Thanks Jim.
W dniu wtorek, 3 marca 2015 01:29:04 UTC+1 użytkownik Jim Hazen napisał:
I think the answer is in the {Scala, Java}Docs of mapAsync:
Transform this stream by applying the given function to each of the
elements as they pass through this processing step. The function returns a
I think the answer is in the {Scala, Java}Docs of mapAsync:
Transform this stream by applying the given function to each of the
elements as they pass through this processing step. The function returns a
Future and the value of that future will be emitted downstreams. As many
futures as
You should use mapAsync instead of map.
On 2 Mar 2015 18:03, kermitas kermi...@gmail.com wrote:
Hello,
as an input I have
Source[PageDetails, Unit]
and I also have method which transform PageDetails to
Future[CategoryVector]:
def categorizePage(page: PageDetails): Future[CategoryVector]
Hello,
as an input I have
Source[PageDetails, Unit]
and I also have method which transform PageDetails to Future[CategoryVector]
:
def categorizePage(page: PageDetails): Future[CategoryVector]
By using map method I can change Source[PageDetails, Unit] to Source[Future[
CategoryVector], Unit]:
THANK YOU Luis!! :) That is what I needed.
Let's imagine that I have just one stream run in one JVM. Do you know how
many futures will be evaluated and wait for completion? You know, I can not
have billion of futures in memory :/. Will this be auto-regulated by back
pressure of if not then
