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optimize cycle detection

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chris meyers
2018-11-08 11:22:48 -05:00
committed by mabashian
parent b84fc3b111
commit 9f3e272665
3 changed files with 73 additions and 23 deletions
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26
awx/main/scheduler/dag_workflow.py
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@@ -1,6 +1,7 @@
# Python
import copy
from awx.main.models import WorkflowJobTemplateNode
# AWX
from awx.main.scheduler.dag_simple import SimpleDAG
@@ -14,21 +15,28 @@ class WorkflowDAG(SimpleDAG):
def _init_graph(self, workflow_job_or_jt):
if hasattr(workflow_job_or_jt, 'workflow_job_template_nodes'):
node_qs = workflow_job_or_jt.workflow_job_template_nodes
workflow_nodes = workflow_job_or_jt.workflow_job_template_nodes
elif hasattr(workflow_job_or_jt, 'workflow_job_nodes'):
node_qs = workflow_job_or_jt.workflow_job_nodes
workflow_nodes = workflow_job_or_jt.workflow_job_nodes
else:
raise RuntimeError("Unexpected object {} {}".format(type(workflow_job_or_jt), workflow_job_or_jt))
workflow_nodes = node_qs.prefetch_related('success_nodes', 'failure_nodes', 'always_nodes').all()
for workflow_node in workflow_nodes:
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')
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')
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')
wfn_by_id = dict()
for workflow_node in workflow_nodes.all():
wfn_by_id[workflow_node.id] = workflow_node
self.add_node(workflow_node)
for node_type in ['success_nodes', 'failure_nodes', 'always_nodes']:
for workflow_node in workflow_nodes:
related_nodes = getattr(workflow_node, node_type).all()
for related_node in related_nodes:
self.add_edge(workflow_node, related_node, node_type)
for edge in success_nodes:
self.add_edge(wfn_by_id[edge[0]], wfn_by_id[edge[1]], 'success_nodes')
for edge in failure_nodes:
self.add_edge(wfn_by_id[edge[0]], wfn_by_id[edge[1]], 'failure_nodes')
for edge in always_nodes:
self.add_edge(wfn_by_id[edge[0]], wfn_by_id[edge[1]], 'always_nodes')
'''
Determine if all, relevant, parents node are finished.