Flip each bit in Spark dataframe calling a custom function - scala

Question

I have a spark Dataframe that looks like
ID |col1|col2|col3|col4.....
A |0 |1 |0 |0....
C |1 |0 |0 |0.....
E |1 |0 |1 |1......
ID is a unique key and other columns have binary values 0/1
now,I want to iterate over each row and if the column value is 0 i want to apply some function passing this single row as a data frame to that function
like col1 ==0 in above data frame for ID A
now the DF of line should look like
newDF.show()
ID |col1|col2|col3|col4.....
A |1 |1 |0 |0....
myfunc(newDF)
next 0 is encountered at col3 for ID A so new DF look like
newDF.show()
ID |col1|col2|col3|col4.....
A |0 |1 |1 |0....
val max=myfunc(newDF) //function returns a double.
so on...
Note:- Each 0 bit is flipped once at row level for function
calling resetting last flipped bit effect
P.S:- I tried using withcolumn calling a UDF but serialisation issues of Df inside DF
actually the myfunc i'm calling is send for scoring for ML model that i have that returns probability for that user if a particular bit is flipped .So i have to iterate through each 0 set column ad set it 1 for that particular instance .

Answer 1

I'm not sure you need anything particularly complex for this. Given that you have imported the SQL functions and the session implicits
val spark: SparkSession = ??? // your session
import spark.implicits._
import org.apache.spark.sql.functions._
you should be able to "flip the bits" (although I'm assuming those are actually encoded as numbers) by applying the following function
def flip(col: Column): Column = when(col === 1, lit(0)).otherwise(lit(1))
as in this example
df.select($"ID", flip($"col1") as "col1", flip($"col2") as "col2")
You can easily rewrite the flip function to deal with edge cases or use different type (if, for example, the "bits" are encoded with booleans or strings).