def test_get_outbound_context(self): output = data_flow.get_task_output(TASK, {'new_key1': 'new_val1'}) self.assertDictEqual( { 'new_key11': 'new_val1', 'task': { 'my_task': { 'new_key1': 'new_val1' } } }, output)
def convey_task_result(cls, workbook_name, execution_id, task_id, state, result): db_api.start_tx() workbook = cls._get_workbook(workbook_name) try: #TODO(rakhmerov): validate state transition task = db_api.task_get(workbook_name, execution_id, task_id) task_output = data_flow.get_task_output(task, result) # Update task state. task = db_api.task_update(workbook_name, execution_id, task_id, {"state": state, "output": task_output}) execution = db_api.execution_get(workbook_name, execution_id) # Calculate task outbound context. outbound_context = data_flow.get_outbound_context(task) cls._create_next_tasks(task, workbook) # Determine what tasks need to be started. tasks = db_api.tasks_get(workbook_name, execution_id) new_exec_state = cls._determine_execution_state(execution, tasks) if execution['state'] != new_exec_state: execution = \ db_api.execution_update(workbook_name, execution_id, { "state": new_exec_state }) LOG.info("Changed execution state: %s" % execution) tasks_to_start = workflow.find_resolved_tasks(tasks) data_flow.prepare_tasks(tasks_to_start, outbound_context) db_api.commit_tx() except Exception as e: raise exc.EngineException("Failed to create necessary DB objects:" " %s" % e) finally: db_api.end_tx() if states.is_stopped_or_finished(execution["state"]): return task if tasks_to_start: cls._run_tasks(tasks_to_start) return task
def convey_task_result(self, cntx, **kwargs): """Conveys task result to Mistral Engine. This method should be used by clients of Mistral Engine to update state of a task once task action has been performed. One of the clients of this method is Mistral REST API server that receives task result from the outside action handlers. Note: calling this method serves an event notifying Mistral that it possibly needs to move the workflow on, i.e. run other workflow tasks for which all dependencies are satisfied. :param cntx: a request context dict :type cntx: dict :param kwargs: a dict of method arguments :type kwargs: dict :return: Task. """ task_id = kwargs.get('task_id') state = kwargs.get('state') result = kwargs.get('result') db_api.start_tx() try: # TODO(rakhmerov): validate state transition task = db_api.task_get(task_id) workbook = self._get_workbook(task['workbook_name']) wf_trace_msg = "Task '%s' [%s -> %s" % \ (task['name'], task['state'], state) wf_trace_msg += ']' if state == states.ERROR \ else ", result = %s]" % result WORKFLOW_TRACE.info(wf_trace_msg) action_name = wb_task.TaskSpec(task['task_spec'])\ .get_full_action_name() if not a_f.get_action_class(action_name): action = a_f.resolve_adhoc_action_name(workbook, action_name) if not action: msg = 'Unknown action [workbook=%s, action=%s]' % \ (workbook, action_name) raise exc.ActionException(msg) result = a_f.convert_adhoc_action_result(workbook, action_name, result) task_output = data_flow.get_task_output(task, result) # Update task state. task, context = self._update_task(workbook, task, state, task_output) execution = db_api.execution_get(task['execution_id']) self._create_next_tasks(task, workbook) # Determine what tasks need to be started. tasks = db_api.tasks_get(execution_id=task['execution_id']) new_exec_state = self._determine_execution_state(execution, tasks) if execution['state'] != new_exec_state: wf_trace_msg = \ "Execution '%s' [%s -> %s]" % \ (execution['id'], execution['state'], new_exec_state) WORKFLOW_TRACE.info(wf_trace_msg) execution = db_api.execution_update(execution['id'], { "state": new_exec_state }) LOG.info("Changed execution state: %s" % execution) # Create a list of tasks that can be executed immediately (have # their requirements satisfied) along with the list of tasks that # require some delay before they'll be executed. tasks_to_start, delayed_tasks = workflow.find_resolved_tasks(tasks) # Populate context with special variables such as `openstack` and # `__execution`. self._add_variables_to_data_flow_context(context, execution) # Update task with new context and params. executables = data_flow.prepare_tasks(tasks_to_start, context, workbook) db_api.commit_tx() except Exception as e: msg = "Failed to create necessary DB objects: %s" % e LOG.exception(msg) raise exc.EngineException(msg) finally: db_api.end_tx() if states.is_stopped_or_finished(execution['state']): return task for task in delayed_tasks: self._schedule_run(workbook, task, context) for task_id, action_name, action_params in executables: self._run_task(task_id, action_name, action_params) return task
def convey_task_result(cls, workbook_name, execution_id, task_id, state, result): db_api.start_tx() try: workbook = cls._get_workbook(workbook_name) #TODO(rakhmerov): validate state transition task = db_api.task_get(workbook_name, execution_id, task_id) wf_trace_msg = "Task '%s' [%s -> %s" % \ (task['name'], task['state'], state) wf_trace_msg += ']' if state == states.ERROR \ else ", result = %s]" % result WORKFLOW_TRACE.info(wf_trace_msg) task_output = data_flow.get_task_output(task, result) # Update task state. task, outbound_context = cls._update_task(workbook, task, state, task_output) execution = db_api.execution_get(workbook_name, execution_id) cls._create_next_tasks(task, workbook) # Determine what tasks need to be started. tasks = db_api.tasks_get(workbook_name, execution_id) new_exec_state = cls._determine_execution_state(execution, tasks) if execution['state'] != new_exec_state: wf_trace_msg = \ "Execution '%s' [%s -> %s]" % \ (execution_id, execution['state'], new_exec_state) WORKFLOW_TRACE.info(wf_trace_msg) execution = \ db_api.execution_update(workbook_name, execution_id, { "state": new_exec_state }) LOG.info("Changed execution state: %s" % execution) tasks_to_start, delayed_tasks = workflow.find_resolved_tasks(tasks) cls._add_variables_to_data_flow_context(outbound_context, execution) data_flow.prepare_tasks(tasks_to_start, outbound_context) db_api.commit_tx() except Exception as e: LOG.exception("Failed to create necessary DB objects.") raise exc.EngineException("Failed to create necessary DB objects:" " %s" % e) finally: db_api.end_tx() if states.is_stopped_or_finished(execution["state"]): return task for task in delayed_tasks: cls._schedule_run(workbook, task, outbound_context) if tasks_to_start: cls._run_tasks(tasks_to_start) return task
def convey_task_result(self, cntx, **kwargs): """Conveys task result to Mistral Engine. This method should be used by clients of Mistral Engine to update state of a task once task action has been performed. One of the clients of this method is Mistral REST API server that receives task result from the outside action handlers. Note: calling this method serves an event notifying Mistral that it possibly needs to move the workflow on, i.e. run other workflow tasks for which all dependencies are satisfied. :param cntx: a request context dict :type cntx: dict :param kwargs: a dict of method arguments :type kwargs: dict :return: Task. """ task_id = kwargs.get('task_id') state = kwargs.get('state') result = kwargs.get('result') db_api.start_tx() try: # TODO(rakhmerov): validate state transition task = db_api.task_get(task_id) workbook = self._get_workbook(task['workbook_name']) wf_trace_msg = "Task '%s' [%s -> %s" % \ (task['name'], task['state'], state) wf_trace_msg += ']' if state == states.ERROR \ else ", result = %s]" % result WORKFLOW_TRACE.info(wf_trace_msg) task_output = data_flow.get_task_output(task, result) # Update task state. task, outbound_context = self._update_task(workbook, task, state, task_output) execution = db_api.execution_get(task['execution_id']) self._create_next_tasks(task, workbook) # Determine what tasks need to be started. tasks = db_api.tasks_get(workbook_name=task['workbook_name'], execution_id=task['execution_id']) new_exec_state = self._determine_execution_state(execution, tasks) if execution['state'] != new_exec_state: wf_trace_msg = \ "Execution '%s' [%s -> %s]" % \ (execution['id'], execution['state'], new_exec_state) WORKFLOW_TRACE.info(wf_trace_msg) execution = \ db_api.execution_update(execution['id'], { "state": new_exec_state }) LOG.info("Changed execution state: %s" % execution) tasks_to_start, delayed_tasks = workflow.find_resolved_tasks(tasks) self._add_variables_to_data_flow_context(outbound_context, execution) data_flow.prepare_tasks(tasks_to_start, outbound_context) db_api.commit_tx() except Exception as e: msg = "Failed to create necessary DB objects: %s" % e LOG.exception(msg) raise exc.EngineException(msg) finally: db_api.end_tx() if states.is_stopped_or_finished(execution["state"]): return task for task in delayed_tasks: self._schedule_run(workbook, task, outbound_context) if tasks_to_start: self._run_tasks(tasks_to_start) return task