One thing: do note that `FlinkKafkaConsumer#setStartFromLatest()` does not have any effect when starting from savepoints. i.e., the consumer will still start from whatever offset is written in the savepoint.
On 15 July 2017 at 12:38:10 AM, Tzu-Li (Gordon) Tai (tzuli...@apache.org) wrote: Can you try starting from the savepoint, but telling Kafka to start from the latest offset? (@gordon: Is that possible in Flink 1.3.1 or only in 1.4-SNAPSHOT ?) This is already possible in Flink 1.3.x. `FlinkKafkaConsumer#setStartFromLatest()` would be it. On 15 July 2017 at 12:33:53 AM, Stephan Ewen (se...@apache.org) wrote: Can you try starting from the savepoint, but telling Kafka to start from the latest offset? (@gordon: Is that possible in Flink 1.3.1 or only in 1.4-SNAPSHOT ?) On Fri, Jul 14, 2017 at 11:18 AM, Kien Truong <duckientru...@gmail.com> wrote: Hi, Sorry for the version typo, I'm running 1.3.1. I did not test with 1.2.x. The jobs runs fine with almost 0 back-pressure if it's started from scratch or if I reuse the kafka consumers group id without specifying the safe point. Best regards, Kien On Jul 14, 2017, at 15:59, Stephan Ewen <se...@apache.org> wrote: Hi! Flink 1.3.2 does not yet exist. Do you mean 1.3.1 or latest master? Can you tell us whether this occurs only in 1.3.x and worked well in 1.2.x? If you just keep the job running without savepoint/restore, you do not get into backpressure situations? Thanks, Stephan On Fri, Jul 14, 2017 at 1:15 AM, Kien Truong <duckientru...@gmail.com> wrote: Hi Fabian, This happens to me even when the restore is immediate, so there's not much data in Kafka to catch up (5 minutes max) Regards Kien On Jul 13, 2017, at 23:40, Fabian Hueske < fhue...@gmail.com> wrote: I would guess that this is quite usual because the job has to "catch-up" work. For example, if you took a save point two days ago and restore the job today, the input data of the last two days has been written to Kafka (assuming Kafka as source) and needs to be processed. The job will now read as fast as possible from Kafka to catch-up to the presence. This means the data is much fast ingested (as fast as Kafka can read and ship it) than during regular processing (as fast as your sources produce). The processing speed is bound by your Flink job which means there will be backpressure. Once the job caught-up, the backpressure should disappear. Best, Fabian 2017-07-13 15:48 GMT+02:00 Kien Truong <duckientru...@gmail.com>: Hi all, I have one job where back-pressure is significantly higher after resuming from a save point. Because that job makes heavy use of stateful functions with RocksDBStateBackend , I'm suspecting that this is the cause of performance degradation. Does anyone encounter simillar issues or have any tips for debugging ? I'm using Flink 1.3.2 with YARN in detached mode. Regards, Kien
