@Michal

Yes, we have public VectorUDT in spark.mllib package at 1.6, and this class is still existing in 2.0.
And from 2.0, we provide a new VectorUDT in spark.ml package and make it private temporary (will be public in the near future).
Since from 2.0, spark.mllib package will be in maintenance mode, so we strongly recommend users to use the DataFrame-based spark.ml API.

Thanks
Yanbo 

2016-08-17 11:46 GMT-07:00 Michał Zieliński <zielinski.michal0@gmail.com>:
I'm using Spark 1.6.2 for Vector-based UDAF and this works:

def inputSchema: StructType = new StructType().add("input", new VectorUDT())

Maybe it was made private in 2.0

On 17 August 2016 at 05:31, Alexey Svyatkovskiy <alexeys@princeton.edu> wrote:
Hi Yanbo,

Thanks for your reply. I will keep an eye on that pull request.
For now, I decided to just put my code inside org.apache.spark.ml to be able to access private classes.

Thanks,
Alexey

On Tue, Aug 16, 2016 at 11:13 PM, Yanbo Liang <ybliang8@gmail.com> wrote:
It seams that VectorUDT is private and can not be accessed out of Spark currently. It should be public but we need to do some refactor before make it public. You can refer the discussion at https://github.com/apache/spark/pull/12259 .

Thanks
Yanbo

2016-08-16 9:48 GMT-07:00 alexeys <alexeys@princeton.edu>:
I am writing an UDAF to be applied to a data frame column of type Vector
(spark.ml.linalg.Vector). I rely on spark/ml/linalg so that I do not have to
go back and forth between dataframe and RDD.

Inside the UDAF, I have to specify a data type for the input, buffer, and
output (as usual). VectorUDT is what I would use with
spark.mllib.linalg.Vector:
https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala

However, when I try to import it from spark.ml instead: import
org.apache.spark.ml.linalg.VectorUDT
I get a runtime error (no errors during the build):

class VectorUDT in package linalg cannot be accessed in package
org.apache.spark.ml.linalg

Is it expected/can you suggest a workaround?

I am using Spark 2.0.0

Thanks,
Alexey



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