[jira] [Resolved] (SPARK-19217) Offer easy cast from vector to array

["Hyukjin Kwon (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-19217?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon resolved SPARK-19217.
----------------------------------
    Resolution: Later

Let's track this in another JIRA. It's been inactive for so long time. At least work around is easy and to natively support this in cast, it looks quite big challenge.

> 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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