mgmt | Next generation distributed, event-driven, parallel config management! | Architecture library

Use join('\n') to join the elements of the list into desired string. Check out the output of 2nd debug.

This is by design. One such scenario for this if rolling back changes to a previous pipeline run it would be best practice to have an approval before redeploying code to the environment. As for the scenario where you have 3 stages and each one requires an approval this is by design:

A stage can consist of many jobs, and each job can consume several resources. Before the execution of a stage can begin, all checks on all the resources used in that stage must be satisfied. Azure Pipelines pauses the execution of a pipeline prior to each stage, and waits for all pending checks to be completed. Checks are re-evaluation based on the retry interval specified in each check. If all checks are not successful till the timeout specified, then that stage is not executed. If any of the checks terminally fails (for example, if you reject an approval on one of the resources), then that stage is not executed.

In your given scenario may I suggest the Terraform, function app and APIM deployments be part of the same stage? Each one of these jobs could also be templatized so can reuse them in your additional environments. This would eliminate the possibility a user approves these incorrectly (Unless you have dependsOn outlined) or the possibility that the Terraform Apply is the only one that is release.

Right - the admin console is listening on 127.0.0.1. This is not the outside world interface. This is "localhost".

You have two choices here. You can start Keycloak with a command line argument like:

There is no problem with the code and it works fine on my side. And the error message shows the reason:

azure.core.exceptions.ResourceNotFoundError: (SubscriptionNotFound) The subscription 'xxxx' could not be found. Code: SubscriptionNotFound Message: The subscription 'xxxx' could not be found.

It seems you run the python code in your local machine. I recommend you log in with the Azure CLI first and then check if the subscription id that you used in your python code is right.

Is the query below solve your issues ?

Apache Arrow utilizes a pretty similar idea of Java off-heap storage as Apache Ignite does. For Apache Arrow it means that objects like IntVector don't actually store data in their on-heap layout. They just store a reference to a buffer containing an off-heap address of a physical representation. Technically it's a long offset pointing to a chunk of memory within JVM address space.

When you restart your JVM, address space changes. But in your Apache Ignite native persistence there's a record holding an old pointer. It leads to a SIGSEGV because it's not in the JVM address anymore (in fact it doesn't even exist after a restart).

You could use Apache Arrow serialization machinery to store data permanently in Apache Ignite or even somewhere else. But in fact after that you're going to lose Apache Arrow preciousness as a fast in-memory columnar store. It was initially designed to share off-heap data across multiple data-processing solutions.

Therefore I believe that technically it could be possible to leverage Apache Ignite binary storage format. In that case a custom BinarySerializer should be implemented. After that it would be possible to use it with the Apache Arrow vector classes.

Solved.

The version 6 changed somethings in security. Now some permissions have to be set explicitly, in this case, to allow management requests:

Try this :

If you've properly set up your Department model like this: