18da9b3836
It is not at *all* clear how multiple optimizations for a single task should interact. No simple logical operation is right in all cases, and in fact in most imaginable cases the desired behavior turns out to be independent of all but one of the optimizations. For example, given both `seta` and `skip-unless-files-changed` optimizations, if SETA says to skip a test, it is low value and should be skipped regardless of what files have changed. But if SETA says to run a test, then it has likely been skipped in previous pushes, so it should be run regardless of what has changed in this push. This also adds a bit more output about optimization, that may be useful for anyone wondering why a particular job didn't run. MozReview-Commit-ID: 3OsvRnWjai4
87 lines
3.2 KiB
Python
87 lines
3.2 KiB
Python
# This Source Code Form is subject to the terms of the Mozilla Public
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# License, v. 2.0. If a copy of the MPL was not distributed with this
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# file, You can obtain one at http://mozilla.org/MPL/2.0/.
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from __future__ import absolute_import, print_function, unicode_literals
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class Task(object):
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"""
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Representation of a task in a TaskGraph. Each Task has, at creation:
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- kind: the name of the task kind
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- label; the label for this task
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- attributes: a dictionary of attributes for this task (used for filtering)
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- task: the task definition (JSON-able dictionary)
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- optimization: optimization to apply to the task (see taskgraph.optimize)
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- dependencies: tasks this one depends on, in the form {name: label}, for example
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{'build': 'build-linux64/opt', 'docker-image': 'build-docker-image-desktop-test'}
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And later, as the task-graph processing proceeds:
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- task_id -- TaskCluster taskId under which this task will be created
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- optimized -- true if this task need not be performed
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This class is just a convenience wraper for the data type and managing
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display, comparison, serialization, etc. It has no functionality of its own.
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"""
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def __init__(self, kind, label, attributes, task,
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optimization=None, dependencies=None):
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self.kind = kind
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self.label = label
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self.attributes = attributes
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self.task = task
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self.task_id = None
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self.optimized = False
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self.attributes['kind'] = kind
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self.optimization = optimization
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self.dependencies = dependencies or {}
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def __eq__(self, other):
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return self.kind == other.kind and \
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self.label == other.label and \
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self.attributes == other.attributes and \
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self.task == other.task and \
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self.task_id == other.task_id and \
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self.optimization == other.optimization and \
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self.dependencies == other.dependencies
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def __repr__(self):
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return ('Task({kind!r}, {label!r}, {attributes!r}, {task!r}, '
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'optimization={optimization!r}, '
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'dependencies={dependencies!r})'.format(**self.__dict__))
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def to_json(self):
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rv = {
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'kind': self.kind,
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'label': self.label,
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'attributes': self.attributes,
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'dependencies': self.dependencies,
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'optimization': self.optimization,
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'task': self.task,
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}
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if self.task_id:
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rv['task_id'] = self.task_id
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return rv
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@classmethod
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def from_json(cls, task_dict):
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"""
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Given a data structure as produced by taskgraph.to_json, re-construct
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the original Task object. This is used to "resume" the task-graph
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generation process, for example in Action tasks.
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"""
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rv = cls(
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kind=task_dict['kind'],
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label=task_dict['label'],
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attributes=task_dict['attributes'],
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task=task_dict['task'],
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optimization=task_dict['optimization'],
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dependencies=task_dict.get('dependencies'))
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if 'task_id' in task_dict:
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rv.task_id = task_dict['task_id']
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return rv
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