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https://issues.apache.org/jira/browse/SPARK-19492?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15868503#comment-15868503
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Niek Bartholomeus commented on SPARK-19492:
-------------------------------------------
I'm having this issue since starting to use spark a year ago. I thought it was
a minor issue that would get solved in the next update but it's still there in
2.1.0. The workaround is indeed to create a val func as described above or even
simpler to wrap it with a match clause:
{code}
departments.filter{ x => x match {case Department(_, name)=>
name == "hr"
}}
{code}
> 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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