M.Rez M.Rez - 1 year ago 116
Scala Question

Spark DataFrames when udf functions do not accept large enough input variables

I am preparing a DataFrame with an id and a vector of my features to be used later for doing predictions. I do a groupBy on my dataframe, and in my groupBy I am merging couple of columns as lists into a new column:

def mergeFunction(...) // with 14 input variables

val myudffunction( mergeFunction ) // Spark doesn't support this

collect_list(df(...)) as ...
... // too many of these (something like 14 of them)
, col(...) )
.select("id", "feature_labels")

This is how I am creating my feature vectors and their labels. It has been working for me so far but this is the first time that my feature vector with this method is getting bigger than number 10 which is what at maximum a udf function in Spark accepts.

I am not sure how else I can fix this? Is the size of udf inputs in
Spark going to get bigger, am have I understood them incorrectly, or
there is a better way?

Answer Source

User defined functions are defined for up to 22 parameters. For example

val dummy = ((
  x0: Int, x1: Int, x2: Int, x3: Int, x4: Int, x5: Int, x6: Int, x7: Int, 
  x8: Int, x9: Int, x10: Int, x11: Int, x12: Int, x13: Int, x14: Int, 
  x15: Int, x16: Int, x17: Int, x18: Int, x19: Int, x20: Int, x21: Int) => 1)

Can be registered:

spark.udf.register("dummy", dummy)

and used with SQL or called by name:

val exprs =  (0 to 21).map(_ => lit(1))
  callUDF("dummy", exprs:  _*).alias("dummy")

You can also create an UserDefinedFunction object:

import org.apache.spark.sql.expressions.UserDefinedFunction

Seq(1).toDF.select(UserDefinedFunction(dummy, IntegerType, None)(exprs: _*))

In practice having a function with 22 arguments is not very useful and unless you want to use Scala reflection to generate these there are maintenance nightmare.

I would either consider using collections (array, map) or struct as an input or divide this into multiple modules.

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