Well, I kinda get the "level of abstraction" but what about a cluster 
node failure running my Akka streams stages? 
They are strictly local – how would you deal with an Iterator's iteration 
blowing up? 

Having that said, we do actually work on "Acknowledged Sources / Sinks", the 
first of which is currently being worked on in: github.com/akka/reactive-kafka

But that's the first step, the protocol and patterns that emerge from there 
will be general – to be applied in any kind of situation you need to ACK a 
source that you processed some data.



How would you cope with it? 
See above.

Now, Is my only option to use Kafka and some kind of partition and offset 
syncing?
Of course it's not the only option to "use Kafka", you can build resiliency and 
at-most / at-least once processing any way you want, however that is not the 
core goal of Akka Streams, we're adding it as acknowlaged sink patterns etc. 
Don't confuse the level of abstraction and problems Akka Streams tackle with 
Spark for example (it's entire goal is assuring what you just asked for).

Are you suggesting that event sourcing and reactive streams are not really to 
be "compatible"? 
What? Why?

Totally the opposite is true actually – even sourcing is awesome for 
resiliency, elasticity and scalability.

How do people solve it (resiliency and scalability) in real production systems? 
Could you share some recipes? 
Also see above, but you didn't state the exact problem so the answers will also 
be "fluffy" :-)

If your goal fits spark, use spark; if it doesn't, here's another tool in the 
toolbox – with very different semantics and capabilities (note - all above 
comments about Akka Streams speak about any Reactive Streams implementation).



-- Konrad

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