openstack/deb-python-taskflow
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fd852f9b88d8e5954b6bd54d25c462083240400b
deb-python-taskflow/taskflow/engines/action_engine/compiler.py
Joshua Harlow fd852f9b88 Use 'match_type' utility function instead of staticmethod 
The utility method allows for moving the logic of which
item compiler handles which type to the main compiler
class instead of having it in each per compiler instance.

Change-Id: Iec578b846a879cbff40f2c50ce865eb260b073e8
2016-01-16 04:49:52 +00:00

343 lines
14 KiB
Python
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# -*- coding: utf-8 -*-
# Copyright (C) 2014 Yahoo! Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import threading
import fasteners
from oslo_utils import excutils
import six
from taskflow import flow
from taskflow import logging
from taskflow import task
from taskflow.types import graph as gr
from taskflow.types import tree as tr
from taskflow.utils import iter_utils
from taskflow.utils import misc
from taskflow.flow import (LINK_INVARIANT, LINK_RETRY) # noqa
LOG = logging.getLogger(__name__)
# Constants attached to node attributes in the execution graph (and tree
# node metadata), provided as constants here and constants in the compilation
# class (so that users will not have to import this file to access them); but
# provide them as module constants so that internal code can more
# easily access them...
TASK = 'task'
RETRY = 'retry'
FLOW = 'flow'
# Quite often used together, so make a tuple everyone can share...
ATOMS = (TASK, RETRY)
class Compilation(object):
"""The result of a compilers compile() is this *immutable* object."""
#: Task nodes will have a ``kind`` attribute/metadata key with this value.
TASK = TASK
#: Retry nodes will have a ``kind`` attribute/metadata key with this value.
RETRY = RETRY
#: Flow nodes will have a ``kind`` attribute/metadata key with this value.
FLOW = FLOW
def __init__(self, execution_graph, hierarchy):
self._execution_graph = execution_graph
self._hierarchy = hierarchy
@property
def execution_graph(self):
"""The execution ordering of atoms (as a graph structure)."""
return self._execution_graph
@property
def hierarchy(self):
"""The hierachy of patterns (as a tree structure)."""
return self._hierarchy
def _overlap_occurence_detector(to_graph, from_graph):
"""Returns how many nodes in 'from' graph are in 'to' graph (if any)."""
return iter_utils.count(node for node in from_graph.nodes_iter()
if node in to_graph)
def _add_update_edges(graph, nodes_from, nodes_to, attr_dict=None):
"""Adds/updates edges from nodes to other nodes in the specified graph.
It will connect the 'nodes_from' to the 'nodes_to' if an edge currently
does *not* exist (if it does already exist then the edges attributes
are just updated instead). When an edge is created the provided edge
attributes dictionary will be applied to the new edge between these two
nodes.
"""
# NOTE(harlowja): give each edge its own attr copy so that if it's
# later modified that the same copy isn't modified...
for u in nodes_from:
for v in nodes_to:
if not graph.has_edge(u, v):
if attr_dict:
graph.add_edge(u, v, attr_dict=attr_dict.copy())
else:
graph.add_edge(u, v)
else:
# Just update the attr_dict (if any).
if attr_dict:
graph.add_edge(u, v, attr_dict=attr_dict.copy())
class TaskCompiler(object):
"""Non-recursive compiler of tasks."""
def compile(self, task, parent=None):
graph = gr.DiGraph(name=task.name)
graph.add_node(task, kind=TASK)
node = tr.Node(task, kind=TASK)
if parent is not None:
parent.add(node)
return graph, node
class FlowCompiler(object):
"""Recursive compiler of flows."""
def __init__(self, deep_compiler_func):
self._deep_compiler_func = deep_compiler_func
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
class PatternCompiler(object):
"""Compiles a flow pattern (or task) into a compilation unit.
Let's dive into the basic idea for how this works:
The compiler here is provided a 'root' object via its __init__ method,
this object could be a task, or a flow (one of the supported patterns),
the end-goal is to produce a :py:class:`.Compilation` object as the result
with the needed components. If this is not possible a
:py:class:`~.taskflow.exceptions.CompilationFailure` will be raised.
In the case where a **unknown** type is being requested to compile
a ``TypeError`` will be raised and when a duplicate object (one that
has **already** been compiled) is encountered a ``ValueError`` is raised.
The complexity of this comes into play when the 'root' is a flow that
contains itself other nested flows (and so-on); to compile this object and
its contained objects into a graph that *preserves* the constraints the
pattern mandates we have to go through a recursive algorithm that creates
subgraphs for each nesting level, and then on the way back up through
the recursion (now with a decomposed mapping from contained patterns or
atoms to there corresponding subgraph) we have to then connect the
subgraphs (and the atom(s) there-in) that were decomposed for a pattern
correctly into a new graph and then ensure the pattern mandated
constraints are retained. Finally we then return to the
caller (and they will do the same thing up until the root node, which by
that point one graph is created with all contained atoms in the
pattern/nested patterns mandated ordering).
Also maintained in the :py:class:`.Compilation` object is a hierarchy of
the nesting of items (which is also built up during the above mentioned
recusion, via a much simpler algorithm); this is typically used later to
determine the prior atoms of a given atom when looking up values that can
be provided to that atom for execution (see the scopes.py file for how this
works). Note that although you *could* think that the graph itself could be
used for this, which in some ways it can (for limited usage) the hierarchy
retains the nested structure (which is useful for scoping analysis/lookup)
to be able to provide back a iterator that gives back the scopes visible
at each level (the graph does not have this information once flattened).
Let's take an example:
Given the pattern ``f(a(b, c), d)`` where ``f`` is a
:py:class:`~taskflow.patterns.linear_flow.Flow` with items ``a(b, c)``
where ``a`` is a :py:class:`~taskflow.patterns.linear_flow.Flow` composed
of tasks ``(b, c)`` and task ``d``.
The algorithm that will be performed (mirroring the above described logic)
will go through the following steps (the tree hierachy building is left
out as that is more obvious)::
Compiling f
- Decomposing flow f with no parent (must be the root)
- Compiling a
- Decomposing flow a with parent f
- Compiling b
- Decomposing task b with parent a
- Decomposed b into:
Name: b
Nodes: 1
- b
Edges: 0
- Compiling c
- Decomposing task c with parent a
- Decomposed c into:
Name: c
Nodes: 1
- c
Edges: 0
- Relinking decomposed b -> decomposed c
- Decomposed a into:
Name: a
Nodes: 2
- b
- c
Edges: 1
b -> c ({'invariant': True})
- Compiling d
- Decomposing task d with parent f
- Decomposed d into:
Name: d
Nodes: 1
- d
Edges: 0
- Relinking decomposed a -> decomposed d
- Decomposed f into:
Name: f
Nodes: 3
- c
- b
- d
Edges: 2
c -> d ({'invariant': True})
b -> c ({'invariant': True})
"""
def __init__(self, root, freeze=True):
self._root = root
self._history = set()
self._freeze = freeze
self._lock = threading.Lock()
self._compilation = None
self._matchers = [
(flow.Flow, FlowCompiler(self._compile)),
(task.BaseTask, TaskCompiler()),
]
self._level = 0
def _compile(self, item, parent=None):
"""Compiles a item (pattern, task) into a graph + tree node."""
item_compiler = misc.match_type(item, self._matchers)
if item_compiler is not None:
self._pre_item_compile(item)
graph, node = item_compiler.compile(item, parent=parent)
self._post_item_compile(item, graph, node)
return graph, node
else:
raise TypeError("Unknown object '%s' (%s) requested to compile"
% (item, type(item)))
def _pre_item_compile(self, item):
"""Called before a item is compiled; any pre-compilation actions."""
if item in self._history:
raise ValueError("Already compiled item '%s' (%s), duplicate"
" and/or recursive compiling is not"
" supported" % (item, type(item)))
self._history.add(item)
if LOG.isEnabledFor(logging.BLATHER):
LOG.blather("%sCompiling '%s'", " " * self._level, item)
self._level += 1
def _post_item_compile(self, item, graph, node):
"""Called after a item is compiled; doing post-compilation actions."""
self._level -= 1
if LOG.isEnabledFor(logging.BLATHER):
prefix = ' ' * self._level
LOG.blather("%sDecomposed '%s' into:", prefix, item)
prefix = ' ' * (self._level + 1)
LOG.blather("%sGraph:", prefix)
for line in graph.pformat().splitlines():
LOG.blather("%s %s", prefix, line)
LOG.blather("%sHierarchy:", prefix)
for line in node.pformat().splitlines():
LOG.blather("%s %s", prefix, line)
def _pre_compile(self):
"""Called before the compilation of the root starts."""
self._history.clear()
self._level = 0
def _post_compile(self, graph, node):
"""Called after the compilation of the root finishes successfully."""
self._history.clear()
self._level = 0
@fasteners.locked
def compile(self):
"""Compiles the contained item into a compiled equivalent."""
if self._compilation is None:
self._pre_compile()
try:
graph, node = self._compile(self._root, parent=None)
except Exception:
with excutils.save_and_reraise_exception():
# Always clear the history, to avoid retaining junk
# in memory that isn't needed to be in memory if
# compilation fails...
self._history.clear()
else:
self._post_compile(graph, node)
if self._freeze:
graph.freeze()
node.freeze()
self._compilation = Compilation(graph, node)
return self._compilation
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