We are Using Parallel Chunk Loop(use automatically chunk count) to call workflows by using “Call workflow (table based)” .
Table Creator(6 Rows) -> Parallel Chunk Start-> Call workflow (table based) -> Parallel Chunk End
※ CPU has a 4-core processor, 8 logical processors by the result of msinfo32 command
This workflow worked well for months, but today it crashes with an error: ERROR Call Workflow (Table Based) 0:18: Execute failed: Read timed out
How to solve the problem? Do I need to increase time-out value?
Thanks in advance.
Ryu
Thank you very much for your quick response.
Yes, We are calling a local workflow,
But the Java version is 1.8.0_152 , and OS is Windows Server 2016 in our environment,
It seems the error has nothing to do with the issue.
The workflow worked well in this morning, but failed in the afternoon.
BTW:
KNIME Sever : 4.82
KNIME ANALYTICS PLATFORM: 3.72
Hi Ryu,
Thank you for that information. Would you mind also sharing your KNIME log? Do you use the KNIME AP locally and as the server’s executor? I am wondering if the actual invokation of the workflow fails or if the workflow itself fails because some node throws the error. Can you trigger the workflow you are trying to call manually, e.g. from the AP and find out which node it is that gives you problems?
Kind regards
Alexander
We use as the server’s executor.
And the error is not easy to reproducible because it worked well for months and also worked well in yesterday evening and today morning.
I am doubting whether some parallel processed workflow made system resources heavily utilized, so the calling another workflow is time out.
BTW: Node ID of the “Call Workflow(Table Based)” node is 18 which printed in the error message.
Hi,
I got the log but unfortunately it does not give away more than the error message. You raise good questions. If the workflows are mounted on a network drive, this could cause performance issues. @laughsmile could you give us some more info on the setup?
Kind regards
Alexander
The called workflow is located in the local server , so I think the connection is stable.
and the Server is a physical server(AWS Dedicated Instances) with good spec, which uses SSD storage.
In the server, there is a antivirus software ( ServerProtect made by Trend micro) ,
When the knime workflow was executed , the antivirus software was also running.
In the called workflow, it access redshift by using “Amazon Redshift Connector” Node.
The full log is very large , so I sent the logs from 3:00 pm to the error happened time.
Which part of the logs or other things is needed more?
BTW:
there are six workflow instances(same workflow) called in parallel , the five of them were executed successfully, just one failed.
Hi,
I don’t think this is possible, as it is not expected to take long to run a local workflow. I will talk to some colleagues and try to find out what’s up.
Kind regards
Alexander
I think you would have to take a step back and think about how to construct your workflow and at which point to call parallel workflows. Maybe you could provide us with an example or a screenshot about what the workflow does.
If the thing continues to be unstable you might have to think about ‘communicating’ with the workflow(s) and make them to report back when they finished successfully and maybe have a Variable Condition Loop End at the end, and maybe also some Error catcher.
Hi,
You can also try to give the KNIME Server instance more memory. This might help if you call the workflow using the knime:// protocol. Please see here how to do that.
Kind regards
Alexander
Hi,
And does that solve the problem? If you have the opportunity, you could also try out 4 GB in order to let the server not consume too many resources.
Kind regards
Alexander
The server has 16G memory, so I changed the memory setting to 8G.
But today the error appeared again.(the third time this month)
It seems the only way is to modify workflow as I mentioned above.
Hi,
One more question: how did you configure the node? It has options for short and long running workflows. Did you specify that the workflow runs long? See here for more info: https://kni.me/n/M66aoaj-gqFbGR5j
Kind regards
Alexander