def simple_all_convergence(self, wf_node_generator):
g = WorkflowDAG()
nodes = [wf_node_generator() for i in range(4)]
for n in nodes:
g.add_node(n)
r'''
0
/\
S / \ S
/ \
1 2
\ /
F \ / S
\/
3
'''
g.add_edge(nodes[0], nodes[1], "success_nodes")
g.add_edge(nodes[0], nodes[2], "success_nodes")
g.add_edge(nodes[1], nodes[3], "failure_nodes")
g.add_edge(nodes[2], nodes[3], "success_nodes")
nodes[3].all_parents_must_converge = True
nodes[0].job = Job(status='successful')
nodes[1].job = Job(status='failed')
nodes[2].job = Job(status='successful')
return (g, nodes)
def workflow_dag_1(wf_node_generator):
g = WorkflowDAG()
nodes = [wf_node_generator() for i in range(4)]
for n in nodes:
g.add_node(n)
r'''
0
/\
S / \
/ \
1 |
| |
F | | S
| |
3 |
\ |
F \ |
\/
2
'''
g.add_edge(nodes[0], nodes[1], "success_nodes")
g.add_edge(nodes[0], nodes[2], "success_nodes")
g.add_edge(nodes[1], nodes[3], "failure_nodes")
g.add_edge(nodes[3], nodes[2], "failure_nodes")
return (g, nodes)
def workflow_dag_canceled(self, wf_node_generator):
g = WorkflowDAG()
nodes = [wf_node_generator() for i in range(1)]
for n in nodes:
g.add_node(n)
r'''
F0
'''
nodes[0].job = Job(status='canceled')
return (g, nodes)
def workflow_dag_canceled(self, wf_node_generator):
g = WorkflowDAG()
nodes = [wf_node_generator() for i in range(4)]
for n in nodes:
g.add_node(n)
r'''
C0
/ | \
F / A| \ S
/ | \
1 2 3
'''
g.add_edge(nodes[0], nodes[1], "failure_nodes")
g.add_edge(nodes[0], nodes[2], "always_nodes")
g.add_edge(nodes[0], nodes[3], "success_nodes")
nodes[0].job = Job(status='canceled')
return (g, nodes)
def workflow_dag_root_children(self, wf_node_generator):
g = WorkflowDAG()
wf_root_nodes = [wf_node_generator() for i in range(0, 10)]
wf_leaf_nodes = [wf_node_generator() for i in range(0, 10)]
for n in wf_root_nodes + wf_leaf_nodes:
g.add_node(n)
'''
Pair up a root node with a single child via an edge
R1 R2 ... Rx
| | |
| | |
C1 C2 Cx
'''
for i, n in enumerate(wf_leaf_nodes):
g.add_edge(wf_root_nodes[i], n, 'label')
return (g, wf_root_nodes, wf_leaf_nodes)
def workflow_all_converge_1(self, wf_node_generator):
g = WorkflowDAG()
nodes = [wf_node_generator() for i in range(3)]
for n in nodes:
g.add_node(n)
r'''
0
|\ F
| \
S| 1
| /
|/ A
2
'''
g.add_edge(nodes[0], nodes[1], "failure_nodes")
g.add_edge(nodes[0], nodes[2], "success_nodes")
g.add_edge(nodes[1], nodes[2], "always_nodes")
nodes[2].all_parents_must_converge = True
nodes[0].job = Job(status='successful')
return (g, nodes)
def complex_dag(self, wf_node_generator):
g = WorkflowDAG()
nodes = [wf_node_generator() for i in range(10)]
for n in nodes:
g.add_node(n)
g.add_edge(nodes[0], nodes[1], "failure_nodes")
g.add_edge(nodes[0], nodes[2], "success_nodes")
g.add_edge(nodes[0], nodes[3], "always_nodes")
g.add_edge(nodes[1], nodes[4], "success_nodes")
g.add_edge(nodes[1], nodes[5], "failure_nodes")
g.add_edge(nodes[2], nodes[6], "failure_nodes")
g.add_edge(nodes[3], nodes[6], "success_nodes")
g.add_edge(nodes[4], nodes[6], "always_nodes")
g.add_edge(nodes[6], nodes[7], "always_nodes")
g.add_edge(nodes[6], nodes[8], "success_nodes")
g.add_edge(nodes[6], nodes[9], "failure_nodes")
return (g, nodes)
def workflow_all_converge_deep_dnr_tree(self, wf_node_generator):
g = WorkflowDAG()
nodes = [wf_node_generator() for i in range(7)]
for n in nodes:
g.add_node(n)
r'''
0 1 2
\ | /
S \ S| / F
\ | /
\|/
|
3
/\
S / \ S
/ \
4| | 5
\ /
S \ / S
\/
6
'''
g.add_edge(nodes[0], nodes[3], "success_nodes")
g.add_edge(nodes[1], nodes[3], "success_nodes")
g.add_edge(nodes[2], nodes[3], "failure_nodes")
g.add_edge(nodes[3], nodes[4], "success_nodes")
g.add_edge(nodes[3], nodes[5], "success_nodes")
g.add_edge(nodes[4], nodes[6], "success_nodes")
g.add_edge(nodes[5], nodes[6], "success_nodes")
nodes[3].all_parents_must_converge = True
nodes[4].all_parents_must_converge = True
nodes[5].all_parents_must_converge = True
nodes[6].all_parents_must_converge = True
nodes[0].job = Job(status='successful')
nodes[1].job = Job(status='successful')
nodes[2].job = Job(status='successful')
return (g, nodes)
def workflow_all_converge_dnr(self, wf_node_generator):
g = WorkflowDAG()
nodes = [wf_node_generator() for i in range(4)]
for n in nodes:
g.add_node(n)
r'''
0 1 2
S \ F | / F
\ | /
\ | /
\|/
|
3
'''
g.add_edge(nodes[0], nodes[3], "success_nodes")
g.add_edge(nodes[1], nodes[3], "failure_nodes")
g.add_edge(nodes[2], nodes[3], "failure_nodes")
nodes[3].all_parents_must_converge = True
nodes[0].job = Job(status='successful')
nodes[1].job = Job(status='running')
nodes[2].job = Job(status='failed')
return (g, nodes)
def workflow_all_converge_2(self, wf_node_generator):
"""The ordering of _1 and this test, _2, is _slightly_ different.
The hope is that topological sorting results in 2 being processed before 3
and/or 3 before 2.
"""
g = WorkflowDAG()
nodes = [wf_node_generator() for i in range(3)]
for n in nodes:
g.add_node(n)
r'''
0
|\ S
| \
F| 1
| /
|/ A
2
'''
g.add_edge(nodes[0], nodes[1], "success_nodes")
g.add_edge(nodes[0], nodes[2], "failure_nodes")
g.add_edge(nodes[1], nodes[2], "always_nodes")
nodes[2].all_parents_must_converge = True
nodes[0].job = Job(status='successful')
return (g, nodes)
