198 lines
6.3 KiB
Python
198 lines
6.3 KiB
Python
# Copyright 2014 - Mirantis, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import networkx as nx
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from networkx.algorithms import traversal
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from mistral import exceptions as exc
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from mistral.workflow import base
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from mistral.workflow import commands
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from mistral.workflow import data_flow
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from mistral.workflow import lookup_utils
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from mistral.workflow import states
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class ReverseWorkflowController(base.WorkflowController):
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"""'Reverse workflow controller.
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This controller implements the workflow pattern which is based on
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dependencies between tasks, i.e. each task in a workflow graph
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may be dependent on other tasks. To run this type of workflow
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user must specify a task name that serves a target node in the
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graph that the algorithm should come to by resolving all
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dependencies.
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For example, if there's a workflow consisting of two tasks
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'A' and 'B' where 'A' depends on 'B' and if we specify a target
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task name 'A' then the controller first will run task 'B' and then,
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when a dependency of 'A' is resolved, will run task 'A'.
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"""
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__workflow_type__ = "reverse"
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def _find_next_commands(self, task_ex):
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"""Finds all tasks with resolved dependencies.
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This method finds all tasks with resolved dependencies and
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returns them in the form of workflow commands.
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"""
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cmds = super(ReverseWorkflowController, self)._find_next_commands(
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task_ex
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)
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# TODO(rakhmerov): Adapt reverse workflow to non-locking model.
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# 1. Task search must use task_ex parameter.
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# 2. When a task has more than one dependency it's possible to
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# get into 'phantom read' phenomena and create multiple instances
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# of the same task. So 'unique_key' in conjunction with 'wait_flag'
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# must be used to prevent this.
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task_specs = self._find_task_specs_with_satisfied_dependencies()
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return cmds + [
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commands.RunTask(
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self.wf_ex,
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self.wf_spec,
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t_s,
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self.get_task_inbound_context(t_s)
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)
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for t_s in task_specs
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]
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def _get_target_task_specification(self):
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task_name = self.wf_ex.params.get('task_name')
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task_spec = self.wf_spec.get_tasks().get(task_name)
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if not task_spec:
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raise exc.WorkflowException(
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'Invalid task name [wf_spec=%s, task_name=%s]' %
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(self.wf_spec, task_name)
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)
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return task_spec
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def _get_upstream_task_executions(self, task_spec):
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t_specs = [
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self.wf_spec.get_tasks()[t_name]
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for t_name in self.wf_spec.get_task_requires(task_spec)
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or []
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]
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return list(
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filter(
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lambda t_e: t_e.state == states.SUCCESS,
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lookup_utils.find_task_executions_by_specs(
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self.wf_ex.id,
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t_specs
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)
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)
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)
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def evaluate_workflow_final_context(self):
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task_execs = lookup_utils.find_task_executions_by_spec(
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self.wf_ex.id,
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self._get_target_task_specification()
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)
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# NOTE: For reverse workflow there can't be multiple
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# executions for one task.
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assert len(task_execs) <= 1
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if len(task_execs) == 1:
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return data_flow.evaluate_task_outbound_context(task_execs[0])
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else:
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return {}
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def get_logical_task_state(self, task_ex):
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# TODO(rakhmerov): Implement.
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return base.TaskLogicalState(task_ex.state, task_ex.state_info)
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def find_indirectly_affected_task_executions(self, task_name):
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return set()
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def is_error_handled_for(self, task_ex):
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return task_ex.state != states.ERROR
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def all_errors_handled(self):
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task_execs = lookup_utils.find_error_task_executions(self.wf_ex.id)
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return len(task_execs) == 0
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def _find_task_specs_with_satisfied_dependencies(self):
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"""Given a target task name finds tasks with no dependencies.
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:return: Task specifications with no dependencies.
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"""
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tasks_spec = self.wf_spec.get_tasks()
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graph = self._build_graph(tasks_spec)
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# Unwind tasks from the target task
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# and filter out tasks with dependencies.
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return [
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t_s for t_s in
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traversal.dfs_postorder_nodes(
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graph.reverse(),
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self._get_target_task_specification()
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)
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if self._is_satisfied_task(t_s)
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]
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def _is_satisfied_task(self, task_spec):
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if lookup_utils.find_task_executions_by_spec(
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self.wf_ex.id, task_spec):
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return False
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if not self.wf_spec.get_task_requires(task_spec):
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return True
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success_t_names = set()
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for t_ex in self.wf_ex.task_executions:
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if t_ex.state == states.SUCCESS:
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success_t_names.add(t_ex.name)
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return not (
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set(self.wf_spec.get_task_requires(task_spec)) - success_t_names
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)
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def _build_graph(self, tasks_spec):
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graph = nx.DiGraph()
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# Add graph nodes.
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for t in tasks_spec:
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graph.add_node(t)
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# Add graph edges.
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for t_spec in tasks_spec:
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for dep_t_spec in self._get_dependency_tasks(tasks_spec, t_spec):
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graph.add_edge(dep_t_spec, t_spec)
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return graph
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def _get_dependency_tasks(self, tasks_spec, task_spec):
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dep_task_names = self.wf_spec.get_task_requires(task_spec)
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if len(dep_task_names) == 0:
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return []
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dep_t_specs = set()
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for t_spec in tasks_spec:
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for t_name in dep_task_names:
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if t_name == t_spec.get_name():
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dep_t_specs.add(t_spec)
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return dep_t_specs
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