We have a Kafka stream of events that we want to process with a Flink 
datastream process. However, the stream is populated by an upstream batch 
process that only executes every few hours. So the stream has very 'bursty' 
behaviour. We need a window based on event time to await the next events for 
the same key. Due to this batch population of the stream, these windows can 
remain open (with no event activity on the stream) for many hours. From what I 
understand we could indeed leave the Flink datastream process up and running 
all this time and the window would remain open. We could even use a savepoint 
and then stop the process and restart it (with the window state being restored) 
when we get the next batch and the events start appearing in the stream again.

One rationale for this mode of operation is that we have a future usecase where 
this stream will be populated in real-time and would behave like a normal 
stream.

Is that a best-practice approach for this scenario? Or should we be treating 
these batches as individual batches (Flink job that ends with the end of the 
batch) and manually handle the windowing that needs to cross multiple batches.

Thanks,
Jonny

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