Hi Sihua, You are right. The incremental checkpoint might release machine from high cpu loading and make the bad machines recover quickly, but I was wondering why the first checkpoint failed by timeout. You can see when the bad machine recovered, the cpu loading for each checkpoint is not so high, although there were still peeks in each checkpoint happened. I think the high cpu loading that might be caused by those timeout checkpointing threads is not the root cause. I will use the incremental checkpoint eventually but I will decide if change my persistence filesystem after we find out the root cause or stop the investigation and make the conclusion in this mailing thread. What do you think?
Best Regards, Tony Wei 2018-03-06 15:13 GMT+08:00 周思华 <summerle...@163.com>: > Hi Tony, > > Sorry for missing the factor of cpu, I found that the "bad tm"'s cpu load > is so much higher that the 'good tm', so I think maybe it also a reason > that could lead to timeout. Since you were using "full checkpoint", it need > to iterate all the records in the RocksDB with some `if` check, when the > state is huge this is cpu costly. Let me try to explain the full checkpoint > a bit more, it contains two parts. > > Part 1. Take snapshot of the RocksDB. (This can map to the "Checkpoint > Duration (sync) " on the checkpoint detail page) > > Part2. Loop the records of the snapshot, along with some `if` check to ensure > that the data is sent to s3 in the order of the key group. (This can map to > the "Checkpoint Duration(Async)"). > > So part2 could be cpu costly and network costly, if the CPU load is too > high, then sending data will slow down, because there are in a single loop. > If cpu is the reason, this phenomenon will disappear if you use increment > checkpoint, because it almost only send data to s3. In the all, for now > trying out the incremental checkpoint is the best thing to do I think. > > Best Regards, > Sihua Zhou > > > 发自网易邮箱大师 > > On 03/6/2018 14:45，Tony Wei<tony19920...@gmail.com> > <tony19920...@gmail.com> wrote： > > Sent to the wrong mailing list. Forward it to the correct one. > > ---------- Forwarded message ---------- > From: Tony Wei <tony19920...@gmail.com> > Date: 2018-03-06 14:43 GMT+08:00 > Subject: Re: checkpoint stuck with rocksdb statebackend and s3 filesystem > To: 周思华 <summerle...@163.com>, Stefan Richter <s.rich...@data-artisans.com > > > Cc: "user-subscr...@flink.apache.org" <user-subscr...@flink.apache.org> > > > Hi Sihua, > > Thanks a lot. I will try to find out the problem from machines' > environment. If you and Stefan have any new suggestions or thoughts, please > advise me. Thank you ! > > Best Regards, > Tony Wei > > 2018-03-06 14:34 GMT+08:00 周思华 <summerle...@163.com>: > >> Hi Tony, >> >> I think the two things you mentioned can both lead to a bad network. But >> from my side, I think it more likely that it is the unstable network env >> that cause the poor network performance itself, because as far as I know >> I can't found out the reason that the flink would slow down the network so >> much (even It does, the effect should not be that so much). >> >> Maybe stefan could tell more about that. ;) >> >> Best Regards, >> Sihua Zhou >> >> 发自网易邮箱大师 >> >> On 03/6/2018 14:04，Tony Wei<tony19920...@gmail.com> >> <tony19920...@gmail.com> wrote： >> >> Hi Sihua, >> >> >>> Hi Tony, >>> >>> About to your question: average end to end latency of checkpoint is less >>> than 1.5 mins, doesn't means that checkpoint won't timeout. indeed, it >>> determined byt the max end to end latency (the slowest one), a checkpoint >>> truly completed only after all task's checkpoint have completed. >>> >> >> Sorry for my poor expression. What I mean is the average duration of >> "completed" checkpoints, so I guess there are some problems that make some >> subtasks of checkpoint take so long, even more than 10 mins. >> >> >>> >>> About to the problem: after a second look at the info you privode, we >>> can found from the checkpoint detail picture that there is one task which >>> cost 4m20s to transfer it snapshot (about 482M) to s3 and there are 4 >>> others tasks didn't complete the checkpoint yet. And from the >>> bad_tm_pic.png vs good_tm_pic.png, we can found that on "bad tm" the >>> network performance is far less than the "good tm" (-15 MB vs -50MB). So I >>> guss the network is a problem, sometimes it failed to send 500M data to s3 >>> in 10 minutes. (maybe you need to check whether the network env is stable) >>> >> >> That is what I concerned. Because I can't determine if checkpoint is >> stuck makes network performance worse or network performance got worse >> makes checkpoint stuck. >> Although I provided one "bad machine" and one "good machine", that >> doesn't mean bad machine is always bad and good machine is always good. See >> the attachments. >> All of my TMs met this problem at least once from last weekend until now. >> Some machines recovered by themselves and some recovered after I restarted >> them. >> >> Best Regards, >> Tony Wei >> >> 2018-03-06 13:41 GMT+08:00 周思华 <summerle...@163.com>: >> >>> >>> Hi Tony, >>> >>> About to your question: average end to end latency of checkpoint is less >>> than 1.5 mins, doesn't means that checkpoint won't timeout. indeed, it >>> determined byt the max end to end latency (the slowest one), a checkpoint >>> truly completed only after all task's checkpoint have completed. >>> >>> About to the problem: after a second look at the info you privode, we >>> can found from the checkpoint detail picture that there is one task which >>> cost 4m20s to transfer it snapshot (about 482M) to s3 and there are 4 >>> others tasks didn't complete the checkpoint yet. And from the >>> bad_tm_pic.png vs good_tm_pic.png, we can found that on "bad tm" the >>> network performance is far less than the "good tm" (-15 MB vs -50MB). So I >>> guss the network is a problem, sometimes it failed to send 500M data to s3 >>> in 10 minutes. (maybe you need to check whether the network env is stable) >>> >>> About the solution: I think incremental checkpoint can help you a lot, >>> it will only send the new data each checkpoint, but you are right if the >>> increment state size is huger than 500M, it might cause the timeout problem >>> again (because of the bad network performance). >>> >>> Best Regards, >>> Sihua Zhou >>> >>> 发自网易邮箱大师 >>> >>> On 03/6/2018 13:02，Tony Wei<tony19920...@gmail.com> >>> <tony19920...@gmail.com> wrote： >>> >>> Hi Sihua, >>> >>> Thanks for your suggestion. "incremental checkpoint" is what I will try >>> out next and I know it will give a better performance. However, it might >>> not solve this issue completely, because as I said, the average end to end >>> latency of checkpointing is less than 1.5 mins currently, and it is far >>> from my timeout configuration. I believe "incremental checkpoint" will >>> reduce the latency and make this issue might occur seldom, but I can't >>> promise it won't happen again if I have bigger states growth in the future. >>> Am I right? >>> >>> Best Regards, >>> Tony Wei >>> >>> 2018-03-06 10:55 GMT+08:00 周思华 <summerle...@163.com>: >>> >>>> Hi Tony, >>>> >>>> Sorry for jump into, one thing I want to remind is that from the log >>>> you provided it looks like you are using "full checkpoint", this means that >>>> the state data that need to be checkpointed and transvered to s3 will grow >>>> over time, and even for the first checkpoint it performance is slower that >>>> incremental checkpoint (because it need to iterate all the record from the >>>> rocksdb using the RocksDBMergeIterator). Maybe you can try out "incremental >>>> checkpoint", it could help you got a better performance. >>>> >>>> Best Regards, >>>> Sihua Zhou >>>> >>>> 发自网易邮箱大师 >>>> >>>> On 03/6/2018 10:34，Tony Wei<tony19920...@gmail.com> >>>> <tony19920...@gmail.com> wrote： >>>> >>>> Hi Stefan, >>>> >>>> I see. That explains why the loading of machines grew up. However, I >>>> think it is not the root cause that led to these consecutive checkpoint >>>> timeout. As I said in my first mail, the checkpointing progress usually >>>> took 1.5 mins to upload states, and this operator and kafka consumer are >>>> only two operators that have states in my pipeline. In the best case, I >>>> should never encounter the timeout problem that only caused by lots of >>>> pending checkpointing threads that have already timed out. Am I right? >>>> >>>> Since these logging and stack trace was taken after nearly 3 hours from >>>> the first checkpoint timeout, I'm afraid that we couldn't actually find out >>>> the root cause for the first checkpoint timeout. Because we are >>>> preparing to make this pipeline go on production, I was wondering if you >>>> could help me find out where the root cause happened: bad machines or s3 or >>>> flink-s3-presto packages or flink checkpointing thread. It will be great if >>>> we can find it out from those informations the I provided, or a >>>> hypothesis based on your experience is welcome as well. The most important >>>> thing is that I have to decide whether I need to change my persistence >>>> filesystem or use another s3 filesystem package, because it is the last >>>> thing I want to see that the checkpoint timeout happened very often. >>>> >>>> Thank you very much for all your advices. >>>> >>>> Best Regards, >>>> Tony Wei >>>> >>>> 2018-03-06 1:07 GMT+08:00 Stefan Richter <s.rich...@data-artisans.com>: >>>> >>>>> Hi, >>>>> >>>>> thanks for all the info. I had a look into the problem and opened >>>>> https://issues.apache.org/jira/browse/FLINK-8871 to fix this. From >>>>> your stack trace, you can see many checkpointing threads are running on >>>>> your TM for checkpoints that have already timed out, and I think this >>>>> cascades and slows down everything. Seems like the implementation of some >>>>> features like checkpoint timeouts and not failing tasks from checkpointing >>>>> problems overlooked that we also require to properly communicate that >>>>> checkpoint cancellation to all task, which was not needed before. >>>>> >>>>> Best, >>>>> Stefan >>>>> >>>>> >>>>> Am 05.03.2018 um 14:42 schrieb Tony Wei <tony19920...@gmail.com>: >>>>> >>>>> Hi Stefan, >>>>> >>>>> Here is my checkpointing configuration. >>>>> >>>>> Checkpointing Mode Exactly Once >>>>> Interval 20m 0s >>>>> Timeout 10m 0s >>>>> Minimum Pause Between Checkpoints 0ms >>>>> Maximum Concurrent Checkpoints 1 >>>>> Persist Checkpoints Externally Enabled (delete on cancellation) >>>>> Best Regards, >>>>> Tony Wei >>>>> >>>>> 2018-03-05 21:30 GMT+08:00 Stefan Richter <s.rich...@data-artisans.com >>>>> >: >>>>> >>>>>> Hi, >>>>>> >>>>>> quick question: what is your exact checkpointing configuration? In >>>>>> particular, what is your value for the maximum parallel checkpoints and >>>>>> the >>>>>> minimum time interval to wait between two checkpoints? >>>>>> >>>>>> Best, >>>>>> Stefan >>>>>> >>>>>> > Am 05.03.2018 um 06:34 schrieb Tony Wei <tony19920...@gmail.com>: >>>>>> > >>>>>> > Hi all, >>>>>> > >>>>>> > Last weekend, my flink job's checkpoint start failing because of >>>>>> timeout. I have no idea what happened, but I collect some informations >>>>>> about my cluster and job. Hope someone can give me advices or hints about >>>>>> the problem that I encountered. >>>>>> > >>>>>> > My cluster version is flink-release-1.4.0. Cluster has 10 TMs, each >>>>>> has 4 cores. These machines are ec2 spot instances. The job's parallelism >>>>>> is set as 32, using rocksdb as state backend and s3 presto as checkpoint >>>>>> file system. >>>>>> > The state's size is nearly 15gb and still grows day-by-day. >>>>>> Normally, It takes 1.5 mins to finish the whole checkpoint process. The >>>>>> timeout configuration is set as 10 mins. >>>>>> > >>>>>> > <chk_snapshot.png> >>>>>> > >>>>>> > As the picture shows, not each subtask of checkpoint broke caused >>>>>> by timeout, but each machine has ever broken for all its subtasks during >>>>>> last weekend. Some machines recovered by themselves and some machines >>>>>> recovered after I restarted them. >>>>>> > >>>>>> > I record logs, stack trace and snapshot for machine's status (CPU, >>>>>> IO, Network, etc.) for both good and bad machine. If there is a need for >>>>>> more informations, please let me know. Thanks in advance. >>>>>> > >>>>>> > Best Regards, >>>>>> > Tony Wei. >>>>>> > <bad_tm_log.log><bad_tm_pic.png><bad_tm_stack.log><good_tm_l >>>>>> og.log><good_tm_pic.png><good_tm_stack.log> >>>>>> >>>>>> >>>>> >>>>> >>>> >>> >> > >
