Hello.
I have some questions about memory autotuning in the Operator.
1. Does the autotuner try to upgrade the job with more memory allocated if it
intercepts OutOfMemoryError? Say I initially provided too little memory for TM
`resource` - will the job fail and stop on initializing or will the
My guess it’s a major known issue. Need a workaround.
https://issues.apache.org/jira/browse/FLINK-32212
/Maxim
From: prashant parbhane
Date: Tuesday, April 23, 2024 at 11:09 PM
To: user@flink.apache.org
Subject: [External] Regarding java.lang.IllegalStateException
Hello,
We have been facing
oyment
[INFO ][flink/f-d7681d0f-c093-5d8a-b5f5-2b66b4547bf6] Deleting Kubernetes HA
metadata
Any ideas?
Thanks,
Maxim
From: Gyula Fóra
Date: Friday, April 26, 2024 at 1:10 AM
To: Maxim Senin
Cc: Maxim Senin via user
Subject: Re: [External] Exception during autoscaling operation - Flink
1.18/
We are also seeing something similar:
2024-04-26 16:30:44,401 INFO
org.apache.flink.runtime.executiongraph.ExecutionGraph [] - Source: Power
Consumption:power_consumption -> Ingest Power Consumption -> PopSysFields ->
WindowingWatermarkPreCheck (1/1)
still a mystery.
Thanks,
Maxim
From: Gyula Fóra
Date: Friday, April 26, 2024 at 1:10 AM
To: Maxim Senin
Cc: Maxim Senin via user
Subject: Re: [External] Exception during autoscaling operation - Flink
1.18/Operator 1.8.0
Hi Maxim!
Regarding the status update error, it could be related
: Maxim Senin via user
Date: Thursday, April 25, 2024 at 12:01 PM
To: Maxim Senin via user
Subject: [External] Exception during autoscaling operation - Flink
1.18/Operator 1.8.0
Hi.
I already asked before but never got an answer. My observation is that the
operator, after collecting some stats
Hi.
I already asked before but never got an answer. My observation is that the
operator, after collecting some stats, is trying to restart one of the
deployments. This includes taking a savepoint (`takeSavepointOnUpgrade: true`,
`upgradeMode: savepoint`) and “gracefully” shutting down the
Hi. My Flink Deployment is set to use savepoint for upgrades and for taking
savepoint before stopping.
When rescaling happens, for some reason it scales the JobManager to zero
(“Scaling JobManager Deployment to zero with 300 seconds timeout”) and the job
goes into FINISHED state. It doesn’t
Hi.
Does it make sense to specify `parallelism` for task managers or the `job`,
and, similarly, to specify memory amount for the task managers, or it’s better
to leave it to autoscaler and autotuner to pick the best values? How many times
would the autoscaler need to restart task managers
