Spark Dataset and java.sql.Date

Question

Let's say I have a Spark Dataset like this:

scala> import java.sql.Date
scala> case class Event(id: Int, date: Date, name: String)
scala> val ds = Seq(Event(1, Date.valueOf("2016-08-01"), "ev1"), Event(2, Date.valueOf("2018-08-02"), "ev2")).toDS

I want to create a new Dataset with only the name and date fields. As far as I can see, I can either use ds.select() with TypedColumn or I can use ds.select() with Column and then convert the DataFrame to Dataset.

However, I can't get the former option working with the Date type. For example:

scala> ds.select($"name".as[String], $"date".as[Date])
<console>:31: error: Unable to find encoder for type stored in a Dataset.  Primitive types (Int, String, etc) and Product types (case classes) are supported by importing spark.implicits._  Support for serializing other types will be added in future releases.
              ds.select($"name".as[String], $"date".as[Date])
                                                      ^

The later option works:

scala> ds.select($"name", $"date").as[(String, Date)]
res2: org.apache.spark.sql.Dataset[(String, java.sql.Date)] = [name: string, date: date]

Is there a way to select Date fields from Dataset without going to DataFrame and back?

Answer 1

Been bashing my head against problems like these for the whole day. I think you can solve your problem with one line:

implicit val e: Encoder[(String, Date)] = org.apache.spark.sql.Encoders.kryo[(String,Date)]

At least that has been working for me.

EDIT

In these cases, the problem is that for most Dataset operations, Spark 2 requires an Encoder that stores schema information (presumably for optimizations). The schema information takes the form of an implicit parameter (and a bunch of Dataset operations have this sort of implicit parameter).

In this case, the OP found the correct schema for java.sql.Date so the following works:

implicit val e = org.apache.spark.sql.Encoders.DATE