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mirror of https://github.com/ansible/awx.git synced 2024-11-02 09:51:09 +03:00

fully message driven job execution

TODO:
* Need a distributed lock (leverage postgres)
* Less memory-intensive graph representation
* Maybe serializer/deserializer graph to database
  * Iterative graph building instead of full rebuild.
This commit is contained in:
Chris Meyers 2016-09-22 15:36:58 -04:00
parent cdb65ccac9
commit 04f69727f2
5 changed files with 100 additions and 37 deletions

View File

@ -798,34 +798,43 @@ class UnifiedJob(PolymorphicModel, PasswordFieldsModel, CommonModelNameNotUnique
status=self.status,
traceback=self.result_traceback)
def start(self, error_callback, success_callback, **kwargs):
'''
Start the task running via Celery.
'''
task_class = self._get_task_class()
def pre_start(self, **kwargs):
if not self.can_start:
self.job_explanation = u'%s is not in a startable status: %s, expecting one of %s' % (self._meta.verbose_name, self.status, str(('new', 'waiting')))
self.save(update_fields=['job_explanation'])
return False
return (False, None)
needed = self.get_passwords_needed_to_start()
try:
start_args = json.loads(decrypt_field(self, 'start_args'))
except Exception:
start_args = None
if start_args in (None, ''):
start_args = kwargs
opts = dict([(field, start_args.get(field, '')) for field in needed])
if not all(opts.values()):
missing_fields = ', '.join([k for k,v in opts.items() if not v])
self.job_explanation = u'Missing needed fields: %s.' % missing_fields
self.save(update_fields=['job_explanation'])
return False
#extra_data = dict([(field, kwargs[field]) for field in kwargs
# if field not in needed])
return (False, None)
if 'extra_vars' in kwargs:
self.handle_extra_data(kwargs['extra_vars'])
task_class().apply_async((self.pk,), opts, link_error=error_callback, link=success_callback)
return True
return (True, opts)
def start(self, error_callback, success_callback, **kwargs):
'''
Start the task running via Celery.
'''
task_class = self._get_task_class()
(res, opts) = self.pre_start(**kwargs)
if res:
task_class().apply_async((self.pk,), opts, link_error=error_callback, link=success_callback)
return res
def signal_start(self, **kwargs):
"""Notify the task runner system to begin work on this task."""
@ -852,6 +861,7 @@ class UnifiedJob(PolymorphicModel, PasswordFieldsModel, CommonModelNameNotUnique
self.update_fields(start_args=json.dumps(kwargs), status='pending')
self.socketio_emit_status("pending")
print("Running job launch for job %s" % self.name)
from awx.main.scheduler.tasks import run_job_launch
run_job_launch.delay(self.id)

View File

@ -240,3 +240,11 @@ class WorkflowJob(UnifiedJob, WorkflowJobOptions, JobNotificationMixin, Workflow
def get_notification_friendly_name(self):
return "Workflow Job"
def start(self, *args, **kwargs):
(res, opts) = self.pre_start(**kwargs)
if res:
self.status = 'running'
self.save()
self.socketio_emit_status("running")
return res

View File

@ -4,13 +4,14 @@
# Python
import datetime
import logging
import struct, fcntl, os
# Django
from django.conf import settings
from django.db import transaction
# AWX
from awx.main.models import * # noqa
from awx.main.tasks import handle_work_error, handle_work_success
from awx.main.utils import get_system_task_capacity
from awx.main.scheduler.dag_simple import SimpleDAG
from awx.main.scheduler.dag_workflow import WorkflowDAG
@ -47,8 +48,8 @@ def get_running_workflow_jobs():
WorkflowJob.objects.filter(status='running')]
return graph_workflow_jobs
def do_spawn_workflow_jobs():
workflow_jobs = get_running_workflow_jobs()
def spawn_workflow_graph_jobs(workflow_jobs):
# TODO: Consider using transaction.atomic
for workflow_job in workflow_jobs:
dag = WorkflowDAG(workflow_job)
spawn_nodes = dag.bfs_nodes_to_run()
@ -69,6 +70,16 @@ def do_spawn_workflow_jobs():
# TODO: should we emit a status on the socket here similar to tasks.py tower_periodic_scheduler() ?
#emit_websocket_notification('/socket.io/jobs', '', dict(id=))
# See comment in tasks.py::RunWorkflowJob::run()
def process_finished_workflow_jobs(workflow_jobs):
for workflow_job in workflow_jobs:
dag = WorkflowDAG(workflow_job)
if dag.is_workflow_done():
with transaction.atomic():
# TODO: detect if wfj failed
workflow_job.status = 'completed'
workflow_job.save()
workflow_job.socketio_emit_status('completed')
def rebuild_graph():
"""Regenerate the task graph by refreshing known tasks from Tower, purging
@ -88,8 +99,6 @@ def rebuild_graph():
logger.warn("Ignoring celery task inspector")
active_task_queues = None
do_spawn_workflow_jobs()
all_sorted_tasks = get_tasks()
if not len(all_sorted_tasks):
return None
@ -106,12 +115,13 @@ def rebuild_graph():
return None
running_tasks = filter(lambda t: t.status == 'running', all_sorted_tasks)
running_celery_tasks = filter(lambda t: type(t) != WorkflowJob, running_tasks)
waiting_tasks = filter(lambda t: t.status != 'running', all_sorted_tasks)
new_tasks = filter(lambda t: t.status == 'pending', all_sorted_tasks)
# Check running tasks and make sure they are active in celery
logger.debug("Active celery tasks: " + str(active_tasks))
for task in list(running_tasks):
for task in list(running_celery_tasks):
if (task.celery_task_id not in active_tasks and not hasattr(settings, 'IGNORE_CELERY_INSPECTOR')):
# NOTE: Pull status again and make sure it didn't finish in
# the meantime?
@ -122,7 +132,7 @@ def rebuild_graph():
))
task.save()
task.socketio_emit_status("failed")
running_tasks.pop(running_tasks.index(task))
running_tasks.pop(task)
logger.error("Task %s appears orphaned... marking as failed" % task)
# Create and process dependencies for new tasks
@ -171,6 +181,8 @@ def process_graph(graph, task_capacity):
"""Given a task dependency graph, start and manage tasks given their
priority and weight.
"""
from awx.main.tasks import handle_work_error, handle_work_success
leaf_nodes = graph.get_leaf_nodes()
running_nodes = filter(lambda x: x['node_object'].status == 'running', leaf_nodes)
running_impact = sum([t['node_object'].task_impact for t in running_nodes])
@ -190,33 +202,57 @@ def process_graph(graph, task_capacity):
node_dependencies = graph.get_dependents(node_obj)
# Allow other tasks to continue if a job fails, even if they are
# other jobs.
if graph.get_node_type(node_obj) == 'job':
node_type = graph.get_node_type(node_obj)
if node_type == 'job':
# clear dependencies because a job can block (not necessarily
# depend) on other jobs that share the same job template
node_dependencies = []
# Make the workflow_job look like it's started by setting status to
# running, but don't make a celery Task for it.
# Introduce jobs from the workflow so they are candidates to run.
# Call process_graph() again to allow choosing for run, the
# created candidate jobs.
elif node_type == 'workflow_job':
node_obj.start()
spawn_workflow_graph_jobs([node_obj])
return process_graph(graph, task_capacity)
dependent_nodes = [{'type': graph.get_node_type(node_obj), 'id': node_obj.id}] + \
[{'type': graph.get_node_type(n['node_object']),
'id': n['node_object'].id} for n in node_dependencies]
error_handler = handle_work_error.s(subtasks=dependent_nodes)
success_handler = handle_work_success.s(task_actual={'type': graph.get_node_type(node_obj),
'id': node_obj.id})
start_status = node_obj.start(error_callback=error_handler, success_callback=success_handler)
if not start_status:
node_obj.status = 'failed'
if node_obj.job_explanation:
node_obj.job_explanation += ' '
node_obj.job_explanation += 'Task failed pre-start check.'
node_obj.save()
continue
with transaction.atomic():
start_status = node_obj.start(error_callback=error_handler, success_callback=success_handler)
if not start_status:
node_obj.status = 'failed'
if node_obj.job_explanation:
node_obj.job_explanation += ' '
node_obj.job_explanation += 'Task failed pre-start check.'
node_obj.save()
continue
remaining_volume -= impact
running_impact += impact
logger.info('Started Node: %s (capacity hit: %s) '
'Remaining Capacity: %s' %
(str(node_obj), str(impact), str(remaining_volume)))
def schedule():
lockfile = open("/tmp/tower_scheduler.lock", "w")
fcntl.lockf(lockfile, fcntl.LOCK_EX)
task_capacity = get_system_task_capacity()
workflow_jobs = get_running_workflow_jobs()
process_finished_workflow_jobs(workflow_jobs)
spawn_workflow_graph_jobs(workflow_jobs)
graph = rebuild_graph()
if graph:
process_graph(graph, task_capacity)
fcntl.lockf(lockfile, fcntl.LOCK_UN)

View File

@ -1665,21 +1665,30 @@ class RunSystemJob(BaseTask):
def build_cwd(self, instance, **kwargs):
return settings.BASE_DIR
'''
class RunWorkflowJob(BaseTask):
name = 'awx.main.tasks.run_workflow_job'
model = WorkflowJob
def run(self, pk, **kwargs):
print("I'm a running a workflow job")
'''
Run the job/task and capture its output.
'''
pass
#Run the job/task and capture its output.
instance = self.update_model(pk, status='running', celery_task_id=self.request.id)
instance.socketio_emit_status("running")
# FIXME: Detect workflow run completion
# FIXME: Currently, the workflow job busy waits until the graph run is
# complete. Instead, the workflow job should return or never even run,
# because all of the "launch logic" can be done schedule().
# However, other aspects of our system depend on a 1-1 relationship
# between a Job and a Celery Task.
#
# * If we let the workflow job task (RunWorkflowJob.run()) complete
# then how do we trigger the handle_work_error and
# handle_work_success subtasks?
#
# * How do we handle the recovery process? (i.e. there is an entry in
# the database but not in celery).
while True:
dag = WorkflowDAG(instance)
if dag.is_workflow_done():
@ -1689,4 +1698,4 @@ class RunWorkflowJob(BaseTask):
time.sleep(1)
instance.socketio_emit_status(instance.status)
# TODO: Handle cancel
'''

View File

@ -360,7 +360,7 @@ CELERY_ROUTES = ({'awx.main.tasks.run_job': {'queue': 'jobs',
'routing_key': 'scheduler.job.launch'},
'awx.main.scheduler.tasks.run_job_complete': {'queue': 'scheduler',
'routing_key': 'scheduler.job.complete'},})
CELERYBEAT_SCHEDULE = {
'tower_scheduler': {
'task': 'awx.main.tasks.tower_periodic_scheduler',