Case classes are serializable by default (they extend java Serializable
trait)
I am not using RDD or Dataset because I need to transform one column out of
200 or so.
Dataset has the mechanisms to convert rows to case classes as needed (and
make sure it's consistent with the schema). Why would this code not be used
to make udfs a lot nicer?
On Sep 26, 2016 1:16 AM, "Bedrytski Aliaksandr" <sp...@bedryt.ski> wrote:
> Hi Koert,
>
> these case classes you are talking about, should be serializeable to be
> efficient (like kryo or just plain java serialization).
>
> DataFrame is not simply a collection of Rows (which are serializeable by
> default), it also contains a schema with different type for each column.
> This way any columnar data may be represented without creating custom case
> classes each time.
>
> If you want to manipulate a collection of case classes, why not use good
> old RDDs? (Or DataSets if you are using Spark 2.0)
> If you want to use sql against that collection, you will need to explain
> to your application how to read it as a table (by transforming it to a
> DataFrame)
>
> Regards
> --
> Bedrytski Aliaksandr
> sp...@bedryt.ski
>
>
>
> On Sun, Sep 25, 2016, at 23:41, Koert Kuipers wrote:
>
> after having gotten used to have case classes represent complex structures
> in Datasets, i am surprised to find out that when i work in DataFrames with
> udfs no such magic exists, and i have to fall back to manipulating Row
> objects, which is error prone and somewhat ugly.
> for example:
> case class Person(name: String, age: Int)
>
> val df = Seq((Person("john", 33), 5), (Person("mike", 30),
> 6)).toDF("person", "id")
> val df1 = df.withColumn("person", udf({ (p: Person) => p.copy(age = p.age
> + 1) }).apply(col("person")))
> df1.printSchema
> df1.show
> leads to:
> java.lang.ClassCastException:
> org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema
> cannot be cast to Person
>
>
>
