We Keep Coding

How to determine the name of a task in celery?

I have a fastAPI app where I want to call a celery task
I can not import the task as they are in two different code base. So I have to call it using its name.
in tasks.py
imagery = Celery(
"imagery", broker=os.getenv("BROKER_URL"), backend=os.getenv("REDIS_URL")
)
...
#imagery.task(bind=True, name="filter")
def filter_task(self, **kwargs) -> Dict[str, Any]:
print('running task')
The celery worker is running with this command:
celery worker -A worker.imagery -P threads --loglevel=INFO --queues=imagery
Now in my FastAPI code base I want to run the filter task.
So my understanding is I have to use the celery.send_task() function
In app.py I have
from celery import Celery, states
from celery.execute import send_task
from fastapi import FastAPI
from starlette.responses import JSONResponse, PlainTextResponse
from app import models
app = FastAPI()
tasks = Celery(broker=os.getenv("BROKER_URL"), backend=os.getenv("REDIS_URL"))
#app.post("/filter", status_code=201)
async def upload_images(data: models.FilterProductsModel):
"""
TODO: use a celery task(s) to query the database and upload the results to S3
"""
data = ['ok', 'un test']
data = ['ok', 'un test']
result = tasks.send_task('workers.imagery.filter', args=list(data))
return PlainTextResponse(f"here is the id: {str(result.ready())}")
After calling the /filter endpoint, I don't see any task being picked up by the worker.
So I tried different name in send_task()
filter
imagery.filter
worker.imagery.filter
How come my task never get picked up by the worker and nothing shows in the log?
Is my task name wrong?
Edit:
The worker process run in docker. Here is the fullpath of the file on its disk.
tasks.py : /workers/worker.py
So if I follow the import schema. the name of the task would be workers.worker.filter but this does not work, nothing get printed in the logs of docker. Is a print supposed to appear in the STDOUT of the celery cli?

Your Celery worker is subscribed to the imagery queue only . On the other hand, you try to send the task to the default queue (if you did not change configuration, the name of that queue is celery) with result = tasks.send_task('workers.imagery.filter', args=list(data)). It is not surprising you do not see task being executed by your worker as you have been sending tasks to the default queue whole time.
To fix this, try the following:
result = tasks.send_task('workers.imagery.filter', args=list(data), queue='imagery')

OP Here.
This is the solution I used.
task = signature("filter", kwargs=data.dict() ,queue="imagery")
res = task.delay()
As mentioned by #DejanLekic I had to specify the queue.

Setting up periodic tasks in Celery (celerybeat) dynamically using add_periodic_task

I'm using Celery 4.0.1 with Django 1.10 and I have troubles scheduling tasks (running a task works fine). Here is the celery configuration:
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'myapp.settings')
app = Celery('myapp')
app.autodiscover_tasks(lambda: settings.INSTALLED_APPS)
app.conf.BROKER_URL = 'amqp://{}:{}#{}'.format(settings.AMQP_USER, settings.AMQP_PASSWORD, settings.AMQP_HOST)
app.conf.CELERY_DEFAULT_EXCHANGE = 'myapp.celery'
app.conf.CELERY_DEFAULT_QUEUE = 'myapp.celery_default'
app.conf.CELERY_TASK_SERIALIZER = 'json'
app.conf.CELERY_ACCEPT_CONTENT = ['json']
app.conf.CELERY_IGNORE_RESULT = True
app.conf.CELERY_DISABLE_RATE_LIMITS = True
app.conf.BROKER_POOL_LIMIT = 2
app.conf.CELERY_QUEUES = (
Queue('myapp.celery_default'),
Queue('myapp.queue1'),
Queue('myapp.queue2'),
Queue('myapp.queue3'),
)
Then in tasks.py I have:
#app.task(queue='myapp.queue1')
def my_task(some_id):
print("Doing something with", some_id)
In views.py I want to schedule this task:
def my_view(request, id):
app.add_periodic_task(10, my_task.s(id))
Then I execute the commands:
sudo systemctl start rabbitmq.service
celery -A myapp.celery_app beat -l debug
celery worker -A myapp.celery_app
But the task is never scheduled. I don't see anything in the logs. The task is working because if in my view I do:
def my_view(request, id):
my_task.delay(id)
The task is executed.
If in my configuration file if I schedule the task manually, like this it works:
app.conf.CELERYBEAT_SCHEDULE = {
'add-every-30-seconds': {
'task': 'tasks.my_task',
'schedule': 10.0,
'args': (66,)
},
}
I just can't schedule the task dynamically. Any idea?

EDIT: (13/01/2018)
The latest release 4.1.0 have addressed the subject in this ticket #3958 and has been merged
Actually you can't not define periodic task at the view level, because the beat schedule setting will be loaded first and can not be rescheduled at runtime:
The add_periodic_task() function will add the entry to the beat_schedule setting behind the scenes, and the same setting can also can be used to set up periodic tasks manually:
app.conf.CELERYBEAT_SCHEDULE = {
'add-every-30-seconds': {
'task': 'tasks.my_task',
'schedule': 10.0,
'args': (66,)
},
}
which means if you want to use add_periodic_task() it should be wrapped within an on_after_configure handler at the celery app level and any modification on runtime will not take effect:
app = Celery()
#app.on_after_configure.connect
def setup_periodic_tasks(sender, **kwargs):
sender.add_periodic_task(10, my_task.s(66))
As mentioned in the doc the the regular celerybeat simply keep track of task execution:
The default scheduler is the celery.beat.PersistentScheduler, that simply keeps track of the last run times in a local shelve database file.
In order to be able to dynamically manage periodic tasks and reschedule celerybeat at runtime:
There’s also the django-celery-beat extension that stores the schedule in the Django database, and presents a convenient admin interface to manage periodic tasks at runtime.
The tasks will be persisted in django database and the scheduler could be updated in task model at the db level. Whenever you update a periodic task a counter in this tasks table will be incremented, and tells the celery beat service to reload the schedule from the database.
A possible solution for you could be as follow:
from django_celery_beat.models import PeriodicTask, IntervalSchedule
schedule= IntervalSchedule.objects.create(every=10, period=IntervalSchedule.SECONDS)
task = PeriodicTask.objects.create(interval=schedule, name='any name', task='tasks.my_task', args=json.dumps([66]))
views.py
def update_task_view(request, id)
task = PeriodicTask.objects.get(name="task name") # if we suppose names are unique
task.args=json.dumps([id])
task.save()

How to list the queued items in celery?

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.

Django Celery get task count

I am currently using django with celery and everything works fine.
However I want to be able to give the users an opportunity to cancel a task if the server is overloaded by checking how many tasks are currently scheduled.
How can I achieve this ?
I am using redis as broker.
I just found this :
Retrieve list of tasks in a queue in Celery
It is somehow relate to my issue but I don't need to list the tasks , just count them :)

Here is how you can get the number of messages in a queue using celery that is broker-agnostic.
By using connection_or_acquire, you can minimize the number of open connections to your broker by utilizing celery's internal connection pooling.
celery = Celery(app)
with celery.connection_or_acquire() as conn:
conn.default_channel.queue_declare(
queue='my-queue', passive=True).message_count
You can also extend Celery to provide this functionality:
from celery import Celery as _Celery
class Celery(_Celery)
def get_message_count(self, queue):
'''
Raises: amqp.exceptions.NotFound: if queue does not exist
'''
with self.connection_or_acquire() as conn:
return conn.default_channel.queue_declare(
queue=queue, passive=True).message_count
celery = Celery(app)
num_messages = celery.get_message_count('my-queue')

If your broker is configured as redis://localhost:6379/1, and your tasks are submitted to the general celery queue, then you can get the length by the following means:
import redis
queue_name = "celery"
client = redis.Redis(host="localhost", port=6379, db=1)
length = client.llen(queue_name)
Or, from a shell script (good for monitors and such):
$ redis-cli -n 1 -h localhost -p 6379 llen celery

If you have already configured redis in your app, you can try this:
from celery import Celery
QUEUE_NAME = 'celery'
celery = Celery(app)
client = celery.connection().channel().client
length = client.llen(QUEUE_NAME)

Get a redis client instance used by Celery, then check the queue length. Don't forget to release the connection every time you use it (use .acquire):
# Get a configured instance of celery:
from project.celery import app as celery_app
def get_celery_queue_len(queue_name):
with celery_app.pool.acquire(block=True) as conn:
return conn.default_channel.client.llen(queue_name)
Always acquire a connection from the pool, don't create it manually. Otherwise, your redis server will run out of connection slots and this will kill your other clients.

I'll expand on the answer of #StephenFuhry around the not-found error, because more or less broker-agnostic way of retrieving queue length is beneficial even if Celery suggests to mess with brokers directly. In Celery 4 (with Redis broker) this error looks like:
ChannelError: Channel.queue_declare: (404) NOT_FOUND - no queue 'NAME' in vhost '/'
Observations:
ChannelError is a kombu exception (if fact, it's amqp's and kombu "re-exports" it).
On Redis broker Celery/Kombu represent queues as Redis lists
Redis collection type keys are removed whenever the collection becomes empty
If we look at what queue_declare does, it has these lines:
if passive and not self._has_queue(queue, **kwargs):
raise ChannelError(...)
Kombu Redis virtual transport's _has_queue is this:
def _has_queue(self, queue, **kwargs):
with self.conn_or_acquire() as client:
with client.pipeline() as pipe:
for pri in self.priority_steps:
pipe = pipe.exists(self._q_for_pri(queue, pri))
return any(pipe.execute())
The conclusion is that on a Redis broker ChannelError raised from queue_declare is okay (for an existing queue of course), and just means that the queue is empty.
Here's an example of how to output all active Celery queues' lengths (normally should be 0, unless your worker can't cope with the tasks).
from kombu.exceptions import ChannelError
def get_queue_length(name):
with celery_app.connection_or_acquire() as conn:
try:
ok_nt = conn.default_channel.queue_declare(queue=name, passive=True)
except ChannelError:
return 0
else:
return ok_nt.message_count
for queue_info in celery_app.control.inspect().active_queues().values():
print(queue_info[0]['name'], get_queue_length(queue_info[0]['name']))

Retrieve list of tasks in a queue in Celery

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']