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https://issues.apache.org/jira/browse/FLINK-17321?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17454487#comment-17454487
]
Francesco Guardiani commented on FLINK-17321:
---------------------------------------------
Hi [~Sergey Nuyanzin],
this issue is referring to ROW to ROW (which is already being taken care of
with FLINK-25052), MAP to MAP, MULTISET to MULTISET and ARRAY to ARRAY (already
implemented as {{ArrayToArrayCastRule}}), where inner types are "cast
compatible". Effectively, to solve this issue we miss MAP to MAP, MULTISET to
MULTISET and we need to make sure these cast rules are exposed in SQL and Table
API (eventually fixing {{LogicalTypeCasts}}).
Apart those, perhaps there are some combinations of casting across collection
types which might make sense, for example I can think of ARRAY to MULTISET (but
not the inverse) and MULTISET to MAP (which is identity casting, as MULTISET
and MAP are both represented as {{MapData}}). But I would scope these out of
this specific issue, as each of these combinations deserve an ad hoc discussion.
> Support for casting collection types.
> -------------------------------------
>
> Key: FLINK-17321
> URL: https://issues.apache.org/jira/browse/FLINK-17321
> Project: Flink
> Issue Type: Sub-task
> Components: Table SQL / Runtime
> Reporter: Dawid Wysakowicz
> Priority: Critical
>
> Casts of collection types are not supported yet.
> E.g. query: {{"SELECT cast (a as ARRAY<double>) FROM (VALUES (array[3, 2,
> 1])) AS T(a)"}}
> fails with:
> {code}
> org.apache.flink.table.planner.codegen.CodeGenException: Unsupported cast
> from 'ARRAY<INT NOT NULL> NOT NULL' to 'ARRAY<DOUBLE> NOT NULL'.
> at
> org.apache.flink.table.planner.codegen.calls.ScalarOperatorGens$.generateCast(ScalarOperatorGens.scala:1284)
> at
> org.apache.flink.table.planner.codegen.ExprCodeGenerator.generateCallExpression(ExprCodeGenerator.scala:691)
> at
> org.apache.flink.table.planner.codegen.ExprCodeGenerator.visitCall(ExprCodeGenerator.scala:486)
> at
> org.apache.flink.table.planner.codegen.ExprCodeGenerator.visitCall(ExprCodeGenerator.scala:52)
> at org.apache.calcite.rex.RexCall.accept(RexCall.java:288)
> at
> org.apache.flink.table.planner.codegen.ExprCodeGenerator.generateExpression(ExprCodeGenerator.scala:132)
> at
> org.apache.flink.table.planner.codegen.CalcCodeGenerator$$anonfun$5.apply(CalcCodeGenerator.scala:152)
> at
> org.apache.flink.table.planner.codegen.CalcCodeGenerator$$anonfun$5.apply(CalcCodeGenerator.scala:152)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
> at scala.collection.AbstractTraversable.map(Traversable.scala:104)
> at
> org.apache.flink.table.planner.codegen.CalcCodeGenerator$.produceProjectionCode$1(CalcCodeGenerator.scala:152)
> at
> org.apache.flink.table.planner.codegen.CalcCodeGenerator$.generateProcessCode(CalcCodeGenerator.scala:179)
> at
> org.apache.flink.table.planner.codegen.CalcCodeGenerator$.generateCalcOperator(CalcCodeGenerator.scala:49)
> at
> org.apache.flink.table.planner.plan.nodes.physical.batch.BatchExecCalc.translateToPlanInternal(BatchExecCalc.scala:62)
> at
> org.apache.flink.table.planner.plan.nodes.physical.batch.BatchExecCalc.translateToPlanInternal(BatchExecCalc.scala:38)
> at
> org.apache.flink.table.planner.plan.nodes.exec.ExecNode$class.translateToPlan(ExecNode.scala:58)
> at
> org.apache.flink.table.planner.plan.nodes.physical.batch.BatchExecCalcBase.translateToPlan(BatchExecCalcBase.scala:42)
> at
> org.apache.flink.table.planner.plan.nodes.physical.batch.BatchExecSink.translateToTransformation(BatchExecSink.scala:131)
> at
> org.apache.flink.table.planner.plan.nodes.physical.batch.BatchExecSink.translateToPlanInternal(BatchExecSink.scala:97)
> at
> org.apache.flink.table.planner.plan.nodes.physical.batch.BatchExecSink.translateToPlanInternal(BatchExecSink.scala:49)
> at
> org.apache.flink.table.planner.plan.nodes.exec.ExecNode$class.translateToPlan(ExecNode.scala:58)
> at
> org.apache.flink.table.planner.plan.nodes.physical.batch.BatchExecSink.translateToPlan(BatchExecSink.scala:49)
> at
> org.apache.flink.table.planner.delegation.BatchPlanner$$anonfun$translateToPlan$1.apply(BatchPlanner.scala:72)
> at
> org.apache.flink.table.planner.delegation.BatchPlanner$$anonfun$translateToPlan$1.apply(BatchPlanner.scala:71)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at scala.collection.Iterator$class.foreach(Iterator.scala:891)
> at scala.collection.AbstractIterator.foreach(Iterator.scala:1334)
> at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
> at scala.collection.AbstractIterable.foreach(Iterable.scala:54)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
> at scala.collection.AbstractTraversable.map(Traversable.scala:104)
> at
> org.apache.flink.table.planner.delegation.BatchPlanner.translateToPlan(BatchPlanner.scala:71)
> at
> org.apache.flink.table.planner.delegation.PlannerBase.translate(PlannerBase.scala:153)
> ...
> {code}
> Similar behaviour can be observed for MULTISET, MAP, ROW
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