Hi，
I‘m confused about a problem, occuring a exception
"org.apache.flink.table.api.TableException: Table of atomic type can only have
a single field."
Both BillCount and Record are class object. Following is code.
case class BillCount(logisId: Int, provinceId: Int, cityId: Int,
orderRequVari: Int, orderRecAmount: Double, orderRecDate: Long)
val kafkaInputStream: DataStream[Record] = env.addSource(source)
//source is FlinkKafkaConsumer010 source
val tbDataStream : DataStream[BillCount] = kafkaInputStream.map(
new MapFunction[Record, BillCount] {
override def map(value: Record) = {
BillCount(value.getLogis_id, value.getProvince_id,
value.getCity_id,
value.getOrder_require_varieties,
value.getOrder_rec_amount, value.getStore_rec_date.getTime)
}
})
val stream = tbDataStream.toTable(tbEnv, 'logisId, 'provinceId, 'cityId,
'orderRequVari, 'orderRecAmount, 'orderRecDate) // occur error here
Error :
Exception in thread "main" org.apache.flink.table.api.TableException: Table
of atomic type can only have a single field.
at
org.apache.flink.table.api.TableEnvironment$$anonfun$1.apply(TableEnvironment.scala:627)
at
org.apache.flink.table.api.TableEnvironment$$anonfun$1.apply(TableEnvironment.scala:624)
at
scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:251)
at
scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:251)
at
scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:251)
at scala.collection.mutable.ArrayOps$ofRef.flatMap(ArrayOps.scala:108)
at
org.apache.flink.table.api.TableEnvironment.getFieldInfo(TableEnvironment.scala:624)
at
org.apache.flink.table.api.StreamTableEnvironment.registerDataStreamInternal(StreamTableEnvironment.scala:398)
at
org.apache.flink.table.api.scala.StreamTableEnvironment.fromDataStream(StreamTableEnvironment.scala:85)
at
org.apache.flink.table.api.scala.DataStreamConversions.toTable(DataStreamConversions.scala:58)
Thanks.
[email protected]
