Github user fhueske commented on a diff in the pull request:
https://github.com/apache/flink/pull/3040#discussion_r104723484
--- Diff:
flink-libraries/flink-table/src/main/scala/org/apache/flink/table/plan/nodes/dataset/DataSetCorrelate.scala
---
@@ -97,18 +103,41 @@ class DataSetCorrelate(
val sqlFunction = rexCall.getOperator.asInstanceOf[TableSqlFunction]
val pojoFieldMapping = sqlFunction.getPojoFieldMapping
val udtfTypeInfo =
sqlFunction.getRowTypeInfo.asInstanceOf[TypeInformation[Any]]
+ val returnType = FlinkTypeFactory.toInternalRowTypeInfo(getRowType)
val mapFunc = correlateMapFunction(
config,
inputDS.getType,
udtfTypeInfo,
+ returnType,
getRowType,
joinType,
rexCall,
condition,
Some(pojoFieldMapping),
ruleDescription)
- inputDS.flatMap(mapFunc).name(correlateOpName(rexCall, sqlFunction,
relRowType))
+ def getIndices = {
--- End diff --
A correlate forwards all fields from the input and the table function like
this `[in1, in2, in3, tf1, tf2]` for an input `[in1, in2, in3]` and table
function `[tf1, tf2]`. So we can do a simple position based mapping of the
fields of the input type against the output type (field names might change).
Basically similar to what you are doing with the single row join.
We do not need to look at the table function or the condition.
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