maybe you can combine the fields you want to use into one field
Something Something <mailinglist...@gmail.com> 于2020年3月3日周二 上午6:37写道：
> I am writing a Stateful Streaming application in which I am using
> mapGroupsWithState to create aggregates for Groups but I need to create
> *Groups
> based on more than one column in the Input Row*. All the examples in the
> 'Spark: The Definitive Guide' use only one column such as 'User' or
> 'Device'. I am using code similar to what's given below. *How do I
> specify more than one field in the 'groupByKey'?*
>
> There are other challenges as well. The book says we can use
> 'updateAcrossEvents' the way given below but I get compile time error
> saying:
>
>
> *Error:(43, 65) missing argument list for method updateAcrossEvents in
> object MainUnapplied methods are only converted to functions when a
> function type is expected.You can make this conversion explicit by writing
> `updateAcrossEvents _` or `updateAcrossEvents(_,_,_,_,_)` instead of
> `updateAcrossEvents`.
> .mapGroupsWithState(GroupStateTimeout.EventTimeTimeout())(updateAcrossEvents)*
>
> Another challenge: Compiler also complains about the my *MyReport*:
> *Error:(41,
> 12) Unable to find encoder for type stored in a Dataset. Primitive types
> (Int, String, etc) and Product types (case classes) are supported by
> importing spark.implicits._ Support for serializing other types will be
> added in future releases.*
>
> Help in resolving these errors would be greatly appreciated. Thanks in
> advance.
>
>
> withEventTime
> .as[MyReport]
> .groupByKey(_.getKeys.getKey1). // How do I add _.getKeys.getKey2?
>
> .mapGroupsWithState(GroupStateTimeout.EventTimeTimeout())(updateAcrossEvents)
> .writeStream
> .queryName("test_query")
> .format("memory")
> .outputMode("update")
> .start()
>
>
