I'm building a flask app which processes some big files from users. Since this process can take a few seconds, I want the task to be done by an executor. I'm using Flask-executor and it works very well on my Windows developing machine.
But on the Ubuntu host, it simply doesn't: After submitting a task the job method gets called, logs one line to the log file and then stops. No return statement, no exception thrown or something else. How is this even possible?
Here I create the executor:
from flask_executor import Executor
executor = Executor()
executor.init_app(app)
After the file upload, I submit the job:
task_list.append((process_upload, full_file_path, current_user, readme_path))
Nothing special, so here's my method:
def process_upload(file_path: str, user: User, readme_path: str):
try:
_process_upload(file_path, user, readme_path)
except BaseException:
logger.error(traceback.format_exc())
def _process_upload(file_path: str, user: User, readme_path: str):
logger.info(f" --------- processing file {file_path} in background from user {user.username}")
logger.info("1")
path_only, file_name_with_ext = os.path.split(file_path)
logger.info("2")
The process method is actually bigger and does some fancy things, but I'm not even getting that far. Here's the log file:
2022-02-22 10:56:02,055 - ThreadPoolExecutor-0_0 - INFO - --------- processing file /var/www/myproject/files/test/test.dat in background from user test
I know that it's not just log files because the task isn't done after all.
So how the hell is it possible that the submitted task just stops? And why does it work on Windows but not on a 08/15 Ubuntu? How can i find and fix the problem?
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 have the following scripts:
celery_tasks.py
from celery import Celery
app = Celery(broker='amqp://guest:guest#localhost:5672//')
app.conf.task_default_queue = 'test_queue'
#app.task(acks_late=True)
def test(a):
return a
publish.py
from celery_tasks import test
test.delay('abc')
When i run publish.py and start the worker (celery -A celery_tasks worker --loglevel=DEBUG), the 'abc' content is published in the 'test_queue' and is consumed by the worker.
Is there a way for the worker to consume something from a queue that was not posted by Celery? For example, when I put something in the test_queue straight through RabbitMQ, without going through the Celery publisher, and run the Celery worker, it gave me the following warning:
WARNING/MainProcess] Received and deleted unknown message. Wrong destination?!?
The full contents of the message body was: body: 'abc' (3b)
{content_type:None content_encoding:None
delivery_info:{'exchange': '', 'redelivered': False, 'delivery_tag': 1, 'consumer_tag': 'None2', 'routing_key': 'test_queue'} headers={}}
Is there a way to solve this?
Celery has a specific format and a set of headers that needs to be maintained to comply with it. Therefore you would have to reverse engineer it to make celery-compliant message not produced by celery.
Keep in mind that celery is not really made to send messages across the broker, but to send tasks, which are enhanced messages therefore have extras in the header part of the amqp message
It's a late answer but custom consumers might help you. I'm using this for consuming messages from rabbitmq. Where these messages are being populated from another app with pika.
http://docs.celeryproject.org/en/latest/userguide/extending.html#custom-message-consumers
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']))
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']