Yes, it should be an improvement to add some timeout/wait mechanism to avoid
such exception as you mentioned.Currently the LAZY_FROM_SOURCES schedule mode
can avoid this issue, but for streaming job it is not suggested to use this
schedule mode.Maybe there are other ways to work around after you find the
reason for your case. Wish your sharing!
cheers,Zhijiang------------------------------------------------------------------发件人：Kamil
Dziublinski <kamil.dziublin...@gmail.com>发送时间：2017年4月5日(星期三)
16:07收件人：Zhijiang(wangzhijiang999) <wangzhijiang...@aliyun.com>抄 送：user
<user@flink.apache.org>主 题：Re: PartitionNotFoundException on deploying
streaming job
Ok thanks I will try to debug it.But my initial thought was that it should be
possible to increase some timeout/wait value to not have it. If it only occurs
during initial start and after restarting works fine.Any idea of such property
in flink?
On Tue, Apr 4, 2017 at 6:03 PM, Zhijiang(wangzhijiang999)
<wangzhijiang...@aliyun.com> wrote:
Hi Kamil,
When the producer receives the PartitionRequest from downstream task,
first it will check whether the requested partition is already registered. If
not, it will reponse PartitionNotFoundException.And the upstream task is
submitted and begins to run, it will registered all its partitions into
ResultPartitionManager. So your case is that the partition request is arrived
before the partition registration.Maybe the upstream task is submitted delay by
JobManager or some logics delay before register task in NetworkEnvironment. You
can debug the specific status in upstream when response the PartitionNotFound
to track the reason. Wish your further findings!
Cheers,Zhijiang
------------------------------------------------------------------发件人：Kamil
Dziublinski <kamil.dziublin...@gmail.com>发送时间：2017年4月4日(星期二) 17:20收件人：user
<user@flink.apache.org>主 题：PartitionNotFoundException on deploying streaming job
Hi guys,
When I run my streaming job I almost always have initially
PartitionNotFoundException. Job fails, after that restarts and it runs ok.I
wonder what is causing that and if I can adjust some parameters to not have
this initial failure.
I have flink session on yarn with 55 task managers. 4 cores and 4gb per TM.This
setup is using 77% of my yarn cluster.
Any ideas?
Thanks,Kamil.
