Hey Jins,
our current back pressure tracking mechanism does not work with Kafka
sources. To gather back pressure indicators we sample the main task
thread of a subtask. For most tasks, this is the thread that emits
records downstream (e.g. if you have a map function) and everything
works as
I have a Beam Pipeline consuming records from Kafka doing some
transformations and writing it to Hbase. I faced an issue in which
records were writing to Hbase at a slower rate than the incoming
messages to Kafka due to a temporary surge in the incoming traffic.
From the flink UI, if I check
Hi, i am exploring the possibility to use Flink as the main framework to
drive a (highly reactive) application. I started out with Akka and RxJava,
but it seemed to me that Flink in general supports the same features, but
is more robust and has more potential. I would also get an excellent
runtime
