Hi Enrico,
that's (for now) the right approach. I agree, that the KafkaTableSource
should implement both DefinedXTimeAttribute interfaces.
Best, Fabian
2017-05-25 3:20 GMT+02:00 enrico canzonieri <ecanzoni...@gmail.com>:
> I solved this implementing a new Kafka09TableSource in my application. The
> class I implemented extends both DefinedRowTimeAttribute and
> DefinedProcTimeAttribute and it exposes the consumer so that I can assign
> the timestamp extractor.
>
> I'm not sure if this is the right approach, but if that's the case I
> wonder if we could make those changes into KafkaTableSource to make it more
> generic.
>
> On Wed, May 24, 2017 at 12:23 PM, enrico canzonieri <ecanzoni...@gmail.com
> > wrote:
>
>> Hi Timo, thanks for your help!
>>
>> I tried to follow the examples in the tests but I still have the same
>> issue.
>> I changed my schema and added an additional field "rowtime". My schema
>> now is:
>> root
>> |-- rowtime: org.apache.flink.table.expressions.RowtimeAttribute(expr:
>> GenericType<org.apache.flink.table.expressions.Expression>)
>> |-- time: Long
>> |-- host: String
>>
>> If I run the code:
>> table.select('rowtime).toDataStream[Row].print()
>> I get:
>> RowtimeAttribute(1495580133000)
>> RowtimeAttribute(1495580143000)
>> RowtimeAttribute(1495580153000)
>>
>> But If I run:
>> table.window(Tumble over 1.minutes on 'rowtime as 'w).groupBy('host,
>> 'w).select('host)
>> I still get the previous error:
>> TumblingGroupWindow('w, 'rowtime, 60000.millis) is invalid: Tumbling
>> window expects a time attribute for grouping in a stream environment.
>>
>> I'm using a Kafka09TableSource as data source, but it doesn't allow me to
>> specify the timestamp assigner. I think the actual consumer is not exposed
>> to the user so I cannot really call assignTimestampsAndWatermarks. May
>> that be the problem? Should we expose that function so that we can assign
>> timestamp and watermark to a TableSource?
>>
>> The time characteristic in the execution environment is set to EventTime
>> in my code.
>>
>> Cheers,
>> Enrico
>>
>> On Wed, May 24, 2017 at 2:08 AM, Timo Walther <twal...@apache.org> wrote:
>>
>>> Hi Enrico,
>>>
>>> the docs of the 1.3-SNAPSHOT are a bit out of sync right now, but they
>>> will be updated in the next days/1-2 weeks.
>>>
>>> We recently introduced so-called "time indicators". These are attributes
>>> that correspond to Flink's time and watermarks. You declare a logical field
>>> that represents Flink's internal time in a table program.
>>>
>>> In your example you need to append a "time.rowtime" or "time.proctime"
>>> to your table schema definition.
>>>
>>> You can find some examples here:
>>> https://github.com/apache/flink/blob/master/flink-libraries/
>>> flink-table/src/test/scala/org/apache/flink/table/runtime/
>>> datastream/TimeAttributesITCase.scala
>>>
>>> If you have further question, feel free to ask them. It helps us to
>>> improve the documenation.
>>>
>>> Regards,
>>> Timo
>>>
>>>
>>>
>>> Am 24.05.17 um 04:15 schrieb enrico canzonieri:
>>>
>>> Hi,
>>> I'm trying to window and groupBy a stream using the table api, but I get
>>> ValidationException in the windowing function.
>>> Here is the relevant code:
>>>
>>> tableEnv.registerTableSource(schema.getName, src)
>>> val table = tableEnv.scan(schema.getName)
>>> val t = table.window(Tumble over 1.minutes on 'time as
>>> 'w).groupBy('host, 'w).select('host)
>>>
>>> "time" is defined as Long in my schema. The error I get is:
>>> Exception in thread "main" org.apache.flink.table.api.ValidationException:
>>> TumblingGroupWindow('w, 'time, 60000.millis) is invalid: Tumbling window
>>> expects a time attribute for grouping in a stream environment.
>>>
>>> I also tried to define a window that was using processing time, but what
>>> described in the documentation "Tumble over 1.minutes as 'w" doesn't
>>> seem to work anymore. Specifically it seems that a window now always
>>> expects the "on" call.
>>>
>>> Has anybody encountered this issue? I'm using Flink 1.3-SNAPSHOT.
>>>
>>> thanks
>>>
>>>
>>>
>>
>
