SparkSQL:使用两列的条件求和

Question

我希望你能帮助我.我有一个DF如下:

val df = sc.parallelize(Seq(
  (1, "a", "2014-12-01", "2015-01-01", 100), 
  (2, "a", "2014-12-01", "2015-01-02", 150),
  (3, "a", "2014-12-01", "2015-01-03", 120), 
  (4, "b", "2015-12-15", "2015-01-01", 100)
)).toDF("id", "prodId", "dateIns", "dateTrans", "value")
.withColumn("dateIns", to_date($"dateIns")
.withColumn("dateTrans", to_date($"dateTrans"))

我很乐意做一个groupBy prodId并汇总'value',将日期范围总结为'dateIns'和'dateTrans'列之间的差异.特别是,我想有一种方法来定义一个条件和,它总结了上述列之间预定义的最大差异内的所有值.即从dateIns('dateTrans' - 'dateIns'<= 10,20,30)10天,20天,30天之间发生的所有值.

在spark中是否有任何预定义的聚合函数允许进行条件求和？你建议开发一个aggr.UDF(如果是这样,任何建议)？我正在使用pySpqrk,但也很高兴获得Scala解决方案.非常感谢!

Answer 1

让我们更有趣一点,所以窗口中有一些事件:

val df = sc.parallelize(Seq(
  (1, "a", "2014-12-30", "2015-01-01", 100), 
  (2, "a", "2014-12-21", "2015-01-02", 150),
  (3, "a", "2014-12-10", "2015-01-03", 120), 
  (4, "b", "2014-12-05", "2015-01-01", 100)
)).toDF("id", "prodId", "dateIns", "dateTrans", "value")
.withColumn("dateIns", to_date($"dateIns"))
.withColumn("dateTrans", to_date($"dateTrans"))

你需要的或多或少是这样的:

import org.apache.spark.sql.functions.{col, datediff, lit, sum}

// Find difference in tens of days 
val diff = (datediff(col("dateTrans"), col("dateIns")) / 10)
  .cast("integer") * 10

val dfWithDiff = df.withColumn("diff", diff)

val aggregated = dfWithDiff 
  .where((col("diff") < 30) && (col("diff") >= 0))
  .groupBy(col("prodId"), col("diff"))
  .agg(sum(col("value")))

结果

aggregated.show
// +------+----+----------+
// |prodId|diff|sum(value)|
// +------+----+----------+
// |     a|  20|       120|
// |     b|  20|       100|
// |     a|   0|       100|
// |     a|  10|       150|
// +------+----+----------+

其中diff是范围的下限(0 - > [0,10),10 - > [10,20],...).如果您删除val并调整导入,这也适用于PySpark .

编辑(每列聚合):

val exprs = Seq(0, 10,  20).map(x => sum(
  when(col("diff") === lit(x), col("value"))
    .otherwise(lit(0)))
    .alias(x.toString))

dfWithDiff.groupBy(col("prodId")).agg(exprs.head, exprs.tail: _*).show

// +------+---+---+---+
// |prodId|  0| 10| 20|
// +------+---+---+---+
// |     a|100|150|120|
// |     b|  0|  0|100|
// +------+---+---+---+

与Python等价:

from pyspark.sql.functions import *

def make_col(x):
   cnd = when(col("diff") == lit(x), col("value")).otherwise(lit(0))
   return sum(cnd).alias(str(x))

exprs = [make_col(x) for x in range(0, 30, 10)]
dfWithDiff.groupBy(col("prodId")).agg(*exprs).show()   

## +------+---+---+---+
## |prodId|  0| 10| 20|
## +------+---+---+---+
## |     a|100|150|120|
## |     b|  0|  0|100|
## +------+---+---+---+