How to convert a simple DataFrame to a DataSet Spa

Question

I am trying to convert a simple DataFrame to a DataSet from the example in Spark: https://spark.apache.org/docs/latest/sql-programming-guide.html

case class Person(name: String, age: Int)    
import spark.implicits._

val path = "examples/src/main/resources/people.json"

val peopleDS = spark.read.json(path).as[Person]
peopleDS.show()

But the following problem arises:

Exception in thread "main" org.apache.spark.sql.AnalysisException: Cannot up cast `age` from bigint to int as it may truncate
The type path of the target object is:
- field (class: "scala.Int", name: "age")
- root class: ....

Can anyone help me out?

Edit I noticed that with Long instead of Int works! Why is that?

Also:

val primitiveDS = Seq(1,2,3).toDS()
val augmentedDS = primitiveDS.map(i => ("var_" + i.toString, (i + 1).toLong))
augmentedDS.show()

augmentedDS.as[Person].show()

Prints:

+-----+---+
|   _1| _2|
+-----+---+
|var_1|  2|
|var_2|  3|
|var_3|  4|
+-----+---+

Exception in thread "main"
org.apache.spark.sql.AnalysisException: cannot resolve '`name`' given input columns: [_1, _2];

Can Anyone Help me out understand here?

Answer 1

This is how you create dataset from case class

case class Person(name: String, age: Long) 

Keep the case class outside of the class that has below code

val primitiveDS = Seq(1,2,3).toDS()
val augmentedDS = primitiveDS.map(i => Person("var_" + i.toString, (i + 1).toLong))
augmentedDS.show()

augmentedDS.as[Person].show()

Hope this helped

Answer 2

If you change Int to Long (or BigInt) it works fine:

case class Person(name: String, age: Long)
import spark.implicits._

val path = "examples/src/main/resources/people.json"

val peopleDS = spark.read.json(path).as[Person]
peopleDS.show()

Output:

+----+-------+
| age|   name|
+----+-------+
|null|Michael|
|  30|   Andy|
|  19| Justin|
+----+-------+

EDIT: Spark.read.json by default parses numbers as Long types - it's safer to do so. You can change the col type after using casting or udfs.

EDIT2:

To answer your 2nd question, you need to name the columns correctly before the conversion to Person will work:

val primitiveDS = Seq(1,2,3).toDS()
val augmentedDS = primitiveDS.map(i => ("var_" + i.toString, (i + 1).toLong)).
 withColumnRenamed ("_1", "name" ).
 withColumnRenamed ("_2", "age" )
augmentedDS.as[Person].show()

Outputs:

+-----+---+
| name|age|
+-----+---+
|var_1|  2|
|var_2|  3|
|var_3|  4|
+-----+---+