Best Practices¶

While there are many different development interfaces in Nautobot that each expose unique functionality, there are a common set of a best practices that have broad applicability to users and developers alike. This includes elements of writing Jobs, Apps, and scripts for execution through nautobot-server nbshell.

The below best practices apply to test code as well as feature code, and there are additional test-specific best practices to be aware of as well.

Base Classes¶

For models that support change-logging, custom fields, and relationships (which includes all subclasses of OrganizationalModel and PrimaryModel), the "Full-featured models" base classes below should always be used. For less full-featured models, refer to the "Minimal models" column instead.

Feature	Full-featured models	Minimal models
Data models	`PrimaryModel`	`BaseModel`
FilterSets	`NautobotFilterSet`	`BaseFilterSet`
Object create/edit forms	`NautobotModelForm`	`BootstrapMixin`
Object bulk-edit forms	`NautobotBulkEditForm`	`BootstrapMixin`
Table filter forms	`NautobotFilterForm`	`BootstrapMixin`
Read-only serializers	`BaseModelSerializer`	`BaseModelSerializer`
All other serializers	`NautobotModelSerializer`	`ValidatedModelSerializer`
List view tables	`BaseTable`	`BaseTable`
API ViewSets	`NautobotModelViewSet`	`ModelViewSet`
UI ViewSets	`NautobotUIViewSet`	individual mixins as needed

Data Model Best Practices¶

Model Existence in the Database¶

A common Django pattern is to check whether a model instance's primary key (pk) field is set as a proxy for whether the instance has been written to the database or whether it exists only in memory. Because of the way Nautobot's UUID primary keys are implemented, this check will not work as expected because model instances are assigned a UUID in memory at instance creation time, not at the time they are written to the database (when the model's save() method is called). Instead, for any model which inherits from nautobot.core.models.BaseModel, you should check an instance's present_in_database property which will be either True or False.

Instead of:

if instance.pk:
    # Are we working with an existing instance in the database?
    # Actually, the above check doesn't tell us one way or the other!
    ...
else:
    # Will never be reached!
    ...

Use:

if instance.present_in_database:
    # We're working with an existing instance in the database!
    ...
else:
    # We're working with a newly created instance not yet written to the database!
    ...

Note

There is one case where a model instance will have a null primary key, and that is the case where it has been removed from the database and is in the process of being deleted. For most purposes, this is not the case you are intending to check!

Model Validation¶

Django offers several places and mechanism in which to exert data and model validation. All model specific validation should occur within the model's clean() method or field specific validators. This ensures the validation logic runs and is consistent through the various Nautobot interfaces (Web UI, REST API, ORM, etc).

Consuming Model Validation¶

Django places specific separation between validation and the saving of an instance and this means it is a common Django pattern to make explicit calls first to a model instance's clean()/full_clean() methods and then the save() method. Calling only the save() method does not automatically enforce validation and may lead to data integrity issues.

Nautobot provides a convenience method that both enforces model validation and saves the instance in a single call to validated_save(). Any model which inherits from nautobot.core.models.BaseModel has this method available. This includes all core models and it is recommended that all new Nautobot core models and App-provided models also inherit from BaseModel or one of its descendants such as nautobot.core.models.generics.OrganizationalModel or nautobot.core.models.generics.PrimaryModel.

The intended audience for the validated_save() convenience method is Job authors and anyone writing scripts for, or interacting with the ORM directly through the nautobot-server nbshell command. It is generally not recommended however, to use validated_save() as a blanket replacement for the save() method in the core of Nautobot.

During execution, should model validation fail, validated_save() will raise django.core.exceptions.ValidationError in the normal Django fashion.

Field Naming in Data Models¶

Model field names must always follow the following conventions:

Use lowercase letters, numbers, and underscores only
Separate words with underscores for readability/clarity
For foreign keys and their corresponding reverse-relations, match the verbose_name or verbose_name_plural of the related model

Instead of:

Rack.group
DeviceType.consoleserverporttemplates
Device.ipaddress_set

Use:

Rack.rack_group
DeviceType.console_server_port_templates
Device.ip_addresses

Foreign Key `related_name` for Abstract Model Classes¶

Changed in version 2.0.0

If an abstract model class defines a foreign key to a concrete model class, Django's default related_name functionality doesn't provide great options - the best you could normally do for a related_name on a ForeignKey from an abstract base class, for example, would be "%(class)ss" (potentially resulting in related names like "devices" or, less optimally, "ipaddresss") or "%(app_label)s_%(class)s_related" (resulting in related names like "dcim_device_related" or "ipam_ipaddress_related", which while at least consistent, are rather clunky).

Fortunately, Nautobot provides a ForeignKeyWithAutoRelatedName model field class that solves this problem. On any concrete subclass of an abstract base class that uses ForeignKeyWithAutoRelatedName instead of ForeignKey, the related_name will be automatically set based on the concrete subclass's verbose_name_plural value (which in many cases Django is clever enough to automatically derive from the class name, but can also be specified directly on your Meta class if needed). Thus, if your model's verbose_name_plural is "IP addresses", the related_name for the ForeignKeyWithAutoRelatedName will automatically be ip_addresses.

Note

At this time we only recommend using ForeignKeyWithAutoRelatedName for this abstract model case; for foreign keys between concrete models, it's still best to use a regular ForeignKey with an explicitly specified related_name string.

Nautobot doesn't currently have a similar class provided for ManyToManyField; in this case you'll probably be best, for now, to just use related_name="%(app_label)s_%(class)s_related" for any abstract base class's ManyToManyField if a reverse relation is desired.

Slug Field¶

Changed in version 2.0.0

Models should generally not have a slug field, and should use the model's primary key (UUID) in URL patterns for both the UI and the REST API. All models should have a human-friendly natural key, either a single unique field (typically name) or a minimally complex unique-together set of fields (such as DeviceType's (manufacturer, model)).

For models that have a strong use case for a slug or similar field (such as GitRepository.slug, which defines a module name for Python imports from the repository, or CustomField.key, which defines the key used to access this field in the REST API and GraphQL), Nautobot provides the AutoSlugField to handle automatically populating the slug field from another field(s). Generally speaking model slugs should be populated from the name field. Below is an example on defining the slug field.

from django.db import models

from nautobot.core.models.fields import AutoSlugField
from nautobot.core.models.generics import PrimaryModel

class ExampleModel(PrimaryModel):
    name = models.CharField(max_length=100, unique=True)
    slug = AutoSlugField(populate_from='name')

CharField and SlugField Max Length¶

When constructing a CharField or SlugField, always utilize the CHARFIELD_MAX_LENGTH constant unless your field requires a value greater than CHARFIELD_MAX_LENGTH, which is 255.

Getting URL Routes¶

When developing new models a need often arises to retrieve a reversible route for a model to access it in either the web UI or the REST API. When this time comes, you must use nautobot.core.utils.lookup.get_route_for_model. You must not write your own logic to construct route names.

Changed in version 2.0.0

get_route_for_model was moved from the nautobot.utilities.utils module to nautobot.core.utils.lookup.

from nautobot.core.utils.lookup import get_route_for_model

This utility function supports both UI and API views for both Nautobot core apps and Nautobot Apps.

Added in version 1.4.3

Support for generating API routes was added to get_route_for_model() by passing the argument api=True.

UI Routes¶

Instead of:

route = f"{model._meta.app_label}:{model._meta.model_name}_list"
if model._meta.app_label in settings.PLUGINS:
    route = f"plugins:{route}"

Use:

route = get_route_for_model(model, "list")

REST API Routes¶

Instead of:

api_route = f"{model._meta.app_label}-api:{model._meta.model_name}-list"
if model._meta.app_label in settings.PLUGINS:
    api_route = f"plugins-api:{api_route}"

Use:

api_route = get_route_for_model(model, "list", api=True)

Examples¶

Core models:

>>> get_route_for_model(Device, "list")
"dcim:device_list"
>>> get_route_for_model(Device, "list", api=True)
"dcim-api:device-list"

App models:

>>> get_route_for_model(ExampleModel, "list")
"plugins:example_app:examplemodel_list"
>>> get_route_for_model(ExampleModel, "list", api=True)
"plugins-api:example_app-api:examplemodel-list"

Tip

The first argument may also optionally be an instance of a model, or a string using the dotted notation of {app_label}.{model} (e.g. dcim.device).

Using an instance:

>>> instance = Device.objects.first()
>>> get_route_for_model(instance, "list")
"dcim:device_list"

Using dotted notation:

>>> get_route_for_model("dcim.device", "list")
"dcim:device_list"

REST API Best Practices¶

Generally the field names on a REST API serializer should correspond directly to the field names on the model, subject to the best practices described above.
For related count fields, use the related model name suffixed with _count.

Instead of:

Interface.count_ipaddresses

Use:

Interface.ip_address_count

Filtering Models with FilterSets¶

The following best practices must be considered when establishing new FilterSet classes for model classes.

Mapping Model Fields to Filters¶

FilterSets must inherit from nautobot.core.filters.BaseFilterSet or nautobot.extras.filters.NautobotFilterSet (which inherits from nautobot.core.filters.BaseFilterSet)
- This affords that automatically generated lookup expressions (ic, nic, iew, niew, etc.) are always included
- This also asserts that the correct underlying Form class that maps the generated form field types and widgets will be included
FilterSets must publish all model fields from a model, including related fields.
- All fields should be provided using Meta.fields = "__all__" and this would be preferable for the first and common case as it requires the least maintenance and overhead and asserts parity between the model fields and the filterset filters.
- In some cases simply excluding certain fields would be the next most preferable e.g. Meta.exclude = ["unwanted_field", "other_unwanted_field"]
- Finally, the last resort should be explicitly declaring the desired fields using Meta.fields =. This should be avoided because it incurs the highest technical debt in maintaining alignment between model fields and filters.
In the event that fields do need to be customized to extend lookup expressions, a dictionary of field names mapped to a list of lookups may be used, however, this pattern is only compatible with explicitly declaring all fields, which should also be avoided for the common case. For example:

class UserFilter(NautobotFilterSet):
    class Meta:
        model = User
        fields = {
            'username': ['exact', 'contains'],
            'last_login': ['exact', 'year__gt'],
        }

It is acceptable that default filter mappings may need to be overridden with custom filter declarations, but filter_overrides (see below) should be used as a first resort.

Filter Naming and Definition¶

Custom filter definitions must not shadow the name of an existing model field if it is also changing the type.
- For example (before Nautobot 2.0.0), DeviceFilterSet.interfaces was a BooleanFilter that was shadowing the Device.interfaces related manager. This caused problems with automatic introspection of the filterset and was fixed in 2.0 by introducing a separate has_interfaces filter and changing the interfaces filter to show the correct behavior. Shadowing database fields with a filter field of a different type must be avoided in all new filters.
In Nautobot 2.0 and later, for all foreign-key related fields and their corresponding reverse-relations:
- If there is no appropriate single field that could be used as a natural key (e.g. a globally-unique name or slug), then the default filtering behavior for this field (using django_filters.ModelMultipleChoiceFilter) can be used for now, until issue 2875 is implemented to allow for the use of multiple fields with NaturalKeyOrPKMultipleChoiceFilter.
- Otherwise, the field must be shadowed with a Nautobot NaturalKeyOrPKMultipleChoiceFilter which will automatically try to lookup by UUID or name depending on the value of the incoming argument (e.g. UUID string vs. name string).
  - This provides an advantage over the default django_filters.ModelMultipleChoiceFilter which only supports a UUID (pk) value as an input.
- Fields that use slug or some other natural key field instead of name can set the to_field_name argument on NaturalKeyOrPKMultipleChoiceFilter accordingly.

# Typical usage
from nautobot.core.filters import NaturalKeyOrPKMultipleChoiceFilter

    provider = NaturalKeyOrPKMultipleChoiceFilter(
        queryset=Provider.objects.all(),
        to_field_name="name",
        label="Provider (name or ID)",
    )

# Optionally, using the to_field_name argument to look up by "slug" instead of by "name"
from nautobot.core.filters import NaturalKeyOrPKMultipleChoiceFilter

    git_repository = NaturalKeyOrPKMultipleChoiceFilter(
        to_field_name="slug",
        queryset=GitRepository.objects.all(),
        label="Git repository (slug or ID)",
    )

Boolean filters for membership must be named with has_{related_name} (e.g. has_interfaces) and should use the RelatedMembershipBooleanFilter filter class.
- One exception to this naming convention may be made for Boolean filters for identity, which may be named is_{name} instead (e.g. is_virtual_chassis_member versus has_virtual_chassis). Although this is semantically identical to has_ filters, there may be occasions where naming the filter is_ would be more intuitive.

from nautobot.core.filters import RelatedMembershipBooleanFilter

    has_interfaces = RelatedMembershipBooleanFilter(
        field_name="interfaces",
        label="Has interfaces",
    )

    is_virtual_chassis_member = RelatedMembershipBooleanFilter(
        field_name="virtual_chassis",
        label="Is a virtual chassis member",
    )

Whenever possible otherwise, filter names must correspond exactly to the underlying model field they are referencing.
If there's necessarily a mismatch between the filter name and the model field name (such as in the has_* and is_* cases described above):
1. The suffix component of the filter name must correspond exactly to the underlying model field name (for example, for a field of console_port_templates, the filter must be has_console_port_templates, not has_consoleporttemplates or has_console_ports).
2. The filter itself must declare field_name to identify unambiguously the underlying model field.
Filters must be declared using the appropriate lookup expression (lookup_expr) if any other expression than exact (the default) is required.
Filters must be declared using exclude=True if a queryset .exclude() is required to be called vs. queryset .filter() which is the default when the filter default exclude=False is passed through. If you require Foo.objects.exclude(), you must pass exclude=True instead of defining a filterset method to explicitly hard-code such a query.

For example, for a boolean filter that checks to see whether the console_ports field is null if False and not null if True, you would need to combine all of the above rules, resulting in:

   has_console_ports = BooleanFilter(field_name="console_ports", lookup_expr="isnull", exclude=True)

Tip

For boolean filters on related memberships (has_*/is_*), you should always use RelatedMembershipBooleanFilter, which is a BooleanFilter subclass that defaults to the correct lookup_expr and exclude values for this common case.

Filters must be declared using distinct=True if a queryset .distinct()is required to be called on the queryset.
Filters must not be set to be required using required=True
Filter methods defined using the method= keyword argument may only be used as a last resort (see below) when correct usage of field_name, lookup_expr, exclude, or other filter keyword arguments do not suffice. In other words: filter methods should used as the exception and not the rule.
Use of filter_overrides must be considered in cases where more-specific class-local overrides. The need may occasionally arise to change certain filter-level arguments used for filter generation, such such as changing a filter class, or customizing a UI widget. Any extra arguments are sent to the filter as keyword arguments at instance creation time. (Hint: extra must be a callable)

For example:

class ProductFilter(NautobotFilterSet):

     class Meta:
         model = Interface
         fields = "__all__"
         filter_overrides = {
             # This would change the default to all CharFields to use lookup_expr="icontains". It
             # would also pass in the custom `choices` generated by the `generate_choices()`
             # function.
             models.CharField: {
                 "filter_class": filters.MultiValueCharFilter,
                 "extra": lambda f: {
                     "lookup_expr": "icontains",
                     "choices": generate_choices(),
                 },
             },
             # This would make BooleanFields use a radio select widget vs. the default of checkbox
             models.BooleanField: {
                 "extra": lambda f: {
                     "widget": forms.RadioSelect,
                 },
             },
         }

Warning

Existing features of filtersets and filters must be exhausted first using keyword arguments before resorting to customizing, re-declaring/overloading, or defining filter methods.

Filter Methods¶

Filters on a filterset can reference a method (either a callable, or the name of a method on the filterset) to perform custom business logic for that filter field. However, many uses of filter methods in Nautobot are problematic because they break the ability for such filter fields to be properly reversible.

Consider this example from nautobot.dcim.filters.DeviceFilterSet.pass_through_ports:

    # Filter field definition is a BooleanFilter, for which an "isnull" lookup_expr
    # is the only valid filter expression
    pass_through_ports = django_filters.BooleanFilter(
        method="_pass_through_ports",
        label="Has pass-through ports",
    )

    # Method definition loses context and further the field's lookup_expr
    # falls back to the default of "exact" and the `name` value is irrelevant here.
    def _pass_through_ports(self, queryset, name, value):
        breakpoint()  # This was added to illustrate debugging with pdb below
        return queryset.exclude(frontports__isnull=value, rearports__isnull=value)

The default lookup_expr unless otherwise specified is “exact”, as seen in django_filters.conf:

  'DEFAULT_LOOKUP_EXPR': 'exact',

When this method is called, the internal state is default, making reverse introspection impossible, because the lookup_expr is defaulting to “exact”:

(Pdb) field = self.filters[name]
(Pdb) field.exclude
False
(Pdb) field.lookup_expr
'exact'

This means that the arguments for the field are being completely ignored and the hard-coded queryset queryset.exclude(frontports__isnull=value, rearports__isnull=value) is all that is being run when this method is called.

Additionally, name variable that gets passed to the method cannot be used here because there are two field names at play (frontports and rearports). This hard-coding is impossible to introspect and therefore impossible to reverse.

So while this filter definition could be improved like so, there is still no way to know what is going on in the method body:

    pass_through_ports = django_filters.BooleanFilter(
        method="_pass_through_ports",  # The method that is called
        exclude=True,                  # Perform an `.exclude()` vs. `.filter()``
        lookup_expr="isnull",          # Perform `isnull` vs. `exact``
        label="Has pass-through ports",
    )

For illustration, if we use another breakpoint, you can see that the filter field now has the correct attributes that can be used to help reverse this query:

(Pdb) field = self.filters[name]
(Pdb) field.exclude
True
(Pdb) field.lookup_expr
'isnull'

Except that it stops there because of the method body. Here are the problems:

There's no way to identify either of the field names required here
The name that is incoming to the method is the filter name as defined (pass_through_ports in this case) does not map to an actual model field
So the filter can be introspected for lookup_expr value using self.filters[name].lookup_expr, but it would have to be assumed that applies to both fields.
Same with exclude (self.filters[name].exclude)

It would be better to just eliminate pass_through_ports=True entirely in exchange for front_ports=True&rear_ports=True (current) or has_frontports=True&has_rearports=True (future).

Generating Reversible Q Objects¶

With consistent and proper use of filter field arguments when defining them on a filterset, a query could be constructed using the field_name and lookup_expr values. For example:

    def generate_query(self, field, value):
        query = Q()
        predicate = {f"{field.field_name}__{field.lookup_expr}": value}
        if field.exclude:
            query |= ~Q(**predicate)
        else:
            query |= Q(**predicate)
        return query


## Somewhere else in business logic:
field = filterset.filters[name]
value = filterset.data[name]
query = generate_query(field, value)
filterset.qs.filter(query).count()  # 339

Summary¶

For the vast majority of cases where we have method filters, it’s for Boolean filters
For the common case method filters are unnecessary technical debt and should be eliminated where better suited by proper use of filter field arguments
Reversibility may not always necessarily be required, but by properly defining field_name, lookup_expr, and exclude on filter fields, introspection becomes deterministic and reversible queries can be reliably generated as needed.
For exceptions such as DeviceFilterSet.has_primary_ip where it checks for both Device.primary_ip4 OR Device.primary_ip6, method filters may still be necessary, however, they would be the exception and not the norm.
The good news is that in the core there are not that many of these filter methods defined, but we also don’t want to see them continue to proliferate.

Using NautobotUIViewSet for App Development¶

Added in version 1.4.0

Using NautobotUIViewSet for App development is strongly recommended.

Best Practices¶

Base Classes¶

Data Model Best Practices¶

Model Existence in the Database¶

Model Validation¶

Consuming Model Validation¶

Field Naming in Data Models¶

Foreign Key related_name for Abstract Model Classes¶

Slug Field¶

CharField and SlugField Max Length¶

Getting URL Routes¶

UI Routes¶

REST API Routes¶

Examples¶

REST API Best Practices¶

Filtering Models with FilterSets¶

Mapping Model Fields to Filters¶

Filter Naming and Definition¶

Filter Methods¶

Generating Reversible Q Objects¶

Summary¶

Using NautobotUIViewSet for App Development¶

Foreign Key `related_name` for Abstract Model Classes¶