Hi Kunal and Johannes,

THANKS for your posts on this -- as I was also wondering how 
exception-handling mid-Flow is supposed to work!

Followup questions for you, please:

- Let's say for simplicity that we have only 3 events flowing through a 
Streaming Flow with 5 Stages. 
- We put a Try block around the myGraph.run() materialization
- Then, say an exception happens on Stage 3, and Event 2.

1) Does this mean that the resulting Try collection will contain:
- Success w Result from Event 1, all Stages
- Failure w Exception from Event 2, Stage 3
- NOTHING for Event 3; as Materialized Graph would have just stopped on an 
exception

2) Does this then imply that Source has to somehow know Events 2,3 got 
dropped via the UUID of Event- last processed;
then it has to re-stream those to the Sink?

Please let me know if I'm understanding those two points correctly!

THANKS!
D

On Wednesday, September 14, 2016 at 10:49:18 PM UTC-7, Kunal Deshpande 
wrote:
>
> Hi, 
>
> I have been using Akka streams to implement a saved-search refresh system 
> as well as a notification processing system at my current company. 
>
> Recently we ran into a fast-publisher & slow subscriber problem where the 
> downstream HTTP services were taking a long time to respond resulting in 
> Timeout exceptions in our client. Currently we simply drop the event and 
> resume the stream using Supervision.Resume but I am unsure whether that 
> translates into back pressure.
>
> Few questions on back pressure
> 1. While using Flows in akka-streams using .via will a downstream flow 
> apply back pressure to a flow upstream or is back pressure only signaled to 
> a Source?
> 2. Will exceptions in a Flow trigger back pressure
> 3. Is there a mathematical way to represent back pressure and is it 
> consistent across different reactive streams implementations?
>
> Thanks, and really appreciate your time!
>
> Kunal
>

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