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# -*- coding: utf-8 -*-
# Copyright (C) 2012-2013 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 contextlib
import logging
import os
import random
import sys
import time
logging.basicConfig(level=logging.ERROR)
top_dir = os.path.abspath(os.path.join(os.path.dirname(__file__),
os.pardir,
os.pardir))
sys.path.insert(0, top_dir)
from oslo_utils import reflection
from taskflow import engines
from taskflow.listeners import printing
from taskflow.patterns import unordered_flow as uf
from taskflow import task
# INTRO: These examples show how unordered_flow can be used to create a large
# number of fake volumes in parallel (or serially, depending on a constant that
# can be easily changed).
@contextlib.contextmanager
def show_time(name):
start = time.time()
yield
end = time.time()
print(" -- %s took %0.3f seconds" % (name, end - start))
# This affects how many volumes to create and how much time to *simulate*
# passing for that volume to be created.
MAX_CREATE_TIME = 3
VOLUME_COUNT = 5
# This will be used to determine if all the volumes are created in parallel
# or whether the volumes are created serially (in an undefined ordered since
# a unordered flow is used). Note that there is a disconnection between the
# ordering and the concept of parallelism (since unordered items can still be
# ran in a serial ordering). A typical use-case for offering both is to allow
# for debugging using a serial approach, while when running at a larger scale
# one would likely want to use the parallel approach.
#
# If you switch this flag from serial to parallel you can see the overall
# time difference that this causes.
SERIAL = False
if SERIAL:
engine = 'serial'
else:
engine = 'parallel'
class VolumeCreator(task.Task):
def __init__(self, volume_id):
# Note here that the volume name is composed of the name of the class
# along with the volume id that is being created, since a name of a
# task uniquely identifies that task in storage it is important that
# the name be relevant and identifiable if the task is recreated for
# subsequent resumption (if applicable).
#
# UUIDs are *not* used as they can not be tied back to a previous tasks
# state on resumption (since they are unique and will vary for each
# task that is created). A name based off the volume id that is to be
# created is more easily tied back to the original task so that the
# volume create can be resumed/revert, and is much easier to use for
# audit and tracking purposes.
base_name = reflection.get_callable_name(self)
super(VolumeCreator, self).__init__(name="%s-%s" % (base_name,
volume_id))
self._volume_id = volume_id
def execute(self):
print("Making volume %s" % (self._volume_id))
time.sleep(random.random() * MAX_CREATE_TIME)
print("Finished making volume %s" % (self._volume_id))
# Assume there is no ordering dependency between volumes.
flow = uf.Flow("volume-maker")
for i in range(0, VOLUME_COUNT):
flow.add(VolumeCreator(volume_id="vol-%s" % (i)))
# Show how much time the overall engine loading and running takes.
with show_time(name=flow.name.title()):
eng = engines.load(flow, engine=engine)
# This context manager automatically adds (and automatically removes) a
# helpful set of state transition notification printing helper utilities
# that show you exactly what transitions the engine is going through
# while running the various volume create tasks.
with printing.PrintingListener(eng):
eng.run()
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