq(
    Literal(false)
  )

  override lazy val updateExpressions = Seq(Or(
    belowThreshold,
    If(IsNull(child), Literal(false), LessThan(child, threshold))
  ))

  override lazy val mergeExpressions = Seq(
    Or(belowThreshold.left, belowThreshold.right)
  )

  override lazy val evaluateExpression = belowThreshold
  override def defaultResult: Option[Literal] = Option(Literal(false))
} 

它应该进一步用相当于 withAggregateFunction 的内容包裹起来。 .