Xuannan Su created FLINK-30607:
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Summary: 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
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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