I have a problem with adding days (numbers) to date format columns in Spark. I know that there is a function date_add
that takes two arguments - date column and integer:
date_add(date startdate, tinyint/smallint/int days)
I'd like to use a column value that is of type integer instead (not an integer itself).
Say I have the following dataframe:
val data = Seq(
(0, "2016-01-1"),
(1, "2016-02-2"),
(2, "2016-03-22"),
(3, "2016-04-25"),
(4, "2016-05-21"),
(5, "2016-06-1"),
(6, "2016-03-21"))
).toDF("id", "date")
I can simply add integers to dates:
val date_add_fun =
data.select(
$"id",
$"date",
date_add($"date", 1)
)
But I cannot use a column expression that contains the values:
val date_add_fun =
data.select(
$"id",
$"date",
date_add($"date", $"id")
)
It gives error:
<console>:60: error: type mismatch;
found : org.apache.spark.sql.ColumnName
required: Int
date_add($"date", $"id")
Does anyone know if it is possible to use column is date_add function? Or what is the workaround?
You can use expr
:
import org.apache.spark.sql.functions.expr
data.withColumn("future", expr("date_add(date, id)")).show
// +---+----------+----------+
// | id| date| future|
// +---+----------+----------+
// | 0| 2016-01-1|2016-01-01|
// | 1| 2016-02-2|2016-02-03|
// | 2|2016-03-22|2016-03-24|
// | 3|2016-04-25|2016-04-28|
// | 4|2016-05-21|2016-05-25|
// | 5| 2016-06-1|2016-06-06|
// | 6|2016-03-21|2016-03-27|
// +---+----------+----------+
selectExpr
could be use in a similar way:
data.selectExpr("*", "date_add(date, id) as future").show
The other answers work but aren't a drop in replacement for the existing date_add function.
I had a case where expr
wouldn't work for me, so here is a drop in replacement:
def date_add(date: Column, days: Column) = {
new Column(DateAdd(date.expr, days.expr))
}
Basically, all the machinery is there in Spark to do this already, the function signature for date_add
just forces it to be a literal.
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