Spark - Sum of row values - scala

Question

I have the following DataFrame:
January | February | March
-----------------------------
10 | 10 | 10
20 | 20 | 20
50 | 50 | 50
I'm trying to add a column to this which is the sum of the values of each row.
January | February | March | TOTAL
----------------------------------
10 | 10 | 10 | 30
20 | 20 | 20 | 60
50 | 50 | 50 | 150
As far as I can see, all the built in aggregate functions seem to be for calculating values in single columns. How do I go about using values across columns on a per row basis (using Scala)?
I've gotten as far as
val newDf: DataFrame = df.select(colsToSum.map(col):_*).foreach ...

Answer 1

You were very close with this:
val newDf: DataFrame = df.select(colsToSum.map(col):_*).foreach ...
Instead, try this:
val newDf = df.select(colsToSum.map(col).reduce((c1, c2) => c1 + c2) as "sum")
I think this is the best of the the answers, because it is as fast as the answer with the hard-coded SQL query, and as convenient as the one that uses the UDF. It's the best of both worlds -- and I didn't even add a full line of code!

Answer 2

Alternatively and using Hugo's approach and example, you can create a UDF that receives any quantity of columns and sum them all.
from functools import reduce
def superSum(*cols):
return reduce(lambda a, b: a + b, cols)
add = udf(superSum)
df.withColumn('total', add(*[df[x] for x in df.columns])).show()
+-------+--------+-----+-----+
|January|February|March|total|
+-------+--------+-----+-----+
| 10| 10| 10| 30|
| 20| 20| 20| 60|
+-------+--------+-----+-----+

Answer 3

This code is in Python, but it can be easily translated:
# First we create a RDD in order to create a dataFrame:
rdd = sc.parallelize([(10, 10,10), (20, 20,20)])
df = rdd.toDF(['January', 'February', 'March'])
df.show()
# Here, we create a new column called 'TOTAL' which has results
# from add operation of columns df.January, df.February and df.March
df.withColumn('TOTAL', df.January + df.February + df.March).show()
Output:
+-------+--------+-----+
|January|February|March|
+-------+--------+-----+
| 10| 10| 10|
| 20| 20| 20|
+-------+--------+-----+
+-------+--------+-----+-----+
|January|February|March|TOTAL|
+-------+--------+-----+-----+
| 10| 10| 10| 30|
| 20| 20| 20| 60|
+-------+--------+-----+-----+
You can also create an User Defined Function it you want, here a link of Spark documentation:
UserDefinedFunction (udf)

Answer 4

Working Scala example with dynamic column selection:
import sqlContext.implicits._
val rdd = sc.parallelize(Seq((10, 10, 10), (20, 20, 20)))
val df = rdd.toDF("January", "February", "March")
df.show()
+-------+--------+-----+
|January|February|March|
+-------+--------+-----+
| 10| 10| 10|
| 20| 20| 20|
+-------+--------+-----+
val sumDF = df.withColumn("TOTAL", df.columns.map(c => col(c)).reduce((c1, c2) => c1 + c2))
sumDF.show()
+-------+--------+-----+-----+
|January|February|March|TOTAL|
+-------+--------+-----+-----+
| 10| 10| 10| 30|
| 20| 20| 20| 60|
+-------+--------+-----+-----+

Answer 5

You can use expr() for this.In scala use
df.withColumn("TOTAL", expr("January+February+March"))