I have configured a CentOS server with (server A) :
Redis 6.2.6
Python 3.8.1
Celery 5.2.1
Flower
On another server (server B) I send tasks to redis that are executed by a worker on server A.
I want to try to implement multiple queues and change de name of the default queue : queues "high", "normal" and "low", with "normal" beeing the default queue (instead of celery queue).
If I hardcode the queue in the task, it works (they are send to the right queue).
But if I try to do it with "task_routes", it doesn't.
I tried to:
hardcode route in this settings
implement a routing function
implement a routing class with the routing function
But it never works. Moreover, even though I put the "task_create_missing_queue" parameter to False, all tasks are send to celery queue (the default queue is still celery and not normal...).
Any ideas ?
Here is the code for my celeryconfig.py file (with routing function:
from celery import Celery
from kombu import Exchange, Queue
from celery.exceptions import Reject
import re
task_create_missing_queues = False
task_queues = (
Queue('high', Exchange('high'), routing_key='high'),
Queue('normal', Exchange('normal'), routing_key='normal'),
Queue('low', Exchange('low'), routing_key='low')
)
task_default_queue = 'normal'
task_default_exchange = 'normal'
task_default_routing_key = 'normal'
def route_tasks (name, args, kwargs, options, task=None, **kw):
if ':' not in name:
return {'queue': 'normal'}
namespace, _ = name.split(':')
return {'queue': namespace}
task_routes = (route_tasks,)
And my tasks are defined with a name like this with the decorator (when I hardcode the queue for each task, it is there that I add "queue="queue_name") (my celery app is called celery):
#celery.task(bind=True, name="high:long_task")
I can see in flower that the configuration is well taken into account.
My worker is started with multi and -Q high,normal,low.
Many thanks!
I'm using Python 2.7 (sigh), celery==3.1.19, librabbitmq==1.6.1, rabbitmq-server-3.5.6-1.noarch, and redis 2.8.24 (from redis-cli info).
I'm attempting to send a message from a celery producer to a celery consumer, and obtain the result back in the producer. There is 1 producer and 1 consumer, but 2 rabbitmq's (as brokers) and 1 redis (for results) in between.
The problem I'm facing is:
In the consumer, I get back get an AsyncResult via async_result =
ZipUp.delay(unique_directory), but async_result.ready() never
returns True (at least for 9 seconds it doesn't) - even for a
consumer task that does essentially nothing but return a string.
I can see, in the rabbitmq management web interface, my message
being received by the rabbitmq exchange, but it doesn't show up in
the corresponding rabbitmq queue. Also, a log message sent by the
very beginning of the ZipUp task doesn't appear to be getting
logged.
Things work if I don't try to get a result back from the AsyncResult! But I'm kinda hoping to get the result of the call - it's useful :).
Below are configuration specifics.
We're setting up Celery as follows for returns:
CELERY_RESULT_BACKEND = 'redis://%s' % _SHARED_WRITE_CACHE_HOST_INTERNAL
CELERY_RESULT = Celery('TEST', broker=CELERY_BROKER)
CELERY_RESULT.conf.update(
BROKER_HEARTBEAT=60,
CELERY_RESULT_BACKEND=CELERY_RESULT_BACKEND,
CELERY_TASK_RESULT_EXPIRES=100,
CELERY_IGNORE_RESULT=False,
CELERY_RESULT_PERSISTENT=False,
CELERY_ACCEPT_CONTENT=['json'],
CELERY_TASK_SERIALIZER='json',
CELERY_RESULT_SERIALIZER='json',
)
We have another Celery configuration that doesn't expect a return value, and that works - in the same program. It looks like:
CELERY = Celery('TEST', broker=CELERY_BROKER)
CELERY.conf.update(
BROKER_HEARTBEAT=60,
CELERY_RESULT_BACKEND=CELERY_BROKER,
CELERY_TASK_RESULT_EXPIRES=100,
CELERY_STORE_ERRORS_EVEN_IF_IGNORED=False,
CELERY_IGNORE_RESULT=True,
CELERY_ACCEPT_CONTENT=['json'],
CELERY_TASK_SERIALIZER='json',
CELERY_RESULT_SERIALIZER='json',
)
The celery producer's stub looks like:
#CELERY_RESULT.task(name='ZipUp', exchange='cognition.workflow.ZipUp_%s' % INTERNAL_VERSION)
def ZipUp(directory): # pylint: disable=invalid-name
""" Task stub """
_unused_directory = directory
raise NotImplementedError
It's been mentioned that using queue= instead of exchange= in this stub would be simpler. Can anyone confirm that (I googled but found exactly nothing on the topic)? Apparently you can just use queue= unless you want to use fanout or something fancy like that, since not all celery backends have the concept of an exchange.
Anyway, the celery consumer starts out with:
#task(queue='cognition.workflow.ZipUp_%s' % INTERNAL_VERSION, name='ZipUp')
#StatsInstrument('workflow.ZipUp')
def ZipUp(directory): # pylint: disable=invalid-name
'''
Zip all files in directory, password protected, and return the pathname of the new zip archive.
:param directory Directory to zip
'''
try:
LOGGER.info('zipping up {}'.format(directory))
But "zipping up" doesn't get logged anywhere. I searched every (disk-backed) file on the celery server for that string, and got two hits: /usr/bin/zip, and my celery task's code - and no log messages.
Any suggestions?
Thanks for reading!
It appears that using the following task stub in the producer solved the problem:
#CELERY_RESULT.task(name='ZipUp', queue='cognition.workflow.ZipUp_%s' % INTERNAL_VERSION)
def ZipUp(directory): # pylint: disable=invalid-name
""" Task stub """
_unused_directory = directory
raise NotImplementedError
In short, it's using queue= instead of exchange= .
I have a Django project on an Ubuntu EC2 node, which I have been using to set up an asynchronous using Celery.
I am following http://michal.karzynski.pl/blog/2014/05/18/setting-up-an-asynchronous-task-queue-for-django-using-celery-redis/ along with the docs.
I've been able to get a basic task working at the command line, using:
(env1)ubuntu#ip-172-31-22-65:~/projects/tp$ celery --app=myproject.celery:app worker --loglevel=INFO
I just realized, that I have a bunch of tasks in my queue, that had not executed:
[2015-03-28 16:49:05,916: WARNING/MainProcess] Restoring 4 unacknowledged message(s).
(env1)ubuntu#ip-172-31-22-65:~/projects/tp$ celery -A tp purge
WARNING: This will remove all tasks from queue: celery.
There is no undo for this operation!
(to skip this prompt use the -f option)
Are you sure you want to delete all tasks (yes/NO)? yes
Purged 81 messages from 1 known task queue.
How do I get a list of the queued items from the command line?
If you want to get all scheduled tasks,
celery inspect scheduled
To find all active queues
celery inspect active_queues
For status
celery inspect stats
For all commands
celery inspect
If you want to get it explicitily.Since you are using redis as queue.Then
redis-cli
>KEYS * #find all keys
Then find out something related to celery
>LLEN KEY # i think it gives length of list
Here is a copy-paste solution for Redis:
def get_celery_queue_len(queue_name):
from yourproject.celery import app as celery_app
with celery_app.pool.acquire(block=True) as conn:
return conn.default_channel.client.llen(queue_name)
def get_celery_queue_items(queue_name):
import base64
import json
from yourproject.celery import app as celery_app
with celery_app.pool.acquire(block=True) as conn:
tasks = conn.default_channel.client.lrange(queue_name, 0, -1)
decoded_tasks = []
for task in tasks:
j = json.loads(task)
body = json.loads(base64.b64decode(j['body']))
decoded_tasks.append(body)
return decoded_tasks
It works with Django. Just don't forget to change yourproject.celery.
Is there a way to get all the results from every worker on a Celery Broadcast task? I would like to monitor if everything went ok on all the workers. A list of workers that the task was send to would also be appreciated.
No, that is not easily possible.
But you don't have to limit yourself to the built-in amqp result backend,
you can send your own results using Kombu (http://kombu.readthedocs.org),
which is the messaging library used by Celery:
from celery import Celery
from kombu import Exchange
results_exchange = Exchange('myres', type='fanout')
app = Celery()
#app.task(ignore_result=True)
def something():
res = do_something()
with app.producer_or_acquire(block=True) as producer:
producer.send(
{'result': res},
exchange=results_exchange,
serializer='json',
declare=[results_exchange],
)
producer_or_acquire will create a new kombu.Producer using the celery
broker connection pool.
How can I retrieve a list of tasks in a queue that are yet to be processed?
EDIT: See other answers for getting a list of tasks in the queue.
You should look here:
Celery Guide - Inspecting Workers
Basically this:
my_app = Celery(...)
# Inspect all nodes.
i = my_app.control.inspect()
# Show the items that have an ETA or are scheduled for later processing
i.scheduled()
# Show tasks that are currently active.
i.active()
# Show tasks that have been claimed by workers
i.reserved()
Depending on what you want
If you are using Celery+Django simplest way to inspect tasks using commands directly from your terminal in your virtual environment or using a full path to celery:
Doc: http://docs.celeryproject.org/en/latest/userguide/workers.html?highlight=revoke#inspecting-workers
$ celery inspect reserved
$ celery inspect active
$ celery inspect registered
$ celery inspect scheduled
Also if you are using Celery+RabbitMQ you can inspect the list of queues using the following command:
More info: https://linux.die.net/man/1/rabbitmqctl
$ sudo rabbitmqctl list_queues
if you are using rabbitMQ, use this in terminal:
sudo rabbitmqctl list_queues
it will print list of queues with number of pending tasks. for example:
Listing queues ...
0b27d8c59fba4974893ec22d478a7093 0
0e0a2da9828a48bc86fe993b210d984f 0
10#torob2.celery.pidbox 0
11926b79e30a4f0a9d95df61b6f402f7 0
15c036ad25884b82839495fb29bd6395 1
celerey_mail_worker#torob2.celery.pidbox 0
celery 166
celeryev.795ec5bb-a919-46a8-80c6-5d91d2fcf2aa 0
celeryev.faa4da32-a225-4f6c-be3b-d8814856d1b6 0
the number in right column is number of tasks in the queue. in above, celery queue has 166 pending task.
If you don't use prioritized tasks, this is actually pretty simple if you're using Redis. To get the task counts:
redis-cli -h HOST -p PORT -n DATABASE_NUMBER llen QUEUE_NAME
But, prioritized tasks use a different key in redis, so the full picture is slightly more complicated. The full picture is that you need to query redis for every priority of task. In python (and from the Flower project), this looks like:
PRIORITY_SEP = '\x06\x16'
DEFAULT_PRIORITY_STEPS = [0, 3, 6, 9]
def make_queue_name_for_pri(queue, pri):
"""Make a queue name for redis
Celery uses PRIORITY_SEP to separate different priorities of tasks into
different queues in Redis. Each queue-priority combination becomes a key in
redis with names like:
- batch1\x06\x163 <-- P3 queue named batch1
There's more information about this in Github, but it doesn't look like it
will change any time soon:
- https://github.com/celery/kombu/issues/422
In that ticket the code below, from the Flower project, is referenced:
- https://github.com/mher/flower/blob/master/flower/utils/broker.py#L135
:param queue: The name of the queue to make a name for.
:param pri: The priority to make a name with.
:return: A name for the queue-priority pair.
"""
if pri not in DEFAULT_PRIORITY_STEPS:
raise ValueError('Priority not in priority steps')
return '{0}{1}{2}'.format(*((queue, PRIORITY_SEP, pri) if pri else
(queue, '', '')))
def get_queue_length(queue_name='celery'):
"""Get the number of tasks in a celery queue.
:param queue_name: The name of the queue you want to inspect.
:return: the number of items in the queue.
"""
priority_names = [make_queue_name_for_pri(queue_name, pri) for pri in
DEFAULT_PRIORITY_STEPS]
r = redis.StrictRedis(
host=settings.REDIS_HOST,
port=settings.REDIS_PORT,
db=settings.REDIS_DATABASES['CELERY'],
)
return sum([r.llen(x) for x in priority_names])
If you want to get an actual task, you can use something like:
redis-cli -h HOST -p PORT -n DATABASE_NUMBER lrange QUEUE_NAME 0 -1
From there you'll have to deserialize the returned list. In my case I was able to accomplish this with something like:
r = redis.StrictRedis(
host=settings.REDIS_HOST,
port=settings.REDIS_PORT,
db=settings.REDIS_DATABASES['CELERY'],
)
l = r.lrange('celery', 0, -1)
pickle.loads(base64.decodestring(json.loads(l[0])['body']))
Just be warned that deserialization can take a moment, and you'll need to adjust the commands above to work with various priorities.
To retrieve tasks from backend, use this
from amqplib import client_0_8 as amqp
conn = amqp.Connection(host="localhost:5672 ", userid="guest",
password="guest", virtual_host="/", insist=False)
chan = conn.channel()
name, jobs, consumers = chan.queue_declare(queue="queue_name", passive=True)
A copy-paste solution for Redis with json serialization:
def get_celery_queue_items(queue_name):
import base64
import json
# Get a configured instance of a celery app:
from yourproject.celery import app as celery_app
with celery_app.pool.acquire(block=True) as conn:
tasks = conn.default_channel.client.lrange(queue_name, 0, -1)
decoded_tasks = []
for task in tasks:
j = json.loads(task)
body = json.loads(base64.b64decode(j['body']))
decoded_tasks.append(body)
return decoded_tasks
It works with Django. Just don't forget to change yourproject.celery.
This worked for me in my application:
def get_celery_queue_active_jobs(queue_name):
connection = <CELERY_APP_INSTANCE>.connection()
try:
channel = connection.channel()
name, jobs, consumers = channel.queue_declare(queue=queue_name, passive=True)
active_jobs = []
def dump_message(message):
active_jobs.append(message.properties['application_headers']['task'])
channel.basic_consume(queue=queue_name, callback=dump_message)
for job in range(jobs):
connection.drain_events()
return active_jobs
finally:
connection.close()
active_jobs will be a list of strings that correspond to tasks in the queue.
Don't forget to swap out CELERY_APP_INSTANCE with your own.
Thanks to #ashish for pointing me in the right direction with his answer here: https://stackoverflow.com/a/19465670/9843399
The celery inspect module appears to only be aware of the tasks from the workers perspective. If you want to view the messages that are in the queue (yet to be pulled by the workers) I suggest to use pyrabbit, which can interface with the rabbitmq http api to retrieve all kinds of information from the queue.
An example can be found here:
Retrieve queue length with Celery (RabbitMQ, Django)
I think the only way to get the tasks that are waiting is to keep a list of tasks you started and let the task remove itself from the list when it's started.
With rabbitmqctl and list_queues you can get an overview of how many tasks are waiting, but not the tasks itself: http://www.rabbitmq.com/man/rabbitmqctl.1.man.html
If what you want includes the task being processed, but are not finished yet, you can keep a list of you tasks and check their states:
from tasks import add
result = add.delay(4, 4)
result.ready() # True if finished
Or you let Celery store the results with CELERY_RESULT_BACKEND and check which of your tasks are not in there.
As far as I know Celery does not give API for examining tasks that are waiting in the queue. This is broker-specific. If you use Redis as a broker for an example, then examining tasks that are waiting in the celery (default) queue is as simple as:
connect to the broker
list items in the celery list (LRANGE command for an example)
Keep in mind that these are tasks WAITING to be picked by available workers. Your cluster may have some tasks running - those will not be in this list as they have already been picked.
The process of retrieving tasks in particular queue is broker-specific.
I've come to the conclusion the best way to get the number of jobs on a queue is to use rabbitmqctl as has been suggested several times here. To allow any chosen user to run the command with sudo I followed the instructions here (I did skip editing the profile part as I don't mind typing in sudo before the command.)
I also grabbed jamesc's grep and cut snippet and wrapped it up in subprocess calls.
from subprocess import Popen, PIPE
p1 = Popen(["sudo", "rabbitmqctl", "list_queues", "-p", "[name of your virtula host"], stdout=PIPE)
p2 = Popen(["grep", "-e", "^celery\s"], stdin=p1.stdout, stdout=PIPE)
p3 = Popen(["cut", "-f2"], stdin=p2.stdout, stdout=PIPE)
p1.stdout.close()
p2.stdout.close()
print("number of jobs on queue: %i" % int(p3.communicate()[0]))
If you control the code of the tasks then you can work around the problem by letting a task trigger a trivial retry the first time it executes, then checking inspect().reserved(). The retry registers the task with the result backend, and celery can see that. The task must accept self or context as first parameter so we can access the retry count.
#task(bind=True)
def mytask(self):
if self.request.retries == 0:
raise self.retry(exc=MyTrivialError(), countdown=1)
...
This solution is broker agnostic, ie. you don't have to worry about whether you are using RabbitMQ or Redis to store the tasks.
EDIT: after testing I've found this to be only a partial solution. The size of reserved is limited to the prefetch setting for the worker.
from celery.task.control import inspect
def key_in_list(k, l):
return bool([True for i in l if k in i.values()])
def check_task(task_id):
task_value_dict = inspect().active().values()
for task_list in task_value_dict:
if self.key_in_list(task_id, task_list):
return True
return False
With subprocess.run:
import subprocess
import re
active_process_txt = subprocess.run(['celery', '-A', 'my_proj', 'inspect', 'active'],
stdout=subprocess.PIPE).stdout.decode('utf-8')
return len(re.findall(r'worker_pid', active_process_txt))
Be careful to change my_proj with your_proj
To get the number of tasks on a queue you can use the flower library, here is a simplified example:
from flower.utils.broker import Broker
from django.conf import settings
def get_queue_length(queue):
broker = Broker(settings.CELERY_BROKER_URL)
queues_result = broker.queues([queue])
return queues_result.result()[0]['messages']