In the implementation I am passing just one CoFlatMapFunction, where flatMap1,
which operates on EventA, just emits a None (doesn't do anything practically)
and flatMap2 tries to access the state and throws the NPE.
It wouldn't make sense to use a mapper in this context, I would still want to
f
Ok sorry about that :-). I misunderstood as I am not familiar with Scala
code. Just curious though how are you passing two MapFunction's to the
flatMap function on the connected stream. The interface of ConnectedStream
requires just one CoMap function-
https://ci.apache.org/projects/flink/flink-doc
Hi Sameer,
Thank you for your quick response.
I don't think event ordering is the problem here, the processor doesn't assume
any ordering.
KeyedStream[EventA] stores a state of type Set[InfoA] on its key, which I would
like KeyedStream[EventB] to access.
The code operates on an Option[Set[I
+1
On Aug 26, 2016, at 11:23 PM, Aparup Banerjee (apbanerj)
wrote:
Hi-
Has anyone looked into embedding apache siddhi into Flink.
Thanks,
Aparup
There is no guarantee about the order in which each stream elements arrive
in a connected streams. You have to check if the elements have arrived from
Stream A before using the information to process elements from Stream B.
Otherwise you have to buffer elements from stream B and check if there are
Let's say I have two types sharing the same trait
trait Event {
def id: Id
}
case class EventA(id: Id, info: InfoA) extends Event
case class EventB(id: Id, info: InfoB) extends Event
Each of these events gets pushed to a Kafka topic and gets consumed by a stream
in Flink.
Let's say I have two
