def _predict_hook(self, my_task):
split_n = valueof(my_task, self.times)
if split_n is None:
return
my_task._set_internal_attribute(splits = split_n)
# Create the outgoing nodes.
outputs = []
for i in range(split_n):
outputs += self.outputs
if my_task._has_state(Task.LIKELY):
child_state = Task.LIKELY
else:
child_state = Task.FUTURE
my_task._update_children(outputs, child_state)
def try_fire(self, my_task):
# If the threshold was already reached, there is nothing else to do.
if my_task._has_state(Task.COMPLETED):
return False
if my_task._has_state(Task.READY):
return True
# Retrieve a list of all activated tasks from the associated
# task that did the conditional parallel split.
split_node = my_task._find_ancestor_from_name(self.split_task)
if split_node is None:
msg = 'Join with %s, which was not reached' % self.split_task
raise WorkflowException(self, msg)
nodes = split_node.spec._get_activated_threads(split_node)
# The default threshold is the number of threads that were started.
threshold = valueof(my_task, self.threshold)
if threshold is None:
threshold = len(nodes)
# Look up which tasks have already completed.
waiting_nodes = []
completed = 0
for node in nodes:
# Refresh path prediction.
node.spec._predict(node)
if self._branch_is_complete(node):
completed += 1
else:
waiting_nodes.append(node)
# If the threshold was reached, get ready to fire.
if completed >= threshold:
# If this is a cancelling join, cancel all incoming branches,
# except for the one that just completed.
if self.cancel_remaining:
for node in waiting_nodes:
node.cancel()
return True
# We do NOT set the task state to COMPLETED, because in
# case all other incoming tasks get cancelled (or never reach
# the ThreadMerge for other reasons, such as reaching a stub branch),
# we need to revisit it.
return False
def _try_fire_structured(self, my_task, force = False):
# If the threshold was already reached, there is nothing else to do.
if my_task._has_state(Task.READY):
return True
if my_task._has_state(Task.COMPLETED):
return False
# Retrieve a list of all activated tasks from the associated
# task that did the conditional parallel split.
split_task = my_task._find_ancestor_from_name(self.split_task)
if split_task is None:
msg = 'Join with %s, which was not reached' % self.split_task
raise WorkflowException(self, msg)
tasks = split_task.spec._get_activated_tasks(split_task, my_task)
# The default threshold is the number of branches that were started.
threshold = valueof(my_task, self.threshold)
if threshold is None:
threshold = len(tasks)
# Look up which tasks have already completed.
waiting_tasks = []
completed = 0
for task in tasks:
# Refresh path prediction.
task.spec._predict(task)
if not self._branch_may_merge_at(task):
completed += 1
elif self._branch_is_complete(task):
completed += 1
else:
waiting_tasks.append(task)
# If the threshold was reached, get ready to fire.
if force or completed >= threshold:
self._fire(my_task, waiting_tasks)
return True
# We do NOT set the task state to COMPLETED, because in
# case all other incoming tasks get cancelled (or never reach
# the Join for other reasons, such as reaching a stub branch), we
# need to revisit it.
return False
def _on_ready_before_hook(self, my_task):
file = valueof(my_task, self.file)
xml_reader = XmlReader()
workflow_list = xml_reader.parse_file(file)
workflow = workflow_list[0]
outer_job = my_task.job.outer_job
subjob = SpiffWorkflow.Job(workflow, parent = outer_job)
subjob.signal_connect('completed', self._on_subjob_completed, my_task)
# Integrate the tree of the subjob into the tree of this job.
my_task._update_children(self.outputs, Task.FUTURE)
for child in my_task.children:
child._inherit_attributes()
for child in subjob.task_tree.children:
my_task.children.insert(0, child)
child.parent = my_task
my_task._set_internal_attribute(subjob = subjob)
return True
def _try_fire_unstructured(self, my_task, force = False):
# If the threshold was already reached, there is nothing else to do.
if my_task._has_state(Task.COMPLETED):
return False
if my_task._has_state(Task.READY):
return True
# The default threshold is the number of inputs.
threshold = valueof(my_task, self.threshold)
if threshold is None:
threshold = len(self.inputs)
# Look at the tree to find all places where this task is used.
tasks = []
for input in self.inputs:
for task in my_task.job.task_tree:
if task.thread_id != my_task.thread_id:
continue
if task.spec != input:
continue
tasks.append(task)
# Look up which tasks have already completed.
waiting_tasks = []
completed = 0
for task in tasks:
if task.parent is None or task._has_state(Task.COMPLETED):
completed += 1
else:
waiting_tasks.append(task)
# If the threshold was reached, get ready to fire.
if force or completed >= threshold:
self._fire(my_task, waiting_tasks)
return True
# We do NOT set the task state to COMPLETED, because in
# case all other incoming tasks get cancelled (or never reach
# the Join for other reasons, such as reaching a stub branch), we
# we need to revisit it.
return False
