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https://issues.apache.org/jira/browse/SPARK-19217?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15873159#comment-15873159
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Liang-Chi Hsieh commented on SPARK-19217:
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The native casting of UserDefinedType from/to other non UserDefinedType is
lacking in current Spark SQL. It seems make sense to me because in some cases
we may need to cast UserDefinedType to native data types which Spark SQL
functions only support.
> Offer easy cast from vector to array
> ------------------------------------
>
> Key: SPARK-19217
> URL: https://issues.apache.org/jira/browse/SPARK-19217
> Project: Spark
> Issue Type: Improvement
> Components: ML, PySpark, SQL
> Affects Versions: 2.1.0
> Reporter: Nicholas Chammas
> Priority: Minor
>
> Working with ML often means working with DataFrames with vector columns. You
> can't save these DataFrames to storage (edit: at least as ORC) without
> converting the vector columns to array columns, and there doesn't appear to
> an easy way to make that conversion.
> This is a common enough problem that it is [documented on Stack
> Overflow|http://stackoverflow.com/q/35855382/877069]. The current solutions
> to making the conversion from a vector column to an array column are:
> # Convert the DataFrame to an RDD and back
> # Use a UDF
> Both approaches work fine, but it really seems like you should be able to do
> something like this instead:
> {code}
> (le_data
> .select(
> col('features').cast('array').alias('features')
> ))
> {code}
> We already have an {{ArrayType}} in {{pyspark.sql.types}}, but it appears
> that {{cast()}} doesn't support this conversion.
> Would this be an appropriate thing to add?
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