Spark sql how to explode without losing null value

Question

I have a Dataframe that I am trying to flatten. As part of the process, I want to explode it, so if I have a column of arrays, each value of the array will be used to create a separate row. For instance,

id | name | likes
_______________________________
1  | Luke | [baseball, soccer]

should become

id | name | likes
_______________________________
1  | Luke | baseball
1  | Luke | soccer

This is my code

private DataFrame explodeDataFrame(DataFrame df) {
    DataFrame resultDf = df;
    for (StructField field : df.schema().fields()) {
        if (field.dataType() instanceof ArrayType) {
            resultDf = resultDf.withColumn(field.name(), org.apache.spark.sql.functions.explode(resultDf.col(field.name())));
            resultDf.show();
        }
    }
    return resultDf;
}

The problem is that in my data, some of the array columns have nulls. In that case, the entire row is deleted. So this dataframe:

id | name | likes
_______________________________
1  | Luke | [baseball, soccer]
2  | Lucy | null

becomes

id | name | likes
_______________________________
1  | Luke | baseball
1  | Luke | soccer

instead of

id | name | likes
_______________________________
1  | Luke | baseball
1  | Luke | soccer
2  | Lucy | null

How can I explode my arrays so that I don't lose the null rows?

I am using Spark 1.5.2 and Java 8

Answer 1

Following up on the accepted answer, when the array elements are a complex type it can be difficult to define it by hand (e.g with large structs).

To do it automatically I wrote the following helper method:

  def explodeOuter(df: Dataset[Row], columnsToExplode: List[String]) = {
      val arrayFields = df.schema.fields
          .map(field => field.name -> field.dataType)
          .collect { case (name: String, type: ArrayType) => (name, type.asInstanceOf[ArrayType])}
          .toMap

      columnsToExplode.foldLeft(df) { (dataFrame, arrayCol) =>
      dataFrame.withColumn(arrayCol, explode(when(size(col(arrayCol)) =!= 0, col(arrayCol))
        .otherwise(array(lit(null).cast(arrayFields(arrayCol).elementType)))))    
 }

Edit: it seems that spark 2.2 and newer have this built in.

Answer 2

Spark 2.2+

You can use explode_outer function:

import org.apache.spark.sql.functions.explode_outer

df.withColumn("likes", explode_outer($"likes")).show

// +---+----+--------+
// | id|name|   likes|
// +---+----+--------+
// |  1|Luke|baseball|
// |  1|Luke|  soccer|
// |  2|Lucy|    null|
// +---+----+--------+

Spark <= 2.1

In Scala but Java equivalent should be almost identical (to import individual functions use import static).

import org.apache.spark.sql.functions.{array, col, explode, lit, when}

val df = Seq(
  (1, "Luke", Some(Array("baseball", "soccer"))),
  (2, "Lucy", None)
).toDF("id", "name", "likes")

df.withColumn("likes", explode(
  when(col("likes").isNotNull, col("likes"))
    // If null explode an array<string> with a single null
    .otherwise(array(lit(null).cast("string")))))

The idea here is basically to replace NULL with an array(NULL) of a desired type. For complex type (a.k.a structs) you have to provide full schema:

val dfStruct = Seq((1L, Some(Array((1, "a")))), (2L, None)).toDF("x", "y")

val st =  StructType(Seq(
  StructField("_1", IntegerType, false), StructField("_2", StringType, true)
))

dfStruct.withColumn("y", explode(
  when(col("y").isNotNull, col("y"))
    .otherwise(array(lit(null).cast(st)))))

or

dfStruct.withColumn("y", explode(
  when(col("y").isNotNull, col("y"))
    .otherwise(array(lit(null).cast("struct<_1:int,_2:string>")))))

Note:

If array Column has been created with containsNull set to false you should change this first (tested with Spark 2.1):

df.withColumn("array_column", $"array_column".cast(ArrayType(SomeType, true)))

Answer 3

You can use explode_outer() function.