Hi Tzu-Li,
Huge thanks for the input, I'll try to implement prototype of your idea and
see if it answers my requirements
On 9 January 2017 at 08:02, Tzu-Li (Gordon) Tai <tzuli...@apache.org> wrote:
> Hi Igor!
>
> What you can actually do is let a single FlinkKafkaConsumer consume from
> both topics, producing a single DataStream which you can keyBy afterwards.
> All versions of the FlinkKafkaConsumer support consuming multiple Kafka
> topics simultaneously. This is logically the same as union and then a
> keyBy, like what you described.
>
> Note that this approach requires that the records in both of your Kafka
> topics are of the same type when consumed into Flink (ex., same POJO
> classes, or simply both as Strings, etc.).
> If that isn’t possible and you have different data types / schemas for the
> topics, you’d probably need to use “connect” and then a keyBy.
>
> If you’re applying a window directly after joining the two topic streams,
> you could also use a window join:
>
> dataStream.join(otherStream)
> .where(<key selector>).equalTo(<key selector>)
> .window(TumblingEventTimeWindows.of(Time.seconds(3)))
> .apply (new JoinFunction () {...});
>
> The “where” specifies how to select the key from the first stream, and
> “equalTo” the second one.
>
> Hope this helps, let me know if you have other questions!
>
> Cheers,
> Gordon
>
> On January 9, 2017 at 4:06:34 AM, igor.berman (igor.ber...@gmail.com)
> wrote:
>
> Hi,
> I have usecase when I need to join two kafka topics together by some
> fields.
> In general, I could put content of one topic into another, and partition
> by
> same key, but I can't touch those two topics(i.e. there are other
> consumers
> from those topics), on the other hand it's essential to process same keys
> at
> same "thread" to achieve locality and not to get races when working with
> same key from different machines/threads
>
> my idea is to use union of two streams and then key by the field,
> but is there better approach to achieve "locality"?
>
> any inputs will be appreciated
> Igor
>
>
>
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> kafka-streams-tp10912.html
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