Suppose I have the following DataFrame:
scala> val df1 = Seq("a", "b").toDF("id").withColumn("nums", array(lit(1)))
df1: org.apache.spark.sql.DataFrame = [id: string, nums: array<int>]
scala> df1.show()
+---+----+
| id|nums|
+---+----+
| a| [1]|
| b| [1]|
+---+----+
And I want to add elements to the array in the nums
column, so that I get something like the following:
+---+-------+
| id|nums |
+---+-------+
| a| [1,5] |
| b| [1,5] |
+---+-------+
Is there a way to do this using the .withColumn()
method of the DataFrame? E.g.
val df2 = df1.withColumn("nums", append(col("nums"), lit(5)))
I've looked through the API documentation for Spark, but can't find anything that would allow me to do this. I could probably use split
and concat_ws
to hack something together, but I would prefer a more elegant solution if one is possible. Thanks.
import org.apache.spark.sql.functions.{lit, array, array_union}
val df1 = Seq("a", "b").toDF("id").withColumn("nums", array(lit(1)))
val df2 = df1.withColumn("nums", array_union($"nums", lit(Array(5))))
df2.show
+---+------+
| id| nums|
+---+------+
| a|[1, 5]|
| b|[1, 5]|
+---+------+
The array_union()
was added since spark 2.4.0 release on 11/2/2018, 7 months after you asked the question, :) see https://spark.apache.org/news/index.html
You can do it using a udf
function as
def addValue = udf((array: Seq[Int])=> array ++ Array(5))
df1.withColumn("nums", addValue(col("nums")))
.show(false)
and you should get
+---+------+
|id |nums |
+---+------+
|a |[1, 5]|
|b |[1, 5]|
+---+------+
Updated
Alternative way is to go with dataset way and use map as
df1.map(row => add(row.getAs[String]("id"), row.getAs[Seq[Int]]("nums")++Seq(5)))
.show(false)
where add is a case class
case class add(id: String, nums: Seq[Int])
I hope the answer is helpful