Developing ANTA tests

Info

This documentation applies for both creating tests in ANTA or creating your own test package.

ANTA is not only a Python library with a CLI and a collection of built-in tests, it is also a framework you can extend by building your own tests.

Generic approach¶

A test is a Python class where a test function is defined and will be run by the framework.

ANTA provides an abstract class AntaTest. This class does the heavy lifting and provide the logic to define, collect and test data. The code below is an example of a simple test in ANTA, which is an AntaTest subclass:

from anta.models import AntaTest, AntaCommand
from anta.decorators import skip_on_platforms


class VerifyTemperature(AntaTest):
    """Verifies if the device temperature is within acceptable limits.

    Expected Results
    ----------------
    * Success: The test will pass if the device temperature is currently OK: 'temperatureOk'.
    * Failure: The test will fail if the device temperature is NOT OK.

    Examples
    --------
    ```yaml
    anta.tests.hardware:
      - VerifyTemperature:
    ```
    """

    name = "VerifyTemperature"
    description = "Verifies the device temperature."
    categories: ClassVar[list[str]] = ["hardware"]
    commands: ClassVar[list[AntaCommand | AntaTemplate]] = [AntaCommand(command="show system environment temperature", revision=1)]

    @skip_on_platforms(["cEOSLab", "vEOS-lab", "cEOSCloudLab"])
    @AntaTest.anta_test
    def test(self) -> None:
        """Main test function for VerifyTemperature."""
        command_output = self.instance_commands[0].json_output
        temperature_status = command_output.get("systemStatus", "")
        if temperature_status == "temperatureOk":
            self.result.is_success()
        else:
            self.result.is_failure(f"Device temperature exceeds acceptable limits. Current system status: '{temperature_status}'")

AntaTest also provide more advanced capabilities like AntaCommand templating using the AntaTemplate class or test inputs definition and validation using AntaTest.Input pydantic model. This will be discussed in the sections below.

AntaTest structure¶

Full AntaTest API documentation is available in the API documentation section

Class Attributes¶

name (str): Name of the test. Used during reporting.
description (str): A human readable description of your test.
categories (list[str]): A list of categories in which the test belongs.
commands ([list[AntaCommand | AntaTemplate]]): A list of command to collect from devices. This list must be a list of AntaCommand or AntaTemplate instances. Rendering AntaTemplate instances will be discussed later.

Info

All these class attributes are mandatory. If any attribute is missing, a NotImplementedError exception will be raised during class instantiation.

Instance Attributes¶

Attributes:

Name	Type	Description
`device`	`AntaDevice`	AntaDevice instance on which this test is run.
`inputs`	`Input`	AntaTest.Input instance carrying the test inputs.
`instance_commands`	`list[AntaCommand]`	List of AntaCommand instances of this test.
`result`	`TestResult`	TestResult instance representing the result of this test.
`logger`	`Logger`	Python logger for this test instance.

Logger object

ANTA already provides comprehensive logging at every steps of a test execution. The AntaTest class also provides a logger attribute that is a Python logger specific to the test instance. See Python documentation for more information.

AntaDevice object

Even if device is not a private attribute, you should not need to access this object in your code.

Test Inputs¶

AntaTest.Input is a pydantic model that allow test developers to define their test inputs. pydantic provides out of the box error handling for test input validation based on the type hints defined by the test developer.

The base definition of AntaTest.Input provides common test inputs for all AntaTest instances:

Input model¶

Full Input model documentation is available in API documentation section

Attributes:

Name	Type	Description
`result_overwrite`	`ResultOverwrite \| None`	Define fields to overwrite in the TestResult object.

ResultOverwrite model¶

Full ResultOverwrite model documentation is available in API documentation section

Attributes:

Name	Type	Description
`description`	`str \| None`	Overwrite `TestResult.description`.
`categories`	`list[str] \| None`	Overwrite `TestResult.categories`.
`custom_field`	`str \| None`	A free string that will be included in the TestResult object.

Note

The pydantic model is configured using the extra=forbid that will fail input validation if extra fields are provided.

Methods¶

test(self) -> None: This is an abstract method that must be implemented. It contains the test logic that can access the collected command outputs using the instance_commands instance attribute, access the test inputs using the inputs instance attribute and must set the result instance attribute accordingly. It must be implemented using the AntaTest.anta_test decorator that provides logging and will collect commands before executing the test() method.
render(self, template: AntaTemplate) -> list[AntaCommand]: This method only needs to be implemented if AntaTemplate instances are present in the commands class attribute. It will be called for every AntaTemplate occurrence and must return a list of AntaCommand using the AntaTemplate.render() method. It can access test inputs using the inputs instance attribute.

Test execution¶

Below is a high level description of the test execution flow in ANTA:

ANTA will parse the test catalog to get the list of AntaTest subclasses to instantiate and their associated input values. We consider a single AntaTest subclass in the following steps.
ANTA will instantiate the AntaTest subclass and a single device will be provided to the test instance. The Input model defined in the class will also be instantiated at this moment. If any ValidationError is raised, the test execution will be stopped.
If there is any AntaTemplate instance in the commands class attribute, render() will be called for every occurrence. At this moment, the instance_commands attribute has been initialized. If any rendering error occurs, the test execution will be stopped.
The AntaTest.anta_test decorator will collect the commands from the device and update the instance_commands attribute with the outputs. If any collection error occurs, the test execution will be stopped.
The test() method is executed.

Writing an AntaTest subclass¶

In this section, we will go into all the details of writing an AntaTest subclass.

Class definition¶

Import anta.models.AntaTest and define your own class. Define the mandatory class attributes using anta.models.AntaCommand, anta.models.AntaTemplate or both.

Info

Caching can be disabled per AntaCommand or AntaTemplate by setting the use_cache argument to False. For more details about how caching is implemented in ANTA, please refer to Caching in ANTA.

from anta.models import AntaTest, AntaCommand, AntaTemplate


class <YourTestName>(AntaTest):
    """
    <a docstring description of your test>
    """

    name = "YourTestName"                                           # should be your class name
    description = "<test description in human reading format>"
    categories = ["<arbitrary category>", "<another arbitrary category>"]
    commands = [
        AntaCommand(
            command="<EOS command to run>",
            ofmt="<command format output>",
            version="<eAPI version to use>",
            revision="<revision to use for the command>",           # revision has precedence over version
            use_cache="<Use cache for the command>",
        ),
        AntaTemplate(
            template="<Python f-string to render an EOS command>",
            ofmt="<command format output>",
            version="<eAPI version to use>",
            revision="<revision to use for the command>",           # revision has precedence over version
            use_cache="<Use cache for the command>",
        )
    ]

Command revision and version

Most of EOS commands return a JSON structure according to a model (some commands may not be modeled hence the necessity to use text outformat sometimes.
The model can change across time (adding feature, … ) and when the model is changed in a non backward-compatible way, the revision number is bumped. The initial model starts with revision 1.
A revision applies to a particular CLI command whereas a version is global to an eAPI call. The version is internally translated to a specific revision for each CLI command in the RPC call. The currently supported version values are 1 and latest.
A revision takes precedence over a version (e.g. if a command is run with version=”latest” and revision=1, the first revision of the model is returned)
By default, eAPI returns the first revision of each model to ensure that when upgrading, integrations with existing tools are not broken. This is done by using by default version=1 in eAPI calls.

By default, ANTA uses version="latest" in AntaCommand, but when developing tests, the revision MUST be provided when the outformat of the command is json. As explained earlier, this is to ensure that the eAPI always returns the same output model and that the test remains always valid from the day it was created. For some commands, you may also want to run them with a different revision or version.

For instance, the VerifyBFDPeersHealth test leverages the first revision of show bfd peers:

# revision 1 as later revision introduce additional nesting for type
commands = [AntaCommand(command="show bfd peers", revision=1)]

Inputs definition¶

If the user needs to provide inputs for your test, you need to define a pydantic model that defines the schema of the test inputs:

class <YourTestName>(AntaTest):
    """Verifies ...

    Expected Results
    ----------------
    * Success: The test will pass if ...
    * Failure: The test will fail if ...

    Examples
    --------
    ```yaml
    your.module.path:
      - YourTestName:
        field_name: example_field_value
    ```
    """
    ...
    class Input(AntaTest.Input):
        """Inputs for my awesome test."""
        <input field name>: <input field type>
        """<input field docstring>"""

To define an input field type, refer to the pydantic documentation about types. You can also leverage anta.custom_types that provides reusable types defined in ANTA tests.

Regarding required, optional and nullable fields, refer to this documentation on how to define them.

Note

All the pydantic features are supported. For instance you can define validators for complex input validation.

Template rendering¶

Define the render() method if you have AntaTemplate instances in your commands class attribute:

class <YourTestName>(AntaTest):
    ...
    def render(self, template: AntaTemplate) -> list[AntaCommand]:
        return [template.render(<template param>=input_value) for input_value in self.inputs.<input_field>]

You can access test inputs and render as many AntaCommand as desired.

Test definition¶

Implement the test() method with your test logic:

class <YourTestName>(AntaTest):
    ...
    @AntaTest.anta_test
    def test(self) -> None:
        pass

The logic usually includes the following different stages:

Parse the command outputs using the self.instance_commands instance attribute.
If needed, access the test inputs using the self.inputs instance attribute and write your conditional logic.
Set the result instance attribute to reflect the test result by either calling self.result.is_success() or self.result.is_failure("<FAILURE REASON>"). Sometimes, setting the test result to skipped using self.result.is_skipped("<SKIPPED REASON>") can make sense (e.g. testing the OSPF neighbor states but no neighbor was found). However, you should not need to catch any exception and set the test result to error since the error handling is done by the framework, see below.

The example below is based on the VerifyTemperature test.

class VerifyTemperature(AntaTest):
    ...
    @AntaTest.anta_test
    def test(self) -> None:
        # Grab output of the collected command
        command_output = self.instance_commands[0].json_output

        # Do your test: In this example we check a specific field of the JSON output from EOS
        temperature_status = command_output["systemStatus"] if "systemStatus" in command_output.keys() else ""
        if temperature_status == "temperatureOk":
            self.result.is_success()
        else:
            self.result.is_failure(f"Device temperature exceeds acceptable limits. Current system status: '{temperature_status}'")

As you can see there is no error handling to do in your code. Everything is packaged in the AntaTest.anta_tests decorator and below is a simple example of error captured when trying to access a dictionary with an incorrect key:

class VerifyTemperature(AntaTest):
    ...
    @AntaTest.anta_test
    def test(self) -> None:
        # Grab output of the collected command
        command_output = self.instance_commands[0].json_output

        # Access the dictionary with an incorrect key
        command_output['incorrectKey']

ERROR    Exception raised for test VerifyTemperature (on device 192.168.0.10) - KeyError ('incorrectKey')

Get stack trace for debugging

If you want to access to the full exception stack, you can run ANTA in debug mode by setting the ANTA_DEBUG environment variable to true. Example:

$ ANTA_DEBUG=true anta nrfu --catalog test_custom.yml text

Test decorators¶

In addition to the required AntaTest.anta_tests decorator, ANTA offers a set of optional decorators for further test customization:

anta.decorators.deprecated_test: Use this to log a message of WARNING severity when a test is deprecated.
anta.decorators.skip_on_platforms: Use this to skip tests for functionalities that are not supported on specific platforms.

from anta.decorators import skip_on_platforms

class VerifyTemperature(AntaTest):
    ...
    @skip_on_platforms(["cEOSLab", "vEOS-lab"])
    @AntaTest.anta_test
    def test(self) -> None:
        pass

Access your custom tests in the test catalog¶

This section is required only if you are not merging your development into ANTA. Otherwise, just follow contribution guide.

For that, you need to create your own Python package as described in this hitchhiker’s guide to package Python code. We assume it is well known and we won’t focus on this aspect. Thus, your package must be impartable by ANTA hence available in the module search path sys.path (you can use PYTHONPATH for example).

It is very similar to what is documented in catalog section but you have to use your own package name.2

Let say the custom Python package is anta_custom and the test is defined in anta_custom.dc_project Python module, the test catalog would look like:

anta_custom.dc_project:
  - VerifyFeatureX:
      minimum: 1

And now you can run your NRFU tests with the CLI:

anta nrfu text --catalog test_custom.yml
spine01 :: verify_dynamic_vlan :: FAILURE (Device has 0 configured, we expect at least 1)
spine02 :: verify_dynamic_vlan :: FAILURE (Device has 0 configured, we expect at least 1)
leaf01 :: verify_dynamic_vlan :: SUCCESS
leaf02 :: verify_dynamic_vlan :: SUCCESS
leaf03 :: verify_dynamic_vlan :: SUCCESS
leaf04 :: verify_dynamic_vlan :: SUCCESS