473 lines
14 KiB
Python
473 lines
14 KiB
Python
# Copyright 2016 - Nokia Networks.
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# Copyright 2016 - Brocade Communications Systems, 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 abc
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import copy
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import operator
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from oslo_log import log as logging
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from osprofiler import profiler
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import six
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from mistral.db.v2 import api as db_api
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from mistral.engine import actions
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from mistral.engine import dispatcher
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from mistral.engine import policies
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from mistral import exceptions as exc
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from mistral import expressions as expr
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from mistral import utils
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from mistral.utils import wf_trace
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from mistral.workbook import parser as spec_parser
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from mistral.workflow import base as wf_base
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from mistral.workflow import data_flow
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from mistral.workflow import states
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from mistral.workflow import utils as wf_utils
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from mistral.workflow import with_items
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LOG = logging.getLogger(__name__)
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@six.add_metaclass(abc.ABCMeta)
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class Task(object):
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"""Task.
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Represents a workflow task and defines interface that can be used by
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Mistral engine or its components in order to manipulate with tasks.
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"""
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def __init__(self, wf_ex, task_spec, ctx, task_ex=None):
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self.wf_ex = wf_ex
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self.task_spec = task_spec
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self.ctx = ctx
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self.task_ex = task_ex
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self.wf_spec = spec_parser.get_workflow_spec(wf_ex.spec)
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self.waiting = False
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self.reset_flag = False
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@abc.abstractmethod
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def on_action_complete(self, action_ex):
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"""Handle action completion.
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:param action_ex: Action execution.
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"""
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raise NotImplementedError
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@abc.abstractmethod
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def run(self):
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"""Runs task."""
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raise NotImplementedError
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@profiler.trace('task-defer')
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def defer(self):
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"""Defers task.
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This methods finds task execution or creates new and puts task
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to a waiting state.
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"""
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if not self.task_ex:
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t_execs = wf_utils.find_task_executions_by_spec(
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self.wf_ex,
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self.task_spec
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)
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self.task_ex = t_execs[0] if t_execs else None
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if not self.task_ex:
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self._create_task_execution()
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self.set_state(states.WAITING, 'Task execution is deferred.')
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self.waiting = True
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def reset(self):
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self.reset_flag = True
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@profiler.trace('task-set-state')
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def set_state(self, state, state_info, processed=None):
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"""Sets task state without executing post completion logic.
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:param state: New task state.
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:param state_info: New state information (i.e. error message).
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:param processed: New "processed" flag value.
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"""
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assert self.task_ex
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wf_trace.info(
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self.task_ex.workflow_execution,
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"Task execution '%s' [%s -> %s]: %s" %
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(self.task_ex.id, self.task_ex.state, state, state_info)
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)
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self.task_ex.state = state
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self.task_ex.state_info = state_info
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if processed is not None:
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self.task_ex.processed = processed
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@profiler.trace('task-complete')
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def complete(self, state, state_info=None):
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"""Complete task and set specified state.
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Method sets specified task state and runs all necessary post
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completion logic such as publishing workflow variables and
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scheduling new workflow commands.
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:param state: New task state.
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:param state_info: New state information (i.e. error message).
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"""
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assert self.task_ex
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# Ignore if task already completed.
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if states.is_completed(self.task_ex.state):
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return
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self.set_state(state, state_info)
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data_flow.publish_variables(self.task_ex, self.task_spec)
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if not self.task_spec.get_keep_result():
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# Destroy task result.
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for ex in self.task_ex.executions:
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if hasattr(ex, 'output'):
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ex.output = {}
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self._after_task_complete()
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# Ignore DELAYED state.
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if self.task_ex.state == states.RUNNING_DELAYED:
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return
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# If workflow is paused we shouldn't schedule new commands
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# and mark task as processed.
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if states.is_paused(self.wf_ex.state):
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return
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wf_ctrl = wf_base.get_controller(self.wf_ex, self.wf_spec)
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# Calculate commands to process next.
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cmds = wf_ctrl.continue_workflow()
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# Mark task as processed after all decisions have been made
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# upon its completion.
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self.task_ex.processed = True
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dispatcher.dispatch_workflow_commands(self.wf_ex, cmds)
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def _before_task_start(self):
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policies_spec = self.task_spec.get_policies()
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for p in policies.build_policies(policies_spec, self.wf_spec):
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p.before_task_start(self.task_ex, self.task_spec)
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def _after_task_complete(self):
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policies_spec = self.task_spec.get_policies()
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for p in policies.build_policies(policies_spec, self.wf_spec):
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p.after_task_complete(self.task_ex, self.task_spec)
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def _create_task_execution(self, state=states.RUNNING):
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self.task_ex = db_api.create_task_execution({
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'name': self.task_spec.get_name(),
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'workflow_execution_id': self.wf_ex.id,
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'workflow_name': self.wf_ex.workflow_name,
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'workflow_id': self.wf_ex.workflow_id,
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'state': state,
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'spec': self.task_spec.to_dict(),
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'in_context': self.ctx,
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'published': {},
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'runtime_context': {},
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'project_id': self.wf_ex.project_id
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})
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# Add to collection explicitly so that it's in a proper
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# state within the current session.
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self.wf_ex.task_executions.append(self.task_ex)
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def _get_action_defaults(self):
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action_name = self.task_spec.get_action_name()
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if not action_name:
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return {}
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env = self.task_ex.in_context.get('__env', {})
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return env.get('__actions', {}).get(action_name, {})
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class RegularTask(Task):
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"""Regular task.
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Takes care of processing regular tasks with one action.
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"""
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@profiler.trace('task-on-action-complete')
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def on_action_complete(self, action_ex):
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state = action_ex.state
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# TODO(rakhmerov): Here we can define more informative messages
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# cases when action is successful and when it's not. For example,
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# in state_info we can specify the cause action.
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state_info = (None if state == states.SUCCESS
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else action_ex.output.get('result'))
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self.complete(state, state_info)
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def is_completed(self):
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return self.task_ex and states.is_completed(self.task_ex.state)
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@profiler.trace('task-run')
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def run(self):
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if not self.task_ex:
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self._run_new()
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else:
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self._run_existing()
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def _run_new(self):
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# NOTE(xylan): Need to think how to get rid of this weird judgment
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# to keep it more consistent with the function name.
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self.task_ex = wf_utils.find_task_execution_with_state(
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self.wf_ex,
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self.task_spec,
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states.WAITING
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)
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if self.task_ex:
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self.set_state(states.RUNNING, None)
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self.task_ex.in_context = self.ctx
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else:
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self._create_task_execution()
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LOG.debug(
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'Starting task [workflow=%s, task_spec=%s, init_state=%s]' %
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(self.wf_ex.name, self.task_spec, self.task_ex.state)
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)
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self._before_task_start()
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# Policies could possibly change task state.
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if self.task_ex.state != states.RUNNING:
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return
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self._schedule_actions()
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def _run_existing(self):
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if self.waiting:
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return
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# Explicitly change task state to RUNNING.
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# Throw exception if the existing task already succeeded.
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if self.task_ex.state == states.SUCCESS:
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raise exc.MistralError(
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'Rerunning succeeded tasks is not supported.'
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)
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self.set_state(states.RUNNING, None, processed=False)
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self._reset_actions()
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self._schedule_actions()
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def _reset_actions(self):
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"""Resets task state.
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Depending on task type this method may reset task state. For example,
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delete all task actions etc.
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"""
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# Reset state of processed task and related action executions.
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if self.reset_flag:
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action_exs = self.task_ex.executions
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else:
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action_exs = db_api.get_action_executions(
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task_execution_id=self.task_ex.id,
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state=states.ERROR,
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accepted=True
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)
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for action_ex in action_exs:
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action_ex.accepted = False
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def _schedule_actions(self):
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# Regular task schedules just one action.
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input_dict = self._get_action_input()
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target = self._get_target(input_dict)
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action = self._build_action()
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action.validate_input(input_dict)
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action.schedule(input_dict, target)
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def _get_target(self, input_dict):
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return expr.evaluate_recursively(
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self.task_spec.get_target(),
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utils.merge_dicts(
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copy.deepcopy(input_dict),
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copy.deepcopy(self.ctx)
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)
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)
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def _get_action_input(self, ctx=None):
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ctx = ctx or self.ctx
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input_dict = expr.evaluate_recursively(self.task_spec.get_input(), ctx)
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return utils.merge_dicts(
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input_dict,
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self._get_action_defaults(),
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overwrite=False
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)
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def _build_action(self):
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action_name = self.task_spec.get_action_name()
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wf_name = self.task_spec.get_workflow_name()
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if wf_name:
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return actions.WorkflowAction(wf_name, task_ex=self.task_ex)
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if not action_name:
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action_name = 'std.noop'
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action_def = actions.resolve_action_definition(
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action_name,
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self.wf_ex.name,
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self.wf_spec.get_name()
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)
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if action_def.spec:
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return actions.AdHocAction(action_def, task_ex=self.task_ex)
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return actions.PythonAction(action_def, task_ex=self.task_ex)
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class WithItemsTask(RegularTask):
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"""With-items task.
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Takes care of processing "with-items" tasks.
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"""
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@profiler.trace('task-on-action-complete')
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def on_action_complete(self, action_ex):
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assert self.task_ex
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state = action_ex.state
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# TODO(rakhmerov): Here we can define more informative messages
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# cases when action is successful and when it's not. For example,
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# in state_info we can specify the cause action.
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state_info = (None if state == states.SUCCESS
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else action_ex.output.get('result'))
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with_items.increase_capacity(self.task_ex)
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if with_items.is_completed(self.task_ex):
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self.complete(
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with_items.get_final_state(self.task_ex),
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state_info
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)
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return
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if (with_items.has_more_iterations(self.task_ex)
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and with_items.get_concurrency(self.task_ex)):
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self._schedule_actions()
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def _schedule_actions(self):
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input_dicts = self._get_with_items_input()
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if not input_dicts:
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self.complete(states.SUCCESS)
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return
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for idx, input_dict in input_dicts:
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target = self._get_target(input_dict)
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action = self._build_action()
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action.schedule(input_dict, target, index=idx)
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def _get_with_items_input(self):
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"""Calculate input array for separating each action input.
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Example:
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DSL:
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with_items:
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- itemX in <% $.arrayI %>
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- itemY in <% $.arrayJ %>
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Assume arrayI = [1, 2], arrayJ = ['a', 'b'].
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with_items_input = {
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"itemX": [1, 2],
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"itemY": ['a', 'b']
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}
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Then we get separated input:
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inputs_per_item = [
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{'itemX': 1, 'itemY': 'a'},
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{'itemX': 2, 'itemY': 'b'}
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]
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:return: the list of tuples containing indexes
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and the corresponding input dict.
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"""
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with_items_inputs = expr.evaluate_recursively(
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self.task_spec.get_with_items(),
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self.ctx
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)
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with_items.validate_input(with_items_inputs)
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inputs_per_item = []
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for key, value in with_items_inputs.items():
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for index, item in enumerate(value):
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iter_context = {key: item}
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if index >= len(inputs_per_item):
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inputs_per_item.append(iter_context)
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else:
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inputs_per_item[index].update(iter_context)
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action_inputs = []
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for item_input in inputs_per_item:
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new_ctx = utils.merge_dicts(item_input, self.ctx)
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action_inputs.append(self._get_action_input(new_ctx))
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with_items.prepare_runtime_context(
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self.task_ex,
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self.task_spec,
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action_inputs
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)
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indices = with_items.get_indices_for_loop(self.task_ex)
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with_items.decrease_capacity(self.task_ex, len(indices))
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if indices:
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current_inputs = operator.itemgetter(*indices)(action_inputs)
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return zip(
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indices,
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current_inputs if isinstance(current_inputs, tuple)
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else [current_inputs]
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)
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return []
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