Vishnu,
I would imagine based on Max's explanation and how other systems like MapReduce
and Spark partition keys, if you have 100 keys and 50 slots, 2 keys would be
assigned to each slot. Each slot would maintain one or more windows (more for
time based windows) and each window would have upto
Flink operates in conjunction with sources and sinks. So ,yes, there are
things that an underlying sink (or a source) must support in conjunction
with Flink to enable a particular semantic.
On Jul 30, 2016 11:46 AM, "M Singh" wrote:
> Thanks Konstantin.
>
> Just to clarify - unless the target
Thanks Konstantin.
Just to clarify - unless the target database is resilient to duplicates,
Flink's once-only configuration will not avoid duplicate updates.
Mans
On Saturday, July 30, 2016 7:40 AM, Konstantin Knauf
wrote:
Hi Mans,
depending on the number of operations and the particu
Hi again,
I implemented the scenario with the combiner and answered my 3rd question by
myself:
The combiners start after the mapper finished, so the reducer will not start
processing partial results until the mappers are completely done.
Regards
Robert
Von: Paschek, Robert [mailto:robert.pasc
Hi Mans,
depending on the number of operations and the particular database, you
might be able to use transactions.
Maybe you can also find a data model, which is more resilient to these
kind of failures.
Cheers,
Konstantin
On 29.07.2016 19:26, M Singh wrote:
> Hi:
>
> I have a use case where
Hi Mailing List,
according to my questions (and your answers!) at this topic
http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/Performance-issues-with-GroupBy-td8130.html
I have eliminated my ArrayList in my collect methods. Additional I want to
emit partial results. My mapper
