Hi, We are interested on this too. So far we flag the records with timestamps in different points of the pipeline and use metrics gauges to measure latency between the different components, but would be good to know if there is something more specific to Kafka that we can do out of the box in Flink.
Cheers, Bruno On Fri, 17 Mar 2017 at 10:07 Florian König <florian.koe...@micardo.com> wrote: > Hi, > > thank you Gyula for posting that question. I’d also be interested in how > this could be done. > > You mentioned the dependency on the commit frequency. I’m using > https://github.com/quantifind/KafkaOffsetMonitor. With the 08 Kafka > consumer a job's offsets as shown in the diagrams updated a lot more > regularly than the checkpointing interval. With the 10 consumer a commit is > only made after a successful checkpoint (or so it seems). > > Why is that so? The checkpoint contains the Kafka offset and would be able > to start reading wherever it left off, regardless of any offset stored in > Kafka or Zookeeper. Why is the offset not committed regularly, > independently from the checkpointing? Or did I misconfigure anything? > > Thanks > Florian > > > Am 17.03.2017 um 10:26 schrieb Gyula Fóra <gyf...@apache.org>: > > > > Hi All, > > > > I am wondering if anyone has some nice suggestions on what would be the > simplest/best way of telling if a job is caught up with the Kafka input. > > An alternative question would be how to tell if a job is caught up to > another job reading from the same topic. > > > > The first thing that comes to my mind is looking at the offsets Flink > commits to Kafka. However this will only work if every job uses a different > group id and even then it is not very reliable depending on the commit > frequency. > > > > The use case I am trying to solve is fault tolerant update of a job, by > taking a savepoint for job1 starting job2 from the savepoint, waiting until > it catches up and then killing job1. > > > > Thanks for your input! > > Gyula > > >
