Another solution would be setting the parallelism = #partitions, so that one parallelism would be responsible for reading exactly one partition.
Qingsheng > On Apr 13, 2022, at 17:52, Qingsheng Ren <renqs...@gmail.com> wrote: > > Hi Jin, > > Unfortunately I don’t have any quick bypass in mind except increasing the > tolerance of out of orderness. > > Best regards, > > Qingsheng > >> On Apr 8, 2022, at 18:12, Jin Yi <j...@promoted.ai> wrote: >> >> confirmed that moving back to FlinkKafkaConsumer fixes things. >> >> is there some notification channel/medium that highlights critical >> bugs/issues on the intended features like this pretty readily? >> >> On Fri, Apr 8, 2022 at 2:18 AM Jin Yi <j...@promoted.ai> wrote: >> based on symptoms/observations on the first operator (LogRequestFilter) >> watermark and event timestamps, it does seem like it's the bug. things >> track fine (timestamp > watermark) for the first batch of events, then the >> event timestamps go back into the past and are "late". >> >> looks like the 1.14 backport just got in 11 days ago >> (https://github.com/apache/flink/pull/19128). is there a way to easily test >> this fix locally? based on the threads, should i just move back to >> FlinkKafkaConsumer until 1.14.5? >> >> On Fri, Apr 8, 2022 at 1:34 AM Qingsheng Ren <renqs...@gmail.com> wrote: >> Hi Jin, >> >> If you are using new FLIP-27 sources like KafkaSource, per-partition >> watermark (or per-split watermark) is a default feature integrated in >> SourceOperator. You might hit the bug described in FLINK-26018 [1], which >> happens during the first fetch of the source that records in the first split >> pushes the watermark far away, then records from other splits will be >> treated as late events. >> >> [1] https://issues.apache.org/jira/browse/FLINK-26018 >> >> Best regards, >> >> Qingsheng >> >> >>> On Apr 8, 2022, at 15:54, Jin Yi <j...@promoted.ai> wrote: >>> >>> how should the code look like to verify we're using per-partition >>> watermarks if we moved away from FlinkKafkaConsumer to KafkaSource in >>> 1.14.4? >>> >>> we currently have it looking like: >>> >>> streamExecutionEnvironment.fromSource( >>> KafkaSource.<T>builder().....build(), >>> watermarkStrategy, >>> "whatever", >>> typeInfo); >>> >>> when running this job with the streamExecutionEnviornment parallelism set >>> to 1, and the kafka source having 30 partitions, i'm seeing weird behaviors >>> where the first operator after this source consumes events out of order >>> (and therefore, violates watermarks). the operator simply checks to see >>> what "type" of event something is and uses side outputs to output the >>> type-specific messages. here's a snippet of the event timestamp going back >>> before the current watermark (first instance of going backwards in time in >>> bold): >>> >>> 2022-04-08 05:47:06,315 WARN >>> ai.promoted.metrics.logprocessor.common.functions.filter.LogRequestFilter >>> [] - LogRequestFilter ts: 1649284267139 watermark: 1649284187140 >>> 2022-04-08 05:47:06,315 WARN >>> ai.promoted.metrics.logprocessor.common.functions.filter.LogRequestFilter >>> [] - LogRequestFilter ts: 1649284268138 watermark: 1649284187140 >>> 2022-04-08 05:47:06,315 WARN >>> ai.promoted.metrics.logprocessor.common.functions.filter.LogRequestFilter >>> [] - LogRequestFilter ts: 1649284269138 watermark: 1649284187140 >>> 2022-04-08 05:47:06,315 WARN >>> ai.promoted.metrics.logprocessor.common.functions.filter.LogRequestFilter >>> [] - LogRequestFilter ts: 1649284270139 watermark: 1649284187140 >>> 2022-04-08 05:47:06,315 WARN >>> ai.promoted.metrics.logprocessor.common.functions.filter.LogRequestFilter >>> [] - LogRequestFilter ts: 1649284271139 watermark: 1649284187140 >>> 2022-04-08 05:47:06,315 WARN >>> ai.promoted.metrics.logprocessor.common.functions.filter.LogRequestFilter >>> [] - LogRequestFilter ts: 1649284171037 watermark: 1649284187140 >>> 2022-04-08 05:47:06,316 WARN >>> ai.promoted.metrics.logprocessor.common.functions.filter.LogRequestFilter >>> [] - LogRequestFilter ts: 1649284172057 watermark: 1649284187140 >>> 2022-04-08 05:47:06,316 WARN >>> ai.promoted.metrics.logprocessor.common.functions.filter.LogRequestFilter >>> [] - LogRequestFilter ts: 1649284172067 watermark: 1649284187140 >>> 2022-04-08 05:47:06,316 WARN >>> ai.promoted.metrics.logprocessor.common.functions.filter.LogRequestFilter >>> [] - LogRequestFilter ts: 1649284172171 watermark: 1649284187140 >>> 2022-04-08 05:47:06,316 WARN >>> ai.promoted.metrics.logprocessor.common.functions.filter.LogRequestFilter >>> [] - LogRequestFilter ts: 1649284172174 watermark: 1649284187140 >>> 2022-04-08 05:47:06,317 WARN >>> ai.promoted.metrics.logprocessor.common.functions.filter.LogRequestFilter >>> [] - LogRequestFilter ts: 1649284172666 watermark: 1649284187140 >>> >>> >>> >>> On Sat, Mar 19, 2022 at 10:51 AM Dan Hill <quietgol...@gmail.com> wrote: >>> I dove deeper. I wasn't actually using per-partition watermarks. Thank >>> you for the help! >>> >>> On Fri, Mar 18, 2022 at 12:11 PM Dan Hill <quietgol...@gmail.com> wrote: >>> Thanks, Thias and Dongwon. >>> >>> I'll keep debugging this with the idle watermark turned off. >>> >>> Next TODOs: >>> - Verify that we’re using per-partition watermarks. Our code matches the >>> example but maybe something is disabling it. >>> - Enable logging of partition-consumer assignment, to see if that is the >>> cause of the problem. >>> - Look at adding flags to set the source parallelism to see if that fixes >>> the issue. >>> >>> Yes, I've seen Flink talks on creating our own watermarks through Kafka. >>> Sounds like a good idea. >>> >>> On Fri, Mar 18, 2022 at 1:17 AM Dongwon Kim <eastcirc...@gmail.com> wrote: >>> I totally agree with Schwalbe that per-partition watermarking allows # >>> source tasks < # kafka partitions. >>> >>> Otherwise, Dan, you should suspect other possibilities like what Schwalbe >>> said. >>> >>> Best, >>> >>> Dongwon >>> >>> On Fri, Mar 18, 2022 at 5:01 PM Schwalbe Matthias >>> <matthias.schwa...@viseca.ch> wrote: >>> Hi San, Dongwon, >>> >>> >>> >>> I share the opinion that when per-partition watermarking is enabled, you >>> should observe correct behavior … would be interesting to see why it does >>> not work for you. >>> >>> >>> >>> I’d like to clear one tiny misconception here when you write: >>> >>> >>> >>>>> - The same issue happens even if I use an idle watermark. >>> >>> >>> >>> You would expect to see glitches with watermarking when you enable idleness. >>> >>> Idleness sort of trades watermark correctness for reduces latency when >>> processing timers (much simplified). >>> >>> With idleness enabled you have no guaranties whatsoever as to the quality >>> of watermarks (which might be ok in some cases). >>> >>> BTW we dominantly use a mix of fast and slow sources (that only update once >>> a day) which hand-pimped watermarking and late event processing, and >>> enabling idleness would break everything. >>> >>> >>> >>> Oversight put aside things should work the way you implemented it. >>> >>> >>> >>> One thing I could imagine to be a cause is >>> >>> • that over time the kafka partitions get reassigned to different >>> consumer subtasks which would probably stress correct recalculation of >>> watermarks. Hence #partition == number subtask might reduce the problem >>> • can you enable logging of partition-consumer assignment, to see if >>> that is the cause of the problem >>> • also involuntary restarts of the job can cause havoc as this resets >>> watermarking >>> >>> >>> I’ll be off next week, unable to take part in the active discussion … >>> >>> >>> >>> Sincere greetings >>> >>> >>> >>> Thias >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> From: Dan Hill <quietgol...@gmail.com> >>> Sent: Freitag, 18. März 2022 08:23 >>> To: Dongwon Kim <eastcirc...@gmail.com> >>> Cc: user <user@flink.apache.org> >>> Subject: Re: Weird Flink Kafka source watermark behavior >>> >>> >>> >>> ⚠EXTERNAL MESSAGE – CAUTION: Think Before You Click ⚠ >>> >>> >>> >>> I'll try forcing # source tasks = # partitions tomorrow. >>> >>> >>> >>> Thank you, Dongwon, for all of your help! >>> >>> >>> >>> On Fri, Mar 18, 2022 at 12:20 AM Dongwon Kim <eastcirc...@gmail.com> wrote: >>> >>> I believe your job with per-partition watermarking should be working okay >>> even in a backfill scenario. >>> >>> >>> >>> BTW, is the problem still observed even with # sour tasks = # partitions? >>> >>> >>> >>> For committers: >>> >>> Is there a way to confirm that per-partition watermarking is used in TM log? >>> >>> >>> >>> On Fri, Mar 18, 2022 at 4:14 PM Dan Hill <quietgol...@gmail.com> wrote: >>> >>> I hit this using event processing and no idleness detection. The same >>> issue happens if I enable idleness. >>> >>> >>> >>> My code matches the code example for per-partition watermarking. >>> >>> >>> >>> On Fri, Mar 18, 2022 at 12:07 AM Dongwon Kim <eastcirc...@gmail.com> wrote: >>> >>> Hi Dan, >>> >>> >>> >>> I'm quite confused as you already use per-partition watermarking. >>> >>> >>> >>> What I meant in the reply is >>> >>> - If you don't use per-partition watermarking, # tasks < # partitions can >>> cause the problem for backfill jobs. >>> >>> - If you don't use per-partition watermarking, # tasks = # partitions is >>> going to be okay even for backfill jobs. >>> >>> - If you use per-partition watermarking, # tasks < # partitions shouldn't >>> cause any problems unless you turn on the idleness detection. >>> >>> >>> >>> Regarding the idleness detection which is based on processing time, what is >>> your setting? If you set the value to 10 seconds for example, you'll face >>> the same problem unless the watermark of your backfill job catches up >>> real-time within 10 seconds. If you increase the value to 1 minute, your >>> backfill job should catch up real-time within 1 minute. >>> >>> >>> >>> Best, >>> >>> >>> >>> Dongwon >>> >>> >>> >>> >>> >>> On Fri, Mar 18, 2022 at 3:51 PM Dan Hill <quietgol...@gmail.com> wrote: >>> >>> Thanks Dongwon! >>> >>> >>> >>> Wow. Yes, I'm using per-partition watermarking [1]. Yes, my # source >>> tasks < # kafka partitions. This should be called out in the docs or the >>> bug should be fixed. >>> >>> >>> >>> On Thu, Mar 17, 2022 at 10:54 PM Dongwon Kim <eastcirc...@gmail.com> wrote: >>> >>> Hi Dan, >>> >>> >>> >>> Do you use the per-partition watermarking explained in [1]? >>> >>> I've also experienced a similar problem when running backfill jobs >>> specifically when # source tasks < # kafka partitions. >>> >>> - When # source tasks = # kafka partitions, the backfill job works as >>> expected. >>> >>> - When # source tasks < # kafka partitions, a Kafka consumer consumes >>> multiple partitions. This case can destroying the per-partition patterns as >>> explained in [2]. >>> >>> >>> >>> Hope this helps. >>> >>> >>> >>> p.s. If you plan to use the per-partition watermarking, be aware that >>> idleness detection [3] can cause another problem when you run a backfill >>> job. Kafka source tasks in a backfill job seem to read a batch of records >>> from Kafka and then wait for downstream tasks to catch up the progress, >>> which can be counted as idleness. >>> >>> >>> >>> [1] >>> https://nightlies.apache.org/flink/flink-docs-master/docs/dev/datastream/event-time/generating_watermarks/#using-watermark-strategie >>> >>> [2] >>> https://nightlies.apache.org/flink/flink-docs-master/docs/dev/datastream/event-time/generating_watermarks/#watermark-strategies-and-the-kafka-connector >>> >>> [3] >>> https://nightlies.apache.org/flink/flink-docs-master/docs/dev/datastream/event-time/generating_watermarks/#dealing-with-idle-sources >>> >>> >>> >>> Best, >>> >>> >>> >>> Dongwon >>> >>> >>> >>> On Fri, Mar 18, 2022 at 2:35 PM Dan Hill <quietgol...@gmail.com> wrote: >>> >>> I'm following the example from this section: >>> >>> https://nightlies.apache.org/flink/flink-docs-master/docs/dev/datastream/event-time/generating_watermarks/#watermark-strategies-and-the-kafka-connector >>> >>> >>> >>> On Thu, Mar 17, 2022 at 10:26 PM Dan Hill <quietgol...@gmail.com> wrote: >>> >>> Other points >>> >>> - I'm using the kafka timestamp as event time. >>> >>> - The same issue happens even if I use an idle watermark. >>> >>> >>> >>> On Thu, Mar 17, 2022 at 10:17 PM Dan Hill <quietgol...@gmail.com> wrote: >>> >>> There are 12 Kafka partitions (to keep the structure similar to other low >>> traffic environments). >>> >>> >>> >>> On Thu, Mar 17, 2022 at 10:13 PM Dan Hill <quietgol...@gmail.com> wrote: >>> >>> Hi. >>> >>> >>> >>> I'm running a backfill from a kafka topic with very few records spread >>> across a few days. I'm seeing a case where the records coming from a kafka >>> source have a watermark that's more recent (by hours) than the event time. >>> I haven't seen this before when running this. This violates what I'd >>> assume the kafka source would do. >>> >>> >>> >>> Example problem: >>> >>> 1. I have kafka records at ts=1000, 2000, ... 500000. The actual times are >>> separated by a longer time period. >>> >>> 2. My first operator after the FlinkKafkaConsumer sees: >>> >>> context.timestamp() = 1000 >>> >>> context.timerService().currentWatermark() = 500000 >>> >>> >>> >>> Details about how I'm running this: >>> >>> - I'm on Flink 1.12.3 that's running on EKS and using MSK as the source. >>> >>> - I'm using FlinkKafkaConsumer >>> >>> - I'm using WatermarkStrategy.forBoundedOutOfOrderness(5s). No idleness >>> settings. >>> >>> - I'm running similar code in all the environments. The main difference is >>> low traffic. I have not been able to reproduce this out of the environment. >>> >>> >>> >>> >>> >>> I put the following process function right after my kafka source. >>> >>> >>> >>> -------- >>> >>> >>> AfterSource >>> >>> ts=1647274892728 >>> watermark=1647575140007 >>> record=... >>> >>> >>> >>> >>> public static class TextLog extends ProcessFunction<Record, Record> { >>> private final String label; >>> public TextLogDeliveryLog(String label) { >>> this.label = label; >>> } >>> @Override >>> public void processElement(Record record, Context context, >>> Collector<Record> collector) throws Exception { >>> LOGGER.info("{}\nts={}\nwatermark={}\nrecord={}", >>> label, context.timestamp(), >>> context.timerService().currentWatermark(), record); >>> collector.collect(deliveryLog); >>> } >>> } >>> >>> Diese Nachricht ist ausschliesslich für den Adressaten bestimmt und >>> beinhaltet unter Umständen vertrauliche Mitteilungen. Da die >>> Vertraulichkeit von e-Mail-Nachrichten nicht gewährleistet werden kann, >>> übernehmen wir keine Haftung für die Gewährung der Vertraulichkeit und >>> Unversehrtheit dieser Mitteilung. Bei irrtümlicher Zustellung bitten wir >>> Sie um Benachrichtigung per e-Mail und um Löschung dieser Nachricht sowie >>> eventueller Anhänge. Jegliche unberechtigte Verwendung oder Verbreitung >>> dieser Informationen ist streng verboten. >>> >>> This message is intended only for the named recipient and may contain >>> confidential or privileged information. As the confidentiality of email >>> communication cannot be guaranteed, we do not accept any responsibility for >>> the confidentiality and the intactness of this message. If you have >>> received it in error, please advise the sender by return e-mail and delete >>> this message and any attachments. Any unauthorised use or dissemination of >>> this information is strictly prohibited. >> >
