Is it possible to create a UDF which would return the set of columns?
I.e. having a data frame as follows:
| Feature1 | Feature2 | Feature 3 | | 1.3 | 3.4 | 4.5 |
Now I would like to extract a new feature, which can be described as a vector of let's say two elements (e.g. as seen in a linear regression - slope and offset). Desired dataset shall look as follows:
| Feature1 | Feature2 | Feature 3 | Slope | Offset | | 1.3 | 3.4 | 4.5 | 0.5 | 3 |
Is it possible to create multiple columns with single UDF or do I need to follow the rule: "single column per single UDF"?
UDF can return only a single column at the time.
It is well known that the use of UDFs (User Defined Functions) in Apache Spark, and especially in using the Python API, can compromise our application performace. For this reason, at Damavis we try to avoid their use as much as possible infavour of using native functions or SQL .
Spark SQL supports integration of Hive UDFs, UDAFs and UDTFs. Similar to Spark UDFs and UDAFs, Hive UDFs work on a single row as input and generate a single row as output, while Hive UDAFs operate on multiple rows and return a single aggregated row as a result.
Struct method
You can define the udf
function as
def myFunc: (String => (String, String)) = { s => (s.toLowerCase, s.toUpperCase)} import org.apache.spark.sql.functions.udf val myUDF = udf(myFunc)
and use .*
as
val newDF = df.withColumn("newCol", myUDF(df("Feature2"))).select("Feature1", "Feature2", "Feature 3", "newCol.*")
I have returned Tuple2
for testing purpose (higher order tuples can be used according to how many multiple columns are required) from udf
function and it would be treated as struct
column. Then you can use .*
to select all the elements in separate columns and finally rename them.
You should have output as
+--------+--------+---------+---+---+ |Feature1|Feature2|Feature 3|_1 |_2 | +--------+--------+---------+---+---+ |1.3 |3.4 |4.5 |3.4|3.4| +--------+--------+---------+---+---+
You can rename _1
and _2
Array method
udf
function should return an array
def myFunc: (String => Array[String]) = { s => Array("s".toLowerCase, s.toUpperCase)} import org.apache.spark.sql.functions.udf val myUDF = udf(myFunc)
And the you can select elements of the array
and use alias
to rename them
val newDF = df.withColumn("newCol", myUDF(df("Feature2"))).select($"Feature1", $"Feature2", $"Feature 3", $"newCol"(0).as("Slope"), $"newCol"(1).as("Offset"))
You should have
+--------+--------+---------+-----+------+ |Feature1|Feature2|Feature 3|Slope|Offset| +--------+--------+---------+-----+------+ |1.3 |3.4 |4.5 |s |3.4 | +--------+--------+---------+-----+------+
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