[jira] [Commented] (SPARK-19492) Dataset, filter and pattern matching on elements

["Sean Owen (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-19492?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15856320#comment-15856320 ] 

Sean Owen commented on SPARK-19492:
-----------------------------------

Yeah, was mostly establishing that it had to be inferred. It does. But then I don't see what the difference is between Seq and Dataset in this respect. 

> Dataset, filter and pattern matching on elements
> ------------------------------------------------
>
>                 Key: SPARK-19492
>                 URL: https://issues.apache.org/jira/browse/SPARK-19492
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.0.2, 2.1.0
>            Reporter: Loic Descotte
>            Priority: Minor
>
> It seems it is impossible to use pattern matching to define input parameters for function filter on datasets.
> Example :
> This one is working :
> {code}
> val departments = Seq(
>     Department(1, "hr"),
>     Department(2, "it")
> ).toDS
> departments.filter{ d=> 
>   d.name == "hr"
> }
> {code}
> but not this one :
> {code}
>  departments.filter{ case Department(_, name)=>
>   name == "hr"
> }
> {code}
> Error :
> {code}
> error: missing parameter type for expanded function
> The argument types of an anonymous function must be fully known. (SLS 8.5)
> Expected type was: ?
>     departments.filter{ case Department(_, name)=>
> {code}
> This kind of pattern matching should work (as departements dataset type is known) like Scala collections filter function, or RDD filter function for example.
> Please note that it works on map function : 
> {code}
>  departments.map{ case Department(_, name)=>
>       name
>  }
> {code}



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