author Gregory Szorc <gps@mozilla.com>
Thu, 18 Aug 2016 08:58:59 -0700
changeset 413298 84dfa535f53ae5ec7a03a318e945684a2f8ca519
parent 413226 315bae6d1488fa36f2f90501218159241866a686
permissions -rw-r--r--
Bug 1295486 - Decode YAML files to UTF-8 at read time; r=dustin Before, we'd open files and feed bytes to yaml.load(). When a str is fed to yaml.load(), it attempts to guess the encoding. It defaults to UTF-8 unless somebody set us up the BOM. This is probably OK. Except if the file isn't valid UTF-8, the exception will be raised in the bowels of YAML parsing and it may not be obvious the failure is due to invalid UTF-8 input versus say Python str/unicode coercion foo. We change all call sites that load YAML from a file to use codecs.open() to open the file in UTF-8 and perform UTF-8 decoding/validation at file read time. This should make any UTF-8 failures more obvious. Furthermore, it reinforces that our YAML files are UTF-8 and not some other encoding. I discovered this issue as part of trying to get emoji symbols to render on Treeherder. Unfortunately, it appears pyyaml detects many emoji as unprintable characters and refuses to load them. This makes me sad and makes me want to abandon pyyaml/YAML in favor of something that supports emoji :P MozReview-Commit-ID: AOvAruZFfnK

# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.

from __future__ import absolute_import, print_function, unicode_literals

import codecs
import logging
import os
import yaml

from .graph import Graph
from .taskgraph import TaskGraph
from .optimize import optimize_task_graph
from .util.python_path import find_object

logger = logging.getLogger(__name__)

class Kind(object):

    def __init__(self, name, path, config):
        self.name = name
        self.path = path
        self.config = config

    def _get_impl_class(self):
        # load the class defined by implementation
            impl = self.config['implementation']
        except KeyError:
            raise KeyError("{!r} does not define implementation".format(self.path))
        return find_object(impl)

    def load_tasks(self, parameters, loaded_tasks):
        impl_class = self._get_impl_class()
        return impl_class.load_tasks(self.name, self.path, self.config,
                                     parameters, loaded_tasks)

class TaskGraphGenerator(object):
    The central controller for taskgraph.  This handles all phases of graph
    generation.  The task is generated from all of the kinds defined in
    subdirectories of the generator's root directory.

    Access to the results of this generation, as well as intermediate values at
    various phases of generation, is available via properties.  This encourages
    the provision of all generation inputs at instance construction time.

    # Task-graph generation is implemented as a Python generator that yields
    # each "phase" of generation.  This allows some mach subcommands to short-
    # circuit generation of the entire graph by never completing the generator.

    def __init__(self, root_dir, parameters,
        @param root_dir: root directory, with subdirectories for each kind
        @param parameters: parameters for this task-graph generation
        @type parameters: dict
        @param target_tasks_method: function to determine the target_task_set;
                see `./target_tasks.py`.
        @type target_tasks_method: function

        self.root_dir = root_dir
        self.parameters = parameters
        self.target_tasks_method = target_tasks_method

        # this can be set up until the time the target task set is generated;
        # it defaults to parameters['target_tasks']
        self._target_tasks = parameters.get('target_tasks')

        # start the generator
        self._run = self._run()
        self._run_results = {}

    def full_task_set(self):
        The full task set: all tasks defined by any kind (a graph without edges)

        @type: TaskGraph
        return self._run_until('full_task_set')

    def full_task_graph(self):
        The full task graph: the full task set, with edges representing

        @type: TaskGraph
        return self._run_until('full_task_graph')

    def target_task_set(self):
        The set of targetted tasks (a graph without edges)

        @type: TaskGraph
        return self._run_until('target_task_set')

    def target_task_graph(self):
        The set of targetted tasks and all of their dependencies

        @type: TaskGraph
        return self._run_until('target_task_graph')

    def optimized_task_graph(self):
        The set of targetted tasks and all of their dependencies; tasks that
        have been optimized out are either omitted or replaced with a Task
        instance containing only a task_id.

        @type: TaskGraph
        return self._run_until('optimized_task_graph')

    def label_to_taskid(self):
        A dictionary mapping task label to assigned taskId.  This property helps
        in interpreting `optimized_task_graph`.

        @type: dictionary
        return self._run_until('label_to_taskid')

    def _load_kinds(self):
        for path in os.listdir(self.root_dir):
            path = os.path.join(self.root_dir, path)
            if not os.path.isdir(path):
            kind_name = os.path.basename(path)

            kind_yml = os.path.join(path, 'kind.yml')
            if not os.path.exists(kind_yml):

            logger.debug("loading kind `{}` from `{}`".format(kind_name, path))
            with codecs.open(kind_yml, 'rb', 'utf-8') as f:
                config = yaml.load(f)

            yield Kind(kind_name, path, config)

    def _run(self):
        logger.info("Loading kinds")
        # put the kinds into a graph and sort topologically so that kinds are loaded
        # in post-order
        kinds = {kind.name: kind for kind in self._load_kinds()}
        edges = set()
        for kind in kinds.itervalues():
            for dep in kind.config.get('kind-dependencies', []):
                edges.add((kind.name, dep, 'kind-dependency'))
        kind_graph = Graph(set(kinds), edges)

        logger.info("Generating full task set")
        all_tasks = {}
        for kind_name in kind_graph.visit_postorder():
            logger.debug("Loading tasks for kind {}".format(kind_name))
            kind = kinds[kind_name]
            new_tasks = kind.load_tasks(self.parameters, list(all_tasks.values()))
            for task in new_tasks:
                if task.label in all_tasks:
                    raise Exception("duplicate tasks with label " + task.label)
                all_tasks[task.label] = task
            logger.info("Generated {} tasks for kind {}".format(len(new_tasks), kind_name))
        full_task_set = TaskGraph(all_tasks, Graph(set(all_tasks), set()))
        yield 'full_task_set', full_task_set

        logger.info("Generating full task graph")
        edges = set()
        for t in full_task_set:
            for dep, depname in t.get_dependencies(full_task_set):
                edges.add((t.label, dep, depname))

        full_task_graph = TaskGraph(all_tasks,
                                    Graph(full_task_set.graph.nodes, edges))
        yield 'full_task_graph', full_task_graph

        logger.info("Generating target task set")
        target_tasks = set(self.target_tasks_method(full_task_graph, self.parameters))
        target_task_set = TaskGraph(
            {l: all_tasks[l] for l in target_tasks},
            Graph(target_tasks, set()))
        yield 'target_task_set', target_task_set

        logger.info("Generating target task graph")
        target_graph = full_task_graph.graph.transitive_closure(target_tasks)
        target_task_graph = TaskGraph(
            {l: all_tasks[l] for l in target_graph.nodes},
        yield 'target_task_graph', target_task_graph

        logger.info("Generating optimized task graph")
        do_not_optimize = set()
        if not self.parameters.get('optimize_target_tasks', True):
            do_not_optimize = target_task_set.graph.nodes
        optimized_task_graph, label_to_taskid = optimize_task_graph(target_task_graph,
        yield 'label_to_taskid', label_to_taskid
        yield 'optimized_task_graph', optimized_task_graph

    def _run_until(self, name):
        while name not in self._run_results:
                k, v = self._run.next()
            except StopIteration:
                raise AttributeError("No such run result {}".format(name))
            self._run_results[k] = v
        return self._run_results[name]