Hi Dongwon,
Sorry for the late reply. I did try some experiment and seems like you are
right:
Setting the `.return()` type actually alter the underlying type of the
DataStream from a GenericType into a specific RowTypeInfo. Please see the
JIRA ticket [1] for more info.
Regarding the approach,
Hi Fabian,
Thanks for clarification :-)
I could convert back and forth without worrying about it as I keep using
Row type during the conversion (even though fields are added).
Best,
Dongwon
On Tue, Jul 23, 2019 at 8:15 PM Fabian Hueske wrote:
> Hi Dongwon,
>
> regarding the question about
Hi Dongwon,
regarding the question about the conversion: If you keep using the Row type
and not adding/removing fields, the conversion is pretty much for free
right now.
It will be a MapFunction (sometimes even not function at all) that should
be chained with the other operators. Hence, it should
Hi Rong,
I have to dig deeper into the code to reproduce this error. This seems to
> be a bug to me and will update once I find anything.
Thanks a lot for spending your time on this.
However from what you explained, if I understand correctly you can do all
> of your processing within the
Hi Dongwon,
I have to dig deeper into the code to reproduce this error. This seems to
be a bug to me and will update once I find anything.
However from what you explained, if I understand correctly you can do all
of your processing within the TableAPI scope without converting it back and
forth
Hi Rong,
Thank you for reply :-)
which Flink version are you using?
I'm using Flink-1.8.0.
what is the "sourceTable.getSchema().toRowType()" return?
Row(time1: TimeIndicatorTypeInfo(rowtime))
what is the line *".map(a -> a)" *do and can you remove it?
*".map(a->a)"* is just to illustrate a
Hi Dongwon,
Can you provide a bit more information:
which Flink version are you using?
what is the "sourceTable.getSchema().toRowType()" return?
what is the line *".map(a -> a)" *do and can you remove it?
if I am understanding correctly, you are also using "time1" as the rowtime,
is that want
Hello,
Consider the following snippet:
> Table sourceTable = getKafkaSource0(tEnv);
> DataStream stream = tEnv.toAppendStream(sourceTable, Row.class)
>
> * .map(a -> a) .returns(sourceTable.getSchema().toRowType());*
> stream.print();
>
where sourceTable.printSchema()
