author ffxbld
Tue, 26 Dec 2017 10:57:37 -0800
changeset 397445 02bc97729801a3b45d876f493c89260fa18ac170
parent 383502 112de4d9e9e67a7a10cb9d6dc8eb4d4bee6585e2
child 413270 9fbb232ef1f16e423f55a8b0082f1dca58df1081
permissions -rw-r--r--
No bug, Automated HPKP preload list update from host bld-linux64-spot-303 - a=hpkp-update


This document shows how to define an action in-tree such that it shows up in
supported user interfaces like Treeherder. For details on interface between
in-tree logic and external user interfaces, see `the actions.json spec`_.

At a very high level, the process looks like this:

 * The decision task produces an artifact, ``public/actions.json``, indicating
   what actions are available.

 * A user interface (for example, Treeherder or the Taskcluster tools) 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 test case.

 * The user interface follows the action description to carry out the action.
   In most cases (``action.kind == 'task'``), that entails creating an "action
   task", including the provided information. That 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).

Defining Action Tasks

There are two options for defining actions: creating a callback action, or
creating a custom action task.  A callback action automatically defines an
action task that will invoke a Python function of your devising.

A custom action task is an arbitrary task definition that will be created
directly.  In cases where the callback would simply call ``queue.createTask``,
a custom action task can be more efficient.

Creating a Callback Action
A *callback action* is an action that calls back into in-tree logic. That is,
you register the action with name, title, description, context, input schema and a
python callback. 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, enabling it to do pretty much anything it wants to.

To create a new callback action you must create a file
``taskcluster/taskgraph/actions/my-action.py``, that at minimum contains::

  from registry import register_callback_action

      title='Say Hello',
      symbol='hw',  # Show the callback task in treeherder as 'hw'
      description="Simple **proof-of-concept** callback action",
      order=10000,  # Order in which it should appear relative to other actions
  def hello_world_action(parameters, input, task_group_id, task_id, task):
      # parameters is an instance of taskgraph.parameters.Parameters
      # it carries decision task parameters from the original decision task.
      # input, task_id, and task should all be None
      print "Hello was triggered from taskGroupId: " + taskGroupId

Callback actions are configured in-tree to generate 3 artifacts when they run.
These artifacts are similar to the artifacts generated by decision tasks since
callback actions are basically mini decision tasks. The artifacts are:

  The graph of all tasks created by the action task. Includes tasks
  created to satisfy requirements.
  The set of tasks that the action task requested to build. This does not
  include the requirements.
  This is the mapping from label to ``taskid`` for all tasks involved in
  the task-graph. This includes dependencies.

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`` parameter.

Setting the Action Context
The context parameter should be a list of tag-sets, such as
``context=[{"platform": "linux"}]``, which will make the task show up in the
context-menu for any task with ``task.tags.platform = 'linux'``. Below is
some examples of context parameters 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'``.
  Requires nothing and the action will show up in the context menu for all tasks.
  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 registry import register_callback_action

      symbol='re-c',  # Show the callback task in treeherder as 're-c'
      description="Create a clone of the task",
      context=[{'platform': 'linux'}]
  def retrigger_action(parameters, input, task_group_id, task_id, task):
      # input will be None
      print "Retriggering: {}".format(task_id)
      print "task definition: {}".format(task)

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

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.

Specifying an Input Schema
In call examples so far the ``input`` parameter 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`` parameter.

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 is 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.

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 registry import register_callback_action

      title='Run All Tasks',
      symbol='ra-c',  # Show the callback task in treeherder as 'ra-c'
      description="**Run all tasks** that have been _optimized_ away.",
          'title': 'Action Options',
          'description': 'Options for how you wish to run all tasks',
          'properties': {
              'priority': {
                  'title': 'priority'
                  'description': 'Priority that should be given to the tasks',
                  'type': 'string',
                  'enum': ['low', 'normal', 'high'],
                  'default': 'low',
              'runTalos': {
                  'title': 'Run Talos'
                  'description': 'Do you wish to also include talos tasks?',
                  'type': 'boolean',
                  'default': 'false',
          'required': ['priority', 'runTalos'],
          'additionalProperties': False,
  def retrigger_action(parameters, input, task_group_id, task_id, task):
      print "Create all pruned tasks with priority: {}".format(input['priority'])
      if input['runTalos']:
          print "Also running talos jobs..."

When the ``schema`` parameter is given the callback will always be called with
an ``input`` parameter 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.

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 ``taskgraph.parameters.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 registry import register_callback_action

      title='Say Hello',
      symbol='hw',  # Show the callback task in treeherder as 'hw'
      description="Simple **proof-of-concept** callback action",
      # Define an action that is only included if this is a push to try
      available=lambda parameters: parameters.get('project', None) == 'try',
  def try_only_action(parameters, input, task_group_id, task_id, task):
      print "My try-only action"

Properties of ``parameters``  are documented in the
:doc:`parameters section <parameters>`. You can also examine the
``parameters.yml`` artifact created by decisions tasks.

Creating a Custom Action Task

It is possible to define an action that doesn't take a callback. Instead, you'll
then have to provide a task template. For details on how the task template
language works refer to `the actions.json spec`_. There are two options for
creating this sort of action in-tree. The first option is to create a yaml file
and the second allows you to use Python to do some extra work if you'd like.
The example below illustrates how to create such an action in Python::

  from registry import register_task_action

      description="Create a clone of the task",
      context=[{'platform': 'linux'}],
          'title': 'priority'
          'description': 'Priority that should be given to the tasks',
          'type': 'string',
          'enum': ['low', 'normal', 'high'],
          'default': 'low',
  def task_template_builder(parameters):
      # The task template builder may return None to signal that the action
      # isn't available.
      if parameters.get('project', None) != 'try':
        return None
      return {
          'created': {'$fromNow': ''},
          'deadline': {'$fromNow': '1 hour'},
          'expires': {'$fromNow': '14 days'},
          'provisionerId': '...',
          'workerType': '...',
          'priority': '${input}',
          'payload': {
              'command': '...',
              'env': {
                  'TASK_DEFINITION': {'$json': {'eval': 'task'}}
          # It's now your responsibility to include treeherder routes, as well
          # additional metadata for treeherder in task.extra.treeherder.

An equivalent in yaml. Notice that we can't inspect parameters in this case::

  name: retrigger
  title: Retrigger
  description: Create a clone of the task
  order: 1
    - platform: linux
    title: priority
    description: Priority that should be given to the tasks
    type: string
      - low
      - normal
      - high
    default: low'
  created: {'$fromNow': ''}
  deadline: {'$fromNow': '1 hour'}
  expires: {'$fromNow': '14 days'}
  provisionerId: '...'
  workerType: '...'
  priority: '${input}'
    command: '...'
      TASK_DEFINITION: {'$json': {'eval': 'task'}}

These kinds of actions are useful for creating simple derivative tasks, but are
limited by the expressiveness of the template language. On the other hand, they
are more efficient than an action callback as they do not involve an
intermediate action task before creating the task the user requested.

For further details on the template language, see `the actions.json spec`_.

.. _the actions.json spec: https://docs.taskcluster.net/manual/tasks/actions/spec