aronwc · July 29, 2013 22:12
diff --git a/tasks.py b/tasks.py
 ''' An example of how to use Celery to manage a mix of serial and parallel
 tasks.  This depends on a running instance of a rabbitmq messaging server to
 keep track of task statuses. This can be launched on our ec2 instance with:
 ~/rabbitmq/rabbitmq_server-3.1.3/sbin/rabbitmq-server

 For this script to work, you first need to run a celery worker process to
 await orders:
 $ celery -A tasks worker --loglevel=info

 Then, you can call any of the functions below (see main for an example).

 The worker task will print all the log messages. This is useful for seeing the
 order in which tasks are actually executed.
 '''

 from celery import Celery
 from celery import chain, group


 'This is the extent of the celery configuration!'
 celery = Celery('tasks',
                backend='amqp',
                broker='amqp://guest@localhost//')


 @celery.task
 def guidestar(*args, **kwargs):
    print 'guidestar', args, kwargs
    return True


 @celery.task
 def lookup_twitter_handle(*args, **kwargs):
    print 'lookup_twitter_handle', args, kwargs
    return True


 @celery.task
 def get_twitter_followers(*args, **kwargs):
    print 'get_twitter_followers', args, kwargs
    return True


 @celery.task
 def get_tweets(*args, **kwargs):
    print 'get_tweets', args, kwargs
    return True


 @celery.task
 def download_charity_homepage(*args, **kwargs):
    print 'download_charity_homepage', args, kwargs
    return True


 @celery.task
 def download_charity_news(*args, **kwargs):
    print 'download_charity_news', args, kwargs
    return True


 @celery.task
 def process_ein(ein):
    '''Add a new charity with the given ein. Launches asynchronous
    tasks. Return a GroupResult to monitor the success of each task if
    needed.'''
    # look up twitter handle then (get followers and get tweets in parallel)
    twitter_chain = chain(lookup_twitter_handle.s(),
                          group([get_twitter_followers.s(),
                                 get_tweets.s()])
                          )
    # do these three in parallel
    tasks = group([twitter_chain,
                   download_charity_news.s(),
                   download_charity_homepage.s()])

    # lookup guidestar info before doing anything else.
    return chain(guidestar.s(),
                 tasks
                 ).apply_async(ein)


 if __name__ == '__main__':
    print process_ein('123')
	''' An example of how to use Celery to manage a mix of serial and parallel
	tasks. This depends on a running instance of a rabbitmq messaging server to
	keep track of task statuses. This can be launched on our ec2 instance with:
	~/rabbitmq/rabbitmq_server-3.1.3/sbin/rabbitmq-server

	For this script to work, you first need to run a celery worker process to
	await orders:
	$ celery -A tasks worker --loglevel=info

	Then, you can call any of the functions below (see main for an example).

	The worker task will print all the log messages. This is useful for seeing the
	order in which tasks are actually executed.
	'''

	from celery import Celery
	from celery import chain, group


	'This is the extent of the celery configuration!'
	celery = Celery('tasks',
	backend='amqp',
	broker='amqp://guest@localhost//')


	@celery.task
	def guidestar(args, *kwargs):
	print 'guidestar', args, kwargs
	return True


	@celery.task
	def lookup_twitter_handle(args, *kwargs):
	print 'lookup_twitter_handle', args, kwargs
	return True


	@celery.task
	def get_twitter_followers(args, *kwargs):
	print 'get_twitter_followers', args, kwargs
	return True


	@celery.task
	def get_tweets(args, *kwargs):
	print 'get_tweets', args, kwargs
	return True


	@celery.task
	def download_charity_homepage(args, *kwargs):
	print 'download_charity_homepage', args, kwargs
	return True


	@celery.task
	def download_charity_news(args, *kwargs):
	print 'download_charity_news', args, kwargs
	return True


	@celery.task
	def process_ein(ein):
	'''Add a new charity with the given ein. Launches asynchronous
	tasks. Return a GroupResult to monitor the success of each task if
	needed.'''
	# look up twitter handle then (get followers and get tweets in parallel)
	twitter_chain = chain(lookup_twitter_handle.s(),
	group([get_twitter_followers.s(),
	get_tweets.s()])
	)
	# do these three in parallel
	tasks = group([twitter_chain,
	download_charity_news.s(),
	download_charity_homepage.s()])

	# lookup guidestar info before doing anything else.
	return chain(guidestar.s(),
	tasks
	).apply_async(ein)


	if __name__ == '__main__':
	print process_ein('123')