Python Celery explained for beginners

Hello people,

Learn Python Celery from below articles. I tried to explain Celery as simple as possible.

“Python Celery — Distributed Task Queue demystified for beginners to Professionals(Part-1)” by Chaitanya V

https://link.medium.com/Ld06lZwJEab

“Python Celery explained for beginners to Professionals(Part-2) — Applications of Python Celery” by Chaitanya V

https://link.medium.com/sSXcrhzJEab

“Python Celery explained for beginners to Professionals(Part-3) — Workers, Pool and Concurrency…” by Chaitanya V

https://link.medium.com/2j4MLwAJEab

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👤︎ u/vchaitanya
📅︎ Mar 20 2021
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Managed Celery Infrastructure, launch Python's most popular distributed task queue in seconds.

Hi everyone, I wanted to share an app that I've been working on.

I've been a long time user of Celery and have used it on hundreds of projects, but it always bothered me how much work it was to set up a new broker and backend for each project.

The app launches Redis, RabbitMQ, Celery Flower and/or PostgreSQL. I’m hoping to find a way to support managed Celery workers as well.

I would love to know what everyone thinks and what features I should add.

Site is: https://celeryhost.com

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👤︎ u/lisimia
📅︎ Jan 26 2021
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Meet Celery (Celestia), my dream snake, blue eyed leustic python! Shes so sassy and I love her!
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👤︎ u/Amyugi
📅︎ May 22 2020
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Python Celery Tutorial explained for a layman.

I have used Celery extensively in my company projects. In this series, I’ll explain about Python Celery, it’s applications, my experiences and experiments with Celery in detail.

Please support, comment and suggest.

Python Celery Tutorial explained for a layman.

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👤︎ u/vchaitanya
📅︎ Sep 04 2020
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Why is there no hardcover book on Python Celery Best Practices?

I want to understand forking, preloading , multi threading and multi processing with celery in python and understand the magic of being able to use established database connections for every thread. Where do I go?

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📅︎ Sep 19 2020
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Searching the best task queue in python || Celery vs Huey vs RQ vs any other good alternative

So I am working on a project in which I need to accomplish certain tasks in the background. Each background task runs an ML model and takes around 6 to 8 seconds to complete. Currently I am using the BackgroundTasks feature of FastApi to accomplish this. But I have been reading about that and concluded that it is not very suitable for long tasks and gets blocked as the load increases. The application that I am working on is a heavy load one and should be able to handle thousands of background request every second. Now I am thinking to take this background task and put it on task queue. But not able to decide as to which one to use. The popular options include celery, rq, huey, etc. Celery seems to me a good option but based on my research it appears that it is quite complicated and prone to bugs. On the other hand huey and RQ are easy and lightweight but I am a bit skeptical about their scalability as my workload increases. If anyone has used task queues on a fairly large workload, then please explain how you made your choices for the correct task queues.

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📅︎ Jul 16 2020
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Can we use python asyncio in place of celery?

I'm thinking if i can use asyncio in place of celery for making basic tasks such as sending an email async. This would save me from setting up celery and queue.

what do you guys think? would this have any negative effects?

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👤︎ u/black_loop
📅︎ Dec 11 2019
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How to push app context ? RuntimeError: Working outside of application context with Celery and Flask in Python

I've been struggling for days to implement flask with celery. I keep on getting a RuntimeError: Working outside of application context. By reading, I've seen the main suggestion is to use

with app.app_context: 

However, this doesn't seem to solve my problem and I don't manage to find a solution which works anywhere on the internet.

I posted on Stack Overflow a detailed version of my problem.

If you have any clue on what I should do please let me know

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👤︎ u/Skylixia
📅︎ Jan 21 2020
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The Python Celery Cookbook: Small Tool, Big Possibilities djangostars.com/blog/the-…
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📅︎ Jul 03 2019
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Breaking Down Celery ≥4.x With Python and Django medium.com/better-program…
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👤︎ u/monk8800
📅︎ May 16 2020
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Python-Celery in Windows with Docker managing habr.com/en/post/489964/
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📅︎ Feb 26 2020
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Celery and Python 3.X in a Flask app. Has anyone had any success?

Hi all,

I'm building a new web app whilst following the (excellent) course on Flask web apps by Nick Janetakis. I'm following his tutorial but at the same time am building my own app, piece by piece as I learn.

In doing this I'm trying to use newer tools. For example, I want to use Python 3.x for future proofing the app, but I have an issue where Celery and Python 3.X won't want to work together.

Just checking in if anyone has had any success with getting a Python 3.x to work with any Celery versions or am I resigned to falling back to Python 2.x?

Thanks a tonne...

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📅︎ Mar 03 2019
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[D] Which hosting? Python + ML + celery + rabbitMQ + mysql

Hi Everyone.

So I made a little scrapper that is scrapping a house prices with some features like area, city, sq meters, ratio = price / m2 etc. everything nice and tidy are saved to the mysql database. I want to attach celery and rabbitMQ for task queue (maybe overkill, but I want to learn),

I want to use machine learning for price prediction or something that is not relevant for this moment. But it will be something simple...

End product will be a dashboard that will show some basic statistics or the scrapped houses, heatmap etc.

Then I want to do in Flask environment, and here is some problems...

- Should I use Flask? it should be lightweight and fast

- Machine Learning models as endpoints?

- Which hosting is the best for this kind of problem? Heroku or Amazon? or... ?

I hope I write everything clear, if something is not clear please, I will try to explain this.

Thank you in advance for your help

👍︎ 3
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👤︎ u/s1korrrr
📅︎ Mar 26 2019
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Are there task queues for R similar to Celery for Python?

I have long-running R functions that are exposed as API endpoints via Plumber. My server issues a 504 server error after 5 minutes of keeping an HTTP request opened, so in order to avoid this, I would like to implement some sort of task queue that will allow me to queue up these HTTP requests to run these functions while immediately returning a HTTP response. I found an R package called, jobqueue, but it doesn't look like it's maintained, so I want to avoid using that. One restriction that I have to abide by is that I cannot use Shiny and the Shiny Server. This needs to be an API. I'm going to provide a use case below so there is better understanding of what I'm trying to accomplish.

Use case #1:

A user accesses a frontend UI and clicks on a button to run an analytics job to crunch some data in R. An HTTP request is sent to the R backend to a function that has been exposed as an API endpoint by Plumber. The task is going to take at least 10 minutes to complete, so the user is notified right away on the frontend that their analytics job is now "running". They're shown a status of "running" but they can check on the status of the analytics job by coming back to that UI. Multiple users are able to queue up this analytics job. If there are multiple requests to run that analytics job, any requests that have not been processed yet will show a status of "queued".

I thought about using promises and future but then I realized I still need a way to keep track of the tasks/jobs. I can use Amazon SQS as a message queue broker also, but I still need to track the status.

Any help or direction is appreciated.

Edit 09/25/2019:

I've decided that there is no proper way to have a task queue without turning this into a larger project that I don't have time for, so I am going to go with the following plan for now:

Create an API using Flask (Python) that will then use Celery as the task queue to handle the multiple requests. The Python code would then run the R scripts using the subprocess module in Python. This way we're getting the best of R and Python.

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📅︎ Sep 24 2019
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Any alternatives python task queue similar to pq? (I tried rq and celery)

I have tried rq and celery the only issue w/ them is that there's isn't a priority queue on runtime. It seems my specification needs some kind of priority to be pushed during on runtime and not during when it was invoked. Also i'm having rq issues w/ my process dying due to ChildError (fork) and honcho quitting because of it.

What i'm looking for similar to pq but more of flask and sqlalchemy implementation w/ good scheduling feature. Any suggestions?

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👤︎ u/qatanah
📅︎ Nov 25 2019
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Python/Flask + Celery - maximum concurrencies?

Hello!

I’m build a web tool that involves long processing-time tasks (like 10 mins+). After doing some research, I decided to use celery and delegate the tasks to celery workers. After hours of debugging, I was able to set up celery, put the app on EC2 and everything is working now. However, it seems like Celery has concurrency limit of 4 (the EC2 machine I’m using has 4 cores). When I run 10 tasks at the same time, 6 of them are in waiting queue. My question is: does this mean only 4 users can use this web tool at the same time? How should I deal with this situation? Sorry if my question is too dumb - first time building a web tool.

Any input is much appreciated. Thank you!

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👤︎ u/OccultistX
📅︎ May 30 2019
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Can celery be used to send full python scripts to be run on several different servers, many scripts at a time?

I'm going into a company that does a lot of calculation. The math guys write the calculations they want in python code, and they send the snippets to a server to actually be run.

So currently, a server is running these python scripts in sequential batch, which is a big waste of resources. I'd like to run a ton of python scripts at once on a ton of different servers at once. So they'd have a web interface to write the scripts in, then the web server would send out the jobs to the servers that would make the most sense (least amount of cpu usage, round robin, etc). The servers would then provide a result to that web interface once complete.

Does this look like a job for celery? Are there better ways to approach this?

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📅︎ Mar 31 2019
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Why is python Celery trying to use SSLv3 authentication when trying to connect to RabbitMQ over TLS?

I installed the latest version of Erlang and RabbitMQ from source:

  • Erlang/OTP 22 [erts-10.4.4] [source] [64-bit] [smp:2:2] [ds:2:2:10] [async-threads:1]
  • RabbitMQ 3.7.17
  • Ubuntu 18.04
  • Python 3.6.7
  • celery 4.3.0

My /etc/rabbitmq/rabbitmq.config

[
 {ssl, [{versions, ['tlsv1.2', 'tlsv1.1']}]},
 {rabbit,
  [
    {tcp_listeners, [{"127.0.0.1", 5672}]},
    {ssl_listeners, [5671]},
    {ssl_options, [{cacertfile, "/usr/local/share/ca-certificates/ca.crt"},
                   {certfile, "/usr/local/share/ca-certificates/server.crt"},
                   {keyfile, "/usr/local/share/private/server.key"},
                   {versions, ['tlsv1.2', 'tlsv1.1']},
                   {verify, verify_peer},
                   {fail_if_no_peer_cert, true}
                  ]},
    {auth_mechanisms, ['PLAIN', 'AMQPLAIN', 'EXTERNAL']}
 
  ]
 }
].

I have verified that those .crt and .key are actually in .pem format. And here is my celeryconfig.py:

import ssl
broker_url="amqps://USER:[email protected]:5671//"
result_backend="I am using postgresql"
include=["my_tasks.py"]
task_acks_late=True
worker_prefetch_multiplier=1
worker_max_tasks_per_child=25
timezone="UTC"
broker_use_ssl={'keyfile': 'beep.key', 'certfile': 'beep.crt', 'ca_certs': 'boop.crt', 'cert_reqs': ssl.CERT_REQUIRED}

Whenever I start up my celery workers. I get this message:

consumer: Cannot connect to amqps://USER:**@rabbit-endpoint.com:5671//: [SSL: SSLV3_ALERT_HANDSHAKE_FAILURE] sslv3 alert handshake failure (_ssl.c:847).

I read that the latest version of Erlang/RabbitMQ should not be accepting SSLv3 due to some vulnerabilities, so I am not sure why Celery is trying to authenticate with SSLv3.

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👤︎ u/mrTang5544
📅︎ Aug 14 2019
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Python architecture flask + celery

Hi Folks,

i would like to discuss what could be a better approach to a flask + celery architecture.

At most cases the business rules are stored inside endpoints/ blueprints. I was thinking in a more pythonic way to organize my code, so i thinked in separate the bussines in a module outside the api, as it can be used inside celery tasks.. but i dont know if it can be a great pratice.

so, my project structure would be:

myflaskproject:

-- api

  - clients

       -endpoints.py, etc..

  - billings

       -endpoints.py, etc..

-- bussines

  - billing_rules.py

  - client_rules.py

--tasks

  - celery_tasks.py

what can be the more "pythonic way" to organize these items ?

there's something for indicate me for read and learn more about it ?

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👤︎ u/vermoudh
📅︎ Feb 15 2018
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[Django/Python] Celery task not executing

I've installed Celery, as well as rabbithq which is required to use celery.

I've followed the instruction process to set up celery, however it doesn't execute. My project tree looks like this:

bin
draft1--
        |
         -------draft1 ----
                           |
                            --------celery.py
                            --------tasks.py
                            --------views.py
         -------manage.py
         -------templates
         
include
lib

Here's my code:

settings.py

CELERY_BROKER_URL = 'amqp://localhost' 

celery.py

import os
from celery import Celery

os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'app.settings')

app = Celery('app')
app.config_from_object('django.conf:settings', namespace='CELERY')
app.autodiscover_tasks()

tasks.py

from celery import shared_task

@shared_task
def print_this():
    print('ONE MINUTE')

app.views

print_this.delay()

So my celery function doesn't work, it doesn't execute the print statement. What I want to do is execute the function every minute. Any idea what the problem is?

May have something to do with CELERY_BROKER_URL as that is the bit I'm most confused about, even through I've tried to read through what it does.

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📅︎ Oct 02 2017
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Python 3 - pip - celery - Nota *

pip install celery

============================

from celery import Celery

def make_celery(app):

celery = Celery(app.import_name, backend=app.config['CELERY_RESULT_BACKEND'],

broker=app.config['CELERY_BROKER_URL'])

celery.conf.update(app.config)

TaskBase = celery.Task

class ContextTask(TaskBase):

abstract = True

def __call__(self, *args, **kwargs):

with app.app_context():

return TaskBase.__call__(self, *args, **kwargs)

celery.Task = ContextTask

return celery

===========================

Minimal Example

With what we have above this is the minimal example of using Celery with Flask:

from flask import Flask

flask_app = Flask(__name__)

flask_app.config.update(

CELERY_BROKER_URL='redis://localhost:6379',

CELERY_RESULT_BACKEND='redis://localhost:6379'

)

celery = make_celery(flask_app)

@celery.task()

def add_together(a, b):

return a + b

This task can now be called in the background:

>>> result = add_together.delay(23, 42)

>>> result.wait()

65

Running the Celery Worker

Now if you jumped in and already executed the above code you will be disappointed to learn that your .wait() will never actually return. That’s because you also need to run celery. You can do that by running celery as a worker:

$ celery -A your_application.celery worker

The your_application string has to point to your application’s package or module that creates the celery object.

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👤︎ u/pythoNote
📅︎ Jan 05 2019
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Celery like task manager for new AsyncIO Python module. Still in alpha, but functional. github.com/cr0hn/aiotasks
👍︎ 20
📰︎ r/Python
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👤︎ u/cr0hn
📅︎ Mar 29 2017
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Distributed computing using Celery, dispy, parallel python (pp), or something else?

Hi,

I want to create a project that test algorithms that I'm planning to develop to operate on the data that require a 3rd party software to compute them. An algorithm would generate tasks to be performed on a cluster. These tasks can take between a few minutes to several hours to a day at times and would return back for future processing. In the beginning, I was thinking about using a tool targeting distributed computing such as HTCondor with Python binding. However, I'm starting to think that there might be a good pure Python alternative. I've been reading up on a few libraries available in Python from here but I can't seem to decide whether any of them might suit my needs.

A typical work flow of an algorithm would be as followed:

  1. Generate tasks
  2. Transfer data files and scripts to a worker machine
  3. Start computation via a transferred script (or alternative methods?)
  4. Transfer result files back to the issuing machine
  5. Clean up (these computations generate a lot of junk files)
  6. Evaluate and repeat Step 1, if required.

The major problem I ran into for a Python based solution is the support for files transfer (10-20+MB). It's not clear whether such operation is supported, or how it can be done. A lot of examples seem to assume that I will be transferring a small file (at best) and has nothing to transfer back. I came across a suggestion that I should use a shared network drive, which I'd like to avoid. Note that this project is meant to deploy in an internal network only so large file transfer of around 20MB shouldn't be a big issue.

Here are my more detail requirements:

  • Long run time. The program that I'm executing on the worker machine is a 3rd party software that requires long run time. I need to be able to operate them without timeout.
  • Handle multiple instances of the program on the same machine.
  • File transfer. I need to transfer multiple files at the beginning of each task to my program, and several files back from the worker machine to process them (or keep them for future references).
  • (Non-critical) Resource management and task scheduling. The machines within the cluster maybe shared with multiple users. It would be nice to have such built-in capabilities already.
  • (Non-critical) Load balancing. The machine maybe in used by a user, so an automatic real-time load balancing would be a plus.

Please note that I'm new with this type of

... keep reading on reddit ➡

👍︎ 13
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👤︎ u/aicez
📅︎ Mar 07 2015
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Performing Asynchronous background Tasks on Linux with Python 3, Flask and Celery techarena51.com/index.php…
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📰︎ r/Python
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📅︎ Oct 10 2016
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[Django/Python] I want to call a function every hour. Is there a simple way to do this or do I have to use a package such as Celery (which seems more complex than I need)?
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📅︎ Sep 23 2017
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Slackery - Celery monitoring in slack (python) kray.me/2018/09/celery_mo…
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📰︎ r/slackdev
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👤︎ u/kraymer
📅︎ Sep 11 2018
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Executing asynchronous tasks on Linux with Python and Celery techarena51.com/index.php…
👍︎ 6
📰︎ r/linux
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📅︎ Oct 10 2016
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Replacing Celery with Elixir in a Python Project klibert.pl/statics/python…
👍︎ 4
📰︎ r/Python
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📅︎ Jun 12 2016
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Article series on Python Celery explained for beginners to professionals

Hello my dear pythonistas,

I've good experience with Python celery. I've used extensively in my projects. I'm writing an article series on my journey with Python Celery. Beginners and Professionals can take a look at it.

“Python Celery — Distributed Task Queue demystified for beginners to Professionals(Part-1)” by Chaitanya V

“Python Celery explained for beginners to Professionals(Part-2) — Applications(WHY, WHEN AND WHERE) of Python Celery” by Chaitanya V

👍︎ 21
📰︎ r/Python
💬︎
👤︎ u/vchaitanya
📅︎ Oct 03 2020
🚨︎ report
Python Celery — Distributed Task Queue demystified for beginners to Professionals(Part-1)

Hi all,

I have used Celery extensively in my company projects. In this series, I’ll explain about Python Celery, it’s applications, my experiences and experiments with Celery in detail.

Python Celery Tutorial — Distributed Task Queue demystified for beginners to Professionals(Part-1)

👍︎ 3
📰︎ r/Python
💬︎
👤︎ u/vchaitanya
📅︎ Sep 03 2020
🚨︎ report
Which hosting for python, celery and rabbitmq?

Hi Everyone.

So I made a little scrapper that is scrapping a house prices with some features like area, city, sq meters, ratio = price / m2 etc. everything nice and tidy are saved to the mysql database. I want to attach celery and rabbitMQ for task queue (maybe overkill, but I want to learn),

I want to use machine learning for price prediction or something that is not relevant for this moment. But it will be something simple...

End product will be a dashboard that will show some basic statistics or the scrapped houses, heatmap etc.

Then I want to do in Flask environment, and here is some problems...

- Should I use Flask? it should be lightweight and fast

- Machine Learning models as endpoints?

- Which hosting is the best for this kind of problem? Heroku or Amazon? or... ?

I hope I write everything clear, if something is not clear please, I will try to explain this.

Thank you in advance for your help

👍︎ 2
📰︎ r/Python
💬︎
👤︎ u/s1korrrr
📅︎ Mar 30 2019
🚨︎ report

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