def _flatten_flow(self, flow):
"""Flattens a graph flow."""
graph = gr.DiGraph(name=flow.name)
# Flatten all nodes into a single subgraph per node.
subgraph_map = {}
for item in flow:
subgraph = self._flatten(item)
subgraph_map[item] = subgraph
graph = gr.merge_graphs([graph, subgraph])
# Reconnect all node edges to their corresponding subgraphs.
for (u, v, attrs) in flow.iter_links():
u_g = subgraph_map[u]
v_g = subgraph_map[v]
if any(attrs.get(k) for k in ("invariant", "manual", "retry")):
# Connect nodes with no predecessors in v to nodes with
# no successors in u (thus maintaining the edge dependency).
self._add_new_edges(graph, u_g.no_successors_iter(), v_g.no_predecessors_iter(), edge_attrs=attrs)
else:
# This is dependency-only edge, connect corresponding
# providers and consumers.
for provider in u_g:
for consumer in v_g:
reasons = provider.provides & consumer.requires
if reasons:
graph.add_edge(provider, consumer, reasons=reasons)
if flow.retry is not None:
self._connect_retry(flow.retry, graph)
return graph
def apply_constraints(self, graph, flow, decomposed_members):
# This list is used to track the links that have been previously
# iterated over, so that when we are trying to find a entry to
# connect to that we iterate backwards through this list, finding
# connected nodes to the current target (lets call it v) and find
# the first (u_n, or u_n - 1, u_n - 2...) that was decomposed into
# a non-empty graph. We also retain all predecessors of v so that we
# can correctly locate u_n - 1 if u_n turns out to have decomposed into
# an empty graph (and so on).
priors = []
# NOTE(harlowja): u, v are flows/tasks (also graph terminology since
# we are compiling things down into a flattened graph), the meaning
# of this link iteration via iter_links() is that u -> v (with the
# provided dictionary attributes, if any).
for (u, v, attr_dict) in flow.iter_links():
if not priors:
priors.append((None, u))
v_g = decomposed_members[v]
if not v_g.number_of_nodes():
priors.append((u, v))
continue
invariant = any(attr_dict.get(k) for k in _EDGE_INVARIANTS)
if not invariant:
# This is a symbol *only* dependency, connect
# corresponding providers and consumers to allow the consumer
# to be executed immediately after the provider finishes (this
# is an optimization for these types of dependencies...)
u_g = decomposed_members[u]
if not u_g.number_of_nodes():
# This must always exist, but incase it somehow doesn't...
raise exc.CompilationFailure(
"Non-invariant link being created from '%s' ->"
" '%s' even though the target '%s' was found to be"
" decomposed into an empty graph" % (v, u, u))
for u in u_g.nodes_iter():
for v in v_g.nodes_iter():
depends_on = u.provides & v.requires
if depends_on:
_add_update_edges(graph,
[u], [v],
attr_dict={
_EDGE_REASONS: depends_on,
})
else:
# Connect nodes with no predecessors in v to nodes with no
# successors in the *first* non-empty predecessor of v (thus
# maintaining the edge dependency).
match = self._find_first_decomposed(u, priors,
decomposed_members,
self._is_not_empty)
if match is not None:
_add_update_edges(graph,
match.no_successors_iter(),
list(v_g.no_predecessors_iter()),
attr_dict=attr_dict)
priors.append((u, v))
def apply_constraints(self, graph, flow, decomposed_members):
# This list is used to track the links that have been previously
# iterated over, so that when we are trying to find a entry to
# connect to that we iterate backwards through this list, finding
# connected nodes to the current target (lets call it v) and find
# the first (u_n, or u_n - 1, u_n - 2...) that was decomposed into
# a non-empty graph. We also retain all predecessors of v so that we
# can correctly locate u_n - 1 if u_n turns out to have decomposed into
# an empty graph (and so on).
priors = []
# NOTE(harlowja): u, v are flows/tasks (also graph terminology since
# we are compiling things down into a flattened graph), the meaning
# of this link iteration via iter_links() is that u -> v (with the
# provided dictionary attributes, if any).
for (u, v, attr_dict) in flow.iter_links():
if not priors:
priors.append((None, u))
v_g = decomposed_members[v]
if not v_g.number_of_nodes():
priors.append((u, v))
continue
invariant = any(attr_dict.get(k) for k in _EDGE_INVARIANTS)
if not invariant:
# This is a symbol *only* dependency, connect
# corresponding providers and consumers to allow the consumer
# to be executed immediately after the provider finishes (this
# is an optimization for these types of dependencies...)
u_g = decomposed_members[u]
if not u_g.number_of_nodes():
# This must always exist, but incase it somehow doesn't...
raise exc.CompilationFailure(
"Non-invariant link being created from '%s' ->"
" '%s' even though the target '%s' was found to be"
" decomposed into an empty graph" % (v, u, u))
for u in u_g.nodes_iter():
for v in v_g.nodes_iter():
depends_on = u.provides & v.requires
if depends_on:
_add_update_edges(graph, [u], [v],
attr_dict={
_EDGE_REASONS: depends_on,
})
else:
# Connect nodes with no predecessors in v to nodes with no
# successors in the *first* non-empty predecessor of v (thus
# maintaining the edge dependency).
match = self._find_first_decomposed(u, priors,
decomposed_members,
self._is_not_empty)
if match is not None:
_add_update_edges(graph,
match.no_successors_iter(),
list(v_g.no_predecessors_iter()),
attr_dict=attr_dict)
priors.append((u, v))
def compile(self, flow, parent=None):
"""Decomposes a flow into a graph and scope tree hierarchy."""
graph = gr.DiGraph(name=flow.name)
graph.add_node(flow, kind=FLOW, noop=True)
tree_node = tr.Node(flow, kind=FLOW, noop=True)
if parent is not None:
parent.add(tree_node)
if flow.retry is not None:
tree_node.add(tr.Node(flow.retry, kind=RETRY))
decomposed = dict(
(child, self._deep_compiler_func(child, parent=tree_node)[0])
for child in flow)
decomposed_graphs = list(six.itervalues(decomposed))
graph = gr.merge_graphs(graph,
*decomposed_graphs,
overlap_detector=_overlap_occurence_detector)
for u, v, attr_dict in flow.iter_links():
u_graph = decomposed[u]
v_graph = decomposed[v]
_add_update_edges(graph,
u_graph.no_successors_iter(),
list(v_graph.no_predecessors_iter()),
attr_dict=attr_dict)
if flow.retry is not None:
graph.add_node(flow.retry, kind=RETRY)
_add_update_edges(graph, [flow], [flow.retry],
attr_dict={LINK_INVARIANT: True})
for node in graph.nodes_iter():
if node is not flow.retry and node is not flow:
graph.node[node].setdefault(RETRY, flow.retry)
from_nodes = [flow.retry]
connected_attr_dict = {LINK_INVARIANT: True, LINK_RETRY: True}
else:
from_nodes = [flow]
connected_attr_dict = {LINK_INVARIANT: True}
connected_to = [
node for node in graph.no_predecessors_iter() if node is not flow
]
if connected_to:
# Ensure all nodes in this graph(s) that have no
# predecessors depend on this flow (or this flow's retry) so that
# we can depend on the flow being traversed before its
# children (even though at the current time it will be skipped).
_add_update_edges(graph,
from_nodes,
connected_to,
attr_dict=connected_attr_dict)
return graph, tree_node
def _flatten_flow(self, flow, parent):
"""Flattens a flow."""
graph = gr.DiGraph(name=flow.name)
node = tr.Node(flow)
if parent is not None:
parent.add(node)
if flow.retry is not None:
node.add(tr.Node(flow.retry))
# Flatten all nodes into a single subgraph per item (and track origin
# item to its newly expanded graph).
subgraphs = {}
for item in flow:
subgraph = self._flatten(item, node)[0]
subgraphs[item] = subgraph
graph = gr.merge_graphs([graph, subgraph])
# Reconnect all items edges to their corresponding subgraphs.
for (u, v, attrs) in flow.iter_links():
u_g = subgraphs[u]
v_g = subgraphs[v]
if any(attrs.get(k) for k in _EDGE_INVARIANTS):
# Connect nodes with no predecessors in v to nodes with
# no successors in u (thus maintaining the edge dependency).
self._add_new_edges(graph,
u_g.no_successors_iter(),
v_g.no_predecessors_iter(),
edge_attrs=attrs)
else:
# This is symbol dependency edge, connect corresponding
# providers and consumers.
for provider in u_g:
for consumer in v_g:
reasons = provider.provides & consumer.requires
if reasons:
graph.add_edge(provider, consumer, reasons=reasons)
if flow.retry is not None:
self._connect_retry(flow.retry, graph)
return graph, node
def _flatten_flow(self, flow, parent):
"""Flattens a flow."""
graph = gr.DiGraph(name=flow.name)
node = tr.Node(flow)
if parent is not None:
parent.add(node)
if flow.retry is not None:
node.add(tr.Node(flow.retry))
# Flatten all nodes into a single subgraph per item (and track origin
# item to its newly expanded graph).
subgraphs = {}
for item in flow:
subgraph = self._flatten(item, node)[0]
subgraphs[item] = subgraph
graph = gr.merge_graphs([graph, subgraph])
# Reconnect all items edges to their corresponding subgraphs.
for (u, v, attrs) in flow.iter_links():
u_g = subgraphs[u]
v_g = subgraphs[v]
if any(attrs.get(k) for k in ('invariant', 'manual', 'retry')):
# Connect nodes with no predecessors in v to nodes with
# no successors in u (thus maintaining the edge dependency).
self._add_new_edges(graph,
u_g.no_successors_iter(),
v_g.no_predecessors_iter(),
edge_attrs=attrs)
else:
# This is dependency-only edge, connect corresponding
# providers and consumers.
for provider in u_g:
for consumer in v_g:
reasons = provider.provides & consumer.requires
if reasons:
graph.add_edge(provider, consumer, reasons=reasons)
if flow.retry is not None:
self._connect_retry(flow.retry, graph)
return graph, node
def compile(self, flow, parent=None):
"""Decomposes a flow into a graph and scope tree hierarchy."""
graph = gr.DiGraph(name=flow.name)
graph.add_node(flow, kind=FLOW, noop=True)
tree_node = tr.Node(flow, kind=FLOW, noop=True)
if parent is not None:
parent.add(tree_node)
if flow.retry is not None:
tree_node.add(tr.Node(flow.retry, kind=RETRY))
decomposed = dict(
(child, self._deep_compiler_func(child, parent=tree_node)[0])
for child in flow)
decomposed_graphs = list(six.itervalues(decomposed))
graph = gr.merge_graphs(graph, *decomposed_graphs,
overlap_detector=_overlap_occurence_detector)
for u, v, attr_dict in flow.iter_links():
u_graph = decomposed[u]
v_graph = decomposed[v]
_add_update_edges(graph, u_graph.no_successors_iter(),
list(v_graph.no_predecessors_iter()),
attr_dict=attr_dict)
# Insert the flow(s) retry if needed, and always make sure it
# is the **immediate** successor of the flow node itself.
if flow.retry is not None:
graph.add_node(flow.retry, kind=RETRY)
_add_update_edges(graph, [flow], [flow.retry],
attr_dict={LINK_INVARIANT: True})
for node in graph.nodes_iter():
if node is not flow.retry and node is not flow:
graph.node[node].setdefault(RETRY, flow.retry)
from_nodes = [flow.retry]
attr_dict = {LINK_INVARIANT: True, LINK_RETRY: True}
else:
from_nodes = [flow]
attr_dict = {LINK_INVARIANT: True}
# Ensure all nodes with no predecessors are connected to this flow
# or its retry node (so that the invariant that the flow node is
# traversed through before its contents is maintained); this allows
# us to easily know when we have entered a flow (when running) and
# do special and/or smart things such as only traverse up to the
# start of a flow when looking for node deciders.
_add_update_edges(graph, from_nodes, [
node for node in graph.no_predecessors_iter()
if node is not flow
], attr_dict=attr_dict)
# Connect all nodes with no successors into a special terminator
# that is used to identify the end of the flow and ensure that all
# execution traversals will traverse over this node before executing
# further work (this is especially useful for nesting and knowing
# when we have exited a nesting level); it allows us to do special
# and/or smart things such as applying deciders up to (but not
# beyond) a flow termination point.
#
# Do note that in a empty flow this will just connect itself to
# the flow node itself... and also note we can not use the flow
# object itself (primarily because the underlying graph library
# uses hashing to identify node uniqueness and we can easily create
# a loop if we don't do this correctly, so avoid that by just
# creating this special node and tagging it with a special kind); we
# may be able to make this better in the future with a multidigraph
# that networkx provides??
flow_term = Terminator(flow)
graph.add_node(flow_term, kind=FLOW_END, noop=True)
_add_update_edges(graph, [
node for node in graph.no_successors_iter()
if node is not flow_term
], [flow_term], attr_dict={LINK_INVARIANT: True})
return graph, tree_node
