[英]Cumulative product in Spark
我嘗試在 Spark Scala 中實現一個累積產品,但我真的不知道如何實現。 我有以下數據框:
Input data:
+--+--+--------+----+
|A |B | date | val|
+--+--+--------+----+
|rr|gg|20171103| 2 |
|hh|jj|20171103| 3 |
|rr|gg|20171104| 4 |
|hh|jj|20171104| 5 |
|rr|gg|20171105| 6 |
|hh|jj|20171105| 7 |
+-------+------+----+
我想有以下輸出:
Output data:
+--+--+--------+-----+
|A |B | date | val |
+--+--+--------+-----+
|rr|gg|20171105| 48 | // 2 * 4 * 6
|hh|jj|20171105| 105 | // 3 * 5 * 7
+-------+------+-----+
只要數字是嚴格的正數(也可以處理 0,如果存在,使用coalesce
)如您的示例中所示,最簡單的解決方案是計算對數之和並取指數:
import org.apache.spark.sql.functions.{exp, log, max, sum}
val df = Seq(
("rr", "gg", "20171103", 2), ("hh", "jj", "20171103", 3),
("rr", "gg", "20171104", 4), ("hh", "jj", "20171104", 5),
("rr", "gg", "20171105", 6), ("hh", "jj", "20171105", 7)
).toDF("A", "B", "date", "val")
val result = df
.groupBy("A", "B")
.agg(
max($"date").as("date"),
exp(sum(log($"val"))).as("val"))
由於這使用 FP 算術,因此結果將不准確:
result.show
+---+---+--------+------------------+
| A| B| date| val|
+---+---+--------+------------------+
| hh| jj|20171105|104.99999999999997|
| rr| gg|20171105|47.999999999999986|
+---+---+--------+------------------+
但四舍五入后應該足以滿足大多數應用程序。
result.withColumn("val", round($"val")).show
+---+---+--------+-----+
| A| B| date| val|
+---+---+--------+-----+
| hh| jj|20171105|105.0|
| rr| gg|20171105| 48.0|
+---+---+--------+-----+
如果這還不夠,您可以定義UserDefinedAggregateFunction
或Aggregator
(如何在 Spark SQL 中定義和使用用戶定義的聚合函數? )或使用帶有reduceGroups
函數式 API:
import scala.math.Ordering
case class Record(A: String, B: String, date: String, value: Long)
df.withColumnRenamed("val", "value").as[Record]
.groupByKey(x => (x.A, x.B))
.reduceGroups((x, y) => x.copy(
date = Ordering[String].max(x.date, y.date),
value = x.value * y.value))
.toDF("key", "value")
.select($"value.*")
.show
+---+---+--------+-----+
| A| B| date|value|
+---+---+--------+-----+
| hh| jj|20171105| 105|
| rr| gg|20171105| 48|
+---+---+--------+-----+
您可以使用 collect_list+UDF 或 UDAF 解決此問題。 UDAF 可能更有效,但由於本地聚合而更難實現。
如果您有這樣的數據框:
+---+---+
|key|val|
+---+---+
| a| 1|
| a| 2|
| a| 3|
| b| 4|
| b| 5|
+---+---+
您可以調用 UDF :
val prod = udf((vals:Seq[Int]) => vals.reduce(_ * _))
df
.groupBy($"key")
.agg(prod(collect_list($"val")).as("val"))
.show()
+---+---+
|key|val|
+---+---+
| b| 20|
| a| 6|
+---+---+
從 Spark 2.4 開始,您還可以使用高階函數aggregate
來計算:
import org.apache.spark.sql.functions.{expr, max}
val df = Seq(
("rr", "gg", "20171103", 2),
("hh", "jj", "20171103", 3),
("rr", "gg", "20171104", 4),
("hh", "jj", "20171104", 5),
("rr", "gg", "20171105", 6),
("hh", "jj", "20171105", 7)
).toDF("A", "B", "date", "val")
val result = df
.groupBy("A", "B")
.agg(
max($"date").as("date"),
expr("""
aggregate(
collect_list(val),
cast(1 as bigint),
(acc, x) -> acc * x)""").alias("val")
)
火花 3.2+
product(e: Column): Column
聚合函數:返回一個組中所有數字元素的乘積。
斯卡拉
import spark.implicits._
var df = Seq(
("rr", "gg", 20171103, 2),
("hh", "jj", 20171103, 3),
("rr", "gg", 20171104, 4),
("hh", "jj", 20171104, 5),
("rr", "gg", 20171105, 6),
("hh", "jj", 20171105, 7)
).toDF("A", "B", "date", "val")
df = df.groupBy("A", "B").agg(max($"date").as("date"), product($"val").as("val"))
df.show(false)
// +---+---+--------+-----+
// |A |B |date |val |
// +---+---+--------+-----+
// |hh |jj |20171105|105.0|
// |rr |gg |20171105|48.0 |
// +---+---+--------+-----+
火花
from pyspark.sql import SparkSession, functions as F
spark = SparkSession.builder.getOrCreate()
data = [('rr', 'gg', 20171103, 2),
('hh', 'jj', 20171103, 3),
('rr', 'gg', 20171104, 4),
('hh', 'jj', 20171104, 5),
('rr', 'gg', 20171105, 6),
('hh', 'jj', 20171105, 7)]
df = spark.createDataFrame(data, ['A', 'B', 'date', 'val'])
df = df.groupBy('A', 'B').agg(F.max('date').alias('date'), F.product('val').alias('val'))
df.show()
#+---+---+--------+-----+
#| A| B| date| val|
#+---+---+--------+-----+
#| hh| jj|20171105|105.0|
#| rr| gg|20171105| 48.0|
#+---+---+--------+-----+
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