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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 logging
import os
import string
import sys
import time
top_dir = os.path.abspath(os.path.join(os.path.dirname(__file__),
os.pardir,
os.pardir))
sys.path.insert(0, top_dir)
from taskflow import engines
from taskflow.engines.worker_based import worker
from taskflow.patterns import linear_flow as lf
from taskflow import task
from taskflow.types import notifier
from taskflow.utils import threading_utils
ANY = notifier.Notifier.ANY
# INTRO: These examples show how to use a remote worker's event notification
# attribute to proxy back task event notifications to the controlling process.
#
# In this case a simple set of events is triggered by a worker running a
# task (simulated to be remote by using a kombu memory transport and threads).
# Those events that the 'remote worker' produces will then be proxied back to
# the task that the engine is running 'remotely', and then they will be emitted
# back to the original callbacks that exist in the originating engine
# process/thread. This creates a one-way *notification* channel that can
# transparently be used in-process, outside-of-process using remote workers and
# so-on that allows tasks to signal to its controlling process some sort of
# action that has occurred that the task may need to tell others about (for
# example to trigger some type of response when the task reaches 50% done...).
def event_receiver(event_type, details):
"""This is the callback that (in this example) doesn't do much..."""
print("Recieved event '%s'" % event_type)
print("Details = %s" % details)
class EventReporter(task.Task):
"""This is the task that will be running 'remotely' (not really remote)."""
EVENTS = tuple(string.ascii_uppercase)
EVENT_DELAY = 0.1
def execute(self):
for i, e in enumerate(self.EVENTS):
details = {
'leftover': self.EVENTS[i:],
}
self.notifier.notify(e, details)
time.sleep(self.EVENT_DELAY)
BASE_SHARED_CONF = {
'exchange': 'taskflow',
'transport': 'memory',
'transport_options': {
'polling_interval': 0.1,
},
}
# Until https://github.com/celery/kombu/issues/398 is resolved it is not
# recommended to run many worker threads in this example due to the types
# of errors mentioned in that issue.
MEMORY_WORKERS = 1
WORKER_CONF = {
'tasks': [
# Used to locate which tasks we can run (we don't want to allow
# arbitrary code/tasks to be ran by any worker since that would
# open up a variety of vulnerabilities).
'%s:EventReporter' % (__name__),
],
}
def run(engine_options):
reporter = EventReporter()
reporter.notifier.register(ANY, event_receiver)
flow = lf.Flow('event-reporter').add(reporter)
eng = engines.load(flow, engine='worker-based', **engine_options)
eng.run()
if __name__ == "__main__":
logging.basicConfig(level=logging.ERROR)
# Setup our transport configuration and merge it into the worker and
# engine configuration so that both of those objects use it correctly.
worker_conf = dict(WORKER_CONF)
worker_conf.update(BASE_SHARED_CONF)
engine_options = dict(BASE_SHARED_CONF)
workers = []
# These topics will be used to request worker information on; those
# workers will respond with their capabilities which the executing engine
# will use to match pending tasks to a matched worker, this will cause
# the task to be sent for execution, and the engine will wait until it
# is finished (a response is received) and then the engine will either
# continue with other tasks, do some retry/failure resolution logic or
# stop (and potentially re-raise the remote workers failure)...
worker_topics = []
try:
# Create a set of worker threads to simulate actual remote workers...
print('Running %s workers.' % (MEMORY_WORKERS))
for i in range(0, MEMORY_WORKERS):
# Give each one its own unique topic name so that they can
# correctly communicate with the engine (they will all share the
# same exchange).
worker_conf['topic'] = 'worker-%s' % (i + 1)
worker_topics.append(worker_conf['topic'])
w = worker.Worker(**worker_conf)
runner = threading_utils.daemon_thread(w.run)
runner.start()
w.wait()
workers.append((runner, w.stop))
# Now use those workers to do something.
print('Executing some work.')
engine_options['topics'] = worker_topics
result = run(engine_options)
print('Execution finished.')
finally:
# And cleanup.
print('Stopping workers.')
while workers:
r, stopper = workers.pop()
stopper()
r.join()
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