SparkSQL: conditional sum using two columns - sql

Question

I hope you can help me with this.
I have a DF as follows:
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"))
I would love to do a groupBy prodId and aggregate 'value' summing it for ranges of dates defined by the difference between the column 'dateIns' and 'dateTrans'. In particular, I would like to have a way to define a conditional sum that sums all values within a predefined max difference between the above mentioned columns. I.e. all value that happened between 10, 20, 30 days from dateIns ('dateTrans' - 'dateIns' <=10, 20, 30).
Is there any predefined aggregated function in spark that allows doing conditional sums? Do you recommend develop a aggr. UDF (if so, any suggestions)?
I'm using pySpqrk, but very happy to get Scala solutions as well. Thanks a lot!

Answer 1

Lets make your a little bit more interesting so there are some events in the window:
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"))
What you need is more or less something like this:
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")))
And the results
aggregated.show
// +------+----+----------+
// |prodId|diff|sum(value)|
// +------+----+----------+
// | a| 20| 120|
// | b| 20| 100|
// | a| 0| 100|
// | a| 10| 150|
// +------+----+----------+
where diff is a lower bound for the range (0 -> [0, 10), 10 -> [10, 20), ...). This will work in PySpark as well if you remove val and adjust imports.
Edit (aggregate per column):
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|
// +------+---+---+---+
with Python equivalent:
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|
## +------+---+---+---+