I'm trying to transform a dataframe via a function that takes an array as a parameter. My code looks something like this:
def getCategory(categories:Array[String], input:String): String = {
categories(input.toInt)
}
val myArray = Array("a", "b", "c")
val myCategories =udf(getCategory _ )
val df = sqlContext.parquetFile("myfile.parquet)
val df1 = df.withColumn("newCategory", myCategories(lit(myArray), col("myInput"))
However, lit doesn't like arrays and this script errors. I tried definining a new partially applied function and then the udf after that :
val newFunc = getCategory(myArray, _:String)
val myCategories = udf(newFunc)
val df1 = df.withColumn("newCategory", myCategories(col("myInput")))
This doesn't work either as I get a nullPointer exception and it appears myArray is not being recognized. Any ideas on how I pass an array as a parameter to a function with a dataframe?
On a separate note, any explanation as to why doing something simple like using a function on a dataframe is so complicated (define function, redefine it as UDF, etc, etc)?
Most likely not the prettiest solution but you can try something like this:
def getCategory(categories: Array[String]) = {
udf((input:String) => categories(input.toInt))
}
df.withColumn("newCategory", getCategory(myArray)(col("myInput")))
You could also try an array
of literals:
val getCategory = udf(
(input:String, categories: Array[String]) => categories(input.toInt))
df.withColumn(
"newCategory", getCategory($"myInput", array(myArray.map(lit(_)): _*)))
On a side note using Map
instead of Array
is probably a better idea:
def mapCategory(categories: Map[String, String], default: String) = {
udf((input:String) => categories.getOrElse(input, default))
}
val myMap = Map[String, String]("1" -> "a", "2" -> "b", "3" -> "c")
df.withColumn("newCategory", mapCategory(myMap, "foo")(col("myInput")))
Since Spark 1.5.0 you can also use an array
function:
import org.apache.spark.sql.functions.array
val colArray = array(myArray map(lit _): _*)
myCategories(lit(colArray), col("myInput"))
See also Spark UDF with varargs
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