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Architecture Decision Records

The intention is to document deviations from a standard Model View Controller (MVC) design.

Pivoting Compliance View

The view that was preferred for compliance would be devices on y-axis but the features on the x-axis. However, the x-axis (features) cannot be known when building the Django model. The model ends up looking like:

Device feature Compliance
nyc-rt01 aaa True
nyc-rt01 ntp False
nyc-rt01 dns False
nyc-rt02 aaa True
nyc-rt02 dns False

The expected view required expected is something like:

Device aaa dns ntp
nyc-rt01 True False False
nyc-rt02 True False

In order to accommodate this, django-pivot is used, which greatly simplifies building the query. However, django-pivot requires the ability to "count" versus a boolean. Because of that, there is a "shadow" field created that is set to 0 if False, and 1 if True. This is enforced on the save method of the ConfigCompliance model.

Compliance View

Important to understand Pivoting Compliance View first. There is additional context for how to handle bulk deletes. The logic is to find all of the ConfigCompliance data, given a set of Device objects.

Additionally, this makes use of the alter_queryset method, as at start time of the application all features are not necessarily set and needs to be a runtime query that sets the x and y axis correctly.

The absence of data, meaning, a device that is does not have a feature, is the equivalent of a None.

Dynamic Application Features

There are features within the application that can be turned on/off for backup, compliance, and intended. When they are toggled, this will update what is shown in:

  • Navigation
  • Jobs
  • Data Sources
  • Tables
  • Template Contents

This is generally handled with a pattern similar to:

jobs = []
jobs.extend([AllGoldenConfig, AllDevicesGoldenConfig])

Home View

The Home view is generally based on the model GoldenConfig; however, in reality the view that shows up is based on the core Device model. This is because, when a device is included in the Dynamic Group, this does not mean that there is an entry in GoldenConfig yet. So there is nothing to see yet, such as the ability to click and run job on a device. It was confusing to users as to what was shown in the view vs what is in scope currently.

This complicates things such that the view data is one level nested, from the Model. Meaning, the query is based on Device, but the data is primarily in GoldenConfig. Do accommodate, there is an annotated query, similar to:

        return self.queryset.filter(id__in=qs).annotate(

This allows the tables to be a bit simpler as the data is directly accessible without traversing the foreign key.

Home Tables

There is not a one-to-one for fields to data shown. There is custom logic that sees if the last ran date is the same as last successful data and renders either green or red. Here is an example of the code that is actually rendered (logic is within _render_last_success_date method):

    def render_backup_last_success_date(self, record, column):
        """Pull back backup last success per row record."""
        return self._render_last_success_date(record, column, "backup")

Filtering Logic

The filtering logic happens in the get_job_filter function. Any consumer (job/view/etc) should use this to ensure everything is filtered in the same way.

Diff logic

There is a function mapper for the diff logic. This allows for the diff logic to take on different proccesses for cli, json, and custom. This is enforced on the save method of ConfigCompliance.

Dynamic Group

There was originally a scope associated with the project, this was changed to a Dynamic Group to make use of the features within Core. There is backwards compatibility for the time being.

Management Commands

There is specific management commands to run the jobs associated with the project. In a future version, they will reference the management commands in core.

SoT Aggregation

There is a custom SoT Aggregation method, originally this pre-dated Nautobot Core having saved queries and was a way to handle to have a saved query. Currently, it allows operators to transform data by providing a function to post process that data. This functionality is handled to code similar to:

    if PLUGIN_CFG.get("sot_agg_transposer"):
            data = import_string(PLUGIN_CFG.get("sot_agg_transposer"))(data)
        except Exception as error:
            return (400, {"error": str(error)})

Git Actions

The data source contract is used for reading from Git, but extended for pushing to Git.

Configuration Postprocessing

Intended configuration generated by Golden Config Intended feature is designed for comparing to the "running" configuration (assuring compliance comparing to the backup configuration). Using the default Intended configuration for remediation/provisioning of a network device configuration is not always possible. For instance, no secrets should be rendered to the Intended configuration, as it is stored in Git/Database, or maybe some reordering of commands is required to create a valid configuration artifact.

The PROCESSING feature, that is enabled as a Dynamic Application Feature, exposes a single device UI and API view to manipulate the Intended configuration available for Golden Config, with extra processing steps. There are some default functions (i.e. render_secrets), but those can be expanded, and the order can be changed, via Nautobot configuration settings (postprocessing_subscribed and postprocessing_callables).


This configuration generated after postprocessing is not stored either in the Database or in Git.

Both API views, as commented, are only targeting ONE single device because being a synchronous operation (versus the rest of the features that are run asynchronously as Jobs), it could take too much time, and have an undesired impact in Nautobot performance.

All the functions used in the post-processing chain require a consitent signature: func(config_postprocessing: str, configs: models.GoldenConfig, request: HttpRequest) -> str.

  • config_postprocessing: str: it's the reference configuration to use as template to render.
  • configs: models.GoldenConfig: it contains reference to other configs (backup) that could be used to create remediation, and it contains the Device object to identify the GraphQL information to take from.
  • request: HttpRequest: it could contain special query params, and for the render_secrets one, it contains information about the User requesting it, to validate the permissions.

Finally, it always returns the processing from the config_processing.

The API view, under the path config-postprocessing, uses custom permissions, named ConfigPushPermissions, which ensures the user has general permissions for nautobot_golden_config.view_goldenconfig, and specific permissions to view the Device object requested.

Renders Secrets

It was decided to restrict the usage of Jinja filters to only the ones related to getting Nautobot secrets values (defined here), plus the encrypt_type5 and encrypt_type7 filters from Netutils. Remember that this function is not defined to replace the regular Jinja rendering done for creating the Intended configuration, only to add secrets information on the fly. This avoids undesired behavior on this synchronous operation.

This function performs an additional permission validation, to check if the requesting user has permissions to view the SecretsGroup requested.