celery | Distributed Task Queue | Pub Sub library
kandi X-RAY | celery Summary
kandi X-RAY | celery Summary
Distributed Task Queue (development branch)
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Top functions reviewed by kandi - BETA
- Build a tracer .
- Create write handlers .
- List of worker threads .
- Prepare steps for processing .
- Create the event dispatcher .
- Create a task sender .
- Set the TTL for the table .
- Calls the task .
- Send a single task .
- Moves messages from a queue .
celery Key Features
celery Examples and Code Snippets
Lines of Code : 52License : Non-SPDX (NOASSERTION)
Copy.. contents::
:local:
AMQP
----
.. autoclass:: AMQP
.. attribute:: Connection
Broker connection class used. Default is :class:`kombu.Connection`.
.. attribute:: Consumer
Base Consumer class used. Default is :class:`k .. contents::
:local:
Proxies
-------
.. autodata:: default_app
Functions
---------
.. autofunction:: app_or_default
.. autofunction:: enable_trace
.. autofunction:: disable_trace
Reducing the possibility of data loss.
Acks are now implemented by storing a copy of the message when the message
is consumed. The copy isn't removed until the consumer acknowledges
or rejects it.
This means that unacknowledged messages will be redel import os
# ^^^ The above is required if you want to import from the celery
# library. If you don't have this then `from celery.schedules import`
# becomes `proj.celery.schedules` in Python 2.x since it allows
# for relative imports by default.
# # Example::
# >>> R = A.apply_async()
# >>> list(joinall(R))
# [['A 0', 'A 1', 'A 2', 'A 3', 'A 4', 'A 5', 'A 6', 'A 7', 'A 8', 'A 9'],
# ['B 0', 'B 1', 'B 2', 'B 3', 'B 4', 'B 5', 'B 6', 'B 7', 'B 8', 'B 9'],
# ['C 0 import django
# Django settings for celery_http_gateway project.
DEBUG = True
TEMPLATE_DEBUG = DEBUG
CELERY_RESULT_BACKEND = 'database'
BROKER_URL = 'amqp://guest:guest@localhost:5672//'
ADMINS = (
# ('Your Name', 'your_email@domain.com'),
) service = build('people', 'v1', developerKey='YOUR_API_KEY_HERE')
from django.utils.timezone import now
from datetime import timedelta
class Quote(models.Model):
created = models.DateTimeField()
@property
def status(self):
return 'active' if self.created >= now()-timdeltadef create(self, request, *args, **kwargs):
image = self.request.FILES['image'].read()
byte = base64.b64encode(image)
data = {
'product_id': self.kwargs['product_pk'],
'image': bytfrom time import sleep
from celery import shared_task
from .models import ProductImage
from django.core.files import File
from django.core.files.storage import FileSystemStorage
from pathlib import Path
@shared_task
def upload(product_id,Community Discussions
Trending Discussions on celery
QUESTION
I am using the following docker-compose image, I got this image from: https://github.com/apache/airflow/blob/main/docs/apache-airflow/start/docker-compose.yaml
...ANSWER
Answered 2021-Jun-14 at 16:35Support for _PIP_ADDITIONAL_REQUIREMENTS environment variable has not been released yet. It is only supported by the developer/unreleased version of the docker image. It is planned that this feature will be available in Airflow 2.1.1. For more information, see: Adding extra requirements for build and runtime of the PROD image.
For the older version, you should build a new image and set this image in the docker-compose.yaml. To do this, you need to follow a few steps.
- Create a new
Dockerfilewith the following content:
QUESTION
i use celery in django ,
i add a task to my project and get error, but before add this task my project is work good.
my first task is : ...
ANSWER
Answered 2021-Jun-13 at 13:37You can inline import User inside your first task to avoid the circular import.
QUESTION
I'm running the code below as part of a Celery task.
...ANSWER
Answered 2021-Jun-13 at 09:16I would add the celery user to the sudoers file with the only command allowed being the one needed. Use visudo and add these lines
QUESTION
I'm developing an python-django app running in docker containers (django, celery, postgres, redist...etc). It runs on Windows 10 with WSL2-Debian & Docker Desktop.
During my work I need to observe the consoles of all those containers, so I can monitor apps behavior, like when you run docker-compose up so you got all of them live.
When you click on the container within windowed Docker Desktop app you can see the container's console output, but not actual - it looks like it works until some point of time and there are no updates of the consoles output. I remember it was working live just prior to a two or three Docker Desktop updates, and I'm sure it was real time there, but not now.
Did I change a setting or Docker Desktop was bugged?
PS. When I start my containers with docker-compose up (without -d) I can observe live logs on my shell console, but not in Docker Desktop anymore.
Any help how to restore Docker Desktop live console view?
...ANSWER
Answered 2021-May-20 at 20:40It's a bug in Docker Desktop v3.3.3
GitHub issue: https://github.com/docker/for-win/issues/11251 as pointed by @Drarig29
QUESTION
I have a nested collection that I want to transform, pulling some keys "up a level" and discarding some other keys.
Every item in the collection has an allergens property.
...ANSWER
Answered 2021-Jun-11 at 07:24Since you're posting your collection as JSON, I reverse engineered what your actual collection would look like. Turns out, your transform() works fine as far as I can tell. Maybe that helps you to find differences between my and your collection which might lead you to your problem/solution:
QUESTION
How can I properly kill celery tasks running on containers inside a kubernetes environment? The structure of the whole application (all written in Python) is as follows:
A SDK that makes requests to our API;
A Kubernetes structure with one pod running the API and other pods running celery containers to deal with some long-running tasks that can be triggered by the API. These celery containers autoscale.
Suppose we call a SDK method that in turn makes a request to the API that triggers a task to be run on a celery container. What would be the correct/graceful way to kill this task if need be? I am aware that celery tasks have a revoke() method, but I tried using this approach and it did not work, even using terminate=True and signal=signal.SIGKILL (maybe this has something to do with the fact that I am using Azure Service Bus as a broker?)
Perhaps a mapping between a celery task and its corresponding container name would help, but I could not find a way to get this information as well.
Any help and/or ideas would be deeply appreciated.
...ANSWER
Answered 2021-Mar-30 at 13:32The solution I found was to write to file shared by both API and Celery containers. In this file, whenever an interruption is captured, a flag is set to true. Inside the celery containers I keep periodically checking the contents of such file. If the flag is set to true, then I gracefully clear things up and raise an error.
QUESTION
I have a dockerized Django project and everything works fine because Celery keeps displaying runserver logs instead of celery logs.
Here's my docker-compose.yml:
...ANSWER
Answered 2021-Jun-08 at 02:26Remove the ENTRYPOINT ["sh", "./entrypoint.sh"] from your Dockerfile and rebuild your images again.
I hope that will do the job.
QUESTION
I am trying to introduce dynamic workflows into my landscape that involves multiple steps of different model inference where the output from one model gets fed into another model.Currently we have few Celery workers spread across hosts to manage the inference chain. As the complexity increase, we are attempting to build workflows on the fly. For that purpose, I got a dynamic DAG setup with Celeryexecutor working. Now, is there a way I can retain the current Celery setup and route airflow driven tasks to the same workers? I do understand that the setup in these workers should have access to the DAG folders and environment same as the airflow server. I want to know how the celery worker need to be started in these servers so that airflow can route the same tasks that used to be done by the manual workflow from a python application. If I start the workers using command "airflow celery worker", I cannot access my application tasks. If I start celery the way it is currently ie "celery -A proj", airflow has nothing to do with it. Looking for ideas to make it work.
...ANSWER
Answered 2021-Jun-06 at 17:17Thanks @DejanLekic. I got it working (though the DAG task scheduling latency was too much that I dropped the approach). If someone is looking to see how this was accomplished, here are few things I did to get it working.
- Change the airflow.cfg to change the executor,queue and result back-end settings (Obvious)
- If we have to use Celery worker spawned outside the airflow umbrella, change the celery_app_name setting to celery.execute instead of airflow.executors.celery_execute and change the Executor to "LocalExecutor". I have not tested this, but it may even be possible to avoid switching to celery executor by registering airflow's Task in the project's celery App.
- Each task will now call send_task(), the AsynResult object returned is then stored in either Xcom(implicitly or explicitly) or in Redis(implicitly push to the queue) and the child task will then gather the Asyncresult ( it will be an implicit call to get the value from Xcom or Redis) and then call .get() to obtain the result from the previous step.
Note: It is not necessary to split the send_task() and .get() between two tasks of the DAG. By splitting them between parent and child, I was trying to take advantage of the lag between tasks. But in my case, the celery execution of tasks completed faster than airflow's inherent latency in scheduling dependent tasks.
QUESTION
I am trying to use Celery to create periodic tasks in my application. However, I cannot see the outputs of the periodic task that I wrote.
The backend is on a Windows-based redis-server. The server is up and running.
project/celery.py
...ANSWER
Answered 2021-Jun-04 at 09:08You need to start celery beat, because that him that will read the database and execute your task.
install : https://github.com/celery/django-celery-beat
so in CLI, you need to execute :
QUESTION
I'm having different python programs doing long polling at different machines, and am thinking of a queuing based mechanism to manage the load and provide an async job functionality.
These programs are standalone, and aren't part of any framework.
I'm primarily thinking about Celery due to its ability for multi-processing and sharing tasks across multiple celery workers. Is celery a good choice here, or am I better off simply using an event based system with RabbitMQ directly?
...ANSWER
Answered 2021-Jun-03 at 09:21I would say yes - Celery is definitely a good choice! We do have tasks that run sometimes for over 20 hours, and Celery works just fine. Furthermore it is extremely simple to setup and use (Celery + Redis is supersimple).
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Install celery
You can use celery like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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