How to fill the null value in dataframe to uuid?

Question

There is a dataframe with null values in one column(not all being null), it need to fill the null value with uuid, is there a way?

cala> val df = Seq(("stuff2",null,null), ("stuff2",null,Array("value1","value2")),("stuff3","stuff3",null)).toDF("field","field2","values")
        df: org.apache.spark.sql.DataFrame = [field: string, field2: string, values: array<string>]

        scala> df.show
        +------+------+----------------+
        | field|field2|          values|
        +------+------+----------------+
        |stuff2|  null|            null|
        |stuff2|  null|[value1, value2]|
        |stuff3|stuff3|            null|
        +------+------+----------------+

I tried this way, but each row of the "field2" has the same uuid.

scala> val fillDF = df.na.fill(java.util.UUID.randomUUID().toString(), Seq("field2"))
    fillDF: org.apache.spark.sql.DataFrame = [field: string, field2: string, values: array<string>]

scala> fillDF.show
+------+--------------------+----------------+
| field|              field2|          values|
+------+--------------------+----------------+
|stuff2|d007ffae-9134-4ac...|            null|
|stuff2|d007ffae-9134-4ac...|[value1, value2]|
|stuff3|              stuff3|            null|
+------+--------------------+----------------+

How to make it? in case there is more than 1,000,000 rows

Answer 1

You can do it using UDF and coalesce like below.

import org.apache.spark.sql.functions.udf
val arr = udf(() => java.util.UUID.randomUUID().toString())

val df2 = df.withColumn("field2", coalesce(df("field2"), arr()))
df2.show()

You will get different UUID like below.

+------+--------------------+----------------+
| field|              field2|          values|
+------+--------------------+----------------+
|stuff2|fda6bc42-1265-407...|            null|
|stuff2|3fa74767-abd7-405...|[value1, value2]|
|stuff3|              stuff3|            null|
+------+--------------------+----------------+

Answer 2

You can easily do this by using UDF , it can be something like this :

  def generateUUID(value: String):String = {
    import java.util.UUID
    if (Option(value).isDefined) {
      value
    }
    else {
      UUID.randomUUID().toString
    }
    val funcUDF = generateUUID _
    val generateUUID = udf(funcUDF)

Now pass the fillDF accrodingly:

fillDF.withColumns("field2",generateUUID(fillDF("field2"))).show

P.S: The code is not tested but it should work !

Answer 3

This is more or less the same as the above answers, except that it avoids using a UDF. Perhaps at the time there was no uuid() function available in sparkSQL? In any case, I think this is likely to be more performant, and, imo, easier to read

import org.apache.spark.sql.functions.{col, coalesce, expr}

val updatedDF = df.withColumn("nullable_column", coalesce(col("nullable_column"), expr("uuid()")))