I have a Django application where I defined a few @task
functions under task.py
to execute at given periodic task. I'm 100% sure that the issue is not caused by task.py
or any code related but due to some configuration may be in settings.py
or my celery worker.
Task does execute at periodic task but at multiple times.
Here are the celery worker logs:
celery -A cimexmonitor worker --loglevel=info -B -c 4
[2019-09-19 21:22:16,360: INFO/ForkPoolWorker-5] Project Monitor Started : APPProject1
[2019-09-19 21:22:16,361: INFO/ForkPoolWorker-4] Project Monitor Started : APPProject1
[2019-09-19 21:25:22,108: INFO/ForkPoolWorker-4] Project Monitor DONE : APPProject1
[2019-09-19 21:25:45,255: INFO/ForkPoolWorker-5] Project Monitor DONE : APPProject1
[2019-09-20 00:22:16,395: INFO/ForkPoolWorker-4] Project Monitor Started : APPProject2
[2019-09-20 00:22:16,398: INFO/ForkPoolWorker-5] Project Monitor Started : APPProject2
[2019-09-20 01:22:11,554: INFO/ForkPoolWorker-5] Project Monitor DONE : APPProject2
[2019-09-20 01:22:12,047: INFO/ForkPoolWorker-4] Project Monitor DONE : APPProject2
If you check above time interval, tasks.py executes one task but 2 workers of celery takes the task & executes the same task at the same interval. I'm not sure why 2 workers took for one task?
settings.py
..
..
# Internationalization
# https://docs.djangoproject.com/en/2.1/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'Asia/Kolkata'
USE_I18N = True
USE_L10N = True
USE_TZ = True
..
..
..
######## CELERY : CONFIG
CELERY_BROKER_URL = 'redis://localhost:6379'
CELERY_RESULT_BACKEND = 'redis://localhost:6379'
CELERY_ACCEPT_CONTENT = ['application/json']
CELERY_TASK_SERIALIZER = 'json'
CELERY_RESULT_SERIALIZER = 'json'
CELERY_ENABLE_UTC = True
CELERYBEAT_SCHEDULER = 'django_celery_beat.schedulers:DatabaseScheduler'
from __future__ import absolute_import, unicode_literals
from celery import Celery
import os
from django.conf import settings
os.environ.setdefault('DJANGO_SETTINGS_MODULE','cimexmonitor.settings')
## set the default Django settings module for the 'celery' program.
# Using a string here means the worker don't have to serialize
# the configuration object to child processes.
# - namespace='CELERY' means all celery-related configuration keys
# should have a `CELERY_` prefix.
app = Celery('cimexmonitor')
#app.config_from_object('django.conf:settings', namespace='CELERY')
app.config_from_object('django.conf:settings')
# Load task modules from all registered Django app configs.
app.autodiscover_tasks(settings.INSTALLED_APPS)
@app.task(bind=True)
def debug_task(self):
print('Request: {0!r}'.format(self.request))
→ celery --version
4.3.0 (rhubarb)
→ redis-server --version
Redis server v=3.0.6 sha=00000000:0 malloc=jemalloc-3.6.0 bits=64 build=7785291a3d2152db
django-admin-interface==0.9.2
django-celery-beat==1.5.0
Thanks
revoke cancels the task execution. If a task is revoked, the workers ignore the task and do not execute it. If you don't use persistent revokes your task can be executed after worker's restart. revoke has an terminate option which is False by default.
celery beats only trigger those 1000 tasks (by the crontab schedule), not run them. If you want to run 1000 tasks in parallel, you should have enough celery workers available to run those tasks.
Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operations but supports scheduling as well. The execution units, called tasks, are executed concurrently on one or more worker servers using multiprocessing, Eventlet, or gevent.
Both the worker and beat services need to be running at the same time to execute periodically task as per https://github.com/celery/django-celery-beat
$ celery -A [project-name] worker --loglevel=info -B -c 5
celery -A [project-name] beat -l info --scheduler django_celery_beat.schedulers:DatabaseScheduler
celery worker
started working as a DB scheduler at the same time.celery worker
solved my problem.From the official documentation: Ensuring a task is only executed one at a time.
Also, I hope you are not running multiple workers the same way (celery -A cimexmonitor worker --loglevel=info -B -c 4
) as that would mean you have multiple celery beats scheduling tasks to run... In short - make sure you only have one Celery beat running!
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