Hi,
I am not sure I fully understand the differences between doing something like dataStreamX.connect(dataStreamY.broadcast()).process(new CoProcessFunction{}) and this dataStreamX.connect(dataStreamY.broadcast(descriptor).process(new BroadcastProcessFunction) Couldn't I manage the state exactly the same way using the first option? The only difference I could find was in read/write permissions for different states. But other than that, the difference in possibilities escapes me and I was hoping someone here could clarify. Kind regards, Pieter-Jan ___________________________ Pieter-Jan Van Aeken
Consultant - Data Engineer
EURA NOVA Rue Emile Francqui, 4 1435 Mont-Saint-Guibert (T) +32 10 75 02 00
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Hi Pieter-Jan,
The second variant stores the elements of the broadcasted stream in operator (thus non-keyed) state. On the differences: The Broadcast stream is not a keyed stream, so you are not in a keyed context, thus you have no access to keyed state. Given this, and assuming that you are implementing a CoProcessFunction, then your function should also implement the ListCheckpointed interface and store the broadcasted data as ListState. From a systems perspective, in case of rescaling, then your function should take care of removing duplicated manually as in the case of scaling down for example, the state of 2 nodes may end up on one. In addition, given that elements are re-distributed randomly, you may even end up with some elements missing one some nodes. This would be more evident in case of scaling up. In this case, nodes will be missing data. From an API perspective, assuming that the other stream is keyed and it has state, accessing it in order to do some computation would be impossible. Cheers, Kostas
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