gecko-dev/taskcluster/taskgraph/optimize.py

382 строки
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Python

# 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/.
"""
The objective of optimization is to remove as many tasks from the graph as
possible, as efficiently as possible, thereby delivering useful results as
quickly as possible. For example, ideally if only a test script is modified in
a push, then the resulting graph contains only the corresponding test suite
task.
See ``taskcluster/docs/optimization.rst`` for more information.
"""
from __future__ import absolute_import, print_function, unicode_literals
import logging
import os
from collections import defaultdict
from .graph import Graph
from . import files_changed
from .taskgraph import TaskGraph
from .util.seta import is_low_value_task
from .util.taskcluster import find_task_id
from .util.parameterization import resolve_task_references
from mozbuild.util import memoize
from slugid import nice as slugid
from mozbuild.base import MozbuildObject
logger = logging.getLogger(__name__)
TOPSRCDIR = os.path.abspath(os.path.join(__file__, '../../../'))
def optimize_task_graph(target_task_graph, params, do_not_optimize,
existing_tasks=None, strategies=None):
"""
Perform task optimization, returning a taskgraph and a map from label to
assigned taskId, including replacement tasks.
"""
label_to_taskid = {}
if not existing_tasks:
existing_tasks = {}
# instantiate the strategies for this optimization process
if not strategies:
strategies = _make_default_strategies()
optimizations = _get_optimizations(target_task_graph, strategies)
removed_tasks = remove_tasks(
target_task_graph=target_task_graph,
optimizations=optimizations,
params=params,
do_not_optimize=do_not_optimize)
replaced_tasks = replace_tasks(
target_task_graph=target_task_graph,
optimizations=optimizations,
params=params,
do_not_optimize=do_not_optimize,
label_to_taskid=label_to_taskid,
existing_tasks=existing_tasks,
removed_tasks=removed_tasks)
return get_subgraph(
target_task_graph, removed_tasks, replaced_tasks,
label_to_taskid), label_to_taskid
def _make_default_strategies():
return {
'never': OptimizationStrategy(), # "never" is the default behavior
'index-search': IndexSearch(),
'seta': SETA(),
'skip-unless-changed': SkipUnlessChanged(),
'skip-unless-schedules': SkipUnlessSchedules(),
'skip-unless-schedules-or-seta': Either(SkipUnlessSchedules(), SETA()),
'only-if-dependencies-run': OnlyIfDependenciesRun(),
}
def _get_optimizations(target_task_graph, strategies):
def optimizations(label):
task = target_task_graph.tasks[label]
if task.optimization:
opt_by, arg = task.optimization.items()[0]
return (opt_by, strategies[opt_by], arg)
else:
return ('never', strategies['never'], None)
return optimizations
def _log_optimization(verb, opt_counts):
if opt_counts:
logger.info(
'{} '.format(verb.title()) +
', '.join(
'{} tasks by {}'.format(c, b)
for b, c in sorted(opt_counts.iteritems())) +
' during optimization.')
else:
logger.info('No tasks {} during optimization'.format(verb))
def remove_tasks(target_task_graph, params, optimizations, do_not_optimize):
"""
Implement the "Removing Tasks" phase, returning a set of task labels of all removed tasks.
"""
opt_counts = defaultdict(int)
removed = set()
reverse_links_dict = target_task_graph.graph.reverse_links_dict()
for label in target_task_graph.graph.visit_preorder():
# if we're not allowed to optimize, that's easy..
if label in do_not_optimize:
continue
# if there are remaining tasks depending on this one, do not remove..
if any(l not in removed for l in reverse_links_dict[label]):
continue
# call the optimization strategy
task = target_task_graph.tasks[label]
opt_by, opt, arg = optimizations(label)
if opt.should_remove_task(task, params, arg):
removed.add(label)
opt_counts[opt_by] += 1
continue
_log_optimization('removed', opt_counts)
return removed
def replace_tasks(target_task_graph, params, optimizations, do_not_optimize,
label_to_taskid, removed_tasks, existing_tasks):
"""
Implement the "Replacing Tasks" phase, returning a set of task labels of
all replaced tasks. The replacement taskIds are added to label_to_taskid as
a side-effect.
"""
opt_counts = defaultdict(int)
replaced = set()
links_dict = target_task_graph.graph.links_dict()
for label in target_task_graph.graph.visit_postorder():
# if we're not allowed to optimize, that's easy..
if label in do_not_optimize:
continue
# if this task depends on un-replaced, un-removed tasks, do not replace
if any(l not in replaced and l not in removed_tasks for l in links_dict[label]):
continue
# if the task already exists, that's an easy replacement
repl = existing_tasks.get(label)
if repl:
label_to_taskid[label] = repl
replaced.add(label)
opt_counts['existing_tasks'] += 1
continue
# call the optimization strategy
task = target_task_graph.tasks[label]
opt_by, opt, arg = optimizations(label)
repl = opt.should_replace_task(task, params, arg)
if repl:
if repl is True:
# True means remove this task; get_subgraph will catch any
# problems with removed tasks being depended on
removed_tasks.add(label)
else:
label_to_taskid[label] = repl
replaced.add(label)
opt_counts[opt_by] += 1
continue
_log_optimization('replaced', opt_counts)
return replaced
def get_subgraph(target_task_graph, removed_tasks, replaced_tasks, label_to_taskid):
"""
Return the subgraph of target_task_graph consisting only of
non-optimized tasks and edges between them.
To avoid losing track of taskIds for tasks optimized away, this method
simultaneously substitutes real taskIds for task labels in the graph, and
populates each task definition's `dependencies` key with the appropriate
taskIds. Task references are resolved in the process.
"""
# check for any dependency edges from included to removed tasks
bad_edges = [(l, r, n) for l, r, n in target_task_graph.graph.edges
if l not in removed_tasks and r in removed_tasks]
if bad_edges:
probs = ', '.join('{} depends on {} as {} but it has been removed'.format(l, r, n)
for l, r, n in bad_edges)
raise Exception("Optimization error: " + probs)
# fill in label_to_taskid for anything not removed or replaced
assert replaced_tasks <= set(label_to_taskid)
for label in sorted(target_task_graph.graph.nodes - removed_tasks - set(label_to_taskid)):
label_to_taskid[label] = slugid()
# resolve labels to taskIds and populate task['dependencies']
tasks_by_taskid = {}
named_links_dict = target_task_graph.graph.named_links_dict()
omit = removed_tasks | replaced_tasks
for label, task in target_task_graph.tasks.iteritems():
if label in omit:
continue
task.task_id = label_to_taskid[label]
named_task_dependencies = {
name: label_to_taskid[label]
for name, label in named_links_dict.get(label, {}).iteritems()}
task.task = resolve_task_references(task.label, task.task, named_task_dependencies)
deps = task.task.setdefault('dependencies', [])
deps.extend(sorted(named_task_dependencies.itervalues()))
tasks_by_taskid[task.task_id] = task
# resolve edges to taskIds
edges_by_taskid = (
(label_to_taskid.get(left), label_to_taskid.get(right), name)
for (left, right, name) in target_task_graph.graph.edges
)
# ..and drop edges that are no longer entirely in the task graph
# (note that this omits edges to replaced tasks, but they are still in task.dependnecies)
edges_by_taskid = set(
(left, right, name)
for (left, right, name) in edges_by_taskid
if left in tasks_by_taskid and right in tasks_by_taskid
)
return TaskGraph(
tasks_by_taskid,
Graph(set(tasks_by_taskid), edges_by_taskid))
class OptimizationStrategy(object):
def should_remove_task(self, task, params, arg):
"""Determine whether to optimize this task by removing it. Returns
True to remove."""
return False
def should_replace_task(self, task, params, arg):
"""Determine whether to optimize this task by replacing it. Returns a
taskId to replace this task, True to replace with nothing, or False to
keep the task."""
return False
class Either(OptimizationStrategy):
"""Given one or more optimization strategies, remove a task if any of them
says to, and replace with a task if any finds a replacement (preferring the
earliest). By default, each substrategy gets the same arg, but split_args
can return a list of args for each strategy, if desired."""
def __init__(self, *substrategies, **kwargs):
self.substrategies = substrategies
self.split_args = kwargs.pop('split_args', None)
if not self.split_args:
self.split_args = lambda arg: [arg] * len(substrategies)
if kwargs:
raise TypeError("unexpected keyword args")
def _for_substrategies(self, arg, fn):
for sub, arg in zip(self.substrategies, self.split_args(arg)):
rv = fn(sub, arg)
if rv:
return rv
return False
def should_remove_task(self, task, params, arg):
return self._for_substrategies(
arg,
lambda sub, arg: sub.should_remove_task(task, params, arg))
def should_replace_task(self, task, params, arg):
return self._for_substrategies(
arg,
lambda sub, arg: sub.should_replace_task(task, params, arg))
class OnlyIfDependenciesRun(OptimizationStrategy):
"""Run this taks only if its dependencies run."""
# This takes advantage of the behavior of the second phase of optimization:
# a task can only be replaced if it has no un-optimized dependencies. So if
# should_replace_task is called, then a task has no un-optimized
# dependencies and can be removed (indicated by returning True)
def should_replace_task(self, task, params, arg):
return True
class IndexSearch(OptimizationStrategy):
# A task with no dependencies remaining after optimization will be replaced
# if artifacts exist for the corresponding index_paths.
# Otherwise, we're in one of the following cases:
# - the task has un-optimized dependencies
# - the artifacts have expired
# - some changes altered the index_paths and new artifacts need to be
# created.
# In every of those cases, we need to run the task to create or refresh
# artifacts.
def should_replace_task(self, task, params, index_paths):
"Look for a task with one of the given index paths"
for index_path in index_paths:
try:
task_id = find_task_id(
index_path,
use_proxy=bool(os.environ.get('TASK_ID')))
return task_id
except KeyError:
# 404 will end up here and go on to the next index path
pass
return False
class SETA(OptimizationStrategy):
def should_remove_task(self, task, params, _):
label = task.label
# we would like to return 'False, None' while it's high_value_task
# and we wouldn't optimize it. Otherwise, it will return 'True, None'
if is_low_value_task(label,
params.get('project'),
params.get('pushlog_id'),
params.get('pushdate')):
# Always optimize away low-value tasks
return True
else:
return False
class SkipUnlessChanged(OptimizationStrategy):
def should_remove_task(self, task, params, file_patterns):
# pushlog_id == -1 - this is the case when run from a cron.yml job
if params.get('pushlog_id') == -1:
return False
changed = files_changed.check(params, file_patterns)
if not changed:
logger.debug('no files found matching a pattern in `skip-unless-changed` for ' +
task.label)
return True
return False
class SkipUnlessSchedules(OptimizationStrategy):
@memoize
def scheduled_by_push(self, repository, revision):
changed_files = files_changed.get_changed_files(repository, revision)
mbo = MozbuildObject.from_environment()
# the decision task has a sparse checkout, so, mozbuild_reader will use
# a MercurialRevisionFinder with revision '.', which should be the same
# as `revision`; in other circumstances, it will use a default reader
rdr = mbo.mozbuild_reader(config_mode='empty')
components = set()
for p, m in rdr.files_info(changed_files).items():
components |= set(m['SCHEDULES'].components)
return components
def should_remove_task(self, task, params, conditions):
if params.get('pushlog_id') == -1:
return False
scheduled = self.scheduled_by_push(params['head_repository'], params['head_rev'])
conditions = set(conditions)
# if *any* of the condition components are scheduled, do not optimize
if conditions & scheduled:
return False
return True