Re: checkpoint stuck with rocksdb statebackend and s3 filesystem

Posted by Tony Wei on
URL: http://deprecated-apache-flink-user-mailing-list-archive.369.s1.nabble.com/checkpoint-stuck-with-rocksdb-statebackend-and-s3-filesystem-tp18679p18703.html

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 <[hidden email]>:

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 <[hidden email]>:

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 <[hidden email]>:

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 <[hidden email]>:
>
> 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_log.log><good_tm_pic.png><good_tm_stack.log>