[英]Calculating column value in current row of Spark Dataframe based on the calculated value of a different column in previous row using Scala
[英]Calculate value based on value from same column of the previous row in spark
考慮到每個移動速率是從前一個速率遞歸計算的要求的性質,面向列的 DataFrame API 不會發光,尤其是在數據集很大的情況下。
也就是說,如果數據集不大,一種方法是讓 Spark 通過 UDF 逐行重新計算移動速率,並將窗口分區速率列表作為其輸入:
import org.apache.spark.sql.expressions.Window
val df = Seq(
(1, "blue", 0.4, Some(0.33)),
(2, "blue", 0.3, None),
(3, "blue", 0.7, None),
(4, "blue", 0.9, None),
(1, "red", 0.5, Some(0.2)),
(2, "red", 0.6, None),
(3, "red", 0.8, None)
).toDF("Month", "Type", "MonthlyRate", "MovingRate")
val win = Window.partitionBy("Type").orderBy("Month").
rowsBetween(Window.unboundedPreceding, 0)
def movingRate(factor: Double) = udf( (initRate: Double, monthlyRates: Seq[Double]) =>
monthlyRates.tail.foldLeft(initRate)( _ * factor + _ )
)
df.
withColumn("MovingRate", when($"Month" === 1, $"MovingRate").otherwise(
movingRate(0.7)(last($"MovingRate", ignoreNulls=true).over(win), collect_list($"MonthlyRate").over(win))
)).
show
// +-----+----+-----------+------------------+
// |Month|Type|MonthlyRate| MovingRate|
// +-----+----+-----------+------------------+
// | 1| red| 0.5| 0.2|
// | 2| red| 0.6| 0.74|
// | 3| red| 0.8| 1.318|
// | 1|blue| 0.4| 0.33|
// | 2|blue| 0.3|0.5309999999999999|
// | 3|blue| 0.7|1.0716999999999999|
// | 4|blue| 0.9|1.6501899999999998|
// +-----+----+-----------+------------------+
盡管可以使用寡婦函數(請參閱@Leo C 的答案),但我敢打賭,使用groupBy
為每個Type
聚合一次會更高效。 然后,分解 UDF 的結果以獲取所有行:
val df = Seq(
(1, "blue", 0.4, Some(0.33)),
(2, "blue", 0.3, None),
(3, "blue", 0.7, None),
(4, "blue", 0.9, None)
)
.toDF("Month", "Type", "MonthlyRate", "MovingRate")
// this udf produces an Seq of Tuple3 (Month, MonthlyRate, MovingRate)
val calcMovingRate = udf((startRate:Double,rates:Seq[Row]) => rates.tail
.scanLeft((rates.head.getInt(0),startRate,startRate))((acc,curr) => (curr.getInt(0),curr.getDouble(1),acc._3+0.7*curr.getDouble(1)))
)
df
.groupBy($"Type")
.agg(
first($"MovingRate",ignoreNulls=true).as("startRate"),
collect_list(struct($"Month",$"MonthlyRate")).as("rates")
)
.select($"Type",explode(calcMovingRate($"startRate",$"rates")).as("movingRates"))
.select($"Type",$"movingRates._1".as("Month"),$"movingRates._2".as("MonthlyRate"),$"movingRates._3".as("MovingRate"))
.show()
給出:
+----+-----+-----------+------------------+
|Type|Month|MonthlyRate| MovingRate|
+----+-----+-----------+------------------+
|blue| 1| 0.33| 0.33|
|blue| 2| 0.3| 0.54|
|blue| 3| 0.7| 1.03|
|blue| 4| 0.9|1.6600000000000001|
+----+-----+-----------+------------------+
您要做的是計算一個遞歸公式,如下所示:
x[i] = y[i] + 0.7 * x[i-1]
其中x[i]
是您在第i
行的MovingRate
和y[i]
您在第i
行的MonthlyRate
。
問題是這是一個純粹的順序公式。 每一行都需要前一行的結果,而前一行的結果又需要前一行的結果。 Spark 是一個並行計算引擎,很難使用它來加速無法真正並行化的計算。
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