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https://issues.apache.org/jira/browse/SPARK-20384?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16418691#comment-16418691
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Furcy Pin edited comment on SPARK-20384 at 3/29/18 10:10 AM:
-------------------------------------------------------------
+1 on this issue.
I think the generic use case is that the spark Encoder magic to automatically
transform a DataFrame into a case class currently only work for base types.
This is great if you have a
{code:java}
case class Table(id: Long, attribute: String)
{code}
with simple attributes,
BUT, if you want to wrap your attribute into another simple class like this
{code:java}
case class Attribute(value: String) {
// some specific methods...
}
case class Table(id: Long, attribute: Attribute){code}
Then this won't work automatically, unless the "attribute" column in your
DataFrame is a struct itself.
The problem is that currently there doesn't seem to be any simple way to
achieve this, which really limits the usefulness of the whole Encoder magic.
And if a nice, simple way to achieve this exists, please document it as I did
not find it.
EDIT: after giving it some thought, I tried to do this:
{code:java}
implicit class Attribute(value: String)
case class Table(id: Long, attribute: Attribute){code}
But it does not work either. If it were possible like this, it would be a nice
way to do it.
was (Author: fpin):
+1 on this issue.
I think the generic use case is that the spark Encoder magic to automatically
transform a DataFrame into a case class currently only work for base types.
This is great if you have a
{code:java}
case class Table(id: Long, attribute: String)
{code}
with simple attributes,
BUT, if you want to wrap your attribute into another simple class like this
{code:java}
case class Attribute(value: String) {
// some specific methods...
}
case class Table(id: Long, attribute: Attribute){code}
Then this won't work automatically, unless the "attribute" column in your
DataFrame is a struct itself.
The problem is that currently there doesn't seem to be any simple way to
achieve this, which really limits the usefulness of the whole Encoder magic.
And if a nice, simple way to achieve this exists, please document it as I did
not find it.
> supporting value classes over primitives in DataSets
> ----------------------------------------------------
>
> Key: SPARK-20384
> URL: https://issues.apache.org/jira/browse/SPARK-20384
> Project: Spark
> Issue Type: Improvement
> Components: Optimizer, SQL
> Affects Versions: 2.1.0
> Reporter: Daniel Davis
> Priority: Minor
>
> As a spark user who uses value classes in scala for modelling domain objects,
> I also would like to make use of them for datasets.
> For example, I would like to use the {{User}} case class which is using a
> value-class for it's {{id}} as the type for a DataSet:
> - the underlying primitive should be mapped to the value-class column
> - function on the column (for example comparison ) should only work if
> defined on the value-class and use these implementation
> - show() should pick up the toString method of the value-class
> {code}
> case class Id(value: Long) extends AnyVal {
> def toString: String = value.toHexString
> }
> case class User(id: Id, name: String)
> val ds = spark.sparkContext
> .parallelize(0L to 12L).map(i => (i, f"name-$i")).toDS()
> .withColumnRenamed("_1", "id")
> .withColumnRenamed("_2", "name")
> // mapping should work
> val usrs = ds.as[User]
> // show should use toString
> usrs.show()
> // comparison with long should throw exception, as not defined on Id
> usrs.col("id") > 0L
> {code}
> For example `.show()` should use the toString of the `Id` value class:
> {noformat}
> +---+-------+
> | id| name|
> +---+-------+
> | 0| name-0|
> | 1| name-1|
> | 2| name-2|
> | 3| name-3|
> | 4| name-4|
> | 5| name-5|
> | 6| name-6|
> | 7| name-7|
> | 8| name-8|
> | 9| name-9|
> | A|name-10|
> | B|name-11|
> | C|name-12|
> +---+-------+
> {noformat}
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