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https://issues.apache.org/jira/browse/FLINK-30607?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17706208#comment-17706208
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Xuannan Su commented on FLINK-30607:
------------------------------------
[~dianfu] Thanks for the patch! This is very useful in our use case. May I ask
if we have a plan to backport this to version 1.16?
> Table.to_pandas doesn't support Map type
> ----------------------------------------
>
> Key: FLINK-30607
> URL: https://issues.apache.org/jira/browse/FLINK-30607
> Project: Flink
> Issue Type: Bug
> Components: API / Python
> Affects Versions: 1.15.3
> Reporter: Xuannan Su
> Assignee: Dian Fu
> Priority: Major
> Labels: pull-request-available
> Fix For: 1.17.0
>
>
> It seems that the Table#to_pandas method in PyFlink doesn't support Map type.
> It throws the following exception.
> {code:java}
> py4j.protocol.Py4JJavaError: An error occurred while calling
> z:org.apache.flink.table.runtime.arrow.ArrowUtils.collectAsPandasDataFrame.
> : java.lang.UnsupportedOperationException: Python vectorized UDF doesn't
> support logical type MAP<INT, INT> currently.
> at
> org.apache.flink.table.runtime.arrow.ArrowUtils$LogicalTypeToArrowTypeConverter.defaultMethod(ArrowUtils.java:743)
> at
> org.apache.flink.table.runtime.arrow.ArrowUtils$LogicalTypeToArrowTypeConverter.defaultMethod(ArrowUtils.java:617)
> at
> org.apache.flink.table.types.logical.utils.LogicalTypeDefaultVisitor.visit(LogicalTypeDefaultVisitor.java:167)
> at org.apache.flink.table.types.logical.MapType.accept(MapType.java:115)
> at
> org.apache.flink.table.runtime.arrow.ArrowUtils.toArrowField(ArrowUtils.java:189)
> at
> org.apache.flink.table.runtime.arrow.ArrowUtils.lambda$toArrowSchema$0(ArrowUtils.java:180)
> at
> java.util.stream.ReferencePipeline$3$1.accept(ReferencePipeline.java:193)
> at
> java.util.ArrayList$ArrayListSpliterator.forEachRemaining(ArrayList.java:1384)
> at java.util.stream.AbstractPipeline.copyInto(AbstractPipeline.java:482)
> at
> java.util.stream.AbstractPipeline.wrapAndCopyInto(AbstractPipeline.java:472)
> at
> java.util.stream.ReduceOps$ReduceOp.evaluateSequential(ReduceOps.java:708)
> at java.util.stream.AbstractPipeline.evaluate(AbstractPipeline.java:234)
> at java.util.stream.ReferencePipeline.collect(ReferencePipeline.java:566)
> at
> org.apache.flink.table.runtime.arrow.ArrowUtils.toArrowSchema(ArrowUtils.java:181)
> at
> org.apache.flink.table.runtime.arrow.ArrowUtils.collectAsPandasDataFrame(ArrowUtils.java:483)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:498)
> at
> org.apache.flink.api.python.shaded.py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
> at
> org.apache.flink.api.python.shaded.py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
> at
> org.apache.flink.api.python.shaded.py4j.Gateway.invoke(Gateway.java:282)
> at
> org.apache.flink.api.python.shaded.py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
> at
> org.apache.flink.api.python.shaded.py4j.commands.CallCommand.execute(CallCommand.java:79)
> at
> org.apache.flink.api.python.shaded.py4j.GatewayConnection.run(GatewayConnection.java:238)
> at java.lang.Thread.run(Thread.java:748) {code}
> This can be reproduced with the following code.
> {code:java}
> env = StreamExecutionEnvironment.get_execution_environment()
> t_env = StreamTableEnvironment.create(env)
> table = t_env.from_descriptor(
> TableDescriptor.for_connector("datagen")
> .schema(
> Schema.new_builder()
> .column("val", DataTypes.MAP(DataTypes.INT(), DataTypes.INT()))
> .build()
> )
> .option("number-of-rows", "10")
> .build()
> )
> df = table.to_pandas()
> print(df) {code}
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