Connection delete package

The connection delete package deletes a connection and all its related assets.

Soft-delete (archive) assets

2.2.3 1.9.0

To soft-delete (archive) all assets in a connection:

Java Python Raw REST API

Workflow workflow = ConnectionDelete.creator( // (1)
                "default/snowflake/1234567890", false // (2)
                ).build() // (3)
                .toWorkflow(); // (4)

WorkflowResponse response = workflow.run(); // (5)

1. The ConnectionDelete package will create a workflow to delete a connection and its assets using the creator() method.

2. You need to provide the following:

   • qualified name of the connection whose assets should be deleted.
   • whether to permanently delete the connection and its assets (hard-delete) (true), or only archive (soft-delete) them (false).

3. Build the minimal package object.

4. Convert the package into a Workflow object.

5. Run the workflow using the run() method on the object you've created.

   Workflows run asynchronously

   Remember that workflows run asynchronously. See the packages and workflows introduction for details on how to check the status and wait until the workflow has been completed.

from pyatlan.client.atlan import AtlanClient
from pyatlan.model.packages import ConnectionDelete

client = AtlanClient()

workflow = ConnectionDelete( # (1)
    qualified_name="default/snowflake/1234567890", purge=False # (2)
).to_workflow() # (3)

response = client.workflow.run(workflow) # (4)

1. The ConnectionDelete package will create a workflow to delete a connection and its assets.

2. You need to provide the following:

   • qualified name of the connection whose assets should be deleted.
   • whether to permanently delete the connection and its assets (hard-delete) (True), or only archive (soft-delete) them (False).

3. Convert the package into a Workflow object.

4. Run the workflow by invoking the run() method on the workflow client, passing the created object.

   Workflows run asynchronously

   Remember that workflows run asynchronously. See the packages and workflows introduction for details on how to check the status and wait until the workflow has been completed.

Create the workflow via UI only

We recommend creating the workflow only via the UI. To rerun an existing workflow, see the steps below.

Hard-delete (purge) assets

Permanent and irreversible

A hard-delete (purge) is permanent and irreversible. Be certain that you want to entirely remove all of the assets in a connection before running in this way!

2.2.3 1.9.0

To hard-delete (purge) all assets in a connection:

Java Python Raw REST API

Workflow workflow = ConnectionDelete.creator( // (1)
                "default/snowflake/1234567890", true // (2)
                ).build() // (3)
                .toWorkflow(); // (4)

WorkflowResponse response = workflow.run(); // (5)

1. The ConnectionDelete package will create a workflow to delete a connection and its assets using the creator() method.

2. You need to provide the following:

   • qualified name of the connection whose assets should be deleted.
   • whether to permanently delete the connection and its assets (hard-delete) (true), or only archive (soft-delete) them (false).

3. Build the minimal package object.

4. Convert the package into a Workflow object.

5. Run the workflow using the run() method on the object you've created.

   Workflows run asynchronously

   Remember that workflows run asynchronously. See the packages and workflows introduction for details on how to check the status and wait until the workflow has been completed.

from pyatlan.client.atlan import AtlanClient
from pyatlan.model.packages import ConnectionDelete

client = AtlanClient()

workflow = ConnectionDelete( # (1)
    qualified_name="default/snowflake/1234567890", purge=True # (2)
).to_workflow() # (3)

response = client.workflow.run(workflow) # (4)

1. The ConnectionDelete package will create a workflow to delete a connection and its assets.

2. You need to provide the following:

   • qualified name of the connection whose assets should be deleted.
   • whether to permanently delete the connection and its assets (hard-delete) (True), or only archive (soft-delete) them (False).

3. Convert the package into a Workflow object.

4. Run the workflow by invoking the run() method on the workflow client, passing the created object.

   Workflows run asynchronously

   Remember that workflows run asynchronously. See the packages and workflows introduction for details on how to check the status and wait until the workflow has been completed.

Create the workflow via UI only

We recommend creating the workflow only via the UI. To rerun an existing workflow, see the steps below.

Re-run existing workflow

1.9.5 1.10.6

To re-run an existing connection delete workflow:

Java Python Kotlin Raw REST API

List<WorkflowSearchResult> existing = WorkflowSearchRequest // (1)
            .findByType(ConnectionDelete.PREFIX, 5); // (2)
// Determine which of the results is the Connection delete workflow you want to re-run...
WorkflowRunResponse response = existing.get(n).rerun(); // (3)

1. You can search for existing workflows through the WorkflowSearchRequest class.
2. You can find workflows by their type using the findByType() helper method and providing the prefix for one of the packages. In this example, we do so for the ConnectionDelete. (You can also specify the maximum number of resulting workflows you want to retrieve as results.)

3. Once you've found the workflow you want to re-run, you can simply call the rerun() helper method on the workflow search result. The WorkflowRunResponse is just a subtype of WorkflowResponse so has the same helper method to monitor progress of the workflow run.

   • Optionally, you can use the rerun(true) method with idempotency to avoid re-running a workflow that is already in running or in a pending state. This will return details of the already running workflow if found, and by default, it is set to false

   Workflows run asynchronously

   Remember that workflows run asynchronously. See the packages and workflows introduction for details on how you can check the status and wait until the workflow has been completed.

from pyatlan.client.atlan import AtlanClient
from pyatlan.model.enums import WorkflowPackage

client = AtlanClient()

existing = client.workflow.find_by_type(  # (1)
  prefix=WorkflowPackage.CONNECTION_DELETE, max_results=5
)

# Determine which Connection delete workflow (n)
# from the list of results you want to re-run.
response = client.workflow.rerun(existing[n]) # (2)

1. You can find workflows by their type using the workflow client find_by_type() method and providing the prefix for one of the packages. In this example, we do so for the ConnectionDelete. (You can also specify the maximum number of resulting workflows you want to retrieve as results.)

2. Once you've found the workflow you want to re-run, you can simply call the workflow client rerun() method.

   • Optionally, you can use rerun(idempotent=True) to avoid re-running a workflow that is already in running or in a pending state. This will return details of the already running workflow if found, and by default, it is set to False.

   Workflows run asynchronously

   Remember that workflows run asynchronously. See the packages and workflows introduction for details on how you can check the status and wait until the workflow has been completed.

val existing = WorkflowSearchRequest // (1)
            .findByType(ConnectionDelete.PREFIX, 5); // (2)
// Determine which of the results is the
// connection delete workflow you want to re-run...
val response = existing.get(n).rerun(); // (3)

1. You can search for existing workflows through the WorkflowSearchRequest class.
2. You can find workflows by their type using the findByType() helper method and providing the prefix for one of the packages. In this example, we do so for the ConnectionDelete. (You can also specify the maximum number of resulting workflows you want to retrieve as results.)

3. Once you've found the workflow you want to re-run, you can simply call the rerun() helper method on the workflow search result. The WorkflowRunResponse is just a subtype of WorkflowResponse so has the same helper method to monitor progress of the workflow run.

   • Optionally, you can use the rerun(true) method with idempotency to avoid re-running a workflow that is already in running or in a pending state. This will return details of the already running workflow if found, and by default, it is set to false

   Workflows run asynchronously

   Remember that workflows run asynchronously. See the packages and workflows introduction for details on how you can check the status and wait until the workflow has been completed.

Requires multiple steps through the raw REST API

1. Find the existing workflow.
2. Send through the resulting re-run request.

{
  "from": 0,
  "size": 5,
  "query": {
    "bool": {
      "filter": [
        {
          "nested": {
            "path": "metadata",
            "query": {
              "prefix": {
                "metadata.name.keyword": {
                  "value": "atlan-connection-delete" // (1)
                }
              }
            }
          }
        }
      ]
    }
  },
  "sort": [
    {
      "metadata.creationTimestamp": {
        "nested": {
          "path": "metadata"
        },
        "order": "desc"
      }
    }
  ],
  "track_total_hits": true
}

1. Searching by the atlan-connection-delete prefix will ensure you only find existing connection delete workflows.

   Name of the workflow

   The name of the workflow will be nested within the _source.metadata.name property of the response object. (Remember since this is a search, there could be multiple results, so you may want to use the other details in each result to determine which workflow you really want.)

{
  "namespace": "default",
  "resourceKind": "WorkflowTemplate",
  "resourceName": "atlan-connection-delete-1684500411" // (1)
}

1. Send the name of the workflow as the resourceName to rerun it.

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