Thank you Xingcan.
Regarding that Either, I still see the need to do TypeCasting/CaseClass
matching. Could you please help give a look?
val transformed = dog
.union(cat)
.connect(transformer)
.keyBy(r
=> r.name, r2 => r2.name)
.process(new Transfo
Hi Averell,
With the CoProcessFunction, you could get access to the time-related services
which may be useful when maintaining the elements in Stream_C and you could get
rid of type casting with the Either class.
Best,
Xingcan
> On Aug 15, 2018, at 3:27 PM, Averell wrote:
>
> Thank you Vino
Hi Averell,
What I mean is that if you store stream_c data in an RDBMS, you can access
the RDBMS directly in the CoFlatMapFunction instead of using the Table API.
This is somewhat similar to stream and dimension table joins.
Of course, the premise of adopting this option is that the amount of data
Thank you Vino & Xingcan.
@Vino: could you help explain more details on using DBMS? Would that be with
using TableAPI, or you meant directly reading DBMS data inside the
ProcessFunction?
@Xingcan: I don't know what are the benefits of using CoProcess over
RichCoFlatMap in this case.
Regarding usin
Hi Averell,
I am also in favor of option 2. Besides, you could use CoProcessFunction
instead of CoFlatMapFunction and try to wrap elements of stream_A and stream_B
using the `Either` class.
Best,
Xingcan
> On Aug 15, 2018, at 2:24 PM, vino yang wrote:
>
> Hi Averell,
>
> As far as these two
Hi Averell,
As far as these two solutions are concerned, I think you can only choose
option 2, because as you have stated, the current Flink DataStream API does
not support the replacement of one of the input stream types of
CoFlatMapFunction. Another choice:
1. Split it into two separate jobs. B
Hi,
I have stream_A of type "Dog", which needs to be transformed using data from
stream_C of type "Name_Mapping". As stream_C is a slow one (data is not
being updated frequently), to do the transformation I connect two streams,
do a keyBy, and then use a RichCoFlatMapFunction in which mapping data
