celery | Distributed Task Queue | Pub Sub library

 by   celery Python Version: 5.4.0rc1 License: Non-SPDX

kandi X-RAY | celery Summary

kandi X-RAY | celery Summary

celery is a Python library typically used in Messaging, Pub Sub applications. celery has build file available and it has medium support. However celery has 16 bugs, it has 2 vulnerabilities and it has a Non-SPDX License. You can install using 'pip install celery' or download it from GitHub, PyPI.

Distributed Task Queue (development branch)
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    Quality
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            kandi-support Support

              celery has a medium active ecosystem.
              It has 21685 star(s) with 4493 fork(s). There are 479 watchers for this library.
              There were 4 major release(s) in the last 6 months.
              There are 560 open issues and 4301 have been closed. On average issues are closed in 254 days. There are 66 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of celery is 5.4.0rc1

            kandi-Quality Quality

              OutlinedDot
              celery has 16 bugs (4 blocker, 1 critical, 3 major, 8 minor) and 937 code smells.

            kandi-Security Security

              celery has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              OutlinedDot
              celery code analysis shows 2 unresolved vulnerabilities (2 blocker, 0 critical, 0 major, 0 minor).
              There are 30 security hotspots that need review.

            kandi-License License

              celery has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              celery releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              celery saves you 43207 person hours of effort in developing the same functionality from scratch.
              It has 51047 lines of code, 5998 functions and 329 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed celery and discovered the below as its top functions. This is intended to give you an instant insight into celery implemented functionality, and help decide if they suit your requirements.
            • 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 .
            Get all kandi verified functions for this library.

            celery Key Features

            No Key Features are available at this moment for celery.

            celery Examples and Code Snippets

            celery.app.amqp.rst
            Pythondot img1Lines of Code : 52dot img1License : Non-SPDX (NOASSERTION)
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            .. 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  
            celery.app.rst
            Pythondot img2Lines of Code : 15dot img2License : Non-SPDX (NOASSERTION)
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            .. contents::
                :local:
            
            Proxies
            -------
            
            .. autodata:: default_app
            
            
            Functions
            ---------
            
            .. autofunction:: app_or_default
            .. autofunction:: enable_trace
            .. autofunction:: disable_trace
              
            Important Notes-Redis: Ack emulation improvements
            Pythondot img3Lines of Code : 6dot img3License : Non-SPDX (NOASSERTION)
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            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  
            celery - settings-django-proj
            Pythondot img4Lines of Code : 70dot img4License : Non-SPDX
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            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.
            
            #   
            celery - tasks-resultgraph
            Pythondot img5Lines of Code : 60dot img5License : Non-SPDX
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            # 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  
            celery - settings
            Pythondot img6Lines of Code : 52dot img6License : Non-SPDX
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            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'),
            )  
            Google People API as a Worker/CLI
            Pythondot img7Lines of Code : 2dot img7License : Strong Copyleft (CC BY-SA 4.0)
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            service = build('people', 'v1', developerKey='YOUR_API_KEY_HERE')
            
            how to change the Model field value with logic to time
            Pythondot img8Lines of Code : 14dot img8License : Strong Copyleft (CC BY-SA 4.0)
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            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()-timdelta
            How to use Celery to upload files in Django
            Pythondot img9Lines of Code : 50dot img9License : Strong Copyleft (CC BY-SA 4.0)
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            def create(self, request, *args, **kwargs):
            
                    image = self.request.FILES['image'].read()
            
                    byte = base64.b64encode(image)
                    
                    data = {
                        'product_id': self.kwargs['product_pk'],
                        'image': byt
            How to use Celery to upload files in Django
            Pythondot img10Lines of Code : 56dot img10License : Strong Copyleft (CC BY-SA 4.0)
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            from 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

            QUESTION

            Cannot install additional requirements to apache airflow
            Asked 2021-Jun-14 at 16:35

            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 16:35

            Support 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.

            1. Create a new Dockerfile with the following content:

            Source https://stackoverflow.com/questions/67851351

            QUESTION

            run two celery task
            Asked 2021-Jun-13 at 13:37

            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.

            # app_account.celery_task.py

            my first task is : ...

            ANSWER

            Answered 2021-Jun-13 at 13:37

            You can inline import User inside your first task to avoid the circular import.

            Source https://stackoverflow.com/questions/67955991

            QUESTION

            How to give celery enough permission to run a root file without compromising security?
            Asked 2021-Jun-13 at 11:03

            I'm running the code below as part of a Celery task.

            ...

            ANSWER

            Answered 2021-Jun-13 at 09:16

            I would add the celery user to the sudoers file with the only command allowed being the one needed. Use visudo and add these lines

            Source https://stackoverflow.com/questions/67956531

            QUESTION

            No live output from containers running on Docker Desktop with WSL2
            Asked 2021-Jun-11 at 09:27

            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 20:40

            It's a bug in Docker Desktop v3.3.3

            GitHub issue: https://github.com/docker/for-win/issues/11251 as pointed by @Drarig29

            Source https://stackoverflow.com/questions/67607424

            QUESTION

            Transform nested collection in laravel
            Asked 2021-Jun-11 at 08:25

            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 07:24

            Since 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:

            Source https://stackoverflow.com/questions/67924276

            QUESTION

            How can I properly kill a celery task in a kubernetes environment?
            Asked 2021-Jun-08 at 21:10

            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:

            1. A SDK that makes requests to our API;

            2. 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 13:32

            The 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.

            Source https://stackoverflow.com/questions/66799974

            QUESTION

            Celery showing django's runserver logs instead of celery logs
            Asked 2021-Jun-08 at 02:26

            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 02:26

            Remove the ENTRYPOINT ["sh", "./entrypoint.sh"] from your Dockerfile and rebuild your images again.

            I hope that will do the job.

            Source https://stackoverflow.com/questions/67880500

            QUESTION

            Use existing celery workers for Airflow's Celeryexecutor workers
            Asked 2021-Jun-06 at 17:17

            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 17:17

            Thanks @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.

            1. Change the airflow.cfg to change the executor,queue and result back-end settings (Obvious)
            2. 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.
            3. 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.

            Source https://stackoverflow.com/questions/67725304

            QUESTION

            Django-Celery No Periodic Outputs
            Asked 2021-Jun-04 at 09:08

            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 09:08

            You 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 :

            Source https://stackoverflow.com/questions/67834249

            QUESTION

            Using Celery for long running async jobs
            Asked 2021-Jun-03 at 09:21

            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 09:21

            I 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).

            Source https://stackoverflow.com/questions/67816611

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install celery

            You can install using 'pip install celery' or download it from GitHub, PyPI.
            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.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
            Find more information at:

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            Install
          • PyPI

            pip install celery

          • CLONE
          • HTTPS

            https://github.com/celery/celery.git

          • CLI

            gh repo clone celery/celery

          • sshUrl

            git@github.com:celery/celery.git

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