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26056ddaa6
* WorkflowDAG accepts workflow job template and workflow jobs for which to build a graph out of the nodes. The optimized query for each is different. This changeset adds the differing queries for a workflow job.
228 lines
9.5 KiB
Python
228 lines
9.5 KiB
Python
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# Python
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import copy
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from awx.main.models import (
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WorkflowJobTemplateNode,
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WorkflowJobNode,
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)
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# AWX
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from awx.main.scheduler.dag_simple import SimpleDAG
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class WorkflowDAG(SimpleDAG):
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def __init__(self, workflow_job=None):
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super(WorkflowDAG, self).__init__()
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if workflow_job:
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self._init_graph(workflow_job)
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def _init_graph(self, workflow_job_or_jt):
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if hasattr(workflow_job_or_jt, 'workflow_job_template_nodes'):
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workflow_nodes = workflow_job_or_jt.workflow_job_template_nodes
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success_nodes = WorkflowJobTemplateNode.success_nodes.through.objects.filter(from_workflowjobtemplatenode__workflow_job_template_id=workflow_job_or_jt.id).values_list('from_workflowjobtemplatenode_id', 'to_workflowjobtemplatenode_id')
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failure_nodes = WorkflowJobTemplateNode.failure_nodes.through.objects.filter(from_workflowjobtemplatenode__workflow_job_template_id=workflow_job_or_jt.id).values_list('from_workflowjobtemplatenode_id', 'to_workflowjobtemplatenode_id')
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always_nodes = WorkflowJobTemplateNode.always_nodes.through.objects.filter(from_workflowjobtemplatenode__workflow_job_template_id=workflow_job_or_jt.id).values_list('from_workflowjobtemplatenode_id', 'to_workflowjobtemplatenode_id')
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elif hasattr(workflow_job_or_jt, 'workflow_job_nodes'):
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workflow_nodes = workflow_job_or_jt.workflow_job_nodes
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success_nodes = WorkflowJobNode.success_nodes.through.objects.filter(from_workflowjobnode__workflow_job_id=workflow_job_or_jt.id).values_list('from_workflowjobnode_id', 'to_workflowjobnode_id')
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failure_nodes = WorkflowJobNode.failure_nodes.through.objects.filter(from_workflowjobnode__workflow_job_id=workflow_job_or_jt.id).values_list('from_workflowjobnode_id', 'to_workflowjobnode_id')
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always_nodes = WorkflowJobNode.always_nodes.through.objects.filter(from_workflowjobnode__workflow_job_id=workflow_job_or_jt.id).values_list('from_workflowjobnode_id', 'to_workflowjobnode_id')
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else:
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raise RuntimeError("Unexpected object {} {}".format(type(workflow_job_or_jt), workflow_job_or_jt))
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print("workflow id {}".format(workflow_job_or_jt.id))
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print("Count of success nodes {}".format(len(success_nodes)))
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wfn_by_id = dict()
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for workflow_node in workflow_nodes.all():
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wfn_by_id[workflow_node.id] = workflow_node
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self.add_node(workflow_node)
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for edge in success_nodes:
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self.add_edge(wfn_by_id[edge[0]], wfn_by_id[edge[1]], 'success_nodes')
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for edge in failure_nodes:
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self.add_edge(wfn_by_id[edge[0]], wfn_by_id[edge[1]], 'failure_nodes')
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for edge in always_nodes:
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self.add_edge(wfn_by_id[edge[0]], wfn_by_id[edge[1]], 'always_nodes')
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'''
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Determine if all, relevant, parents node are finished.
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Relevant parents are parents that are marked do_not_run False.
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:param node: a node entry from SimpleDag.nodes (i.e. a dict with property ['node_object']
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Return a boolean
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'''
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def _are_relevant_parents_finished(self, node):
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obj = node['node_object']
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parent_nodes = [p['node_object'] for p in self.get_dependents(obj)]
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for p in parent_nodes:
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if p.do_not_run is True:
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continue
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# Node might run a job
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if p.do_not_run is False and not p.job:
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return False
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# Node decidedly got a job; check if job is done
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if p.job and p.job.status not in ['successful', 'failed', 'error']:
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return False
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return True
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def bfs_nodes_to_run(self):
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nodes = self.get_root_nodes()
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nodes_found = []
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node_ids_visited = set()
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for index, n in enumerate(nodes):
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obj = n['node_object']
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if obj.id in node_ids_visited:
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continue
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node_ids_visited.add(obj.id)
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if obj.do_not_run is True:
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continue
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if obj.job:
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if obj.job.status in ['failed', 'error']:
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nodes.extend(self.get_dependencies(obj, 'failure_nodes') +
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self.get_dependencies(obj, 'always_nodes'))
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elif obj.job.status == 'successful':
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nodes.extend(self.get_dependencies(obj, 'success_nodes') +
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self.get_dependencies(obj, 'always_nodes'))
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else:
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if self._are_relevant_parents_finished(n):
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nodes_found.append(n)
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return [n['node_object'] for n in nodes_found]
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def cancel_node_jobs(self):
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cancel_finished = True
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for n in self.nodes:
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obj = n['node_object']
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job = obj.job
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if not job:
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continue
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elif job.can_cancel:
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cancel_finished = False
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job.cancel()
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return cancel_finished
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def is_workflow_done(self):
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root_nodes = self.get_root_nodes()
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nodes = root_nodes
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is_failed = False
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for index, n in enumerate(nodes):
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obj = n['node_object']
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job = obj.job
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if obj.unified_job_template is None:
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is_failed = True
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continue
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elif obj.do_not_run is False and not job:
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return False, False
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elif obj.do_not_run is True:
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continue
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children_success = self.get_dependencies(obj, 'success_nodes')
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children_failed = self.get_dependencies(obj, 'failure_nodes')
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children_always = self.get_dependencies(obj, 'always_nodes')
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if not is_failed and job.status != 'successful':
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children_all = children_success + children_failed + children_always
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for child in children_all:
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if child['node_object'].job:
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break
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else:
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is_failed = True if children_all else job.status in ['failed', 'canceled', 'error']
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if job.status == 'canceled':
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continue
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elif job.status in ['error', 'failed']:
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nodes.extend(children_failed + children_always)
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elif job.status == 'successful':
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nodes.extend(children_success + children_always)
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else:
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# Job is about to run or is running. Hold our horses and wait for
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# the job to finish. We can't proceed down the graph path until we
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# have the job result.
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return False, False
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return True, is_failed
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'''
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Determine if all nodes have been decided on being marked do_not_run.
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Nodes that are do_not_run False may become do_not_run True in the future.
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We know a do_not_run False node will NOT be marked do_not_run True if there
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is a job run for that node.
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:param workflow_nodes: list of workflow_nodes
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Return a boolean
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'''
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def _are_all_nodes_dnr_decided(self, workflow_nodes):
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for n in workflow_nodes:
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if n.do_not_run is False and not n.job:
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return False
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return True
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#return not any((n.do_not_run is False and not n.job) for n in workflow_nodes)
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'''
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Determine if a node (1) is ready to be marked do_not_run and (2) should
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be marked do_not_run.
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:param node: SimpleDAG internal node
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:param parent_nodes: list of workflow_nodes
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Return a boolean
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'''
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def _should_mark_node_dnr(self, node, parent_nodes):
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for p in parent_nodes:
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if p.job:
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if p.job.status == 'successful':
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if node in (self.get_dependencies(p, 'success_nodes') +
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self.get_dependencies(p, 'always_nodes')):
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return False
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elif p.job.status in ['failed', 'error']:
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if node in (self.get_dependencies(p, 'failure_nodes') +
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self.get_dependencies(p, 'always_nodes')):
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return False
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else:
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return False
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else:
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return False
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return True
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def mark_dnr_nodes(self):
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root_nodes = self.get_root_nodes()
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nodes = copy.copy(root_nodes)
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nodes_marked_do_not_run = []
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node_ids_visited = set()
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for index, n in enumerate(nodes):
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obj = n['node_object']
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if obj.id in node_ids_visited:
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continue
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node_ids_visited.add(obj.id)
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if obj.do_not_run is False and not obj.job and n not in root_nodes:
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parent_nodes = filter(lambda n: not n.do_not_run,
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[p['node_object'] for p in self.get_dependents(obj)])
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if self._are_all_nodes_dnr_decided(parent_nodes):
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if self._should_mark_node_dnr(n, parent_nodes):
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obj.do_not_run = True
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nodes_marked_do_not_run.append(n)
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if obj.do_not_run is True:
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nodes.extend(self.get_dependencies(obj, 'success_nodes') +
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self.get_dependencies(obj, 'failure_nodes') +
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self.get_dependencies(obj, 'always_nodes'))
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elif obj.job:
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if obj.job.status in ['failed', 'error']:
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nodes.extend(self.get_dependencies(obj, 'success_nodes'))
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elif obj.job.status == 'successful':
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nodes.extend(self.get_dependencies(obj, 'failure_nodes'))
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return [n['node_object'] for n in nodes_marked_do_not_run]
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