I think there is a bug in how processing-time timers work. For event-time, we fire timers when the watermark is >= the timestamp, this is correct because a watermark T says that we will not see elements with a timestamp smaller or equal to T. For processing time, a time of T does not say that we won't see an element with timestamp T. Therefore the triggering behaviour is wrong for processing time. I created a Jira issue for this: https://issues.apache.org/jira/browse/FLINK-9857
Best, Aljoscha > On 16. Jul 2018, at 07:36, Yuan,Youjun <yuanyou...@baidu.com> wrote: > > Hi Hequn, > > To my understand, a processing time window is fired at the last millisecond > of the window(maxTimestamp). Then what will happen if more elements arrive at > the last millisecond, but AFTER the window is fired? > > Thanks, > Youjun > 发件人: Hequn Cheng <chenghe...@gmail.com> > 发送时间: Friday, July 13, 2018 9:44 PM > 收件人: Yuan,Youjun <yuanyou...@baidu.com> > 抄送: Timo Walther <twal...@apache.org>; user@flink.apache.org > 主题: Re: 答复: 答复: TumblingProcessingTimeWindow emits extra results for a same > window > > Hi Youjun, > > The rowtime value in udf:EXTRACT(EPOCH FROM rowtime) is different from the > rowtime value of window. Sql will be parsed and translated into some nodes, > Source -> Calc -> Window -> Sink. The Calc is the input node of Window and > the udf is part of Calc instead of Window. So the max_ts and min_ts is > actually the time before entering the window, i.e, not the time in window. > > However, I still can't find anything valuable to solve the problem. It seems > the window has been triggered many times for the same key. I will think more > about it. > > Best, Hequn. > > On Fri, Jul 13, 2018 at 11:53 AM, Yuan,Youjun <yuanyou...@baidu.com > <mailto:yuanyou...@baidu.com>> wrote: > Hi Hequn, > > I am using Flink 1.4. The job was running with 1 parallelism. > > I don’t think the extra records are caused by different keys, because: > I ran 2 jobs consuming the same source, jobA with 2-minute window, and job > with 4-minute window. If there are wired keys, then jobA will get no more > records than jobB, for the same period. But that not true, jobA got 17 > records while jobB got 11. Relevant results could be found below. > For each window, I output the min and max timestamp, and found that those > extra records always start at the last few milliseconds of the 2 or 4-minte > windows, just before window got closed. I also noticed the windows did not > have a clear cut between minutes, as we can see in jobA’s output, ts > 1531448399978 appears in 18 result records, either as start, or end, or both. > > jobA(2-minute window) output > {"timestamp":1531448040000,"cnt":1668052,"userId":"user01","min_ts":1531448040003,"max_ts":1531448159985} > {"timestamp":1531448160000,"cnt":1613188,"userId":"user01","min_ts":1531448159985,"max_ts":1531448279979} > {"timestamp":1531448280000,"cnt":1664652,"userId":"user01","min_ts":1531448280004,"max_ts":1531448399978} > {"timestamp":1531448280000,"cnt":3,"userId":"user01","min_ts":1531448399978,"max_ts":1531448399978} > {"timestamp":1531448280000,"cnt":2,"userId":"user01","min_ts":1531448399978,"max_ts":1531448399978} > {"timestamp":1531448280000,"cnt":4,"userId":"user01","min_ts":1531448399978,"max_ts":1531448399978} > {"timestamp":1531448280000,"cnt":3,"userId":"user01","min_ts":1531448399978,"max_ts":1531448399978} > {"timestamp":1531448280000,"cnt":2,"userId":"user01","min_ts":1531448399978,"max_ts":1531448399978} > {"timestamp":1531448280000,"cnt":2,"userId":"user01","min_ts":1531448399978,"max_ts":1531448399978} > {"timestamp":1531448280000,"cnt":2,"userId":"user01","min_ts":1531448399978,"max_ts":1531448399978} > {"timestamp":1531448280000,"cnt":3,"userId":"user01","min_ts":1531448399978,"max_ts":1531448399978} > {"timestamp":1531448280000,"cnt":2,"userId":"user01","min_ts":1531448399978,"max_ts":1531448399978} > {"timestamp":1531448280000,"cnt":2,"userId":"user01","min_ts":1531448399978,"max_ts":1531448399978} > {"timestamp":1531448280000,"cnt":3,"userId":"user01","min_ts":1531448399978,"max_ts":1531448399978} > {"timestamp":1531448280000,"cnt":3,"userId":"user01","min_ts":1531448399978,"max_ts":1531448399978} > {"timestamp":1531448280000,"cnt":3,"userId":"user01","min_ts":1531448399978,"max_ts":1531448399978} > {"timestamp":1531448280000,"cnt":2,"userId":"user01","min_ts":1531448399978,"max_ts":1531448399978} > {"timestamp":1531448280000,"cnt":3,"userId":"user01","min_ts":1531448399978,"max_ts":1531448399978} > {"timestamp":1531448280000,"cnt":3,"userId":"user01","min_ts":1531448399978,"max_ts":1531448399978} > {"timestamp":1531448400000,"cnt":1593435,"userId":"user01","min_ts":1531448399978,"max_ts":1531448519978} > > jobB(4-minute window) output > {"timestamp":1531447920000,"cnt":3306838,"userId":"user01","min_ts":1531447919981,"max_ts":1531448159975} > {"timestamp":1531448160000,"cnt":3278178,"userId":"user01","min_ts":1531448159098,"max_ts":1531448399977} > {"timestamp":1531448160000,"cnt":4,"userId":"user01","min_ts":1531448399977,"max_ts":1531448399977} > {"timestamp":1531448160000,"cnt":5,"userId":"user01","min_ts":1531448399977,"max_ts":1531448399977} > {"timestamp":1531448160000,"cnt":8,"userId":"user01","min_ts":1531448399977,"max_ts":1531448399978} > {"timestamp":1531448160000,"cnt":7,"userId":"user01","min_ts":1531448399978,"max_ts":1531448399978} > {"timestamp":1531448160000,"cnt":2,"userId":"user01","min_ts":1531448399978,"max_ts":1531448399978} > {"timestamp":1531448160000,"cnt":3,"userId":"user01","min_ts":1531448399978,"max_ts":1531448399978} > {"timestamp":1531448160000,"cnt":3,"userId":"user01","min_ts":1531448399978,"max_ts":1531448399978} > {"timestamp":1531448160000,"cnt":3,"userId":"user01","min_ts":1531448399978,"max_ts":1531448399978} > {"timestamp":1531448160000,"cnt":3,"userId":"user01","min_ts":1531448399978,"max_ts":1531448399978} > {"timestamp":1531448160000,"cnt":3,"userId":"user01","min_ts":1531448399978,"max_ts":1531448399978} > {"timestamp":1531448400000,"cnt":3226735,"userId":"user01","min_ts":1531448399978,"max_ts":1531448639916} > > Thanks > Youjun > > 发件人: Hequn Cheng <chenghe...@gmail.com <mailto:chenghe...@gmail.com>> > 发送时间: Thursday, July 12, 2018 11:31 PM > 收件人: Yuan,Youjun <yuanyou...@baidu.com <mailto:yuanyou...@baidu.com>> > 抄送: Timo Walther <twal...@apache.org <mailto:twal...@apache.org>>; > user@flink.apache.org <mailto:user@flink.apache.org> > 主题: Re: 答复: TumblingProcessingTimeWindow emits extra results for a same window > > Hi Yuan, > > Haven't heard about this before. Which flink version do you use? The cause > may be: > 1. userId not 100% identical, for example contains invisible characters. > 2. The machine clock vibrated. > > Otherwise, there are some bugs we don't know. > > Best, Hequn > > On Thu, Jul 12, 2018 at 8:00 PM, Yuan,Youjun <yuanyou...@baidu.com > <mailto:yuanyou...@baidu.com>> wrote: > Hi Timo, > > This problem happens 4-5 times a day on our online server, with ~15k events > per second load, and it is using PROCESSING time. So I don’t think I can > stably reproduce the issue on my local machine. > The user ids are actually the same, I have doubled checked that. > > Now, I am wondering could it possible that, after a window fires, some last > events came but that still fall to the time range of the just fired window, > hence new windows are assigned, and fired later. This can explain why the > extra records always contain only a few events (cnt is small). > > To verify that, I just modified the SQL to also output the MIN timestamp of > each windows, and I found the MIN timestamp of theextra records are always > point to the LAST second of the window. > Here is the output I just got, note 1531395119 is the last second of a > 2-minute window start from 1531395000. > -------------------------------------------------------------------------------------------------------------------------------- > {"timestamp":1531394760000,"cnt":1536013,"userId":"user01","min_sec":1531394760} > {"timestamp":1531394880000,"cnt":1459623,"userId":"user01","min_sec":1531394879} > {"timestamp":1531395000000,"cnt":1446010,"userId":"user01","min_sec":1531395000} > {"timestamp":1531395000000,"cnt":7,"userId":"user01","min_sec":1531395119} > {"timestamp":1531395000000,"cnt":3,"userId":"user01","min_sec":1531395119} > {"timestamp":1531395000000,"cnt":3,"userId":"user01","min_sec":1531395119} > {"timestamp":1531395000000,"cnt":6,"userId":"user01","min_sec":1531395119} > {"timestamp":1531395000000,"cnt":3,"userId":"user01","min_sec":1531395119} > {"timestamp":1531395000000,"cnt":2,"userId":"user01","min_sec":1531395119} > {"timestamp":1531395000000,"cnt":2,"userId":"user01","min_sec":1531395119} > {"timestamp":1531395000000,"cnt":2,"userId":"user01","min_sec":1531395119} > > The modified SQL: > INSERT INTO sink > SELECT > TUMBLE_START(rowtime, INTERVAL '2' MINUTE) AS `timestamp`, > count(vehicleId) AS cnt, userId, > MIN(EXTRACT(EPOCH FROM rowtime)) AS min_sec > FROM source > GROUP BY > TUMBLE(rowtime, INTERVAL '2' MINUTE), > userId > > thanks > Youjun > > 发件人: Timo Walther <twal...@apache.org <mailto:twal...@apache.org>> > 发送时间: Thursday, July 12, 2018 5:02 PM > 收件人: user@flink.apache.org <mailto:user@flink.apache.org> > 主题: Re: TumblingProcessingTimeWindow emits extra results for a same window > > Hi Yuan, > > this sounds indeed weird. The SQL API uses regular DataStream API windows > underneath so this problem should have come up earlier if this is problem in > the implementation. Does this behavior reproducible on your local machine? > > One thing that comes to my mind is that the "userId"s might not be 100% > identical (same hashCode/equals method) because otherwise they would be > properly grouped. > > Regards, > Timo > > Am 12.07.18 um 09:35 schrieb Yuan,Youjun: > Hi community, > > I have a job which counts event number every 2 minutes, with TumblingWindow > in ProcessingTime. However, it occasionally produces extra DUPLICATED > records. For instance, for timestamp 1531368480000 below, it emits a normal > result (cnt=1641161), and then followed by a few more records with very small > result (2, 3, etc). > > Can anyone shed some light on the possible reason, or how to fix it? > > Below are the sample output. > ----------------------------------------------------------- > {"timestamp":1531368240000,"cnt":1537821,"userId":"user01"} > {"timestamp":1531368360000,"cnt":1521464,"userId":"user01"} > {"timestamp":1531368480000,"cnt":1641161,"userId":"user01"} > {"timestamp":1531368480000,"cnt":2,"userId":"user01"} > {"timestamp":1531368480000,"cnt":3,"userId":"user01"} > {"timestamp":1531368480000,"cnt":3,"userId":"user01"} > > And here is the job SQL: > ----------------------------------------------------------- > INSERT INTO sink > SELECT > TUMBLE_START(rowtime, INTERVAL '2' MINUTE) AS `timestamp`, > count(vehicleId) AS cnt, > userId > FROM source > GROUP BY TUMBLE(rowtime, INTERVAL '2' MINUTE), > userId > > Thanks, > Youjun Yuan
