How to modify a Spark Dataframe with a complex nested structure? - scala

Question

I've a complex DataFrame structure and would like to null a column easily. I've created implicit classes that wire functionality and easily address 2D DataFrame structures but once the DataFrame becomes more complicated with ArrayType or MapType I've not had much luck. For example:
I have schema defined as:
StructType(
StructField(name,StringType,true),
StructField(data,ArrayType(
StructType(
StructField(name,StringType,true),
StructField(values,
MapType(StringType,StringType,true),
true)
),
true
),
true)
)
I'd like to produce a new DF that has the field data.value of MapType set to null, but as this is an element of an array I have not been able to figure out how. I would think it would be similar to:
df.withColumn("data.values", functions.array(functions.lit(null)))
but this ultimately creates a new column of data.values and does not modify the values element of the data array.

Answer 1

Since Spark 1.6, you can use case classes to map your dataframes (called datasets). Then, you can map your data and transform it to the new schema you want. For example:
case class Root(name: String, data: Seq[Data])
case class Data(name: String, values: Map[String, String])
case class NullableRoot(name: String, data: Seq[NullableData])
case class NullableData(name: String, value: Map[String, String], values: Map[String, String])
val nullableDF = df.as[Root].map { root =>
val nullableData = root.data.map(data => NullableData(data.name, null, data.values))
NullableRoot(root.name, nullableData)
}.toDF()
The resulting schema of nullableDF will be:
root
|-- name: string (nullable = true)
|-- data: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- name: string (nullable = true)
| | |-- value: map (nullable = true)
| | | |-- key: string
| | | |-- value: string (valueContainsNull = true)
| | |-- values: map (nullable = true)
| | | |-- key: string
| | | |-- value: string (valueContainsNull = true)

Answer 2

I ran into the same issue and assuming you don't need the result to have any new fields or fields with different types, here is a solution that can do this without having to redefine the whole struct: Change value of nested column in DataFrame