Thank you Biao and Nico for the inputs and clarification. Good to know
that setDefaultLocalParallelism()
will not have any impact on cluster deployment and can be used to solve the
problem on local. I will try it out.
Thanks,
Vinayak
On Thu, Aug 1, 2019, 2:22 PM Nico Kruber wrote:
> Hi Vinayak,
Hi Vinayak,
If `StreamExecutionEnvironment env =
StreamExecutionEnvironment.createLocalEnvironment(2)` works for your case,
you could try as below.
`StreamExecutionEnvironment.setDefaultLocalParallelism(2);`
`StreamExecutionEnvironment env =
StreamExecutionEnvironment.getExecutionEnvironment();`
Hi Andrey and Jeff,
Thank you for the reply.
I agree with Jeff. My concern is to use different code for local and
non-local deployments.
It would help if StreamExecutionEnvironment.getExecutionEnvironment() works
for both local and cluster deployments.
Thanks & Regards,
Vinayak
On Wed,
@Andrey,
Although your approach will work, it requires the user to write different
code for local mode and other modes. This is inconvenient for users.
IMHO, we should not check these kinds of memory configuration in local
mode. Or implicitly set the memory of TM pretty large in local mode to
avoi
Hi Vinayak,
the error message provides a hint about changing config options, you could
try to use StreamExecutionEnvironment.createLocalEnvironment(2,
customConfig); to increase resources.
this issue might also address the problem, it will be part of 1.9 release:
https://issues.apache.org/jira/bro
Hi,
I am using Flink version: 1.7.1
I have a flink job that gets the execution environment as below and
executes the job.
StreamExecutionEnvironment env =
StreamExecutionEnvironment.getExecutionEnvironment();
When I run the code in cluster, it runs fine. But on local machine while
running the j
