Getting Started¶
Git Branches¶
The Nautobot project follows a branching model based on Git-flow. As such, there are three persistent git branches:
main- Serves as a snapshot of the current stable releasedevelop- All bug fixes and minor feature development on the upcoming stable release occurs herenext- All major new feature development for the next feature release occurs here.
You will always base pull requests off of either the develop branch, for fixes and minor features, or next, if you're working on a feature targeted for a later release. Never target fix or feature pull requests into the main branch, which receives merges only from the develop branch and only for new stable releases of Nautobot.
Forking the Repo¶
When developing Nautobot, you'll be working on your own fork, so your first step will be to fork the official GitHub repository. You will then clone your GitHub fork locally for development.
Note
It is highly recommended that you use SSH with GitHub. If you haven't already, make sure that you setup Git and add an SSH key to your GitHub account before proceeding.
In this guide, SSH will be used to interact with Git.
Example output:
Cloning into 'nautobot'...
remote: Enumerating objects: 231, done.
remote: Counting objects: 100% (231/231), done.
remote: Compressing objects: 100% (147/147), done.
remote: Total 56705 (delta 134), reused 145 (delta 84), pack-reused 56474
Receiving objects: 100% (56705/56705), 27.96 MiB | 34.92 MiB/s, done.
Resolving deltas: 100% (44177/44177), done.
Example output:
CHANGELOG.md development nautobot.code-workspace
CODE_OF_CONDUCT.md docker poetry.lock
CONTRIBUTING.md docs pyproject.toml
LICENSE.txt examples renovate.json
NOTICE install.sh scripts
README.md invoke.yml.example tasks.py
SECURITY.md mkdocs.yml
changes nautobot
About Remote Repos¶
Git refers to remote repositories as remotes. When you make your initial clone of your fork, Git defaults to naming this remote origin. Throughout this documentation, the following remote names will be used:
origin- The default remote name used to refer to your fork of Nautobotupstream- The main remote used to refer to the official Nautobot repository
Setting up your Remotes¶
Remote repos are managed using the git remote command.
Upon cloning Nautobot for the first time, you will have only a single remote:
Example output:
origin git@github.com:yourusername/nautobot.git (fetch)
origin git@github.com:yourusername/nautobot.git (push)
Add the official Nautobot repo as a the upstream remote:
View your remotes again to confirm you've got both origin pointing to your fork and upstream pointing to the official repo:
Example output:
origin git@github.com:yourusername/nautobot.git (fetch)
origin git@github.com:yourusername/nautobot.git (push)
upstream git@github.com:nautobot/nautobot.git (fetch)
upstream git@github.com:nautobot/nautobot.git (push)
You're now ready to proceed to the next steps.
Hint
You will always push changes to origin (your fork) and pull changes from upstream (official repo).
Creating a Branch¶
Before you make any changes, always create a new branch. Again, for bug fixes and minor features, you'll want to create your branches from the develop branch, while for major new features, you'll branch from next instead.
Before you ever create a new branch, always checkout the appropriate branch and make sure you you've got the latest changes from upstream:
Warning
If you do not do this, you run the risk of having merge conflicts in your branch, and that's never fun to deal with. Trust us on this one.
Now that you've got the latest upstream changes, create your branch. Whether you're creating a branch off a fork or working against the Nautobot origin repo, you should follow this convention for naming your branch: u/yourusername-0000-branch-summary, where 0000 is the related GitHub issue number and yourusername is your GitHub username. For example:
If you do not have a relevant GitHub issue, please consider opening one to document the context behind your changes.
Prototypes¶
Sometimes code is written as a proof of concept or early implementation candidate but is not quite ready to be merged, or may be picked up by another author sometime in the future. In that case, the convention is to use the prototype/ prefix to the branch name and not requiring the original authors username. In that scenario, using the example above, you would instead:
Enabling Pre-Commit Hooks¶
Nautobot ships with a Git pre-commit hook script that automatically checks for style compliance and missing database migrations prior to committing changes. This helps avoid erroneous commits that result in CI test failures.
Note
This pre-commit hook currently only supports the Python Virtual Environment Workflow.
You are encouraged to enable it by creating a link to scripts/git-hooks/pre-commit:
Setting up your Development Environment¶
Getting started with Nautobot development is pretty straightforward, and should feel very familiar to anyone with Django development experience. We can recommend either a Docker Compose workflow (if you don't want to install dependencies such as PostgreSQL and Redis directly onto your system) or a Python virtual environment workflow.
Windows Development¶
The Docker Compose development workflow on Windows Subsystem for Linux (WSL) has been tested successfully with Docker Desktop using the WSL2 backend and the Ubuntu 20.04 WSL2 distribution. The Poetry workflow has also been tested successfully on the Ubuntu 20.04 WSL2 distribution.
To install WSL2 and Ubuntu follow the instructions from the WSL installation guide or if running Windows 10 version 2004 and higher you can open an administrator Powershell terminal and enter the following command:
This will install the WSL2 Ubuntu distribution. Reboot if prompted. After the image installs successfully you may install Docker Desktop with the WSL2 backend.
Docker Compose Workflow¶
This workflow uses Docker and Docker Compose and assumes that you have them installed.
For the Docker Compose workflow, Nautobot uses Invoke as a replacement for Make. Invoke was chosen because it is less arcane than make. Instead of a Makefile, Invoke reads the tasks.py in the project root.
Note
Although the Docker Compose workflow uses containers, it is important to note that the containers are running the local repository code on your machine. Changes you make to your local code will be picked up and executed by the containers.
Install Invoke¶
Because it is used to execute all common Docker workflow tasks, Invoke must be installed for your user environment. On most systems, if you're installing without root/superuser permissions, the default will install into your local user environment.
If you run into issues, you may also deliberately tell pip3 to install into your user environment by adding the --user flag:
If you encounter an externally-managed-environment error, you may need to install invoke through your OS's package manager. For example, apt-get install python3-invoke for Debian.
Please see the official documentation on Pip user installs for more information.
List Invoke Tasks¶
Now that you have an invoke command, list the tasks defined in tasks.py:
Example output:
Available tasks:
build Build Nautobot docker image.
build-and-check-docs Build docs for use within Nautobot.
build-dependencies
buildx Build Nautobot docker image using the experimental buildx docker functionality (multi-arch
capability).
check-migrations Check for missing migrations.
check-schema Render the REST API schema and check for problems.
cli Launch a bash shell inside the running Nautobot (or other) Docker container.
createsuperuser Create a new Nautobot superuser account (default: "admin"), will prompt for password.
debug Start Nautobot and its dependencies in debug mode.
destroy Destroy all containers and volumes.
docker-push Tags and pushes docker images to the appropriate repos, intended for release use only.
dumpdata Dump data from database to db_output file.
hadolint Check Dockerfile for hadolint compliance and other style issues.
integration-test Run Nautobot integration tests.
loaddata Load data from file.
makemigrations Perform makemigrations operation in Django.
migration-test Test database migration from a given dataset to latest Nautobot schema.
markdownlint Lint Markdown files.
migrate Perform migrate operation in Django.
nbshell Launch an interactive Nautobot shell.
performance-test Run Nautobot performance tests.
post-upgrade Performs Nautobot common post-upgrade operations using a single entrypoint.
pylint Perform static analysis of Nautobot code.
restart Gracefully restart containers.
ruff Run ruff to perform code formatting and/or linting.
serve-docs Runs local instance of mkdocs serve (ctrl-c to stop).
start Start Nautobot and its dependencies in detached mode.
stop Stop Nautobot and its dependencies.
tests Run all linters and unit tests.
unittest Run Nautobot unit tests.
unittest-coverage Report on code test coverage as measured by 'invoke unittest'.
version Show the version of Nautobot Python and NPM packages or bump them when a valid bump rule is provided.
vscode Launch Visual Studio Code with the appropriate Environment variables to run in a container.
yamllint Run yamllint to validate formatting applies to YAML standards.
Using Docker with Invoke¶
Changed in version 2.1.2
The Docker dev environment creates a superuser account by default with the username and password "admin". To disable this behavior, you must set the environment variable NAUTOBOT_CREATE_SUPERUSER=false using an override file.
A development environment can be easily started up from the root of the project using the following commands:
invoke build- Builds Nautobot docker imagesinvoke migrate- Performs database migration operation in Djangoinvoke debug- Starts Docker containers for Nautobot, PostgreSQL, Redis, Celery, and Celery Beat in debug mode and attaches their output to the terminal in the foreground. You may enter Control-C to stop the containers
Additional useful commands for the development environment:
invoke start [-s servicename]- Starts Docker containers for Nautobot, PostgreSQL, Redis, NGINX, Celery, and Celery Beat (or a specific container/service, such asinvoke start -s redis) to run in the background with debug disabledinvoke cli [-s servicename]- Launch abashshell inside the specified service container (if none is specified, defaults to the Nautobot container)invoke stop [-s servicename]- Stops all containers (or a specific container/service) created byinvoke startinvoke createsuperuser- Creates a superuser account for the Nautobot application
Note
The mkdocs container is not started automatically by invoke start or invoke debug. If desired, this container may be started manually with invoke start -s mkdocs.
Tip
The Nautobot server uses a Django webservice and worker uses watchdog to provide automatic reload of your web and worker servers in most cases when using invoke start or invoke debug.
Tip
To learn about advanced use cases within the Docker Compose workflow, see the Docker Compose Advanced Use Cases page.
Proceed to the Working in your Development Environment section
Python Virtual Environment Workflow¶
This workflow uses Python and Poetry to work with your development environment locally. It requires that you install the required system dependencies on your system yourself.
There are a few things you'll need:
- A Linux system or environment
- A MySQL or PostgreSQL server, which can be installed locally per the documentation
- A Redis server, which can also be installed locally
- A supported version of Python
- A recent version of Poetry
Install Poetry¶
Poetry is a tool for dependency management and packaging in Python. It allows you to declare the libraries your project depends on and it will manage (install/update/remove) them for you. It will also manage virtual environments automatically, and allow for publishing packages to the Python Package Index.
You may install Poetry in your user environment by running:
Changed in version 1.5.6
Poetry 1.3+ is required to be able to install packages against the latest lockfile.
Danger
Always utilize this documented method to install Poetry for use when developing Nautobot.
Never use pip to install Poetry into your Nautobot virtual environment, as it will result in dependency version conflicts that will very likely break Nautobot. Poetry is used as a package manager for Python packages, so you should not install it into the Nautobot environment, because it relies upon a number of the same dependencies as Nautobot, but with conflicting versions.
While there are certain cases where running pip install poetry is valid, such as in Nautobot's automated release deployments where Nautobot is not actually installed, installing Poetry into Nautobot's runtime development environment is not one of them!
For detailed installation instructions, please see the official Poetry installation guide.
Install Hadolint¶
Hadolint is a tool used to validate and lint Dockerfiles to ensure we are following best practices. On macOS with Homebrew you can install Hadolint by running:
Install markdownlint-cli¶
markdownlint-cli is a tool used to validate and lint Markdown files, such as Nautobot's documentation, to ensure that they are correctly constructed. On macOS with Homebrew you can install markdownlint-cli by running:
Creating a Python Virtual Environment¶
A Python virtual environment (or virtualenv) is like a container for a set of Python packages. A virtualenv allow you to build environments suited to specific projects without interfering with system packages or other projects. When installed per the documentation, Nautobot uses a virtual environment in production.
For Nautobot development, we have selected Poetry, which will transparently create a virtualenv for you, automatically install all dependencies required for Nautobot to operate, and will also install the nautobot-server CLI command that you will utilize to interact with Nautobot from here on out.
Bootstrap your virtual environment using poetry install:
Hint
If you are doing development or testing using MySQL, you may quickly install the mysqlclient library along with Nautobot by running poetry install --extras mysql.
This will create automatically create a virtualenv in your home directory, which houses a virtual copy of the Python executable and its related libraries and tooling. When running Nautobot for development, it will be run using the Python binary at found within the virtualenv.
Once created, you may activate the virtual environment using poetry shell:
Example output:
Spawning shell within /home/example/.cache/pypoetry/virtualenvs/nautobot-Ams_xyDt-py3.8
. /home/example/.cache/pypoetry/virtualenvs/nautobot-Ams_xyDt-py3.8/bin/activate
(nautobot-Ams_xyDt-py3.8) $
Notice that the console prompt changes to indicate the active environment. This updates the necessary system environment variables to ensure that any Python scripts are run within the virtual environment.
Observe also that the python interpreter is bound within the virtualenv:
Example output:
(nautobot-Ams_xyDt-py3.8) $ /home/example/.cache/pypoetry/virtualenvs/nautobot-Ams_xyDt-py3.8/bin/python
To exit the virtual shell, use exit:
Example output:
Working with Poetry¶
Poetry automatically installs your dependencies. However, if you need to install any additional dependencies this can be done with pip. For example, if you really like using ipython for development:
Example output:
- Install verify that you have the proper dependencies installed and are in the virtual environment via Poetry. This also ensures that you have the proper mkdocs themes installed.
It may not always be convenient to enter into the virtual shell just to run programs. You may also execute a given command ad hoc within the project's virtual shell by using poetry run:
Check out the Poetry usage guide for more tips.
Configuring Nautobot¶
Note
Unless otherwise noted, all following commands should be executed inside the virtualenv.
Hint
Use poetry shell to enter the virtualenv.
Nautobot's configuration file is nautobot_config.py.
Initializing a Config¶
You may also initialize a new configuration using nautobot-server init:
Example output:
Nautobot would like to send anonymized installation metrics to the project's maintainers.
These metrics include the installed Nautobot version, the Python version in use, an anonymous "deployment ID", and a list of one-way-hashed names of enabled Nautobot Apps and their versions.
Allow Nautobot to send these metrics? [y/n]: n
Installation metrics will not be sent by default.
Configuration file created at /home/example/.nautobot/nautobot_config.py
You may also specify alternate file locations. Please refer to Configuring Nautobot for how to do that.
Using the Development Config¶
A nautobot_config.py suitable for development purposes can be found at development/nautobot_config.py. You may customize the values there or utilize environment variables to override the default values.
If you want to use this file, initialize a config first, then copy this file to the default location Nautobot expects to find its config:
Required Settings¶
A newly created configuration includes sane defaults. If you need to customize them, edit your nautobot_config.py and update the following settings as required:
ALLOWED_HOSTS: This can be set to["*"]for development purposes and must be set ifDEBUG=FalseDATABASES: Database connection parameters, if different from the defaults- Redis settings: Redis configuration requires multiple settings. The defaults should be fine for development.
DEBUG: Set toTrueto enable verbose exception logging and, if installed, the Django debug toolbarEXTRA_INSTALLED_APPS: Optionally provide a list of extra Django apps you may desire to use for development
Working in your Development Environment¶
Below are common commands for working your development environment.
Creating a Superuser¶
Changed in version 2.1.2
The Docker dev environment creates a superuser account by default with the username and password "admin". To disable this behavior, you must set the environment variable NAUTOBOT_CREATE_SUPERUSER=false using an override file.
You'll need to create a administrative superuser account to be able to log into the Nautobot Web UI for the first time. Specifying an email address for the user is not required, but be sure to use a very strong password.
| Docker Compose Workflow | Virtual Environment Workflow |
|---|---|
invoke createsuperuser |
nautobot-server createsuperuser |
Starting the Development Server¶
Django provides a lightweight HTTP/WSGI server for development use. The development server automatically reloads Python code for each request, as needed. You don’t need to restart the server for code changes to take effect. However, some actions like adding files don’t trigger a restart, so you’ll have to restart the server in these cases.
Danger
DO NOT USE THIS SERVER IN A PRODUCTION SETTING. The development server and watchdog is for development and testing purposes only. It is neither performant nor secure enough for production use.
You can start the Nautobot development server with the invoke start command (if using Docker), or the nautobot-server runserver management command:
| Docker Compose Workflow | Virtual Environment Workflow |
|---|---|
invoke start |
nautobot-server runserver |
For example:
Example output:
Performing system checks...
System check identified no issues (0 silenced).
November 18, 2020 - 15:52:31
Django version 3.1, using settings 'nautobot.core.settings'
Starting development server at http://127.0.0.1:8080/
Quit the server with CONTROL-C.
Warning
Do not use poetry run nautobot-server runserver as it will crash unless you also pass the --noreload flag, which somewhat defeats the purpose of using the development server. It is recommended to use nautobot-server runserver from within an active virtualenv (e.g. poetry shell). This is a known issue with Django and Poetry.
Please see the official Django documentation on runserver for more information.
You can connect to the development server at localhost:8080.
Starting the Worker Server¶
In order to run Nautobot Jobs or anything that requires a worker you must start a Celery worker.
The worker is started in Docker Workflow with watchdog and can be setup to be started with watchdog in the Virtual Environment Workflow. Watchdog provides a similar experience to the Django lightweight HTTP/WSGI for restarting your application automatically. Watchdog can watch for changes on your filesystem, this is helpful when adjusting existing Python files to not have to restart the celery worker when testing jobs.
| Docker Compose Workflow | Virtual Environment Workflow |
|---|---|
invoke start |
nautobot-server celery worker |
Tip
You can leverage watchdog for your celery worker as described above, with the following watchmedo command in your development environment watchmedo auto-restart --directory './' --pattern '*.py' --recursive -- nautobot-server celery worker -l INFO --events.
Starting the Interactive Shell¶
Nautobot provides an interactive Python shell that sets up the server environment and gives you direct access to the database models for debugging. Nautobot extends this slightly to automatically import models and other utilities.
Run the Nautobot interactive shell with invoke nbshell (Docker) or the nautobot-server nbshell management command:
| Docker Compose Workflow | Virtual Environment Workflow |
|---|---|
invoke nbshell |
nautobot-server nbshell |
For example:
Example output:
# Shell Plus Model Imports
...
# Shell Plus Django Imports
...
# Django version 3.2.16
# Nautobot version 2.0.0a0
# Example Nautobot App version 1.0.0
Python 3.8.16 (default, Mar 23 2023, 04:48:11)
[GCC 10.2.1 20210110] on linux
Type "help", "copyright", "credits" or "license" for more information.
(InteractiveConsole)
>>>
Post-upgrade Operations¶
There will be times where you're working with the bleeding edge of Nautobot from the develop branch or feature branches and will need to pull in database changes or run server operations.
Get into the habit of running nautobot-server post_upgrade (or invoke post-upgrade when using Docker) after you pull in a major set of changes from Nautobot, which performs a handful of common operations (such as migrate) from a single command:
| Docker Compose Workflow | Virtual Environment Workflow |
|---|---|
invoke post-upgrade |
nautobot-server post_upgrade |
Please see the documentation on the nautobot-server post_upgrade command for more information.
Reinstalling Nautobot¶
Note
This mostly applies to working with Nautobot in a virtualenv, since Docker containers are typically rebuilt when the code changes.
Sometimes when files are renamed, moved, or deleted and you've been working in the same environment for a while, you can encounter weird behavior. If this happens, don't panic and nuke your environment.
First, use pip3 to explicitly uninstall the Nautobot package from the environment:
Example output:
Found existing installation: nautobot 1.0.0b2
Uninstalling nautobot-1.0.0b2:
Successfully uninstalled nautobot-1.0.0b2
Then try to just have Poetry do the right thing by telling it to install again:
Example output:
Installing dependencies from lock file
No dependencies to install or update
Installing the current project: nautobot (1.0.0-beta.2)
Running Tests¶
Throughout the course of development, it's a good idea to occasionally run Nautobot's test suite to catch any potential errors. Tests come in two primary flavors: Unit tests and integration tests.
For information about writing tests, refer to the testing documentation.
Unit Tests¶
Unit tests are automated tests written and run to ensure that a section of the Nautobot application (known as the "unit") meets its design and behaves as intended and expected. Most commonly as a developer of or contributor to Nautobot you will be writing unit tests to exercise the code you have written. Unit tests are not meant to test how the application behaves, only the individual blocks of code, therefore use of mock data and phony connections is common in unit test code. As a guiding principle, unit tests should be fast, because they will be executed quite often.
Unit tests are run using the invoke unittest command (if using the Docker development environment) or the nautobot-server test command:
| Docker Compose Workflow | Virtual Environment Workflow |
|---|---|
invoke unittest |
nautobot-server --config=nautobot/core/tests/nautobot_config.py test nautobot |
Info
By default invoke unittest will start and run the unit tests inside the Docker development container; this ensures that PostgreSQL and Redis servers are available during the test. However, if you have your environment configured such that nautobot-server can run locally, outside of the Docker environment, you may wish to set the environment variable INVOKE_NAUTOBOT_LOCAL=True to execute these tests in your local environment instead.
Useful Unit Test Parameters¶
The invoke unittest command supports a number of optional parameters to influence its behavior. Careful use of these parameters can greatly reduce the time it takes to run and re-run tests during development.
--failfast- Fail as soon as any test failure or error condition is encountered, instead of running to completion.--label <module.path>- Only run the specific subset of tests. Can be broad (--label nautobot.core.tests) or specific (--label nautobot.core.tests.test_graphql.GraphQLQueryTestCase).--no-buffer- Allow stdout/stderr output from the test to be seen in your terminal, instead of being hidden. If you're debugging code withbreakpoint(), you should use this option, as otherwise you'll never see the breakpoint happen.--no-cache-test-fixtures- Prevent caching test factory data to disk, or if a cache is already present, prevent loading from that cache when initializing the test environment. The--no-cache-test-fixturesoption is mandatory if you're actively making changes to factories or model migrations.--no-keepdb- Prevent saving the initialized test database after a test run for later reuse in a test re-run.--no-parallel- By default, Nautobot splits test execution across multiple parallel subprocesses to reduce the runtime of the test suite. When troubleshooting obscure test exceptions, it may be desirable to disable that behavior temporarily using this parameter.--parallel-workers- Explicitly specify the number of workers to use when running tests in parallel.--pattern- Only run tests which match the given substring. Can be used multiple times.--no-reusedb- Prevent reusing the initialized test database from a previous test run; instead, create a new database. The--no-reusedboption is mandatory if you're actively making changes to model migrations, or when switching between branches that differ in their included migrations.--skip-docs-build- Skip building/rebuilding the static Nautobot documentation before running the test. Saves some time on reruns when you haven't changed the documentation source files.--verbose- Run tests more verbosely, including describing each test case as it is run.
Unit Test Invocation Examples¶
In general, when you first run the Nautobot tests in your local copy of the repository, we'd recommend:
When there are too many cores on the testing machine, you can limit the number of parallel workers:
On subsequent reruns, you can add the other performance-related options:
When switching between significantly different branches of the code base (e.g. main vs develop vs next), you'll need to for once include the --no-keepdb option so that the test database can be destroyed and recreated appropriately:
To not use the cached test fixture, you will need to include the --no-cache-test-fixtures flag
To limit the test to a specific pattern or label, you can use the --label and --pattern options:
invoke unittest --verbose --skip-docs-build --label nautobot.core.tests.dcim.test_views.DeviceTestCase
invoke unittest --verbose --skip-docs-build --pattern Controller
Integration Tests¶
Integration tests are automated tests written and run to ensure that the Nautobot application behaves as expected when being used as it would be in practice. By contrast to unit tests, where individual units of code are being tested, integration tests rely upon the server code actually running, and web UI clients or API clients to make real connections to the service to exercise actual workflows, such as navigating to the login page, filling out the username/passwords fields, and clicking the "Log In" button.
Running integrations tests requires the use of Docker at this time. They can be directly invoked using nautobot-server test just as unit tests can, however, a headless Firefox browser provided by Selenium is required. Because Selenium installation and setup is complicated, we have included a configuration for this to work out of the box using Docker.
The Selenium container is running a standalone, headless Firefox "web driver" browser that can be remotely controlled by Nautobot for use in integration testing.
Before running integration tests, the selenium container must be running. If you are using the Docker Compose workflow, it is automatically started for you. For the Virtual Environment workflow, you must start it manually.
| Docker Compose Workflow | Virtual Environment Workflow |
|---|---|
| (automatic) | invoke start --service selenium |
Integration tests are run using the invoke integration-test command. All integration tests must inherit from nautobot.core.testing.integration.SeleniumTestCase, which itself is tagged with integration. A custom test runner has been implemented to automatically skip any test case tagged with integration by default, so normal unit tests run without any concern. To run the integration tests the --tag integration argument must be passed to nautobot-server test.
Changed in version 2.0.0
SeleniumTestCase was moved from the nautobot.utilities.testing.integration module to nautobot.core.testing.integration.
| Docker Compose Workflow | Virtual Environment Workflow |
|---|---|
invoke integration-test |
nautobot-server --config=nautobot/core/tests/nautobot_config.py test --tag integration nautobot |
Info
The same arguments supported by invoke unittest are supported by invoke integration-test, with the exception of --parallel at this time. The key difference being the dependency upon the Selenium container, and inclusion of the integration tag.
Tip
You may also use invoke integration-test in the Virtual Environment workflow given that the selenium container is running, and that the INVOKE_NAUTOBOT_LOCAL=True environment variable has been set.
Changed in version 1.5.11
The --cache-test-fixtures argument was added to the invoke integration-test and nautobot-server test commands to allow for caching of test factory data between test runs. See the factories documentation for more information.
Customizing Integration Test Executions¶
The following environment variables can be provided when running tests to customize where Nautobot looks for Selenium and where Selenium looks for Nautobot. If using the default setup documented above, there is no need to customize these.
NAUTOBOT_SELENIUM_URL- The URL used by the Nautobot test runner to remotely control the headless Selenium Firefox node. You can provide your own, but it must be aRemoteWebDriver. (Default:http://localhost:4444/wd/hub; for Docker:http://selenium:4444/wd/hub)NAUTOBOT_SELENIUM_HOST- The hostname used by the Selenium WebDriver to access Nautobot using Firefox. (Default:host.docker.internal; for Docker:nautobot)
Verifying the REST API Schema¶
If you make changes to the REST API, you should verify that the REST API OpenAPI schema renders correctly without errors. To verify that there are no errors, you can run the invoke check-schema command (if using the Docker development environment) or the nautobot-server spectacular command. In the latter case you should run the command for each supported REST API version that Nautobot provides (e.g. "2.0", "2.1")
| Docker Compose Workflow | Virtual Environment Workflow |
|---|---|
invoke check-schema |
nautobot-server spectacular --api-version 2.0 --validate --fail-on-warn --file /dev/null |
Verifying Code Style and Static Analysis¶
To enforce best practices around consistent coding style, Nautobot uses Ruff. Additionally, static analysis of Nautobot code is performed by Ruff and Pylint. You should run all of these commands and ensure that they pass fully with regard to your code changes before opening a pull request upstream.
| Docker Compose Workflow | Virtual Environment Workflow |
|---|---|
invoke ruff |
ruff format --check nautobot/ development/ examples/ tasks.pyand ruff check nautobot/ development/ examples/ tasks.py |
invoke pylint |
nautobot-server pylint nautobot tasks.pyand nautobot-server pylint --recursive development/ examples/ |
Handling Migrations¶
Checking Whether a New Migration is Needed¶
If you're unsure whether a database schema migration is needed based on your changes, you can run the following command:
| Docker Compose Workflow | Virtual Environment Workflow |
|---|---|
invoke check-migrations |
nautobot-server --config=nautobot/core/tests/nautobot_config.py makemigrations --dry-run --check |
Autogenerating Migrations¶
To generate new migrations if the above command indicates that a change is needed, you can run the following command:
| Docker Compose Workflow | Virtual Environment Workflow |
|---|---|
invoke makemigrations |
nautobot-server --config=nautobot/core/tests/nautobot_config.py makemigrations |
If your branch modifies a Django model (and as a result requires a database schema modification), please be sure to provide a meaningful name to the migration before pushing.
- If you have yet to run
invoke makemigrations, you can pass in a name for the migration with the-noption, exampleinvoke makemigrations -n provider_increase_account_length. - If you have already run
invoke makemigrations, rename the generated migration files, for example0004_provider_increase_account_lengthinstead of0004_auto_20211220_2104.
You’ll also want to run invoke ruff --autoformat (or ruff format) against the generated migration file as the autogenerated code doesn’t follow our style guide by default.
When modifying model field attributes, modify the test data in the tests too to reflect these changes and also any forms which refer to the model.
Testing Migrations¶
Nautobot includes a number of data sets under the development/datasets/ directory that represent various snapshots of historical database contents. You should validate whether your migrations correctly and successfully handle these datasets by running the invoke migration-test --db-engine [postgres|mysql] --dataset development/datasets/<filename> command (which handles both the Docker Compose workflow as well as the Virtual Environment workflow).
Working on Documentation¶
Some features require documentation updates or new documentation to be written. The documentation files can be found in the docs directory. To preview these changes locally, you can use mkdocs.
For substantial changes to the code (including new features, removal of existing features, or significant changes in behavior) you should always make corresponding documentation updates. Nautobot's documentation pipeline includes a custom plugin for mkdocs that adds a few useful macros for annotating such changes:
+++ 1.4.3, on a line by itself, is a shorthand for!!! version-added "Added in version 1.4.3"+/- 1.4.3, on a line by itself, is a shorthand for!!! version-changed "Changed in version 1.4.3"--- 1.4.3, on a line by itself, is a shorthand for!!! version-removed "Removed in version 1.4.3"
These admonitions in turn appear in the rendered documentation as follows:
Added in version 1.4.3
Changed in version 1.4.3
Removed in version 1.4.3
You can also add text to any of these admonitions for further clarity, for example:
??? version-added "Added in version 1.4.3"
The custom `mkdocs` plugin was added.
will render as:
Added in version 1.4.3
The custom mkdocs plugin was added.
Caution
While you can use the version-added / version-changed / version-removed admonitions directly to add a custom title to a specific admonition, in general, you should use the macros for consistency across the documentation.
Writing Documentation¶
You can preview the documentation using the server built into mkdocs, which should start a web server at http://localhost:8001.
| Docker Compose Workflow | Virtual Environment Workflow |
|---|---|
invoke start -s mkdocs |
mkdocs serve |
Documentation is written in Markdown. If you need to add additional pages or sections to the documentation, you can add them to mkdocs.yml at the root of the repository.
Verifying Documentation¶
Nautobot uses markdownlint-cli to verify correctness of the documentation. You should run this command and ensure that it passes fully with regard to your documentation changes before opening a pull request upstream.
| Docker Compose Workflow | Virtual Environment Workflow |
|---|---|
invoke markdownlint |
markdownlint --ignore nautobot/project-static --config .markdownlint.yml nautobot examples *.md |
Submitting Pull Requests¶
Once you're happy with your work and have verified that all tests pass, commit your changes and push it upstream to your fork. Always provide descriptive (but not excessively verbose) commit messages. When working on a specific issue, be sure to reference it.
Once your fork has the new commit, submit a pull request to the Nautobot repo to propose the changes. Be sure to provide a detailed accounting of the changes being made and the reasons for doing so.
Once submitted, a maintainer will review your pull request and either merge it or request changes. If changes are needed, you can make them via new commits to your fork: The pull request will update automatically.
Note
Remember, pull requests are entertained only for accepted issues. If an issue you want to work on hasn't been approved by a maintainer yet, it's best to avoid risking your time and effort on a change that might not be accepted.
Troubleshooting¶
Below are common issues you might encounter in your development environment and how to address them.
FATAL: sorry, too many clients already¶
When using nautobot-server runserver to do development you might run into a traceback that looks something like this:
Exception Type: OperationalError at /extras/tags/
Exception Value: FATAL: sorry, too many clients already
The runserver development server is multi-threaded by default, which means that every request is creating its own connection. If you are doing some local testing or development that is resulting in a lot of connections to the database, pass --nothreading to the runserver command to disable threading: