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()
