Hi Christophe,
Have you looked at Kite SDK? We do something like this but using Gobblin and
Kite SDK, which is a parallel pipeline to Flink. It feels like if you partition
by something logical like topic name, you should be able to sink using Kite
SDK. Kite allows you good ways to handle further partitoning like using
timestamp and also schema evolution if you are using AVRO.
-- Ashish
On Mon, Mar 26, 2018 at 4:57 AM, Timo Walther<twal...@apache.org> wrote:
Hi Christophe,
I think this will require more effort. As far as I know there is no such
"dynamic" feature. Have you looked in to the bucketing sink code? Maybe you can
adapt it to your needs?
Otherwise it might also make sense to open an issue for it to discuss a design
for it. Maybe other contributors are interested in this feature as well.
Regards,
Timo
Am 23.03.18 um 18:20 schrieb Christophe Jolif:
Hi all,
I'm using the nice topic pattern feature on the KafkaConsumer to read from
multiple topics, automatically discovering new topics added into the system.
At the end of the processing I'm sinking the result into a Hadoop Filesystem
using a BucketingSink.
All works great until I get the requirement to sink into a different Hadoop
Filesystem based on the input topic.
One way to do this would obviously be to get rid of the topic pattern and
start a (similar) job per topic which would each get its own sink to its own
filesystem. And start new jobs when new topics are added. But that's far from
being ideal. This would lead to the usual issues with Flink and a dynamic
number of jobs (requiring new task slots...) also obviously it would require
some external machinery to know new topics have been added and create new jobs
etc...
What would be the recommended way to have a "dynamic" BucketingSink that can
not only write to several basePath (not too hard I guess) but also dynamically
add new base path when new topics are coming into the system.
Thanks, --
Christophe
