Getting started with charm testing

Testing charm code is an essential part of charming. Here we will see how to get started with it. We will look at the templates we have available and the frameworks we can use to write good unit, integration, and functional tests.


What you will learn:

  • What are the starting points for adding tests to a charm?
  • What do you typically want to test in a charm?
  • How can you do that?
    • What can you unit-test?
      • How to effectively use the Harness.
    • What can you only integration-test?
      • What integration testing frameworks are there?
    • Where can you apply functional testing?
  • How to automate this in a CI pipeline.


The two most popular ways to set up a charm project are via charmcraft init and by using the template-operator template repo.

Currently, so-called charmcraft profiles are being developed – these will consolidate some standard templates and make them available via charmcraft init --profile <profile name>.


The template you get with charmcraft init is rather compact. The testing infrastructure consists of a tests/ file, and a run_tests shell script that contains:

if [ -z "$VIRTUAL_ENV" -a -d venv/ ]; then            
    . venv/bin/activate                               
if [ -z "$PYTHONPATH" ]; then                         
    export PYTHONPATH="lib:src"                       
    export PYTHONPATH="lib:src:$PYTHONPATH"           
coverage run --branch --source=src -m unittest -v "$@"
coverage report -m                                    

In words, this will run:

  • flake8 linter checks.
  • unittest with coverage in /tests/.


The template-operator template provides a more robust starting point for most charming needs. It comes with:

  • example unit tests (/tests/unit/
  • example integration tests (/tests/integration/
  • instead of a run_tests script, a tox.ini file that contains pre-set environments for:
    • formatting (black)
    • linting (flake8, isort)
    • unit (pytest)
    • integration (pytest, pytest-operator)

So template-operator contains all of charmcraft init's template tooling, and more. It also contains github actions specifications that will configure GH to run the whole suite on each PR to master/main (more on that later).

template-operator is in practice a k8s charm template. Its example code is that of a kubernetes charm, and its CI will run the integration tests on microk8s. If you’re developing a machine charm, you are going to have to change all that.

In the rest of this tutorial we will assume that our project is set up with template-operator.

Unit Testing

A charm as an input → output function

In production, a charm is a stateless (modulo stored state) object that comes to life when the juju agent decides to execute it with a given context (we call that an event). The “inputs” of a charm run are therefore:

  • the event context
  • charm configuration
  • relation data
  • stored state

Only the event context is guaranteed to be present. The other input sources are optional, but typically a charm will have at least some config and a few integrations adding to its inputs.

The charm code executes and typically produces side-effects aimed at its workload (i.e. it writes files to a disk, runs commands on a system, or reconfigures a process) or at other charms it integrates with (i.e. it writes relation data). We call this ‘operating’ a workload, and that is what a charm is meant to do. The ways in which a charm operates can be roughly categorized as:

  • system operations (e.g. kill a process, restart a service, write a file, emit to a syslog server, make a HTTP request)
  • cloud operations (e.g. deploy a kubernetes service, launch a VM)
  • workload operations (e.g. send a request to a local server, write a config file)
  • juju operations (write relation data)

If the charm is a machine charm, workload operation calls can be done directly, while if we’re talking about a k8s charm, they will likely be mediated by Pebble.

“juju operations” are the most ‘meta’ of them all: they do not affect the workload in and of itself, but they share data which is meant to affect the operation of other charms that this charm is related to.

For the bigger picture of a charm’s lifecycle, you can read this and that.

What we are testing when we unit-test.

A ‘live’, deployed juju application will have access to all the inputs we discussed above, including environment variables, host system access, et cetera. Unittests will typically want to mock all that and focus on mapping inputs to expected outputs. Any combination of the input sources we mentioned above can result in any combination of operations. A few examples of increasing complexity of scenarios we may want to unittest:

  • if this event occurs, assert that the charm emits that system call
  • if this event occurs, given this config, assert that the charm writes to the filesystem a config file with this expected content
  • if this event occurs, given this relation data and that config value, assert that that system call is made and this relation data is written (to another relation)

You will notice that the starting point is typically always an event. A charm doesn’t do anything unless it’s being run, and it is only run when an event occurs. So there is always an event context to be mocked. This has important consequences for the unit-testing framework, as we will see below.

The harness.

In the charming world, unit testing means using the Harness.

The harness is the ‘mocker’ for most inputs and outputs you will need. Where a live charm would gather its input through context vars and calls to the juju api (by running the hook tools), a charm under unit-test will gather data via a mocked backend managed by the harness. Where a live charm would produce output by writing files to a filesystem, the harness exposes a mock filesystem the charm will be able to interact with without knowing the difference. More specific outputs, however, will be need to be mocked individually

A typical test with the harness will look like this:

  • set up:
    • set up the charm and its metadata
    • set up the harness
    • mock any ‘output’ callable that you know would misfire or break (e.g. a system call – you don’t want a unittest to reboot your laptop)
    • configure the charm
    • mock any relation data
  • mock an event
  • get the output
  • run assertions on the output

Obviously, other flows are possible, e.g. where you unittest individual charm methods without going through the whole event context setup, but this is the characteristic one.

Understanding the harness

When you instantiate a Harness object, the charm instance does not exist yet. Just like in a live charm, it is possible that when the charm is executed for the first time, the juju model already has given it storage, relations, some config, or leadership. This delay is meant to give us a chance to simulate this in our test setup. You create the harness, then you prepare the ‘initial state’ of the model mock, then you finally initialize the charm and simulate one or more events.

There are two ways to initialize a harnessed charm. When a charm is deployed, it goes through the Setup phase (for a description of the phases, see a charm’s life). That is a fixed sequence of events. The harness has a method, begin_with_initial_hooks(), that runs said sequence. Alternatively, you can initialize the charm by calling begin(). This will instantiate the charm without firing any Setup phase event.

After the Setup phase, the charm goes into Operation. To test operation-phase-related events, the harness provides some methods to simulate the most common scenarios. For example:

  • the cloud admin changes the charm config: harness.update_config
  • the cloud admin relates this charm to some other: harness.add_relation
  • a remote unit joins in a relation (e.g. because the cloud admin has scaled up a remote charm): harness.add_relation_unit
  • a remote unit touches its relation data: harness.update_relation_data
  • the cloud admin removes a relation: harness.remove_relation
  • a resource is attached/detached: harness.attach_storage/harness.detach_storage
  • a container becomes ready: harness.container_pebble_ready

Therefore, one typically will not have to manually .emit() events, but can rely on the harness’ utilities and focus on the higher level abstractions that they expose.

A note on the Harness’ double role: realistic backend mock and allmighty simulator

The Harness plays a double role in unittests, one that can cause some confusion to starters. On the one hand, the Harness provides a mock backend for the charm to talk with. If the charm-under-test wants to know what data is in a relation’s databag, it will ask the Harness for it. Since a real, deployed charm has to play by juju’s rules and cannot, for example, write a remote unit’s databag, if the charm attempts to do so via the Harness, the Harness will not allow it and raise the same error the charm would meet when deployed.

If you, as tester, want to mock the remote unit’s databag, so that when the charm attempts to read it, it will give some data back, then we want the Harness to not fail and tell you the remote databag is read-only. This is achieved by checking, from ops code, of whether we are in an event context or not.

When a live charm executes it is, invariably, because an event has occurred. A harnessed charm, however, can be kept alive while multiple events are ran on it, its methods are called in arbitrary orders, and the object is otherwise freely manipulated. The harness achieves event-runtime behaviour consistency with a ‘live’ charm by controlling the event context.

  • When the event context is unset, the harness operates in ‘allmighty’ mode and we assume that everything is allowed: all databags can be read/written at will.
  • When the event context is set, the harness operates in ‘strict’ mode and the backend will behave in a realistic way, raising errors on forbidden calls.

Therefore, if the Harness knows that an event is being fired, it will be able to set the strict mode and the code will be validated as it should. But if you bypass the Harness by manually calling an event handler, for example, then the code will run with superpowers and the test may miss some bugs.

class MyCharm(CharmBase):
     def _on_event(self, event):
         """This event handler attempts to write a remote app's databag and should therefore fail."""
         relation = self.get_relation()[]['foo'] = 'bar'
def test_allmighty():
     harness = Harness(MyCharm)
     harness.charm._on_event(None)  # no exception raised
def test_strict():
    harness = Harness(MyCharm)
    with pytest.raises(Exception):
        harness.charm.on.event.emit()   # will raise an exception

    with pytest.raises(Exception):
        # The same will happen if instead of emitting the event directly we 'cause' it to be emitted by the harness. E.g:
        # or whatever we need to do to trigger `event`

We are currently working on how to expose these knobs to the test environment, so that one can tell the Harness to temporarily pretend we are (or we are not) in a certain event context. see this

Writing a test

The typical way in which we want to structure a test is:

  • configure the required inputs
  • mock any function or system call you need to
  • initialize the charm
  • fire some event OR use one of the harness methods to trigger a predefined event sequence
  • assert some output matches what is expected, or some function is called with the expected parameters, etc…

This is how a simple test might look like:

from charm import MyCharm
from ops.testing import Harness

def test_pebble_ready_writes_config_file():
    """Test that on pebble-ready, a config file is written"""
    harness: Harness[MyCharm] = Harness(MyCharm)
    # If you want to mock charm config:
    harness.update_config({'foo': 'bar'})
    # if you want to mock charm leadership:

    # if you want to mock relation data:
    relation_ID = harness.add_relation('relation-name', 'remote-app-name')
    harness.add_relation_unit(relation_ID, 'remote-app-name/0')
    harness.update_relation_data(relation_ID, 'remote-app-name/0', {'baz': 'qux'})
    # we are done setting up the inputs.

    charm = harness.charm  # this is a MyCharm instance
    # this will fire a `workload-pebble-ready` event.

    # suppose that MyCharm has dropped a YAML config file via Pebble.push()
    container = charm.unit.get_container("workload")
    file = "/opt/workload/path_to_config_file.yaml"
    config = yaml.safe_load(container.pull(file).read())
    assert config[0]['foo']['bar'] == 'baz'  # or whatever

Don’t forget, in the setup part, to specify the charm’s metadata. A charm’s available configuration depends on its config.yaml metadata file, and the relations it can have depend on its metadata.yaml file, and so on. You need to pass yaml-encoded strings containing the necessary data to the Harness constructor in order for the harness to be aware of what config is possible and which relations and actions are available.

An important difference between a harnessed charm and a ‘live’, deployed charm is that the harness holds on to the charm instance between events, while a deployed charm garbage-collects the charm instance between hooks. So if your charm were to set some states in, say, instance attributes, and rely on it on subsequent event handling loops, the unit tests based on the harness would not be able to catch that mistake. An integration test would.

Integration Testing

Where unit testing focuses on black-box method-by-method verification, integration testing focuses on the big picture. Typically integration tests check that the charm does not break (i.e. blocks with status blocked/error) when a (mocked) cloud admin performs certain operations. These operations are scripted by using, in order of abstraction:

Things you typically want to test with integration tests:

  • The charm can be packed (i.e. charmcraft pack does not error)
  • The charm can be deployed (i.e. juju deploy ./packed_charm.charm deploys an application that reaches active or waiting within a reasonable time frame)

These are ‘smoke tests’ that should always be present. The following are non-smokey, proper integration tests.

  • The charm can be related to other applications without erroring
    • and the relation has the expected effect on the charm’s operation logic
  • The charm can be configured
    • and the config has the expected effect on the charm’s operation logic
  • The actions supported by the charm can be executed
    • and return the expected results
  • Given any combination of the above relations, configs, etc, the charm ‘works’, i.e. the workload it operates does whatever it is supposed to do.

Testing with pytest-operator

In template-operator, the tox.ini file contains the following lines:

description = Run integration tests
deps =
commands =
    pytest -v --tb native --ignore={[vars]tst_path}unit --log-cli-level=INFO -s {posargs}

So you can run tox -e integration and the integration tests will run, provided that you have a cloud available on your local juju client. In other words, you should be able to open a shell and execute juju add-model foo.

The entry point for all pytest-operator tests is the ops_test fixture. The fixture is a module-scoped context which, on entry, adds to juju a randomly-named new model and destroys it on exit. All tests in that module, and all interactions with the ops_test object, will take place against that model.

If you run tox -e integration --model foo, ops_test will not generate a random new model, but instead will connect to an existing model called foo and the tests will be run there. This means that multiple modules will all be executed in the same model, which can give headaches in CIs, where the --model argument is often used to provide a predictable model name for logging collection purposes. This is the case for example in template-operator.

Pro tip: you can prevent ops_test from tearing down the model on exit by passing the --keep-models argument. This is useful when the tests fail and the logs don’t provide a sufficient post-mortem and a real live autopsy is required.

Detailed documentation of how to use ops_test and pytest-operator is out of scope for this document. However, this is how a typical integration test could look like (yes, pytest-operator has an async API):

async def test_operation(ops_test: OpsTest):
    # tweak the config
    app: Application = ops_test.model.applications.get("tester")
    await app.set_config({"my-key": "my-value"})
    # add another charm and relate them
    await ops_test.model.deploy('other-app')
    await ops_test.model.relate('tester:endpoint1', 'other-charm:endpoint2')
    # scale it up
    await app.add_unit(2)
    # remove another app
    await ops_test.model.remove_application('yet-another-app')
    # run an action on a unit
    unit: Unit = app.units[1]
    action = await'my-action')
    assert action.results == <foo>
    # what this means depends on the workload
    assert charm_operates_correctly()  

ops_test has, of course, API for all inverse operations: remove an app, scale it down, remove a relation…

A good integration testing suite will check that the charm continues to operate as expected whenever possible, by combining these simple elements.

Functional Testing

Some charms represent their workload by means of an object-oriented wrapper, which mediates between operator code and the implementation of operation logic. In such cases, it can be useful to add a third category of tests, namely functional tests, that black-box test that workload wrapper without worrying about the substrate it runs on (the charm, the cloud, the machine or pod…). For an example charm adopting this strategy, see parca-operator.

Nowadays, the preferred tool to do functional testing is scenario. See this tutorial for a good start.


Typically, you want the tests to be ran automatically against any PR into your repository’s main branch, and sometimes, to trigger a new release whenever that succeeds. CD is out of scope for this article, but we will look at how to set up a basic CI.

template-operator once again has the essential plumbing already in place.

Let’s look at .github/workflows/ci.yaml, it contains three jobs: lint, unit-test and integration-test-microk8s.

    name: Lint
    runs-on: ubuntu-latest
      - name: Checkout
        uses: actions/checkout@v3
      - name: Install dependencies
        run: python3 -m pip install tox
      - name: Run linters
        run: tox -e lint

unit-test is very similar, and both are rather self-explanatory. They work closely together with tox.ini, which does most of the job of specifying what it means to run a given test suite.

integration-test-microk8s is a bit more complex, because in order to run itests we need to deploy a kubernetes cluster. That is achieved by this step:

      - name: Setup operator environment
        uses: charmed-kubernetes/actions-operator@main
          provider: microk8s

This will install microk8s on the test runner and pytest-operator will be able to connect to it.

You can find more actions, advanced documentation and use cases in charming-actions

Secondly, the test runner step passes --model testing to tox to, as we mentioned above, later be able to use the logdump action on the model to gather debug logs.

      - name: Run integration tests
        run: tox -e integration -- --model testing
      - name: Dump logs
        uses: canonical/charm-logdump-action@main
        if: failure()
          app: <name of the app being tested>
          model: testing


We have examined all angles one might take when testing a charm, and given a brief overview of the most popular frameworks for implementing unit and integration tests, all the way to how one would link them up with a CI system to make sure the repository remains clean and tested.


Thank you for putting this together!

After an initial read through, I have the following comments:

  1. The language is a little too informal. I think that we can tighten it up, while still keeping the tone approachable and friendly.

  2. The intro to the unit testing section is overly long. I think that it could be rewritten to be much more concise.

If I were to rewrite it, I would switch the focus from what the harness cannot do to what it enables. E.g., instead of talking about how some things must be mocked, talk about how the harness eliminates the need for mocking a lot of calls. That should both streamline the doc, and provide a more compelling case for writing tests with the harness.

  1. I would like to see examples of unit testing and integration testing throughout their respective sections. I think that it’s a mistake to wait until the end of the unit testing section to show an example.

~ Penny

I saw there it’s said that

Could you provide some additional info for this? This would be helpful but I didn’t find any relevant documentation on this mocking feature.

Good starting point: Container.push docs

In a nutshell:

  • from test code, you can harness.model.get_container('x').push(...), or pull, or list files, or anything you’d normally do with a container from charm code, but with god powers
  • from charm code, you shouldn’t have to change anything for things to ‘work’. The charm can push, pull, etc… from all containers it has access to (after you’ve set them up with the harness).

For an example of testing code making use of containers: grafana unittests

1 Like

This is a fantastic write up. I’ve entered into the space a little and managed to create a build->release->contious-integration pipeline for the Nextcloud charm. It’s still very basic (which I like).

For reference,

Here is the Nextcloud release pipeline, using the reusable workflows from the data-platform workflows at Canonical and @carlcsaposs .

It will:

  1. Build the charm on a push to main branch.
  2. Upload and create a new revision on the edge channel in charmhub.
  3. Create a github release with the same revision number as the uploaded charm.

Here is the Nextcloud Continous Integration (CI) pipeline (manually triggered) using the “actions-operator

It will:

  1. Spawn a juju environment and a LXD controller (This is what the actions-operator do).
  2. Deploy Nextcloud and Posgresql and relates them.
  3. Wait for the Nextcloud to become “active” within a fixed timeframe.

Much help from @carlcsaposs (github actions) and @taurus (more github) and @tmihoc (docs) and the @ppasotti for this above. Also thanx for the hints from @barrettj12 about the various examples and workflows.