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https://issues.apache.org/jira/browse/SPARK-18131?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15847473#comment-15847473
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Shivaram Venkataraman commented on SPARK-18131:
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Hmm - this is tricky. We ran into a similar issue in SQL and we added a reader,
writer object in SQL that was registered to the method in core. See
https://github.com/apache/spark/blob/ce112cec4f9bff222aa256893f94c316662a2a7e/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala#L39
for how we did that. We could do a similar thing in MLlib as well ?
cc [~mengxr]
> Support returning Vector/Dense Vector from backend
> --------------------------------------------------
>
> Key: SPARK-18131
> URL: https://issues.apache.org/jira/browse/SPARK-18131
> Project: Spark
> Issue Type: New Feature
> Components: SparkR
> Reporter: Miao Wang
>
> For `spark.logit`, there is a `probabilityCol`, which is a vector in the
> backend (scala side). When we do collect(select(df, "probabilityCol")),
> backend returns the java object handle (memory address). We need to implement
> a method to convert a Vector/Dense Vector column as R vector, which can be
> read in SparkR. It is a followup JIRA of adding `spark.logit`.
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