r-cluster | distributively schedule R scripts on a mesos cluster | Job Orchestrator library

I think it might be an open issue in JDK 8 . Please see the below link .

https://github.com/bcgit/bc-java/issues/941

Issue reported for JDK 8u292 , but it is working in earlier versions( JDK 8u282 ) and newer versions (JDK 9+).

Try downgrading/upgrading the JDK based on your need.

In the comment section, I have already mentioned what was my issue and just for future reference I'm explaining it here:

1. high disk watermark [90%] exceeded […] shards will be relocated away from this node: This error will come whenever you are encountering low disk space available in your system. So, just to overcome this issue I had to clean up some data to make more disk space and my current free space is 40GB which is working fine.
2. After fixing the Disk space I countered another issue that was "Max virtual memory areas vm.max_map_count [65530] is too low, increase to at least [262144]".
   So here we need to increase the virtual memory of the Docker. In windows first, we need to go to the Docker terminal before executing the increased virtual memory command.

If your Docker using wsl subsystem then

1. open power shell
2. Run: wsl -d docker-desktop, this will take to Docker terminal.
3. Run: sysctl -w vm.max_map_count=262144

Restart your Docker and you are all set.

Note: The increase the virtual memory is already answered here: https://stackoverflow.com/a/66547784/596495

The Connect container starts Connect Distributed Server already. You should use HTTP and JSON properties to configure the Elastic connector rather than exec into the container shell and issue connect-standalone commands which default to using a broker running in the container itself.

Similarly, the Elastic quickstart file expects Elasticsearch running within the Connect container, by default

For the ELK stack to work you need all three PersistentVolumeClaim's to be bound as I recall. Instead of creating 1 30 GB of PV create 3 of the same size with the claims and then re-install. Other nodes have unmet dependincies.

Also please do not handle the volumes by hand. There are guidelines to deploy dynamic volums. Use OpenEBS for example. That way you wont need to worry about the pvc's. After giving the pv's if anything happens write again with your cluster installation process.

I was wrong obviously, in this particular problem, filesystems and cgroups take role and the main problem of this is an old problem. From 5.2.1 to 8.0.0. Reinstall the chart by pulling the chart. Edit values file and definitely change the container version. It should be fine or create another error log stack.

damn boy, i have things to say :)

1. I think the main cause of your issue is interaction between loops / callbacks / cluster here is an exemple to clarify my point on loops

Please comment if you see any issues anywhere in the configuration or if you would like to provide any suggestions. Increase disk storage configuration according to your usage.

Based on the comments.

The issue was caused by execution AWS CLI in different region than intended. The solution was to use --region option to provide correct region for the CLI.

I recommend you to show the image using cv2.imshow, instead of using matplotlib.

cv2.imshow shows the image "pixel to pixel" by default, while matplotlib.pyplot matches the image dimensions to the size of the axes.

To give a pod control over something in Kubernetes you need at least four things:

1. Create or select existing Role/ClusterRole (you picked administer-cluster, which rules are unknown to me).
2. Create or select existing ServiceAccount (you created k8s-deployer in namespace tooling).
3. Put the two together with RoleBinding/ClusterRoleBinding.
4. Assign the ServiceAccount to a pod.

Here's an example that can manage namespaces: