Create Actions#

This page demonstrates how to define an action in-tree such that it shows up in supported user interfaces like Treeherder or Taskcluster web.

At a high level, the process looks like this:

The Decision task produces an artifact, public/actions.json, indicating what actions are available. The format of this file is described in the actions.json spec.

A user interface (for example, Treeherder or the Taskcluster web UI) consults actions.json and presents appropriate choices to the user, if necessary gathering additional data from the user, such as the number of times to re-trigger a task.

The user interface follows the action description to carry out the action. In most cases that entails creating an “action task” encapsulating the provided information. The action task is responsible for carrying out the named action, and may create new sub-tasks if necessary (for example, to re-trigger a task).

Note

You can generate actions.json locally by running taskgraph actions.

Defining Action Tasks#

Taskgraph provides a built-in mechanism for defining action tasks called callback actions. These are actions that call back into in-tree logic.

First an action is registered with a name, title, description, context, input schema and a Python callback function. When the action is triggered in a user interface, input matching the schema is collected, passed to a new task which then calls your Python callback. This system is powerful and enables actions to perform a wide range of operations.

Suppose we want to create a new “Hello World” action. First, create a file taskcluster/project_taskgraph/actions/hello_world.py. It should look something like:

from taskgraph.actions.registry import register_callback_action

@register_callback_action(
    name='hello',
    title='Say Hello',
    symbol='hello-world',  # Symbol to display in Treeherder.
    description="Says hello",
    order=10000,  # Order in which it should appear relative to other actions
)
def hello_world_action(parameters, graph_config, input, task_group_id, task_id, task):
    print(f"Hello from {task_group_id}!")

The arguments passed into this function are:

parameters: an instance of taskgraph.parameters.Parameters, carrying Decision task parameters from the original decision task.
graph_config: an instance of taskgraph.config.GraphConfig, carrying configuration for this tree
input: the input from the user triggering the action (if any)
task_group_id: the target task group on which this action should operate
task_id: the target task on which this action should operate (or None if it is operating on the whole group)
task: the definition of the target task (or None, as for task_id)

The order value is the sort key defining the order of actions in the resulting actions.json file. If multiple actions have the same name and match the same task, the action with the smallest order will be used.

Setting the Action Context#

The example above defines an action that is available in the context-menu for the entire task group (result-set or push in Treeherder terminology). To create an action that shows up in the context menu for a task we would specify the context argument.

The context argument should be a list of tag-sets, such as context=[{"platform": "linux"}], which will make the action show up in the context-menu for any task with task.tags.platform = 'linux'. Below is some examples of context arguments and the resulting conditions on task.tags (tags used below are just illustrative).

context=[{"platform": "linux"}]:: Requires task.tags.platform = 'linux'.
context=[{"kind": "test", "platform": "linux"}]:: Requires task.tags.platform = 'linux' and task.tags.kind = 'test'.
context=[{"kind": "test"}, {"platform": "linux"}]:: Requires task.tags.platform = 'linux' or task.tags.kind = 'test'.
context=[{}]:: Requires nothing and the action will show up in the context menu for all tasks.
context=[]:: Is the same as not setting the context parameter, which will make the action show up in the context menu for the task-group. (i.e., the action is not specific to some task)

The example action below will be shown in the context-menu for tasks with task.tags.platform = 'linux':

from taskgraph.actions.registry import register_callback_action

@register_callback_action(
    name='retrigger',
    title='Retrigger',
    symbol='rt',  # Show the callback task in Treeherder as 'rt'
    description="Create a clone of the task",
    order=1,
    context=[{'platform': 'linux'}]
)
def retrigger_action(parameters, graph_config, input, task_group_id, task_id, task):
    # input will be None
    print(f"Retriggering: {task_id}")
    # code to perform the re-trigger

When the context parameter is set, the task_id and task parameters will be provided to the callback. In this case the task_id and task parameters will be the taskId and task definition of the task whose context-menu the action was triggered on.

Typically, the context parameter is used for actions that operate on tasks, such as retriggering, running a specific test case, creating a loaner, bisection, etc. You can think of the context as a place the action should appear, but it’s also very much a form of input the action can use.

It’s also possible to pass a function which takes the set of parameters as input and returns the context as output:

context=lambda params:
    [{}] if int(params['level']) < 3 else [{'worker-implementation': 'docker-worker'}],

Specifying an Input Schema#

In all the examples so far the input argument for the callbacks has been None. To make an action that takes input you must specify an input schema. This is done by passing a JSON schema as the schema argument.

When designing a schema for the input it is important to exploit as many of the JSON schema validation features as reasonably possible. Furthermore, it is strongly encouraged that the title and description properties in JSON schemas be used to provide a detailed explanation of what the input value will do. Authors can reasonably expect JSON schema description properties to be rendered as markdown before being presented (though this may not be guaranteed for all interfaces).

The example below illustrates how to specify an input schema. Notice that while this example doesn’t specify a context it is perfectly legal to specify both input and context:

from taskgraph.actions.registry import register_callback_action

@register_callback_action(
    name='run-missing',
    title='Run Missing Tasks',
    symbol='rm',  # Show the callback task in Treeherder as 'rm'
    description="Run tasks that have been optimized away.",
    order=1,
    input={
        'title': 'Action Options',
        'description': 'Options for how you wish to run missing tasks',
        'properties': {
            'priority': {
                'title': 'priority'
                'description': 'Priority that should be given to the tasks',
                'type': 'string',
                'enum': ['low', 'normal', 'high'],
                'default': 'low',
            },
            'runAll': {
                'title': 'Run Missing'
                'description': 'When set also re-trigger tasks non-missing tasks',
                'type': 'boolean',
                'default': 'false',
            }
        },
        'required': ['priority', 'runAll'],
        'additionalProperties': False,
    },
)
def retrigger_action(parameters, graph_config, input, task_group_id, task_id, task):
    word = "all" if input["runAll"] else "missing"
    print(f"Run {word} tasks with priority '{input['priority']}'")
    # code to run tasks

The format of the schema follows the json-schema specification.

When the schema argument is given the callback will always be called with an input argument that satisfies the previously given JSON schema. It is encouraged to set additionalProperties: false, as well as specifying all properties as required in the JSON schema. Furthermore, it’s good practice to provide default values for properties, as user interface generators will often take advantage of such properties.

It is possible to specify the schema parameter as a callable that returns the JSON schema. It will be called with a keyword parameter graph_config with the graph configuration <taskgraph-graph-config> of the current taskgraph.

Once you have specified input and context as applicable for your action you can do pretty much anything you want from within your callback. Whether you want to create one or more tasks or run a specific piece of code like a test.

Conditional Availability#

The decision parameters passed to the callback are also available when the decision task generates the list of actions to be displayed in the user interface. When registering an action callback the availability option can be used to specify a callable which, given the decision parameters, determines if the action should be available. The feature is illustrated below:

from taskgraph.actions.registry import register_callback_action

@register_callback_action(
    name='hello',
    title='Say Hello',
    symbol='hw',  # Show the callback task in treeherder as 'hw'
    description="Simple **proof-of-concept** callback action",
    order=2,
    # Define an action that is only included if this is a pull request.
    available=lambda params: params["tasks_for"] == "github-pull-request",
)
def try_only_action(parameters, graph_config, input, task_group_id, task_id, task):
    print "My try-only action"

See Parameters for a reference on the available parameters.

Creating Tasks#

Actions often involve creating other tasks in some fashion. The create_tasks() utility function provides a full-featured way to create new tasks. Its features include creating prerequisite tasks, operating in a “testing” mode with ./mach taskgraph test-action-callback, and generating artifacts that can be used by later action tasks to figure out what happened. See the source for more detailed docmentation.

The artifacts it can produce are:

task-graph.json (or task-graph-<suffix>.json:: The graph of all tasks created by the action task. Includes tasks created to satisfy requirements.
to-run.json (or to-run-<suffix>.json:: The set of tasks that the action task requested to build. This does not include the requirements.
label-to-taskid.json (or label-to-taskid-<suffix>.json:: This is the mapping from label to taskid for all tasks involved in the task-graph. This includes dependencies.

Testing Actions#

If you are working on an action task and wish to test it out locally, use the taskgraph test-action-callback command, e.g:

taskgraph test-action-callback \
    --task-id I4gu9KDmSZWu3KHx6ba6tw --task-group-id sMO4ybV9Qb2tmcI1sDHClQ \
    --input input.yml hello_world_action

This invocation will run the hello world callback with the given inputs and print any created tasks to stdout, rather than actually creating them.

Scopes and More Information#

As with most things, triggering actions need to satisfy a scope expression. If you are creating a new action and getting scope errors, or are getting scope errors when trying to trigger one; talk to your Taskcluster administrator.

For further details on actions in general, see the actions.json spec.

Create Actions

Contents

Create Actions#

Defining Action Tasks#

Setting the Action Context#

Specifying an Input Schema#

Conditional Availability#

Creating Tasks#

Testing Actions#

Scopes and More Information#