Hi Gerard, the behaviour you are describing sounds odd to me. I have a couple of questions:
1. Which Flink and Kafka version are you using? 2. How many partitions do you have? --> Try to set the parallelism of your job to the number of partitions. That way, you will have one partition per source task. 3. How are the source operators distributed? Are they running on different nodes? 4. What do you mean with "until it (the blue one) was finished consuming the partition"? I assume that you don't ingest into the Kafka topic live but want to read persisted data. 5. Are you using Flink's metrics to monitor the different source tasks? Check what the source operator's output rate is (should be visible from the web UI). Cheers, Till On Tue, Oct 30, 2018 at 10:27 AM Gerard Garcia <ger...@talaia.io> wrote: > I think my problem is not the same, yours is that you want to consume from > partitions with more data faster to avoid consuming first the one with less > elements which could advance the event time too fast. Mine is that Kafka > only consumes from some partitions even if it seems that it has resources > to read and process from all of them at the same time. > > Gerard > > On Tue, Oct 30, 2018 at 9:36 AM bupt_ljy <bupt_...@163.com> wrote: > >> Hi, >> >> If I understand your problem correctly, there is a similar JIRA >> issue FLINK-10348, reported by me. Maybe you can take a look at it. >> >> >> Jiayi Liao,Best >> >> Original Message >> *Sender:* Gerard Garcia<ger...@talaia.io> >> *Recipient:* fearsome.lucidity<fearsome.lucid...@gmail.com> >> *Cc:* user<user@flink.apache.org> >> *Date:* Monday, Oct 29, 2018 17:50 >> *Subject:* Re: Unbalanced Kafka consumer consumption >> >> The stream is partitioned by key after ingestion at the finest >> granularity that we can (which is finer than how stream is partitioned when >> produced to kafka). It is not perfectly balanced but still is not so >> unbalanced to show this behavior (more balanced than what the lag images >> show). >> >> Anyway, let's assume that the problem is that the stream is so unbalanced >> that one operator subtask can't handle the ingestion rate. It is expected >> then that all the others operators reduce its ingestion rate even if they >> have resources to spare? The task is configured with processing time and >> there are no windows. If that is the case, is there a way to let operator >> subtasks process freely even if one of them is causing back pressure >> upstream? >> >> The attached images shows how Kafka lag increases while the throughput is >> stable until some operator subtasks finish. >> >> Thanks, >> >> Gerard >> >> On Fri, Oct 26, 2018 at 8:09 PM Elias Levy <fearsome.lucid...@gmail.com> >> wrote: >> >>> You can always shuffle the stream generated by the Kafka source >>> (dataStream.shuffle()) to evenly distribute records downstream. >>> >>> On Fri, Oct 26, 2018 at 2:08 AM gerardg <ger...@talaia.io> wrote: >>> >>>> Hi, >>>> >>>> We are experience issues scaling our Flink application and we have >>>> observed >>>> that it may be because Kafka messages consumption is not balanced across >>>> partitions. The attached image (lag per partition) shows how only one >>>> partition consumes messages (the blue one in the back) and it wasn't >>>> until >>>> it finished that the other ones started to consume at a good rate >>>> (actually >>>> the total throughput multiplied by 4 when these started) . Also, when >>>> that >>>> ones started to consume, one partition just stopped an accumulated >>>> messages >>>> back again until they finished. >>>> >>>> We don't see any resource (CPU, network, disk..) struggling in our >>>> cluster >>>> so we are not sure what could be causing this behavior. I can only >>>> assume >>>> that somehow Flink or the Kafka consumer is artificially slowing down >>>> the >>>> other partitions. Maybe due to how back pressure is handled? >>>> >>>> < >>>> http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/file/t1007/consumer_max_lag.png> >>>> >>>> >>>> Gerard >>>> >>>> >>>> >>>> >>>> >>>> -- >>>> Sent from: >>>> http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/ >>>> >>>
